(This article was first published on

**Recology**, and kindly contributed to R-bloggers)I have up to recently always done my phenotypic selection analyses in SAS. I finally got some code I think works to do everything SAS would do. Feedback much appreciated!

########################Selection analyses#############################

install.packages(c("car","reshape","ggplot2"))

require(car)

require(reshape)

require(ggplot2)

# Create data set

dat <- data.frame(plant = seq(1,100,1),

trait1 = rep(c(0.1,0.15,0.2,0.21,0.25,0.3,0.5,0.6,0.8,0.9,1,3,4,10,11,12,13,14,15,16), each = 5), trait2 = runif(100),

fitness = rep(c(1,5,10,20,50), each = 20))

# Make relative fitness column

dat_ <- cbind(dat, dat$fitness/mean(dat$fitness))

names(dat_)[5] <- "relfitness"

# Standardize traits

dat_ <- cbind(dat_[,-c(2:3)], rescaler(dat_[,c(2:3)],"sd"))

####Selection differentials and correlations among traits, cor.prob uses function in functions.R file

############################################################################

####### Function for calculating correlation matrix, corrs below diagonal,

####### and P-values above diagonal

############################################################################

cor.prob <- function(X, dfr = nrow(X) - 2) {

R <- cor(X)

above <- row(R) < col(R)

r2 <- R[above]^2

Fstat <- r2 * dfr / (1 - r2)

R[above] <- 1 - pf(Fstat, 1, dfr)

R

}

# Get selection differentials and correlations among traits in one data frame

dat_seldiffs <- cov(dat_[,c(3:5)]) # calculates sel'n differentials using cov

dat_selcorrs <- cor.prob(dat_[,c(3:5)]) # use P-values above diagonal for significance of sel'n differentials in dat_seldiffs

dat_seldiffs_selcorrs <- data.frame(dat_seldiffs, dat_selcorrs) # combine the two

##########################################################################

####Selection gradients

dat_selngrad <- lm(relfitness ~ trait1 * trait2, data = dat_)

summary(dat_selngrad) # where "Estimate" is our sel'n gradient

####Check assumptions

shapiro.test(dat_selngrad$residuals) # normality, bummer, non-normal

hist(dat_selngrad$residuals) # plot residuals

vif(dat_selngrad) # check variance inflation factors (need package car), everything looks fine

plot(dat_selngrad) # cycle through diagnostic plots

############################################################################

# Plot data

ggplot(dat_, aes(trait1, relfitness)) +

geom_point() +

geom_smooth(method = "lm") +

labs(x="Trait 1",y="Relative fitness")

ggsave("myplot.jpeg")

Plot of relative fitness vs. trait 1 standardized

To

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