I'd like to perform a t-test between groups 'A' and 'B'. The difficulty is that
although there is only one response variable, there are many observations, and
the grouping (A or B) differs with each observation. My code for generating the
input data is shown below.
I'd like to know how to approach doing the test, ideally so that the t-test
results for each observation are presented in a table. I'm not sure where to
start as other searches have been futile... something along the lines of
t.test(ind_vars ~ resp_vars) , maybe using rapply, and separating groups by A
and B each time...
# Matrix for response variableN_samples <- 20resp_vars <-
matrix(runif(n=N_samples, min=0, max=1))sample_names <- paste0("sample_",
1:N_samples )rownames(x=resp_vars) <- sample_namescolnames(x=resp_vars) <-
"resp"resp_vars [1:5,]
# Matrix for independent variablesN_observations <- 100ind_vars <- matrix(NA,
N_observations, N_samples)ind_vars <- apply(ind_vars, c(1,2), function(x)
sample(c("A", "B"),1))ind_var_names <- paste0("obs_",
1:N_observations)rownames(x=ind_vars) <- ind_var_namescolnames(x=ind_vars) <-
sample_namesind_vars[1:3,1:5]
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