Interaction between intraspecific variability and influential species - Phylogenetic Logistic Regression

Source: R/intra_influ_phyglm.R

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

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

Arguments

formula

The model formula: response~predictor.
data

Data frame containing species traits with row names matching tips in phy.
phy

A phylogeny (class 'phylo') matching data.
Vx

Name of the column containing the standard deviation or the standard error of the predictor variable. When information is not available for one taxon, the value can be 0 or NA
n.intra

Number of datasets resimulated taking into account intraspecific variation (see: "intra_phylgm")
x.transf

Transformation for the predictor variable (e.g. "log" or "sqrt"). Please use this argument instead of transforming data in the formula directly (see also details below).
distrib

A character string indicating which distribution to use to generate a random value for the response and/or predictor variables. Default is normal distribution: "normal" (function rnorm). Uniform distribution: "uniform" (runif) Warning: we recommend to use normal distribution with Vx = standard deviation of the mean.
cutoff

The cutoff value used to identify for influential species (see Details)
btol

Bound on searching space. For details see phyloglm
track

Print a report tracking function progress (default = TRUE)
...

Further arguments to be passed to phylolm

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.

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.

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).

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

if (FALSE) {
#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)
}
# \dontshow{
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[1:20,], phy = phy,
                                 Vx = "z", n.intra = 2,track = TRUE,
                                 distrib="normal",x.transf=NULL)
#> Warning: distrib=normal: make sure that standard deviation is provided for Vx
#> Warning: Some phylo tips do not match species in data (this can be due to NA removal) species were dropped from phylogeny or data
#> Used dataset has  20  species that match data and phylogeny
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
# To check summary results and most influential species:
summary(intra_influ)
#> $`Most Common Influential species for the Estimate`
#>   Species removed (%) of iterations
#> 1              t5                50
#> 2             t79                50
#> 
#> $`Average Estimates`
#>   Species removed  Estimate DIFestimate Change(%)      Pval
#> 1              t5 0.3112292  0.12277099    239.90 0.1654295
#> 2             t79 0.2306591  0.04220087     27.25 0.4414887
#> 
#> $`Most Common Influential species for the Intercept`
#>   Species removed (%) of iterations
#> 1             t17                50
#> 2              t5                50
#> 3             t79                50
#> 
#> $`Average Intercepts`
#>   Species removed Intercept DIFintercept Change(%)       Pval
#> 1              t5 -1.319858   -0.4108498     55.55 0.09778574
#> 2             t17 -1.113048   -0.2040398     21.35 0.14716088
#> 3             t79 -1.095681   -0.1866725     18.70 0.14986393
#> 
# Visual diagnostics for clade removal:
sensi_plot(intra_influ)
#> Warning: `fun.y` is deprecated. Use `fun` instead.
# }