ESTER (version 0.1.0)

seqERboot: Computes sequential evidence ratios for a given data set and permutation samples

Description

Computes sequential evidence ratios for a given data set as well as for order_nb random permutations of this dataset. When data involve repeated measures (and so multiple lines per subject), a column indicating the subject "id" should be provided to the id argument. If nothing is passed to the id argument, seqERboot will suppose that there is only one observation (i.e., one line) per subject.

Usage

seqERboot(ic, mod1, mod2, nmin, id = NULL, order_nb)

Arguments

ic

Indicates whether to use the aic or the bic.

mod1

A model of class lm or lmerMod.

mod2

A model of class lm or lmerMod (of the same class of mod1).

nmin

Minimum sample size from which start to compute sequential evidence ratios.

id

If applicable (i.e., repeated measures), name of the "id" column of your dataframe, in character string.

order_nb

Number of permutation samples to evaluate.

See Also

seqER

Examples

# NOT RUN {
data(mtcars)
mod1 <- lm(mpg ~ cyl, mtcars)
mod2 <- lm(mpg ~ cyl + disp, mtcars)
seq_boot_mtcars <- seqERboot(ic = bic, mod1, mod2, nmin = 10, order_nb = 20)

# Example with repeated measures
library(lme4)
data(sleepstudy)
mod1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy)
mod2 <- lmer(Reaction ~ Days + I(Days^2) + (1|Subject), sleepstudy)
seqERboot(ic = bic, mod1, mod2, nmin = 10, id = "Subject", order_nb = 20)
# }
# NOT RUN {
# }