Sensitivity Analysis Species Sampling - Phylogenetic Logistic Regression
Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in phylogenetic logistic regression.
The model formula
Data frame containing species traits with row names matching tips
A phylogeny (class 'phylo') matching
The number of times species are randomly deleted for each
A vector containing the percentages of species to remove.
Bound on searching space. For details see
Print a report tracking function progress (default = TRUE)
Further arguments to be passed to
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
repeats this many times (controlled by
times), 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 linear models (i.e. trait~ predictor). In the future we will implement more complex models.
Output can be visualised using
samp_phylm returns a list with the following
formula: The formula
full.model.estimates: Coefficients, aic and the optimised
value of the phylogenetic parameter (e.g.
the full model without deleted species.
samp.model.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 (
difference between simulation intercept and full model intercept (
the percentage of change in intercept compared to the full model (
and intercept p-value (
pval.intercept). All these parameters are also reported
for the regression slope (
DFslope etc.). Additionally, model aic value
AIC) and the optimised value (
optpar) of the phylogenetic
lambda, depending on the phylogenetic model
used) are reported. Lastly we reported the standardised difference in intercept
sDFintercept) and slope (
sign.analysis For each break (i.e. each percentage of species
removed) this reports the percentage of statistically signficant (at p<0.05)
perc.sign.intercept) over all repititions as well as the
percentage of statisticaly significant (at p<0.05) slopes (
data: Original full dataset.
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.
Gustavo Paterno & Gijsbert D.A. Werner
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.
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# 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, times = 10) # To check summary results and most influential species: summary(samp) ## Not run: # Visual diagnostics for clade removal: sensi_plot(samp) ## End(Not run)
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