pairwiseCount: Count number of pairwise cases for a data set with missing (NA) data and impute values.

Description

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.

Usage

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

Arguments

An input matrix, typically a data matrix ready to be correlated.

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.

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

Value

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.

Details

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.

Examples

Run this code

# NOT RUN {
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)

    
# }

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