calculate_stipw: Calculate STIPW based on propensity for being study

In swihart/wfpca: weighted functional prinicpal components analysis

Description

Names are hard coded, so stick to script in the examples.

Usage

calculate_stipw(data_in = NULL, na.action = "omit")

Arguments

data_in	an object returned from apply_censoring()
na.action	"keep" means the dataset returned will be same number of rows as data_in and "omit" discards all data past censoring observation

Value

a dataset similar to data_in with stipw in columns; and potentially fewer rows or NA-filled rows for induced censoring.

Examples

## Not run: 
d<-prep_data()
head(d)
over_samp_mat<-sample_data(d,1000)
with_ses <- calculate_ses(over_samp_mat)
long <-make_long(with_ses)
head(long)
censored <- apply_censoring(long)
head(censored,18)
observed_with_stipw <- calculate_stipw(censored,"keep")

## End(Not run)

swihart/wfpca documentation built on May 30, 2019, 10:38 p.m.