check.nomiss: Check if Variable has No Missings for Each Subject

In wviechtb/esmpack: Functions that Facilitate Preparation and Management of ESM/EMA Data

Check if Variable has No Missings for Each Subject

Description

Function that checks if a variable has no missing values for each subject.

Usage

check.nomiss(x, id, data, out=1)

Arguments

x	argument to specify the variable to check.
id	argument to specify a subject id variable.
data	optional data frame that contains the variables specified above.
out	either a string or an integer (1 = "logical", 2 = "id", 3 = "data") indicating what information should be returned in case there are subjects where the variable is not time-invariant.

Details

The function checks if the values of a variable do not contain any missing values for each subject.

Value

When out = 1 or out = "logical", the function simply returns a logical (i.e., TRUE or FALSE), depending on whether the variable contains no missing values within each subject.

When out = 2 or out = "id", the function returns a vector with the ids of those subjects where the variable contains missing values.

When out = 3 or out = "data", the function returns the data for those subjects where the variable contains missing values.

Author(s)

Wolfgang Viechtbauer [e-mail]

Examples

# illustrative dataset
dat <- data.frame(subj=rep(1:4, each=5),
                  obs = 1:5,
                  age = rep(c(20,31,27,22), each=5),
                  stress = c(2,3,1,4,2, 3,NA,3,3,3, 1,1,2,6,4, 1,2,1,3,1))
dat

# check if the age variable contains no missings
check.nomiss(age, subj, data=dat)

# check if the stress variable contains no missings
check.nomiss(stress, subj, data=dat)

# for which subjects are there missing values?
check.nomiss(stress, subj, data=dat, out=2)

# show the data for those subjects
check.nomiss(stress, subj, data=dat, out=3)

wviechtb/esmpack documentation built on April 12, 2025, 9:48 p.m.