library(lme4) library(ggplot2) ########################## summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) { require(plyr) # New version of length which can handle NA's: if na.rm==T, don't count them length2 <- function (x, na.rm=FALSE) { if (na.rm) sum(!is.na(x)) else length(x) } # This is does the summary; it's not easy to understand... datac <- ddply(data, groupvars, .drop=.drop, .fun= function(xx, col, na.rm) { c( N = length2(xx[,col], na.rm=na.rm), mean = mean (xx[,col], na.rm=na.rm), sd = sd (xx[,col], na.rm=na.rm) ) }, measurevar, na.rm ) # Rename the "mean" column datac <- rename(datac, c("mean"=measurevar)) datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean # Confidence interval multiplier for standard error # Calculate t-statistic for confidence interval: # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1 ciMult <- qt(conf.interval/2 + .5, datac$N-1) datac$ci <- datac$se * ciMult return(datac) } ##################### ab <- read.csv("PFS2012red.green.csv", header=TRUE) ab$culture <- factor(ab$culture, levels = c("AB","BA")) all.marg=summarySE(ab,measurevar="value",groupvars=c("culture", "color"), na.rm=T) all.marg ggplot(all.marg,aes(x=culture,y=value,fill=color))+ geom_point(aes(pch=color), size=4, colour="black")+ geom_point(aes(color=color, pch=color), size=4)+ geom_errorbar(aes(x=culture, ymin=value+se,ymax=value-se), lwd=0.4,width=0.3)+ theme_bw()+ xlab("Culture") + ylab("Colour Values") ###################### red=subset(ab, color=="red") m=t.test(red$value~red$culture) # Welch Two Sample t-test # #data: red$value by red$culture #t = -2.9376, df = 38.244, p-value = 0.005578 #alternative hypothesis: true difference in means is not equal to 0 #95 percent confidence interval: # -26.153025 -4.815783 #sample estimates: #mean in group AB mean in group BA # 75.93748 91.42189