pairwise.BMSC {bmscstan} | R Documentation |
Pairwise contrasts
Description
Calculate pairwise comparisons between marginal posterior distributions divided by group levels
Usage
pairwise.BMSC(mdl, contrast, covariate = NULL, who = "delta")
Arguments
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
|
Value
a pairwise.BMSC
object
Examples
######################################
# 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")
[Package bmscstan version 1.2.1.0 Index]