scale_missing: Chi-square tests for the counts of missing and non-missing....
In MergeGUI: A GUI for Merging Datasets in R

Description Usage Arguments Value Author(s) Examples

View source: R/mergeGUI.R

Chi-square tests for the counts of missing and non-missing. This function is used to detect whether the matched variables from different files have different missing patterns. For each variable, it will firstly count the missing and non-missing values among the sources, and then form a contingency table. The p-value of Chi-square test is computed from the contingency table and finally reported for the variable.

1	scale_missing(nametable.class, dataset.class, name.class)

`nametable.class`	A matrix of the matched variable names. The number of columns is equal to the number of files. Each row represents a variable that is going to be merged. Any elements except NA in nametable.class must be the variable names in dataset.class.
`dataset.class`	The dataset list. The length of the list is equal to the number of files, and the order of the list is the same as the order of columns in nametable.class.
`name.class`	A character vector of variable names. The length of the vector must be equal to the number of rows in nametable.class. Since the variable names in nametable.class may not be consistent, name.class is needed to name the variables.

A vector of p-values from the Chisquare-test for the missings of each variable. The p-values are between 0 and 1.

Xiaoyue Cheng <xycheng@iastate.edu>

a = data.frame(aa = 1:5, ab = LETTERS[6:2], ac = as.logical(c(0, 1, 0, NA, 0)))
b = data.frame(b1 = letters[12:14], b2 = 3:1)
dat = list(a, b)
name = matrix(c("ab", "aa", "ac", "b1", "b2", NA), ncol = 2)
colnames(name) = c("a", "b")
newname = c("letter", "int", "logic")
scale_missing(name, dat, newname)