Here is some code for doing one-way repeated measures analysis with lme4 and custom contrasts. We will use a repeated measures design with three conditions of the factor Treat and 20 participants. The contrasts are Helmert contrasts, but they differ from the built-in Helmert contrasts in that the sum of the absolute values of the contrasts weights equals 2 for each contrast.

The standard error of each contrast equals the square root of the product of the sum of the squared contrast weight w and the residual variance divided by the number of participants n.

The residual variance equals the within treatment variance times 1 minus the correlation between conditions. (Which equals the within treatment variance minus 1 times the covariance ) .

In the example below, the within treatment variance equals 1 and the covariance 0.5 (so the value of the correlation is .50 as well). The residual variance is therefore equal to .50.

For the first contrasts, the weights are equal to {-1, 1, 0}, so the value of the standard error of the contrasts should be equal to the square root of 2*.50/20 = 0.2236.

library(MASS) library(lme4)

# setting up treatment and participants factors nTreat = 3 nPP = 20 Treat <- factor(rep(1:nTreat, each=nPP)) PP <- factor(rep(1:nPP, nTreat)) # generate some random # specify means means = c(0, .20, .50) # create variance-covariance matrix # var = 1, cov = .5 Sigma = matrix(rep(.5, 9), 3, 3) diag(Sigma) <- 1 # generate the data; using empirical = TRUE # so that variance and covariance are known # set to FALSE for "real" random data sco = as.vector(mvrnorm(nPP, means, Sigma, empirical = TRUE)) #setting up custom contrasts for Treatment factor myContrasts <- rbind(c(-1, 1, 0), c(-.5, -.5, 1)) contrasts(Treat) <- ginv(myContrasts) #fit linear mixed effects model: myModel <- lmer(sco ~ Treat + (1|PP)) summary(myModel)

## Linear mixed model fit by REML ['lmerMod'] ## Formula: sco ~ Treat + (1 | PP) ## ## REML criterion at convergence: 157.6 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.6676 -0.4869 0.1056 0.6053 1.9529 ## ## Random effects: ## Groups Name Variance Std.Dev. ## PP (Intercept) 0.5 0.7071 ## Residual 0.5 0.7071 ## Number of obs: 60, groups: PP, 20 ## ## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 0.2333 0.1826 1.278 ## Treat1 0.2000 0.2236 0.894 ## Treat2 0.4000 0.1936 2.066 ## ## Correlation of Fixed Effects: ## (Intr) Treat1 ## Treat1 0.000 ## Treat2 0.000 0.000