Package 'sensiPhy'

Description

A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(alien)

Format

A data frame with 94 rows and 7 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• gestaLen: Mean gestation length (days)

• homeRange: Mean home range (km)

• SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)

• SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)

• SE_range: Standard deviation (intraspecific) for mean home range (km)

A multiphylo containing 101 trees for 94 mammal species.

References

Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105

Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(alien)

Format

A data frame with 94 rows and 7 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• gestaLen: Mean gestation length (days)

• homeRange: Mean home range (km)

• SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)

• SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)

• SE_range: Standard deviation (intraspecific) for mean home range (km)

A multiphylo containing 101 trees for 94 mammal species.

References

Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105

Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

A comparative dataset containing traits for 94 alien mammal species (alien.data) and a multiphylo object with 101 phylogenies matching the data (alien.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Gonzalez-Suarez et al. 2015) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(alien)

Format

A data frame with 94 rows and 7 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• gestaLen: Mean gestation length (days)

• homeRange: Mean home range (km)

• SE_mass: Standard deviation (intraspecific) for mean adult body mass (g)

• SE_gesta: Standard deviation (intraspecific) for mean gestation length (days)

• SE_range: Standard deviation (intraspecific) for mean home range (km)

A multiphylo containing 101 trees for 94 mammal species.

References

Alien mammal data: Gonzalez-Suarez, Manuela, Sven Bacher, and Jonathan M. Jeschke. "Intraspecific trait variation is correlated with establishment success of alien mammals." The American Naturalist 185.6 (2015): 737-746 DOI: 10.1086/681105

Downloaded from: Gonzalez-Surez M, Bacher S, Jeschke J (2015) Data from: Intraspecific trait variation is correlated with establishment success of alien mammals. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.sp963

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

Fits models for trait evolution of continuous characters, detecting influential clades

Usage

clade_continuous(
  data,
  phy,
  model,
  trait.col,
  clade.col,
  n.species = 5,
  n.sim = 20,
  bounds = list(),
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a model of continuous character evolution using fitContinuous, repeats this many times (controlled by n.sim), stores the results and calculates the effects on model parameters Currently, only binary continuous traits are supported.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade.The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Different evolutionary models from fitContinuous can be used, i.e. BM,OU, EB, trend, lambda, kappa, delta and drift.

See fitContinuous for more details on evolutionary models.

Output can be visualised using sensi_plot.

Value

The function tree_continuous returns a list with the following components:

call: The function call

data: The original full data frame.

full.model.estimates: Parameter estimates (rate of evolution sigsq and where applicable optpar), root state z0, AICc for the full model without deleted clades.

sensi.estimates: Parameter estimates (sigsq and optpar), (percentual) difference in parameter estimate compared to the full model (DIFsigsq, sigsq.perc, DIFoptpar, optpar.perc), AICc and z0 for each repeat with a clade removed.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

errors: Clades where deletion resulted in errors.

clade.col: Which column was used to specify the clades?

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

See Also

fitContinuous

Examples

## Not run: 
data("primates")
#Model trait evolution accounting for phylogenetic uncertainty
clade_cont<-clade_continuous(data=primates$data,phy = primates$phy[[1]], model="OU",
trait.col = "adultMass",clade.col="family",n.sim=30,n.species=10,n.cores = 2,track=TRUE)
#Print summary statistics
summary(clade_cont)
sensi_plot(clade_cont,graph="all")
sensi_plot(clade_cont,clade="Cercopithecidae",graph = "sigsq")
sensi_plot(clade_cont,clade="Cercopithecidae",graph = "optpar")
#Change the evolutionary model, tree transformation or minimum number of species per clade
clade_cont2<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="delta",
trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE)
summary(clade_cont2)
sensi_plot(clade_cont2)
clade_cont3<-clade_continuous(data=primates$data,phy = primates$phy[[1]],model="BM",
trait.col = "adultMass",clade.col="family",n.sim=30,n.species=5,n.cores = 2,track=TRUE)
summary(clade_cont3)
sensi_plot(clade_cont3,graph="sigsq")

## End(Not run)

Description

Fits models for trait evolution of discrete (binary) characters, detecting influential clades

Usage

clade_discrete(
  data,
  phy,
  model,
  transform = "none",
  trait.col,
  clade.col,
  n.species = 5,
  n.sim = 20,
  bounds = list(),
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a model of discrete character evolution using fitDiscrete, repeats this this many times (controlled by n.sim), stores the results and calculates the effects on model parameters. Currently, only binary discrete traits are supported.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade.The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Different character model from fitDiscrete can be used, including ER (equal-rates), SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion).

All transformations to the phylogenetic tree from fitDiscrete can be used, i.e. none, EB, lambda, kappa anddelta.

See fitDiscrete for more details on character models and tree transformations.

Output can be visualised using sensi_plot.

Value

The function tree_discrete returns a list with the following components:

call: The function call

data: The original full data frame.

full.model.estimates: Parameter estimates (transition rates q12 and q21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for the full model without deleted clades.

sensi.estimates: Parameter estimates (transition rates q12 and q21),(percentual) difference in parameter estimate compared to the full model (DIFq12, sigsq.q12, DIFq21, optpar.q21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for each repeat with a clade removed.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

errors: Clades where deletion resulted in errors.

clade.col: Which column was used to specify the clades?

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

See Also

fitDiscrete

Examples

## Not run: 
#Load data:
data("primates")
#Create a binary trait factor 
primates$data$adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
clade_disc<-clade_discrete(data=primates$data,phy = primates$phy[[1]],model="SYM",
trait.col = "adultMass_binary",clade.col="family",n.sim=30,n.species=10,n.cores = 2)
summary(clade_disc)
sensi_plot(clade_disc)
sensi_plot(clade_disc, clade = "Cebidae", graph = "q12")
#Change the evolutionary model, tree transformation or minimum number of species per clade
clade_disc_2<-clade_discrete(data=primates$data,phy = primates$phy[[1]],
model="ARD",transform="kappa",
trait.col = "adultMass_binary",clade.col="family",n.sim=30,
n.species=8,n.cores = 2)
summary(clade_disc_2)
sensi_plot(clade_disc_2)
sensi_plot(clade_disc_2, graph = "q12")
sensi_plot(clade_disc_2, graph = "q21")

## End(Not run)

Description

Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis.

Usage

clade_phyglm(
  formula,
  data,
  phy,
  btol = 50,
  track = TRUE,
  clade.col,
  n.species = 5,
  n.sim = 100,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a phylogenetic logistic regression model using phyloglm and stores the results. The impact of of a specific clade on model estimates is calculated by a comparison between the full model (with all species) and the model without the species belonging to a clade.

To account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Currently, only logistic regression using the "logistic_MPLE"-method from phyloglm is implemented.

clade_phyglm detects influential clades based on difference in intercept and/or estimate when removing a given clade compared to the full model including all species.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Currently, this function can only implement simple logistic models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function clade_phyglm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. alpha) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter are reported.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

data: Original full dataset.

errors: Clades where deletion resulted in errors.

clade.col: Which column was used to specify the clades?

Author(s)

Gustavo Paterno & Gijsbert Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phyloglm, clade_phylm, influ_phyglm, sensi_plot

Examples

## Not run: 
# Simulate Data:
set.seed(6987)
phy = rtree(150)
x = rTrait(n=1,phy=phy)
X = cbind(rep(1,150),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
cla <- rep(c("A","B","C","D","E"), each = 30)
dat = data.frame(y, x, cla)
# Run sensitivity analysis:
clade <- clade_phyglm(y ~ x, phy = phy, data = dat, n.sim = 30, clade.col = "cla")
# To check summary results and most influential clades:
summary(clade)
# Visual diagnostics for clade removal:
sensi_plot(clade)
# Specify which clade removal to plot:
sensi_plot(clade, "B")
sensi_plot(clade, "C")
sensi_plot(clade, "D") #The clade with the largest effect on slope and intercept

## End(Not run)

Description

Estimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis.

Usage

clade_phylm(
  formula,
  data,
  phy,
  model = "lambda",
  track = TRUE,
  clade.col,
  n.species = 5,
  n.sim = 100,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a phylogenetic linear regression model using phylolm and stores the results. The impact of of a specific clade on model estimates is calculated by a comparison between the full model (with all species) and the model without the species belonging to a clade.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

clade_phylm detects influential clades based on difference in intercept and/or estimate when removing a given clade compared to the full model including all species.

