View source: R/BMSC-pairwise.R
pairwise.BMSC | R Documentation |
Calculate pairwise comparisons between marginal posterior distributions divided by group levels
pairwise.BMSC(mdl, contrast, covariate = NULL, who = "delta")
mdl |
An object of class |
contrast |
Character value giving the name of the coefficient whose levels need to be compared. |
covariate |
at the moment is silent |
who |
parameter to choose the estimates to contrast
|
a pairwise.BMSC
object
###################################### # simulation of controls' group data ###################################### # Number of levels for each condition and trials NCond1 <- 2 NCond2 <- 2 Ntrials <- 8 NSubjs <- 30 betas <- c( 0 , 0 , 0 , 0.2) data.sim <- expand.grid( trial = 1:Ntrials, ID = factor(1:NSubjs), Cond1 = factor(1:NCond1), Cond2 = factor(1:NCond2) ) contrasts(data.sim$Cond1) <- contr.sum(2) contrasts(data.sim$Cond2) <- contr.sum(2) ### d.v. generation y <- rep( times = nrow(data.sim) , NA ) # cheap simulation of individual random intercepts set.seed(1) rsubj <- rnorm(NSubjs , sd = 0.1) for( i in 1:length( levels( data.sim$ID ) ) ){ sel <- which( data.sim$ID == as.character(i) ) mm <- model.matrix(~ Cond1 * Cond2 , data = data.sim[ sel , ] ) set.seed(1 + i) y[sel] <- mm %*% as.matrix(betas + rsubj[i]) + rnorm( n = Ntrials * NCond1 * NCond2 ) } data.sim$y <- y # just checking the simulated data... boxplot(y~Cond1*Cond2, data = data.sim) ###################################### # simulation of patient data ###################################### betas.pt <- c( 0 , 0.8 , 0 , 0) data.pt <- expand.grid( trial = 1:Ntrials, Cond1 = factor(1:NCond1), Cond2 = factor(1:NCond2) ) contrasts(data.pt$Cond1) <- contr.sum(2) contrasts(data.pt$Cond2) <- contr.sum(2) ### d.v. generation mm <- model.matrix(~ Cond1 * Cond2 , data = data.pt ) set.seed(5) data.pt$y <- (mm %*% as.matrix(betas.pt) + rnorm( n = Ntrials * NCond1 * NCond2 ))[,1] # just checking the simulated data... boxplot(y~Cond1*Cond2, data = data.pt) mdl <- BMSC(y ~ Cond1 * Cond2 + ( 1 | ID ), data_ctrl = data.sim, data_sc = data.pt, seed = 77, typeprior = "cauchy", s = 1) summary(mdl) pp_check(mdl) pairwise.BMSC( mdl, contrast = "Cond11:Cond21")
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