amd_bi: Amount of Missing Data - Bivariate (Pairwise Deletion)

In quest: Prepare Questionnaire Data for Analysis

Amount of Missing Data - Bivariate (Pairwise Deletion)

Description

amd_bi by default computes the proportion of missing data for pairs of variables in a data.frame, with arguments to allow for counts instead of proportions (i.e., prop) or observed data rather than missing data (i.e., ov). It is bivariate in that each pair of variables is treated in isolation.

Usage

amd_bi(data, vrb.nm, prop = TRUE, ov = FALSE)

Arguments

data	data.frame of data.
vrb.nm	character vector of the colnames from data specifying the variables.
prop	logical vector of length 1 specifying whether the frequency of missing values should be returned as a proportion (TRUE) or a count (FALSE).
ov	logical vector of length 1 specifying whether the frequency of observed values (TRUE) should be returned rather than the frequency of missing values (FALSE).

Value

data.frame of nrow = ncol = length(vrb.nm) and rowames = colnames = vrb.nm providing the frequency of missing (or observed if ov = TRUE) values per pair of variables. If prop = TRUE, the values will range from 0 to 1. If prop = FALSE, the values will range from 0 to nrow(data).

See Also

amd_bi amd_multi

Examples

amd_bi(data = airquality, vrb.nm = names(airquality)) # proportion of missing data
amd_bi(data = airquality, vrb.nm = names(airquality),
   ov = TRUE) # proportion of observed data
amd_bi(data = airquality, vrb.nm = names(airquality),
   prop = FALSE) # count of missing data
amd_bi(data = airquality, vrb.nm = names(airquality),
   prop = FALSE, ov = TRUE) # count of observed data

quest documentation built on May 29, 2024, 4:59 a.m.