Currently, this function can only implement simple linear models (i.e. $y = a + bx$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function clade_phylm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

data: Original full dataset.

errors: Clades where deletion resulted in errors.

clade.col: Which column was used to specify the clades?

Author(s)

Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, samp_phylm, influ_phylm, sensi_plot, sensi_plot,clade_phyglm,

Examples

## Not run: 
# Load data:
data(primates)
# run analysis:
clade <- clade_phylm(log(sexMaturity) ~ log(adultMass), 
phy = primates$phy[[1]], data = primates$data, n.sim = 30, clade.col = "family")
# To check summary results and most influential clades:
summary(clade)
# Visual diagnostics for clade removal:
sensi_plot(clade)
# Specify which clade removal to plot:
sensi_plot(clade, "Cercopithecidae")
sensi_plot(clade, "Cebidae")

## End(Not run)

Description

Estimate the influence of clade removal on phylogenetic signal estimates

Usage

clade_physig(
  trait.col,
  data,
  phy,
  clade.col,
  n.species = 5,
  n.sim = 100,
  method = "K",
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, estimates phylogenetic signal (K or lambda) using phylosig and stores the results. The impact of a specific clade on signal estimates is calculated by the comparison between the full data (with all species) and reduced data estimates (without the species belonging to a clade).

To account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution of signal estimates expected by the removal of the same number of species as in a given clade. This is done by estimating phylogenetic signal without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Output can be visualised using sensi_plot.

Value

The function clade_physig returns a list with the following components:

trait.col: Column name of the trait analysed

full.data.estimates: Phylogenetic signal estimate (K or lambda) and the P value (for the full data).

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated phylogenetic signal (K or lambda) (estimate), difference between simulation signal and full data signal (DF), the percentage of change in signal compared to the full data estimate (perc) and p-value of the phylogenetic signal with the reduced data (pval).

null.dist: A data frame with estimates for the null distribution of phylogenetic signal for all clades analysed.

data: Original full dataset.

Note

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Author(s)

Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

See Also

phylosig, clade_phylm,sensi_plot

Examples

data(alien)
alien.data<-alien$data
alien.phy<-alien$phy
# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
clade <- clade_physig(trait.col = "logMass", data = alien.data, n.sim = 20,
                 phy = alien.phy[[1]], clade.col = "family", method = "K")
summary(clade)
sensi_plot(clade, "Bovidae")
sensi_plot(clade, "Sciuridae")

Description

Fits models for trait evolution of continuous characters, detecting influential species.

Usage

influ_continuous(
  data,
  phy,
  model,
  bounds = list(),
  cutoff = 2,
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one species at a time, fits different models of continuous character evolution using fitContinuous, stores the results and calculates the effects on model parameters.

influ_continuous detects influential species based on the standardised difference in the rate parameter sigsq and the optimisation parameter optpar (e.g. lamda, kappa, alpha, depending on which model is set), when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential.

Different evolutionary models from fitContinuous can be used, i.e. BM,OU, EB, trend, lambda, kappa, delta and drift.

See fitContinuous for more details on evolutionary models.

Value

The function tree_discrete returns a list with the following components:

call: The function call

cutoff: The value selected for cutoff

data: The original full data vector

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

full.model.estimates: Parameter estimates (rate of evolution sigsq and where applicable optpar), root state z0, AICc for the full model without deleted species.

influential_species: List of influential species, based on standardised difference in estimates for sigsq and optpar. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: Parameter estimates (sigsq and optpar),(percentual) difference in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq, DIFoptpar, optpar.perc,sDIFoptpar), AICc and z0 for each repeat with a species removed.

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

See Also

fitContinuous

Examples

## Not run: 
#Load data:
data("primates")
#Model trait evolution accounting for influential species
adultMass<-primates$data$adultMass
names(adultMass)<-rownames(primates$data)
influ_cont<-influ_continuous(data = adultMass,phy = primates$phy[[1]],
model = "OU",cutoff = 2,n.cores = 2,track = TRUE)
#Print summary statistics
summary(influ_cont)
sensi_plot(influ_cont)
sensi_plot(influ_cont,graphs="sigsq")
#' sensi_plot(influ_cont,graphs="optpar")
#Use a different evolutionary model or cutoff 
influ_cont2<-influ_continuous(data = adultMass,phy = primates$phy[[1]],
model = "lambda",cutoff = 1.2,n.cores = 2,track = TRUE)
summary(influ_cont2)
sensi_plot(influ_cont2)
influ_cont3<-influ_continuous(data = adultMass,phy = primates$phy[[1]],
model = "BM",cutoff = 2,n.cores = 2,track = TRUE)
summary(influ_cont3)

## End(Not run)

Description

Fits models for trait evolution of discrete (binary) characters, detecting influential species.

Usage

influ_discrete(
  data,
  phy,
  model,
  transform = "none",
  bounds = list(),
  cutoff = 2,
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one species at a time, fits a model of discrete character evolution using fitDiscrete, stores the results and calculates the effects on model parameters. Currently, only binary discrete traits are supported.

influ_discrete detects influential species based on the standardised difference in q12 or q21 when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential.

Different character model from fitDiscrete can be used, including ER (equal-rates), SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion).

Different transformations to the phylogenetic tree from fitDiscrete can be used, i.e. none, EB, lambda, kappa anddelta.

See fitDiscrete for more details on character models and tree transformations.

Value

The function tree_discrete returns a list with the following components:

call: The function call

cutoff: The value selected for cutoff

data: The original full data vector

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

full.model.estimates: Parameter estimates (transition rates q12 and q21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for the full model.

influential_species: List of influential species, based on standardised difference in estimates for q12 and q21. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: Parameter estimates (transition rates q12 and q21),,(percentual) difference in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for each analysis with a species deleted.

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

See Also

fitDiscrete

Examples

## Not run: 
#Load data:
data("primates")
#Create a binary trait factor 
adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
adultMass_binary<-as.factor(as.factor(adultMass_binary))
names(adultMass_binary)<-rownames(primates$data)
#Model trait evolution accounting for influential species
influ_binary<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "SYM",transform = "none",cutoff = 2,n.cores = 2,track = TRUE)
#Print summary statistics
summary(influ_binary)
sensi_plot(influ_binary) #q12 and q21 are, as expected, exactly the same in symmetrical model. 
#Use a different evolutionary model. 
influ_binary2<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "SYM",transform = "delta",n.cores = 2,track = TRUE)
summary(influ_binary2)
sensi_plot(influ_binary2)
#Or change the cutoff and transformation
influ_binary3<-influ_discrete(data = adultMass_binary,phy = primates$phy[[1]],
model = "ARD",transform = "none",cutoff = 1.2,n.cores = 2,track = TRUE)
summary(influ_binary3)
sensi_plot(influ_binary3) 

## End(Not run)

Description

Performs leave-one-out deletion analysis for phylogenetic logistic regression, and detects influential species.

Usage

influ_phyglm(formula, data, phy, btol = 50, cutoff = 2, track = TRUE, ...)

Arguments

Details

This function sequentially removes one species at a time, fits a phylogenetic logistic regression model using phyloglm, stores the results and detects influential species.

Currently only logistic regression using the "logistic_MPLE"-method from phyloglm is implemented.

influ_phyglm detects influential species based on the standardised difference in intercept and/or slope when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences change.

Currently, this function can only implement simple logistic models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function influ_phyglm returns a list with the following components:

cutoff: The value selected for cutoff

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (i.e. alpha) for the full model without deleted species.

influential_species: List of influential species, both based on standardised difference in intercept and in the slope of the regression. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the standardised difference (sDIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (i.e. alpha) are reported.

data: Original full dataset.

errors: Species where deletion resulted in errors.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phyloglm, samp_phyglm, influ_phylm, sensi_plot

Examples

# Simulate Data:
set.seed(6987)
phy = rtree(100)
x = rTrait(n=1,phy=phy)
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x)
# Run sensitivity analysis:
influ <- influ_phyglm(y ~ x, data = dat, phy = phy) 
# To check summary results and most influential species:
summary(influ)
# Visual diagnostics for clade removal:
sensi_plot(influ)

Description

Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species.

