create.sampdata: Create Cluster Bootstrapped Data Sets

In jenniferthompson/ClusterBootMultinom: Multinomial Logistic Regression Using Clustered Bootstrapping

Description

Using an original data frame with N subjects, creates data sets with all records from each of N subject IDs sampled with replacement. Final data sets are saved in a list.

Usage

create.sampdata(org.data, id.var, n.sets, first.seed = 56)

Arguments

org.data	Original data set, of class data.frame.
id.var	Character string; name of subject identifier variable to sample.
n.sets	Integer; number of final data sets desired.
first.seed	Numeric (defaults to 56); set for reproducibility.

Value

List of n.sets data frames.

Examples

df <- data.frame(id = sample(1:20, size = 100, replace = TRUE),
                 x1 = rnorm(n = 100),
                 x2 = rbinom(p = 0.75, n = 100, size = 1),
                 y = sample(LETTERS[1:3], size = 100, replace = TRUE))
df <- df[order(df$id),]
df$time <- unlist(lapply(1:length(unique(df$id)),
                         FUN = function(idnum){ 1:nrow(df[df$id == unique(df$id)[idnum],]) }))

## Using create.sampdata(), generate list of cluster bootstrapped data sets
bootdata.list <- create.sampdata(org.data = df,
                                 id.var = 'id',
                                 n.sets = 25)

jenniferthompson/ClusterBootMultinom documentation built on May 19, 2019, 4:03 a.m.