pairwiseCount {psych} | R Documentation |
When doing cor(x, use= "pairwise"), it is nice to know the number of cases for each pairwise correlation. This is particularly useful when doing SAPA type analyses. More importantly, when there are some missing pairs, it is useful to supply imputed values so that further analyses may be done. This is useful if using the Massively Missing Completely at Random (MMCAR) designs used by the SAPA project.
pairwiseCount(x, y = NULL,diagonal=TRUE) pairwiseDescribe(x,y,diagonal=FALSE,...) pairwiseImpute(keys,R,fix=FALSE) pairwiseReport(x,cut=0) count.pairwise(x, y = NULL,diagonal=TRUE) #deprecated
x |
An input matrix, typically a data matrix ready to be correlated. |
y |
An optional second input matrix |
diagonal |
if TRUE, then report the diagonal, else fill the diagonals with NA |
... |
Other parameters to pass to describe |
keys |
A keys.list specifying which items belong to which scale. |
R |
A correlation matrix to be described or imputed |
cut |
Report the item pairs and numbers with cell sizes less than cut |
fix |
If TRUE, then replace all NA correlations with the mean correlation for that within or between scale |
When using Massively Missing Completely at Random (MMCAR) designs used by the SAPA project, it is important to count the number of pairwise observations (pairwiseCount
). If there are pairs with 1 or fewer observations, these will produce NA values for correlations making subsequent factor analyses fa
or reliability analsyes omega
or scoreOverlap
impossible.
In order to identify item pairs with counts less than a certain value pairwiseReport
reports the names of those pairs with fewer than 'cut' observations.
To remedy the problem of missing correlations, we impute the missing correlations using pairwiseImpute
.
The technique takes advantage of the scale based structure of SAPA items. Items within a scale (e.g. Letter Number Series similar to the ability
items) are imputed to correlate with items from another scale (e.g., Matrix Reasoning) at the average of these two between scale inter-item mean correlations. The average correlations within and between scales are reported by pairwiseImpute
and if the fix paremeter is specified, the imputed correlation matrix is returned.
result |
= matrix of counts of pairwise observations (if pairwiseCount) |
av.r |
The average correlation value of the observed correlations within/between scales |
count |
The numer of observed correlations within/between each scale |
percent |
The percentage of complete data by scale |
imputed |
The original correlation matrix with NA values replaced by the mean correlation for items within/between the appropriate scale. |
Maintainer: William Revelle revelle@northwestern.edu
x <- matrix(rnorm(900),ncol=6) y <- matrix(rnorm(450),ncol=3) x[x < 0] <- NA y[y > 1] <- NA pairwiseCount(x) pairwiseCount(y) pairwiseCount(x,y) pairwiseCount(x,diagonal=FALSE) pairwiseDescribe(x,quant=c(.1,.25,.5,.75,.9)) #examine the structure of the ability data set keys <- list(ICAR16=colnames(ability),reasoning = cs(reason.4,reason.16,reason.17,reason.19), letters=cs(letter.7, letter.33,letter.34,letter.58, letter.7), matrix=cs(matrix.45,matrix.46,matrix.47,matrix.55), rotate=cs(rotate.3,rotate.4,rotate.6,rotate.8)) pairwiseImpute(keys,ability)