Usage

influ_phylm(
  formula,
  data,
  phy,
  model = "lambda",
  cutoff = 2,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one species at a time, fits a phylogenetic linear regression model using phylolm, stores the results and detects influential species.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

influ_phylm detects influential species based on the standardised difference in intercept and/or slope when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences change.

Currently, this function can only implement simple linear models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function influ_phylm returns a list with the following components:

cutoff: The value selected for cutoff

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

influential_species: List of influential species, both based on standardised difference in intercept and in the slope of the regression. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the standardised difference (sDIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

data: Original full dataset.

errors: Species where deletion resulted in errors.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, samp_phylm, influ_phyglm, sensi_plot

Examples

# Load data:
data(alien)
# run analysis:
influ <- influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], 
data = alien$data)
# To check summary results:
summary(influ)
# Most influential speciesL
influ$influential.species
# Visual diagnostics
sensi_plot(influ)
# You can specify which graph and parameter ("estimate" or "intercept") to print: 
sensi_plot(influ, param = "estimate", graphs = 2)

Description

Performs leave-one-out deletion analysis for phylogenetic signal estimates, and detects influential species for K or lambda.

Usage

influ_physig(trait.col, data, phy, method = "K", cutoff = 2, track = TRUE, ...)

Arguments

Details

This function sequentially removes one species at a time, ans estimates phylogenetic signal (K or lambda) using phylosig, stores the results and detects the most influential species.

influ_physig detects influential species based on the standardised difference in signal estimate (K or lambda) when removing a given species compared to the full data estimate (with all species). Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences in signal estimate.

Output can be visualised using sensi_plot.

Value

The function influ_physig returns a list with the following components:

cutoff: The value selected for cutoff

trait.col: Column name of the trait analysed

full.data.estimates: Phylogenetic signal estimate (K or lambda) and the P value (for the full data).

influential_species: List of influential species, based on standardised difference in K or lambda. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

influ.physig.estimates: A data frame with all simulation estimates. Each row represents a deleted species Columns report the calculated signal estimate (k) or (lambda), difference between signal estimation of the reduced and full data (DF), the percentage of change in signal compared to the full data signal (perc) and p-value for the phylogenetic signal test (pval)

data: Original full dataset.

Note

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Author(s)

Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

See Also

phylosig, influ_phylm,sensi_plot

Examples

## Not run: 
# Load data:
data(alien)
# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
influ <- influ_physig("logMass", data = alien.data, phy = alien.phy[[1]])
# To check summary results:
summary(influ)
# Most influential speciesL
influ$influential.species
# Visual diagnostics
sensi_plot(influ)
# You can specify which graph to print: 
sensi_plot(influ, graphs = 1)
sensi_plot(influ, graphs = 2)

## End(Not run)

Description

Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis, while taking into account potential interactions with intraspecific variability.

Usage

intra_clade_phyglm(
  formula,
  data,
  phy,
  clade.col,
  n.species = 5,
  n.sim = 100,
  n.intra = 2,
  Vx = NULL,
  distrib = "normal",
  x.transf = NULL,
  btol = 50,
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a phylogenetic logistic regression model using phyloglm and stores the results. The impact of of a specific clade on model estimates is calculated by the comparison between the full model (with all species) and the model without the species belonging to a clade. This operation is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and detect the influential species within that iteration.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

All phylogenetic models from phyloglm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phyloglm for details.

clade_phyglm detects influential clades based on difference in intercept and/or slope when removing a given clade compared to the full model including all species.

Currently, this function can only implement simple logistic models (i.e. $y = a + bx$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_clade_phyglm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

data: Original full dataset.

errors: Clades and/or iterations where deletion resulted in errors.

Author(s)

Gustavo Paterno, Caterina Penone

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phyloglm, intra_phyglm, clade_phyglm, intra_clade_phylm, sensi_plot

Examples

## Not run: 
set.seed(6987)
phy = rtree(100)
x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1))
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x))
cla <- rep(c("A","B","C","D"), each = 25)
dat = data.frame(y, x, z, cla)
intra_clade <- intra_clade_phyglm(formula=y ~ x, data = dat, phy = phy,
                                  clade.col = "cla", n.sim = 30, n.intra = 3,
                                  x.transf = log, Vx = "z", distrib="normal")
sensi_plot(intra_clade)
sensi_plot(intra_clade, clade = "B", graphs = 2)

## End(Not run)

Description

Estimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis, while taking into account potential interactions with intraspecific variability.

Usage

intra_clade_phylm(
  formula,
  data,
  phy,
  clade.col,
  n.species = 5,
  n.sim = 100,
  n.intra = 2,
  Vy = NULL,
  Vx = NULL,
  distrib = "normal",
  y.transf = NULL,
  x.transf = NULL,
  model = "lambda",
  track = TRUE,
  ...
)

Arguments

Details

This function sequentially removes one clade at a time, fits a phylogenetic linear regression model using phylolm and stores the results. The impact of of a specific clade on model estimates is calculated by the comparison between the full model (with all species) and the model without the species belonging to a clade. This operation is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and detect the influential species within that iteration.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

clade_phylm detects influential clades based on difference in intercept and/or slope when removing a given clade compared to the full model including all species.

Currently, this function can only implement simple linear models (i.e. $y = a + bx$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_clade_phylm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

data: Original full dataset.

errors: Clades and/or iterations where deletion resulted in errors.

Author(s)

Gustavo Paterno, Caterina Penone

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, intra_phylm, clade_phylm, intra_clade_phyglm, sensi_plot

Examples

## Not run: 
#load data
data(alien)
intra_clade <- intra_clade_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]],
 data = alien$data, clade.col = "family", n.sim = 30, n.intra = 5, 
 y.transf = log, x.transf = log, Vy="SD_gesta")
summary(intra_clade)
sensi_plot(intra_clade)
sensi_plot(intra_clade, clade = "Bovidae", graphs = 2)
sensi_plot(intra_clade, clade = "Mustelidae", graphs = 2)

## End(Not run)

Description

Performs leave-one-out deletion analysis for phylogenetic logistic regression, and detects influential species, while taking into account potential interactions with intraspecific variability.

Usage

intra_influ_phyglm(
  formula,
  data,
  phy,
  Vx = NULL,
  n.intra = 30,
  x.transf = NULL,
  distrib = "normal",
  cutoff = 2,
  btol = 50,
  track = TRUE,
  ...
)

Arguments

Details

This function fits a phylogenetic linear regression model using phylolm, and detects influential species by sequentially deleting one at a time. The regression is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and detect the influential species within that iteration.

influ_phylm detects influential species based on the standardised difference in intercept and/or slope when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences change.

Currently, this function can only implement simple models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_influ_phylm returns a list with the following components:

cutoff: The value selected for cutoff

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

influential_species: List of influential species, both based on standardised difference in intercept and in the slope of the regression. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade for an iteration of resimulated data. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the standardised difference (sDIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

data: Original full dataset.

errors: Species where deletion resulted in errors.

Warning

When Vx exceeds X negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can be solved by increasing n.intra, changing the transformation type and/or checking the target species in output$sp.pb.

Setting n.intra at high values can take a long time to execute, since the total number of iterations equals n.intra * nrow(data).

Author(s)

Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, intra_phyglm, influ_phyglm,intra_influ_phylm,sensi_plot.

Examples

## Not run: 
#Generate data
set.seed(6987)
phy = rtree(100)
x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1))
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x))
dat = data.frame(y, x, z)
# Run sensitivity analysis:
intra_influ <- intra_influ_phyglm(formula = y ~ x, data = dat, phy = phy, 
                       Vx = "z", n.intra = 5,track = TRUE,distrib="normal",x.transf=NULL) 
# To check summary results and most influential species:
summary(intra_influ)
# Visual diagnostics for clade removal:
sensi_plot(intra_influ)

## End(Not run)

Description

Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species, while taking into account potential interactions with intraspecific variability.

