R/tree_clade_phylm.R
tree_clade_phylm.RdEstimate the impact on model estimates of phylogenetic linear regression after removing clades from the analysis and evaluating uncertainty in trees topology.
tree_clade_phylm( formula, data, phy, clade.col, n.species = 5, n.sim = 100, n.tree = 2, model = "lambda", track = TRUE, ... )
| formula | The model formula |
|---|---|
| data | Data frame containing species traits with row names matching tips
in |
| phy | A phylogeny (class 'multiPhylo', see ? |
| clade.col | The column in the provided data frame which specifies the clades (a character vector with clade names). |
| n.species | Minimum number of species in a clade for the clade to be
included in the leave-one-out deletion analysis. Default is |
| n.sim | Number of simulations for the randomization test. |
| n.tree | Number of times to repeat the analysis with n different trees picked
randomly in the multiPhylo file.
If NULL, |
| model | The phylogenetic model to use (see Details). Default is |
| track | Print a report tracking function progress (default = TRUE) |
| ... | Further arguments to be passed to |
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 for a tree iteration. 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 trees where deletion resulted in errors.
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. It repeats this operation using n trees,
randomly picked in a multiPhylo file.
Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimate a null distribution of slopes 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. This is done for n trees in the multiphylo file.
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.
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.
if (FALSE) { # Load data: data(primates) # run analysis: clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy, data = primates$data, clade.col = "family", n.sim = 50, n.tree = 5) # To check summary results and most influential clades: summary(clade_tree) # Visual diagnostics for clade removal: sensi_plot(clade_tree) # Specify which clade removal to plot: sensi_plot(clade_tree) sensi_plot(clade_tree, "Cercopithecidae") sensi_plot(clade_tree, clade = "Cebidae", graphs = 2) } # \dontshow{ data(primates) # run analysis: clade_tree <- tree_clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy, data = primates$data, clade.col = "family", n.sim = 10, n.tree = 2)#>#> | | | 0% | |=================================== | 50% | |======================================================================| 100%#> $Estimate #> clade.removed N.species estimate DIFestimate Change (%) Pval #> 3 Cercopithecidae 32 0.3051562 0.05732393 23.10 9.604555e-11 #> 2 Cebidae 19 0.2208491 -0.02698319 10.85 6.538060e-07 #> 1 Callitrichidae 9 0.2214056 -0.02642664 10.70 1.089426e-07 #> 4 Lemuridae 8 0.2584135 0.01058129 4.30 1.027548e-09 #> m.null.estimate Pval.randomization Significant (%) #> 3 0.2739287 0.10 50 #> 2 0.2666883 0.00 100 #> 1 0.2534249 0.00 100 #> 4 0.2556822 0.35 0 #> #> $Intercept #> clade.removed N.species intercept DIFintercept Change (%) Pval #> 3 Cercopithecidae 32 4.514581 -0.38997519 7.95 9.604555e-11 #> 2 Cebidae 19 5.042564 0.13800782 2.80 6.538060e-07 #> 1 Callitrichidae 9 5.104811 0.20025393 4.05 1.089426e-07 #> 4 Lemuridae 8 4.830787 -0.07376971 1.50 1.027548e-09 #> m.null.intercept Pval.randomization Significant (%) #> 3 4.702392 0.20 0 #> 2 4.770586 0.00 100 #> 1 4.855895 0.00 100 #> 4 4.840570 0.35 0 #># }