setwd("/Users/matteo/Desktop/") library(dplyr) library(lme4) all_data<-read.table("table_for_glmer.txt",header=TRUE) #equivalent to Table S8 reduced_data <- all_data[complete.cases(all_data), ] #Test removal of interaction with a likelihood ratio test, comparing model fit between full model and model without interaction: full_mixed <- lmer(fd ~ pst*intermediate_Y_or_N + ML_rho + (1|scaffold),data = reduced_data) m1 <- lmer(fd ~ pst + intermediate_Y_or_N + ML_rho + (1|scaffold),data = reduced_data) anova(full_mixed, m1) #Does intermediate expression have an effect on fd? test model with (m1) and without (m2) intermediate_Y_or_N m2 <- lmer(fd ~ pst + ML_rho + (1|scaffold),data = reduced_data) anova(m1, m2) #Test for the effect of pst, i.e. Does selection acting on gene expression predict lower levels of fd? m3 <- lmer(fd ~ ML_rho + (1|scaffold),data = reduced_data) anova(m2,m3) #Test if intermediate genes are associated with lower levels of fd model2 <- glmer (intermediate_Y_or_N ~ fd + (1|scaffold), data=all_data, family="binomial") summary(model2) #Test if intermediate genes are associated with higher levels of pst model3 <- glmer (intermediate_Y_or_N ~ pst + (1|scaffold), data=all_data, family="binomial") summary(model3)