Usage

intra_influ_phylm(
  formula,
  data,
  phy,
  Vy = NULL,
  Vx = NULL,
  y.transf = NULL,
  x.transf = NULL,
  n.intra = 10,
  distrib = "normal",
  model = "lambda",
  cutoff = 2,
  track = TRUE,
  ...
)

Arguments

Details

This function fits a phylogenetic linear regression model using phylolm, and detects influential species by sequentially deleting one at a time. The regression is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and detect the influential species within that iteration.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

influ_phylm detects influential species based on the standardised difference in intercept and/or slope when removing a given species compared to the full model including all species. Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences change.

Currently, this function can only implement simple linear models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_influ_phylm returns a list with the following components:

cutoff: The value selected for cutoff

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

influential_species: List of influential species, both based on standardised difference in intercept and in the slope of the regression. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade for an iteration of resimulated data. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the standardised difference (sDIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

data: Original full dataset.

errors: Species where deletion resulted in errors.

Warning

When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation.

Setting n.intra at high values can take a long time to execute, since the total number of iterations equals n.intra * nrow(data).

Author(s)

Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, intra_phylm, influ_phylm,intra_influ_phyglm,sensi_plot.

Examples

## Not run: 
# Load data:
data(alien)
# run analysis:
intra_influ <- intra_influ_phylm(formula = gestaLen ~ adultMass, phy = alien$phy[[1]],
data=alien$data,model="lambda",y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL,
n.intra=30,distrib = "normal")
summary(intra_influ)
sensi_plot(intra_influ)

## End(Not run)

Description

Performs Phylogenetic logistic regression evaluating intraspecific variability in predictor variables.

Usage

intra_phyglm(
  formula,
  data,
  phy,
  Vx = NULL,
  n.intra = 30,
  x.transf = NULL,
  distrib = "normal",
  btol = 50,
  track = TRUE,
  ...
)

Arguments

Details

This function fits a phylogenetic logistic regression model using phyloglm. The regression is repeated n.intra times. At each iteration the function generates a random value for each row in the dataset using the standard deviation or error supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data, you can use the summarySE function from the package Rmisc.

All phylogenetic models from phyloglm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phyloglm for details.

Currently, this function can only implement simple logistic models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_phyglm returns a list with the following components:

formula: The formula

data: Original full dataset

sensi.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for each regression.

N.obs: Size of the dataset after matching it with tree tips and removing NA's.

stats: Main statistics for model parameters.CI_low and CI_high are the lower and upper limits of the 95

all.stats: Complete statistics for model parameters. sd_intra is the standard deviation due to intraspecific variation. CI_low and CI_high are the lower and upper limits of the 95

sp.pb: Species that caused problems with data transformation (see details above).

Warning

When Vx exceeds X negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can be solved by increasing n.intra, changing the transformation type and/or checking the target species in output$sp.pb.

Author(s)

Caterina Penone & Pablo Ariel Martinez

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phyloglm, sensi_plot

Examples

# Simulate Data:
set.seed(6987)
phy = rtree(150)
x = rTrait(n=1,phy=phy)
x_sd = rnorm(150,mean = 0.8,sd=0.2)
X = cbind(rep(1,150),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x, x_sd)

# Run phylogenetic logistic regression accounting for intraspecific variation:
intra_glm <- intra_phyglm(y~x,Vx = "x_sd",data = dat,phy=phy,distrib = "normal")

#Print summary of sensitivity analysis
summary(intra_glm)
head(intra_glm$sensi.estimates)
#Visual output
sensi_plot(intra_glm)

Description

Performs Phylogenetic linear regression evaluating intraspecific variability in response and/or predictor variables.

Usage

intra_phylm(
  formula,
  data,
  phy,
  Vy = NULL,
  Vx = NULL,
  y.transf = NULL,
  x.transf = NULL,
  n.intra = 30,
  distrib = "normal",
  model = "lambda",
  track = TRUE,
  ...
)

Arguments

Details

This function fits a phylogenetic linear regression model using phylolm. The regression is repeated n.intra times. At each iteration the function generates a random value for each row in the dataset using the standard deviation or errors supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data, you can use the summarySE function from the package Rmisc.

#' All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

Currently, this function can only implement simple linear models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function intra_phylm returns a list with the following components:

formula: The formula

data: Original full dataset

sensi.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for each regression.

N.obs: Size of the dataset after matching it with tree tips and removing NA's.

stats: Main statistics for model parameters.CI_low and CI_high are the lower and upper limits of the 95

all.stats: Complete statistics for model parameters. sd_intra is the standard deviation due to intraspecific variation. CI_low and CI_high are the lower and upper limits of the 95

sp.pb: Species that caused problems with data transformation (see details above).

Warning

When Vy or Vx exceed Y or X, respectively, negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can be solved by increasing n.intra, changing the transformation type and/or checking the target species in output$sp.pb.

Author(s)

Caterina Penone & Pablo Ariel Martinez

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, sensi_plot

Examples

# Load data:
data(alien)
# run PGLS accounting for intraspecific variation:
intra <- intra_phylm(gestaLen ~ adultMass, y.transf = log, x.transf = log, 
phy = alien$phy[[1]], data = alien$data, Vy = "SD_gesta", n.intra = 30)
# To check summary results:
summary(intra)
# Visual diagnostics
sensi_plot(intra)

Description

Performs Phylogenetic signal estimates evaluating trait intraspecific variability

Usage

intra_physig(
  trait.col,
  data,
  phy,
  V = NULL,
  n.intra = 100,
  distrib = "normal",
  method = "K",
  track = TRUE
)

Arguments

Details

This function estimates phylogenetic signal using phylosig. The analysis is repeated n.intra times. At each iteration the function generates a random value for each row in the dataset using the standard deviation or errors supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data, you can use the summarySE function from the package Rmisc.

Output can be visualised using sensi_plot.

Value

The function intra_physig returns a list with the following components:

Trait: Column name of the trait analysed

data: Original full dataset

intra.physig.estimates: Run number, phylogenetic signal estimate (lambda or K) and the p-value for each run with a different simulated datset.

N.obs: Size of the dataset after matching it with tree tips and removing NA's.

stats: Main statistics for signal estimateCI_low and CI_high are the lower and upper limits of the 95

Note

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Author(s)

Caterina Penone & Pablo Ariel Martinez & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

See Also

phylosig, sensi_plot

Examples

## Not run: 
data(alien)
alien.data<-alien$data
alien.phy<-alien$phy
# Run sensitivity analysis:
intra <- intra_physig(trait.col = "gestaLen", V = "SD_gesta" ,
                     data = alien.data, phy = alien.phy[[1]])
summary(intra)
sensi_plot(intra)
sensi_plot(intra, graphs = 1)
sensi_plot(intra, graphs = 2)

## End(Not run)

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic logistic regression, while taking into account potential interactions with intraspecific variability.

Usage

intra_samp_phyglm(
  formula,
  data,
  phy,
  n.sim = 10,
  n.intra = 3,
  breaks = seq(0.1, 0.5, 0.1),
  btol = 50,
  Vx = NULL,
  distrib = "normal",
  x.transf = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full phylogenetic logistic regression, fits a phylogenetic logistic regression model without these species using phylolm, repeats this many times (controlled by n.sim), stores the results and calculates the effects on model parameters. This operation is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and evaluates the effects of sampling within that iteration.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

Currently, this function can only implement simple logistic models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function samp_phyglm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda or kappa) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a model rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported. Lastly we reported the standardised difference in intercept (sDIFintercept) and slope (sDIFestimate).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) intercepts (perc.sign.intercept) over all repetitions as well as the percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate).

data: Original full dataset.

Note

Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Author(s)

Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, samp_phyglm, intra_phyglm,intra_samp_phylm, sensi_plot

Examples

## Not run: 
set.seed(6987)
phy = rtree(100)
x = rTrait(n=1,phy=phy,parameters=list(ancestral.state=2,optimal.value=2,sigma2=1,alpha=1))
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
z = rnorm(n = length(x),mean = mean(x),sd = 0.1*mean(x))
dat = data.frame(y, x, z)
#Run sensitivity analysis:
intra_samp <- intra_samp_phyglm(formula = y ~ x, data = dat, phy = phy, 
                               n.sim=10, n.intra = 3,
                               breaks=seq(.1,.5,.1),
                               Vx = "z", distrib="normal", x.transf=NULL)
summary(intra_samp)
sensi_plot(intra_samp)

## End(Not run)

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic linear regression, while taking into account potential interactions with intraspecific variability.

