#Here is I think an easier way of coding all the components you need.
#The within-group variances, you get with this function:
apply(iris[,1:4],2,function(x) tapply(x,iris$Species,var))
#You can get what you computed by taking the column means.
apply(apply(iris[,1:4],2,function(x) tapply(x,iris$Species,var)),2,mean)
#HOWEVER, note that this takes unweighted column means. If your groups
contain an UNequal number of observations, you do not want to take the
UNweighted means.
#Here is how you get the group and grand means:
apply(iris[,1:4],2,function(x) tapply(x,iris$Species,mean)) #group means
apply(iris[,1:4],2,mean) #grand means
#And you also need the number of observations in each bin, I guess:
apply(iris[,1:4],2,function(x) tapply(x,iris$Species,length))
#I don't want to ruin all the fun for you.
HTH,
Daniel
Coghlan, Avril wrote:
>
> Hello,
>
> I have been looking for functions for calculating the within-groups
> variance and between-groups variance, for the case where you have
> several numerical variables describing samples from a number of groups.
>
> I didn't find such functions in R, so wrote my own versions myself (see
> below). I can calculate the within- and between-groups variance for the
> Sepal.length variable (iris[1]) in the "iris" data set, by typing:
>> calcWithinGroupsVariance(iris[1],iris[5])
> [1] 0.2650082
>> calcBetweenGroupsVariance(iris[1],iris[5])
> [1] 0.4300145
>
> I am wondering however if there are functions for doing this already in
> R?
> I would prefer to use a standard R function if one exists.
>
> Kind Regards,
> Avril
>
>
> Within-Groups Variance:
> =======================
>
> calcWithinGroupsVariance <- function(variable,groupvariable)
> {
> # find out how many values the group variable can take
> groupvariable2 <- as.factor(groupvariable[[1]])
> levels <- levels(groupvariable2)
> numlevels <- length(levels)
> # get the mean and standard deviation for each group:
> numtotal <- 0
> denomtotal <- 0
> for (i in 1:numlevels)
> {
> leveli <- levels[i]
> levelidata <- variable[groupvariable==leveli,]
> levelilength <- length(levelidata)
> # get the mean and standard deviation for group i:
> meani <- mean(levelidata)
> sdi <- sd(levelidata)
> numi <- (levelilength - 1)*(sdi * sdi)
> denomi <- levelilength
> numtotal <- numtotal + numi
> denomtotal <- denomtotal + denomi
> }
> # calculate the within-groups variance
> Vw <- numtotal / (denomtotal - numlevels)
> return(Vw)
> }
>
> Between-Groups-Variance:
> ========================
>
> calcBetweenGroupsVariance <- function(variable,groupvariable)
> {
> # find out how many values the group variable can take
> groupvariable2 <- as.factor(groupvariable[[1]])
> levels <- levels(groupvariable2)
> numlevels <- length(levels)
> # calculate the overall grand mean:
> grandmean <- mean(variable)
> # get the mean and standard deviation for each group:
> numtotal <- 0
> denomtotal <- 0
> for (i in 1:numlevels)
> {
> leveli <- levels[i]
> levelidata <- variable[groupvariable==leveli,]
> levelilength <- length(levelidata)
> # get the mean and standard deviation for group i:
> meani <- mean(levelidata)
> sdi <- sd(levelidata)
> numi <- levelilength * ((meani - grandmean)^2)
> denomi <- levelilength
> numtotal <- numtotal + numi
> denomtotal <- denomtotal + denomi
> }
> # calculate the between-groups variance
> Vb <- numtotal / (denomtotal - numlevels)
> Vb <- Vb[[1]]
> return(Vb)
> }
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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