# clean the workspace
rm(list=ls())
# load R packages
library(nlme)
library(lattice)
library(multcomp)
# choose your .csv data file
dat <- read.csv(file.choose(), header=T)
# data formulation: create variable dog, eye
dat$dog <as.factor(rep(rep(c('Morse','Agnes','Anjou','Alice','Arlo','Sh
erlock','Clem','Clyde'),c(6,3,rep(6,6))), 3))
dat$eye <- as.factor(rep(rep(1:15, each=3), 3))
dat$region <- factor(dat$region, levels=c('AC','VS','SPN'))
# remove missing data(rows)
dat <- data.frame(na.omit(dat))
# data formulation: create variable injection
dat$x <- rep('injected', nrow(dat))
dat$x[which(dat$injected==F)] <- 'uninjected'
dat$x <- factor(dat$x, levels=c('uninjected', 'injected'))
# mixed-effects model with nested random effects and just main
effects
summary(fit0 <- lme(scone ~ region + x,
random=~1|dog/eye/region, data=dat, method = 'ML'))
# F-tests for main effects of region and x
anova(fit0, type = 'marginal')
dat$fit <- as.vector(fitted(fit0))
#1)Calculate the actual means for injected vs. uninjected
areas (these don?°•t need a formal post-hoc comparison because
the xinjected coefficient and associated p-value directly tell
you whether these 2 means differ).
# the descriptive statistics: minimum, lower quartile (25%th),
median (50%th),mean, upper quartile(75%th), maximum
summary(dat$fit[dat$injected==TRUE])
#descriptive statistics
for injected area
summary(dat$fit[dat$injected==FALSE]) #descriptive statistics
for uninjected area
#2)Calculate the actual means for each level of region.
Because there are 3 levels in this variable, you need to do
step 3 below to fully describe the pattern in the main effect.
summary(dat$fit[dat$injected==TRUE & dat$region=='AC'])
#descriptive statistics for injected area, region AC
summary(dat$fit[dat$injected==TRUE & dat$region=='VS'])
#descriptive statistics for injected area, region VS
summary(dat$fit[dat$injected==TRUE & dat$region=='SPN'])
#descriptive statistics for injected area, region SPN
summary(dat$fit[dat$injected==FALSE & dat$region=='AC'])
#descriptive statistics for uninjected area, region AC
summary(dat$fit[dat$injected==FALSE & dat$region=='VS'])
#descriptive statistics for uninjected area, region VS
summary(dat$fit[dat$injected==FALSE & dat$region=='SPN'])
#descriptive statistics for uninjected area, region SPN
# Pairwise comparisons illustrating main effect of region
(differences between regions).
summary(glht(fit0, linfct = mcp(region = "Tukey")))
# mixed-effects model with nested random effects, plus both
main effects and an interaction
summary(fit1 <- lme(scone ~ region*x,
random=~1|dog/eye/region, data=dat, method = 'ML'))
# Likelihood ratio test for overall significance of the
interaction term region*x
# if this is significant, then you want to do the multiple
comparison post-hoc tests that Zhen illustrated,
# but now using fit1 instead of fit0
anova(fit0, fit1)
# multiple comparison post-hoc analysis to LMM
# if you want to know whether there are differences between
injection vs. uninjection
# within each region, and whether this effect differs across
# regions (interactions).
c1 <- rbind('AC: injected vs uninjected effect:' =
c(0,0,0,1,0,0),
'SPN: injected vs uninjected effect:' =
c(0,0,0,1,1,0),
'VS: injected vs uninjected effect:' =
c(0,0,0,1,0,1),
'injected vs uninjected effect, AC vs SPN' =
c(0,0,0,0,1, 0),
'injected vs uninjected effect, AC vs VS' =
c(0,0,0,0,0, 1),
'injected vs uninjected effect, SPN vs VS' =
c(0,0,0,0,-1,1)
)
summary(glht(fit1, c1))
# if you only want to know whether there
between injection vs. uninjection
# within each region
c2 <- rbind('AC: injected vs uninjected
c(0,0,0,1,0,0),
'SPN: injected vs uninjected
c(0,0,0,1,1,0),
'VS: injected vs uninjected
are differences
effect:' =
effect:' =
effect:' =
c(0,0,0,1,0,1))
summary(glht(fit1, c2))