Usage

intra_samp_phylm(
  formula,
  data,
  phy,
  n.sim = 10,
  n.intra = 3,
  breaks = seq(0.1, 0.5, 0.1),
  model = "lambda",
  Vy = NULL,
  Vx = NULL,
  distrib = "normal",
  y.transf = NULL,
  x.transf = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full phylogenetic linear regression, fits a phylogenetic linear regression model without these species using phylolm, repeats this many times (controlled by n.sim), stores the results and calculates the effects on model parameters. This operation is repeated n.intra times for simulated values of the dataset, taking into account intraspecific variation. At each iteration, the function generates a random value for each row in the dataset using the standard deviation or errors supplied, and evaluates the effects of sampling within that iteration.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

Currently, this function can only implement simple linear models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function samp_phylm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda or kappa) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a model rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported. Lastly we reported the standardised difference in intercept (sDIFintercept) and slope (sDIFestimate).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) intercepts (perc.sign.intercept) over all repetitions as well as the percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate).

data: Original full dataset.

Note

Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Author(s)

Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm,samp_phylm, intra_phylm, intra_samp_phyglm, sensi_plot

Examples

## Not run: 
# Load data:
data(alien)
# Run analysis:
samp <- intra_samp_phylm(gestaLen ~ adultMass, phy = alien$phy[[1]],
                         y.transf = log,x.transf = NULL,Vy="SD_gesta",Vx=NULL,
                         data = alien$data, n.intra = 5, n.sim=10)
summary(samp)
head(samp$sensi.estimates)
# Visual diagnostics
sensi_plot(samp)
# You can specify which graph and parameter ("estimate" or "intercept") to print: 
sensi_plot(samp, graphs = 1)
sensi_plot(samp, graphs = 2)

## End(Not run)

Description

Combines phylogeny and data to ensure that tips in phylogeny match data and that observations with missing values are removed. This function uses variables provided in the 'formula' argument to:

• Remove NA's: Check if there is any row with NA in the variables included in the formula. All rows containing NA will be removed from the data

• Match data and phy: Check if tips from phylogeny match rownames in data. Tips not present in data and phy will be removed from the phylogeny and data

• Return matched data and phy: The returned data has no NA in the variables included in 'formula' and only rows that match phylogeny tips. Returned phy has only tips that match data

Used internally in samp_phylm, samp_phyglm, clade_phylm, clade_phyglm, intra_phylm, intra_phyglm, tree_phylm, tree_phyglm and all function analysing interactions. Users can also directly use this function to combine a phylogeny and a dataset.

Usage

match_dataphy(formula, data, phy, verbose = TRUE, ...)

Arguments

Details

This function uses all variables provided in the 'formula' to match data and phylogeny. To avoid cropping the full dataset, 'match_dataphy' searches for NA values only on variables provided by formula. Missing values on other variables, not included in 'formula', will not be removed from data. If no species names are provided as row names in the dataset but the number of rows in the dataset is the same as the number of tips in the phylogeny, the function assumes that the dataset and the phylogeny are in the same order.

This ensures consistency between data and phylogeny only for the variables that are being used in the model (set by 'formula').

If phy is a 'multiphylo' object, all phylogenies will be cropped to match data. But the dataset order will only match the first tree provided. The returned phylogeny will be a 'multiphylo' object.

Value

The function match_dataphy returns a list with the following components:

data: Cropped dataset matching phylogeny

phy: Cropped phylogeny matching data

dropped: Species dropped from phylogeny and removed from data.

Note

If tips are removed from the phylogeny and data or if rows containing missing values are removed from data, a message will be printed with the details. Further, the final number of species that match data and phy will always be reported by a message.

Author(s)

Caterina Penone & Gustavo Paterno

References

This function is largely inspired by the function comparative.data in caper package David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac and Will Pearse (2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. http://CRAN.R-project.org/package=caper

Examples

# Load data:
data(alien)
head(alien$data)
# Match data and phy based on model formula:
comp.data <- match_dataphy(gestaLen ~ homeRange, data = alien$data, alien$phy[[1]])
# Check data:
head(comp.data$data)
# Check phy:
comp.data$phy
# See species dropped from phy or data:
comp.data$dropped
# Example2:
# Match data and phy based on model formula:
comp.data2 <- match_dataphy(gestaLen ~ adultMass, data = alien$data, alien$phy)
# Check data (missing data on variables not included in the formula are preserved)
head(comp.data2$data)
# Check phy:
comp.data2$phy
# See species dropped from phy or data:
comp.data2$dropped

Description

Calculates D statistic (Fritz & Purvis 2010), a measure of phylogenetic signal, for missing data. Missingness is recoded into a binary variable (1=missing, 0=non missing). This function is an adaptation of phylo.d for missing data.

Usage

miss.phylo.d(data, phy, ...)

Arguments

Details

This function builds on phylo.d to calculate a phylogenetic signal in missing data. The variable of interest, usually a trait, is recoded into a binary variable (1=missing data, 0=non missing data). Then the phylo.d function tests the estimated D value for significant departure from both random association and the clumping expected under a Brownian evolution threshold model (Fritz & Purvis, 2010).

Output can be visualised using print() and plot()

Value

The function miss.phylo.d returns an object of class "phylo.d" with the following components, for complete list of arguments see phylo.d :

DEstimate: The estimated D value

Pval1: A p value, giving the result of testing whether D is significantly different from one

Pval0: A p value, giving the result of testing whether D is significantly different from zero The function also prints the percentage of missing data per variable in the dataset.

Author(s)

Caterina Penone & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Fritz, S. A. and Purvis, A. (2010). Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conservation Biology, 24(4):1042-1051.

David Orme, Rob Freckleton, Gavin Thomas, Thomas Petzoldt, Susanne Fritz, Nick Isaac and Will Pearse (2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. https://CRAN.R-project.org/package=caper

Examples

# Load caper:
library(caper)
# Load data
data(primates)
data<-alien$data
phy=alien$phy[[1]]

# Test phylogenetic signal for missing data:
sexNAsig <- miss.phylo.d(data,phy,binvar=homeRange)
print(sexNAsig)
plot(sexNAsig)

massNAsig <- miss.phylo.d(data,phy,binvar=adultMass)
print(massNAsig)
plot(massNAsig)

Description

A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(primates)

Format

A data frame with 95 rows and 3 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• sexMaturity: Age when individuals are first physically capable of reproducing (days)

• homeRange: Mean home range (km)

A multiphylo containing 101 trees for 95 primate species.

References

Data downloaded from: http://esapubs.org/archive/ecol/E090/184/

Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(primates)

Format

A data frame with 95 rows and 3 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• sexMaturity: Age when individuals are first physically capable of reproducing (days)

• homeRange: Mean home range (km)

A multiphylo containing 101 trees for 95 primate species.

References

Data downloaded from: http://esapubs.org/archive/ecol/E090/184/

Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

A comparative dataset containing traits for 95 Primates species (primates.data) and a multiphylo object with 101 phylogenies matching the data (primates.phy). Tip labels are the binomial species names and match with data rownames. Data was taken from (Jones et al. 2009) and phylogenies from (Fritz et al 2009) and (Kuhn et al 2011).

Usage

data(primates)

Format

A data frame with 95 rows and 3 variables:

• family: Taxonomic family

• adultMass: Mean adult body mass (g)

• sexMaturity: Age when individuals are first physically capable of reproducing (days)

• homeRange: Mean home range (km)

A multiphylo containing 101 trees for 95 primate species.

References

Data downloaded from: http://esapubs.org/archive/ecol/E090/184/

Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O'Dell, J., Orme, C. D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly, C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A., Price, S. A., Rigby, E. A., Rist, J., Teacher, A., Bininda-Emonds, O. R. P., Gittleman, J. L., Mace, G. M., Purvis, A. (2009), PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology, 90: 2648. doi: 10.1890/08-1494.1

Phylogeny: Kuhn, Tyler S., Arne O. Mooers, and Gavin H. Thomas. "A simple polytomy resolver for dated phylogenies." Methods in Ecology and Evolution 2.5 (2011): 427-436.

