Closed Ax3man closed 6 years ago
Following issue #10, and PR #11, here is the same parallel bootstrap implementation, but for phyloglm.
phyloglm
Results are the practically the same with the new and old function:
library(phylolm) set.seed(123456) tre = rtree(50) x = rTrait(n=1,phy=tre) X = cbind(rep(1,50),x) y = rbinTrait(n=1,phy=tre, beta=c(-1,0.5), alpha=1 ,X=X) dat = data.frame(trait01 = y, predictor = x) fit_old_MPLE <- phyloglm_old(trait01~predictor,phy=tre,data=dat,boot=5000, method = 'logistic_MPLE') fit_old_IG10 <- phyloglm_old(trait01~predictor,phy=tre,data=dat,boot=5000, method = 'logistic_IG10') fit_new_MPLE <- phyloglm(trait01~predictor,phy=tre,data=dat,boot=5000, method = 'logistic_MPLE', parallel = "SOCK") fit_new_IG10 <- phyloglm(trait01~predictor,phy=tre,data=dat,boot=5000, method = 'logistic_IG10', parallel = "SOCK") library(dplyr) library(tidyr) library(ggplot2) result <- bind_rows(old_MPLE = as.data.frame(fit_old_MPLE$bootstrap), new_MPLE = as.data.frame(fit_new_MPLE$bootstrap), old_IG10 = as.data.frame(fit_old_IG10$bootstrap), new_IG10 = as.data.frame(fit_new_IG10$bootstrap), .id = 'version') %>% mutate(version = factor(version, unique(version))) ggplot(gather(result, 'parameter', 'value', -version), aes(version, value)) + geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), fill = 1, alpha = 0.1) + facet_wrap(~parameter, scales = 'free_y', nr = 1) + theme_minimal()
Let me know if you need anything else related to this!
This is great! Thanks.
Following issue #10, and PR #11, here is the same parallel bootstrap implementation, but for
phyloglm
.Results are the practically the same with the new and old function:
Let me know if you need anything else related to this!