bmscstan (version 1.2.1.0)

pairwise.BMSC: Pairwise contrasts

Description

Calculate pairwise comparisons between marginal posterior distributions divided by group levels

Usage

pairwise.BMSC(mdl, contrast, covariate = NULL, who = "delta")

Value

a pairwise.BMSC object

Arguments

mdl

An object of class BMSC.

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

control

only the controls

singlecase

only the single case \((\beta + \delta)\)

delta

only the difference between the single case and controls

Examples

 # \donttest{

######################################
# 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")

# }