Fritz, Susanne A., Olaf RP Bininda-Emonds, and Andy Purvis. "Geographical variation in predictors of mammalian extinction risk: big is bad, but only in the tropics." Ecology letters 12.6 (2009): 538-549.

Description

Fits models for trait evolution of continuous characters, evaluating sampling uncertainty.

Usage

samp_continuous(
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  model,
  n.cores = NULL,
  bounds = list(),
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks), fits different models of continuous character evolution using fitContinuous, repeats this this many times (controlled by n.sim), stores the results and calculates the effects on model parameters.

Different evolutionary models from fitContinuous can be used, i.e. BM,OU, EB, trend, lambda, kappa, delta and drift.

See fitContinuous for more details on character models and tree transformations.

Output can be visualised using sensi_plot.

Value

The function tree_continuous returns a list with the following components:

call: The function call

data: The original full data vector

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

full.model.estimates: Parameter estimates (rate of evolution sigsq and where applicable optpar), root state z0, AICc for the full model without deleted species.

break.summary.tab: Summary per break of the mean and median effects of species removal on percentage and absolute change parameter estimates.

sensi.estimates: Parameter estimates (sigsq and optpar),(percentual) difference in parameter estimate compared to the full model (DIFsigsq, sigsq.perc,sDIFsigsq, DIFoptpar, optpar.perc,sDIFoptpar), AICc and z0 for each repeat with random species removed.

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

See Also

fitContinuous

Examples

## Not run: 
#Load data:
data("primates")
#Model trait evolution accounting for sampling size 
adultMass<-primates$data$adultMass
names(adultMass)<-rownames(primates$data)
samp_cont<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "OU",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2, track = TRUE)
#Print summary statistics
summary(samp_cont)
sensi_plot(samp_cont)
sensi_plot(samp_cont, graphs = 1)
#Use a different evolutionary model 
samp_cont2<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "kappa",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
summary(samp_cont2)
sensi_plot(samp_cont2)
sensi_plot(samp_cont2, graphs = 2)
samp_cont3<-samp_continuous(data = adultMass,phy = primates$phy[[1]],
model = "BM",n.sim=25,breaks=seq(.05,.2,.05),n.cores = 2,track = TRUE)
summary(samp_cont3)

## End(Not run)

Description

Fits models for trait evolution of discrete (binary) characters, evaluating sampling uncertainty.

Usage

samp_discrete(
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  model,
  transform = "none",
  bounds = list(),
  n.cores = NULL,
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks), fits different models of discrete character evolution using fitDiscrete, repeats this this many times (controlled by n.sim), stores the results and calculates the effects on model parameters Currently, only binary discrete traits are supported.

Different character model from fitDiscrete can be used, including ER (equal-rates), SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion).

Transformations to the phylogenetic tree from fitDiscrete can be used, i.e. none, EB, lambda, kappa anddelta.

See fitDiscrete for more details on character models and tree transformations.

Output can be visualised using sensi_plot.

Value

The function tree_discrete returns a list with the following components:

call: The function call

data: The original full data vector

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

full.model.estimates: Parameter estimates (transition rates q12 and q21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for the full model without deleted species.

break.summary.tab: Summary per break of the mean and median effects of species removal on percentage and absolute change in parameters q12 and q21.

sensi.estimates: Parameter estimates (transition rates q12 and q21),(percentual) difference in parameter estimate compared to the full model (DIFq12, sigsq.q12,sDIFq12, DIFq21, optpar.q21,sDIFq21), AICc and the optimised value of the phylogenetic transformation parameter (e.g. lambda) for each analysis with a species deleted.

optpar: Transformation parameter used (e.g. lambda, kappa etc.)

Author(s)

Gijsbert Werner & Gustavo Paterno

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Yang Z. 2006. Computational Molecular Evolution. Oxford University Press: Oxford.

Harmon Luke J, Jason T Weir, Chad D Brock, Richard E Glor, and Wendell Challenger. 2008. GEIGER: investigating evolutionary radiations. Bioinformatics 24:129-131.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

See Also

fitDiscrete

Examples

## Not run: 
#Load data:
data("primates")
#Create a binary trait factor 
adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
adultMass_binary<-as.factor(as.factor(adultMass_binary))
names(adultMass_binary)<-rownames(primates$data)
#Model trait evolution accounting for sampling size 
samp_binary<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]],
n.sim=25,breaks=seq(.1,.3,.1),model = "SYM",transform = "none",n.cores = 2,track = TRUE)
#Print summary statistics
summary(samp_binary)
sensi_plot(samp_binary)
sensi_plot(samp_binary,graphs=1)
sensi_plot(samp_binary,graphs=2)
#Use a different evolutionary model or transformation 
samp_binary2<-samp_discrete(data = adultMass_binary,phy = primates$phy[[1]],
n.sim=25,breaks=seq(.1,.3,.1),model = "ARD",transform = "lambda",n.cores = 2,track = TRUE)
summary(samp_binary2)
sensi_plot(samp_binary2)
sensi_plot(samp_binary2,graphs=1)
sensi_plot(samp_binary2,graphs=3)

## End(Not run)

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in phylogenetic logistic regression.

Usage

samp_phyglm(
  formula,
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  btol = 50,
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full phylogenetic logistic regression, fits a phylogenetic logistic regression model without these species using phyloglm, repeats this many times (controlled by n.sim), stores the results and calculates the effects on model parameters.

Only logistic regression using the "logistic_MPLE"-method from phyloglm is implemented.

Currently, this function can only implement simple logistic models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function samp_phylm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda or kappa) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a model rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported. Lastly we reported the standardised difference in intercept (sDIFintercept) and slope (sDIFestimate).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) intercepts (perc.sign.intercept) over all repetitions as well as the percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate).

data: Original full dataset.

Note

Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

#' Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phyloglm, samp_phylm, influ_phyglm, sensi_plot

Examples

# Simulate Data:
set.seed(6987)
phy = rtree(100)
x = rTrait(n=1,phy=phy)
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x)
# Run sensitivity analysis:
samp <- samp_phyglm(y ~ x, data = dat, phy = phy, n.sim = 10) 
# To check summary results and most influential species:
summary(samp)
## Not run: 
# Visual diagnostics for clade removal:
sensi_plot(samp)

## End(Not run)

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in a phylogenetic linear regression.

Usage

samp_phylm(
  formula,
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  model = "lambda",
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full phylogenetic linear regression, fits a phylogenetic linear regression model without these species using phylolm, repeats this many times (controlled by n.sim), stores the results and calculates the effects on model parameters.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

Currently, this function can only implement simple linear models (i.e. $trait~ predictor$). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function samp_phylm returns a list with the following components:

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda or kappa) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a model rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported. Lastly we reported the standardised difference in intercept (sDIFintercept) and slope (sDIFestimate).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) intercepts (perc.sign.intercept) over all repetitions as well as the percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate).

data: Original full dataset. #' @note Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, samp_phyglm, influ_phylm,sensi_plot

Examples

# Load data:
data(alien)
# Run analysis:
samp <- samp_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy[[1]], 
data = alien$data, n.sim = 10)
summary(samp)
head(samp$sensi.estimates)
# Visual diagnostics
## Not run: 
sensi_plot(samp)
# You can specify which graph and parameter ("estimate" or "intercept") to print: 
sensi_plot(samp, graphs = 1, param = "estimate")
sensi_plot(samp, graphs = 2, param = "intercept")

## End(Not run)

Description

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on phylogenetic signal estimates

Usage

samp_physig(
  trait.col,
  data,
  phy,
  n.sim = 30,
  breaks = seq(0.1, 0.5, 0.1),
  method = "K",
  track = TRUE,
  ...
)

Arguments

Details

This function randomly removes a given percentage of species (controlled by breaks) from the full data, estimates phylogenetic signal for a given trait (K or lambda) without these species using phylosig, then repeats the analysis many times (controlled by n.sim), stores the results and calculates the effect of random species removal on phylogenetic signal estimates.

Output can be visualised using sensi_plot.

