How can pairwise comparisons with weights be implemented in R?

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Pairwise comparisons with weights can be implemented in R using the pairwise.t.test() function from the stats package.

Here's an example of how to implement pairwise comparisons with weights for three groups (Group A, Group B, Group C) with different sample sizes and weights:

# create sample data with weights
set.seed(123)
Group.A <- c(rnorm(10, mean = 10), rnorm(10, mean = 12))
Group.B <- c(rnorm(5, mean = 8), rnorm(5, mean = 11))
Group.C <- rnorm(6, mean = 9)
weights <- c(rep(1, 20), rep(2, 10), rep(3, 6))
dat <- data.frame(Group = c(rep("A", 20), rep("B", 10), rep("C", 6)),
                  Value = c(Group.A, Group.B, Group.C),
                  Weight = weights)

# pairwise comparisons with weights
pairwise.t.test(dat$Value, dat$Group, wt = dat$Weight, p.adjust.method = "bonferroni")

This code calculates pairwise comparisons with weights using the pairwise.t.test() function. The dat dataframe contains the values for each group (Value), the group labels (Group), and the corresponding weights (Weight).

The wt argument specifies the weights, and the p.adjust.method argument specifies the method for adjusting p-values for multiple comparisons. In this example, we use the Bonferroni adjustment.

The output of the pairwise.t.test() function provides the pairwise comparisons for each group with adjusted p-values.