Value

The function samp_phylosig returns a list with the following components:

Trait: Column name of the trait analysed

full.model.estimates: Phylogenetic signal (K or lambda) and p-value using the full dataset (without deleted species). See phylosig for details.

sensi.estimates: A data frame with all simulation estimates. Each row represents a rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated signal estimate (estimate), difference between reduced data signal estimate and full data signal (DF), the percentage of change in signal compared to the full data estimate (perc) and signal p-value for the reduced data estimate(pval).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) phylogenetic signal over all repetitions with reduced data sets.

data: Original full dataset used in the analysis. #' @note Please be aware that dropping species may reduce power to detect significant signal and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Note

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Author(s)

Gustavo Paterno & Gijsbert D.A. Werner

References

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467.

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

See Also

phylosig, samp_phylm,sensi_plot

Examples

## Not run: 
data(alien)
alien.data<-alien$data
alien.phy<-alien$phy
# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
samp <- samp_physig(trait.col = "logMass", data = alien.data, n.sim = 30,
phy = alien.phy[[1]])
summary(samp)
sensi_plot(samp)
sensi_plot(samp, graphs = 1)
sensi_plot(samp, graphs = 2)

## End(Not run)

Description

Generic function for plotting results from any sensitivity analysis performed with 'sensiPhy'

Usage

sensi_plot(x, ...)

Arguments

Details

sensi_plot recognize and print different sets of graphs depending on the function that generated 'x'. See the links below for details about the graphs generated for each sensiPhy function:

PGLS regressions (single uncertainty):

• clade_phylm: sensi_plot.sensiClade

• influ_phylm: sensi_plot.sensiInflu

• samp_phylm: sensi_plot.sensiSamp

• intra_phylm: sensi_plot.sensiIntra

• tree_phylm: sensi_plot.sensiTree

  PGLS regressions (interacting uncertainties):

• tree_intra_phylm: sensi_plot.sensiTree_Intra

• intra_clade_phylm: sensi_plot.sensiIntra_Clade

• intra_influ_phylm: sensi_plot.sensiIntra_Influ

• intra_samp_phylm: sensi_plot.sensiIntra_Samp

• tree_clade_phylm: sensi_plot.sensiTree_Clade

• tree_influ_phylm: sensi_plot.sensiTree_Influ

• tree_samp_phylm: sensi_plot.sensiTree_Samp

  Phylogenetic signal:

• clade_physig: sensi_plot.clade.physig

• influ_physig: sensi_plot.influ.physig

• samp_physig: sensi_plot.samp.physig

• tree_physig: sensi_plot.tree.physig

• intra_physig: sensi_plot.intra.physig

  trait evolution (continuous & discrete characters):

• clade_continuous & _discrete sensi_plot.sensiClade.TraitEvol

• influ_continuous & _discrete sensi_plot.sensiInflu.TraitEvol

• tree_continuous & _discrete sensi_plot.sensiTree.TraitEvol

• samp_continuous & _discrete sensi_plot.sensiSamp.TraitEvol

Author(s)

Gustavo Paterno

References

The function 'multiplot', developed by Winston Chang, is used inside sensi_plot to print multiple graphs in one frame. The source code is available here: http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/

Description

plot_clade_physig Plot results from clade_physig

Usage

## S3 method for class 'clade.physig'
sensi_plot(x, clade = NULL, ...)

Arguments

Details

For 'x' from clade_physig:

Graph 1: Distribution of the simulated signal estimates (Null distribution for a given clade sample size). The red dashed line represents the estimated signal for the reduced data (without the focal clade) and the black line represents the signal estimate for the full data.

Author(s)

Gustavo Paterno

See Also

ggplot, phylosig

Description

sensi_plot.influ_physig Plot results from influ_physig

Usage

## S3 method for class 'influ.physig'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from influ_physig:

Graph 1: Distribution of estimated phylogenetic signal (K or lambda) for each simulation (leave-one-out deletion). Dashed red vertical line represents the original estimate of phylogenetic signal with the full data (with all species).

Graph 2: Distribution of P values for the phylogenetic signal (K or lambda) for each simulation (leave-one-out deletion). Red vertical line represents the alpha significance level = 0.05.

Author(s)

Gustavo Paterno

See Also

ggplot, phylosig

Description

sensi_plot_intra.physig Plot results from intra_physig.

Usage

## S3 method for class 'intra.physig'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from intra_physig

Graphs 1: Distribution of estimated phylogenetic signal (lambda or K) for each simulation Red vertical line represents the average signal among all estimates.

Graph 2: Distribution of p-values for the phylogenetic signal (K or lambda) for each simulation. Red vertical line represents the alpha significance level = 0.05.

Author(s)

Caterina Penone and Gustavo Paterno

See Also

ggplot, intra_phylm intra_phylm

Description

plot_samp_phylm Plot results from samp_physig

Usage

## S3 method for class 'samp.physig'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from samp_physig:

Graph 1: Estimated phylogenetic signal for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full data estimate (blue = lower than 5, orange = between 5 and 10 and red = higher than 10). The red horizontal line represents the original phylogenetic signal estimated from the full model (with all species).

Graph 2: The proportion of estimated signal in each category across the percentage of species removed.

Graph 3: The percentage of significant signal estimates across the percentage of species removed.

Author(s)

Gustavo Paterno

See Also

ggplot, samp_phylm samp_physig

Description

Plot results from clade_phylm and clade_phyglm

Usage

## S3 method for class 'sensiClade'
sensi_plot(x, clade = NULL, ...)

Arguments

Details

For 'x' from clade_phylm or clade_phyglm:

Graph 1: The original scatterplot $y = a + bx$ (with the full dataset) and a comparison between the regression lines of the full dataset and the rerun without the selected clade (set by clade). For further details about this method, see clade_phylm.

Species from the selected clade are represented in red (removed species), black solid line represents the regression with the full model and red dashed line represents the regression of the model without the species from the selected clade. To check the available clades to plot, see x$sensi.estimates$clade in the object returned from clade_phylm or clade_phyglm.

Graph 2: Distribution of the simulated slopes (Null distribution for a given clade sample size). The red dashed line represents the estimated slope for the reduced model (without the focal clade) and the black line represents the slope for the full model.

Author(s)

Gustavo Paterno

See Also

clade_phylm

Description

Plot results from clade_discrete and clade_continuous

Usage

## S3 method for class 'sensiClade.TraitEvol'
sensi_plot(x, clade = NULL, graph = "all", ...)

Arguments

Details

For 'x' from clade_discrete or clade_continuous:

Graph 1: Distribution of the simulated parameter estimates (Null distribution for a given clade sample size). The red dashed line represents the estimated signal for the reduced data (without the focal clade) and the black line represents the signal estimate for the full data.

Author(s)

Gustavo Paterno & Gijsbert Werner

See Also

clade_continuous or clade_discrete

Description

plot_influ_phylm Plot results from influ_phylm and influ_phyglm

Usage

## S3 method for class 'sensiInflu'
sensi_plot(x, graphs = "all", param = "estimate", ...)

Arguments

Details

For 'x' from influ_phylm or influ_phyglm:

Graph 1: Distribution of estimated slopes (estimates) or intercepts for each simulation (leave-one-out deletion). Red vertical line represents the original slope or intercept from the full model (with all species).

Graph 2: Original regression plot ($trait~predictor$). Standardized difference in slope or intercept is represented by a continuous size scale. The names of the most influential species (sDF > cutoff) are ploted in the graph.

Graph 3: Distribution of standardized difference in slope or intercept. Red colour indicates influential species (with a standardised difference above the value of cutoff).

Graph 4: Distribution of the percentage of change in slope or intercept.

Author(s)

Gustavo Paterno

See Also

ggplot

Description

sensi_plot.sensiTree.TraitEvol Plot results from influ_discrete and influ_continuous.

Usage

## S3 method for class 'sensiInflu.TraitEvol'
sensi_plot(x, graphs = "all", ...)

Arguments

Author(s)

Gijsbert Werner

See Also

ggplot, influ_discrete influ_continuous

The following graphs are printed.

Graph aicc: Distribution of estimated AICc-values across all single-species deletions. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

Graph optpar: Distribution of estimated values for optimisation parameter specified using 'transform' (if applicable) Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

Additionally, only for tree_discrete the function creates the following graphs.

Graph q12: Distribution of estimated parameter values for transition rates q12 across all single-species deletions. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

Graph q21: Distribution of estimated parameter values for transition rates q21. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

While only for tree_continuous the function creates the following graphs.

Graph sigsq: Distribution of estimated parameter values for rate of evolution sigsq across all single-species deletions. . Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

Graph z0: Distribution of estimated parameter values for z0. Red vertical line represents the mean signal among all estimates. Blue vertical line represents the median signal among all estimates.

Description

plot_tree.intra_phylm Plot results from tree_phylm, intra_phylm and intra_phyglm

Usage

## S3 method for class 'sensiIntra'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from tree_phylm, tree_phyglm, intra_phylm or intra_phyglm:

Graphs 1 and 2: Distribution of estimated slopes (estimates) and intercepts for each tree (for tree_phylm) or value generated within a given interval (intra_phylm) Red vertical line represents the mean estimate or intercept for all models.

Graph 3: Scatterplot with mean regression (black line) and standard deviation of the regression (red dotted lines).

Author(s)

Caterina Penone and Gustavo Paterno

See Also

ggplot, tree_phylm intra_phylm

Description

Plot results from intra_clade_phylm and intra_clade_phyglm

Usage

## S3 method for class 'sensiIntra_Clade'
sensi_plot(x, clade = NULL, graphs = "all", ...)

Arguments

Details

For 'x' from intra_clade_phylm or intra_clade_phyglm:

Graph 1: Estimated slopes after clade removal (reduced data) across multiple simulations. Small dots represent estimates reruns between simulations while larger dots represents the average estimate between all simulations for each clade. The solid black line represents the average slope estimate among trees using the full dataset.

Graph 2: The effect of clade removal on slope estimate across all individual simulations for each clade analyzed. The black line indicates estimates with the full dataset while the red line represent estimates without the focal clade (reduced data) across different simulation The blue dots represent null expectation estimates after removing the same number of species of the focal clade, with dots falling outside the red line area indicating a larger than expected absolute effect.

Author(s)

Gustavo Paterno, Caterina Penone

See Also

intra_clade_phylm

Description

sensi_plot.intra_influ Plot results from intra_influ_phylm and intra_influ_phyglm

Usage

## S3 method for class 'sensiIntra_Influ'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from intra_influ_phylm or intra_influ_phyglm:

Graph 1: Most common influential species on model estimates. Percentage of iterations (n.tree or n.intra) where the removal of individual species caused significant change in model estimate (sDIFestimate > cutoff).

Graph 2: Shift on model estimate after species removal for most influential species. Small red dots represent individual reruns between different trees or simulations while large red dots represent the average DIFestimate among all iterations.

Author(s)

Gustavo Paterno, Caterina Penone

See Also

intra_influ_phylm; intra_influ_phyglm

Description

sensi_plot.sensiIntra_Samp Plot results from sensiIntra_Samp_phylm and sensiIntra_Samp_phyglm

Usage

## S3 method for class 'sensiIntra_Samp'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from sensiIntra_Samp_phylm or sensiIntra_Samp_phyglm:

Graph 1: : Estimated estimates for each simulation across percentages of species removed. Small red dots represent individual estimates for each iteration (tree or intra) across percentages of removed species. Large dots represent the average among these estimates within each iteration and percentage of species removal. Different estimates within the same percentage interval represent different iterations (tree or intra).

Graph 2: : The percentage of significant estimates (p < 0.05) across the percentage of species removed. Small red dots represent individual iterations (tree or intra) while large dots represent the average among these iterations.

Author(s)

Gustavo Paterno

See Also

ggplot, samp_phylm samp_phyglm

Description

plot_samp_phylm Plot results from samp_phylm and influ_phyloglm

Usage

## S3 method for class 'sensiSamp'
sensi_plot(x, graphs = "all", param = "estimate", ...)

Arguments

Details

For 'x' from samp_phylm or samp_phyglm:

Graph 1: Estimated slopes or intercepts for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full model (blue = lower than 5, orange = between 5 and 10 and red = higher than 10). The red horizontal line represents the original slope or intercept from the full model (with all species).

Graph 2: The proportion of estimated slopes and intercepts in each category across the percentage of species removed.

Graph 3: Estimated phylogenetic model parameter for each simulation across the percentage of species removed.

Graph 4: The percentage of significant slopes or intercepts across the percentage of species removed.

Note

If model = "BM", only plots 1, 2 and 4 are printed. Plot 3, phylogenetic model parameter is not available for model = "BM"

Author(s)

Gustavo Paterno

See Also

ggplot, samp_phylm samp_phyglm

Description

sensi_plot.sensiSamp.TraitEvol Plot results from samp_continuous and samp_discrete

Usage

## S3 method for class 'sensiSamp.TraitEvol'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from samp_continuous or samp_discrete:

Graph 1: Estimated q12 (discrete) or sigsq (discrete) for each simulation across percentages of species removed. Colours represent percentage of change in comparison with the full model (blue = lower than 5, orange = between 5 and 10 and red = higher than 10).The red horizontal line represents the original value from the full model (with all species).

Graph 2: The proportion of estimated q12 (discrete) or sigsq (discrete) in each category across the percentage of species removed.

Graph 3: Estimated q21 for each simulation across the percentage of species removed (only for samp_discrete).

Graph 4: The percentage of significant q21 across the percentage of species removed (only for samp_discrete).

Author(s)

Gustavo Paterno & Gijsbert Werner

See Also

ggplot, samp_continuous samp_discrete

Description

plot_tree.intra_phylm Plot results from tree_phylm, intra_phylm and intra_phyglm

Usage

## S3 method for class 'sensiTree'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from tree_phylm, tree_phyglm, intra_phylm or intra_phyglm:

Graphs 1 and 2: Distribution of estimated estimates and intercepts for each tree (for tree_phylm) or value generated within a given interval (intra_phylm) Red vertical line represents the mean estimate or intercept for all models.

Graph 3: Scatterplot with mean regression (black line) and standard deviation of the regression (blue dotted lines).

Author(s)

Caterina Penone and Gustavo Paterno

See Also

ggplot, tree_phylm intra_phylm

Description

Plot results from tree_clade_phylm and tree_clade_phyglm

Usage

## S3 method for class 'sensiTree_Clade'
sensi_plot(x, clade = NULL, graphs = "all", ...)

Arguments

Details

For 'x' from tree_clade_phylm or tree_clade_phyglm:

Graph 1: Estimated slopes after clade removal (reduced data) across multiple trees. Small dots represent estimates reruns between phylogenetic trees while larger dots represents the average estimate between all trees for each clade. The solid black line represents the average slope estimate among trees using the full dataset.

Graph 2: The effect of clade removal on slope estimate across all individual phylogenetic trees for each clade analyzed. The black line indicates estimates with the full dataset while the red line represent estimates without the focal clade (reduced data) across different trees. The blue dots represent null expectation estimates after removing the same number of species of the focal clade, with dots falling outside the red line area indicating a larger than expected absolute effect.

Author(s)

Gustavo Paterno, Caterina Penone

See Also

tree_clade_phylm

Description

sensi_plot.sensiTree_Influ Plot results from tree_influ_phylm and tree_influ_phyglm

Usage

## S3 method for class 'sensiTree_Influ'
sensi_plot(x, graphs = "all", ...)

Arguments

Details

For 'x' from sensiTree_Influ_phylm or sensiTree_Influ_phyglm:

Graph 1: Most common influential species on model estimates. Percentage of iterations (n.tree or n.intra) where the removal of individual species caused significant change in model estimate (sDIFestimate > cutoff).

Graph 2: Shift on model estimate after species removal for most influential species. Small red dots represent individual reruns between different trees or simulations while large red dots represent the average DIFestimate among all iterations.

Author(s)

Gustavo Paterno, Caterina Penone

See Also

ggplot

Description

plot_tree.intra_phylm Plot results from tree_intra_phylm, tree_intra_phylm and tree_intra_phyglm

Usage

## S3 method for class 'sensiTree_Intra'
sensi_plot(x, graphs = "all", uncer.type = "all", ...)

Arguments

Details

For 'x' from tree_intra_phylm or tree_intra_phyglm:

Graphs 1 and 2: Distribution of estimated estimates and intercepts for each tree (for tree_phylm) or value generated within a given interval (interaction_intra_tree_phylm) Red vertical line represents the mean estimate or intercept for all models.