mat.regress {psych} | R Documentation |
This function extracts subsets of variables (x and y) from a correlation matrix (m) and then find the multiple correlation and beta weights of the (x) set predicting each member of the (y) set.
mat.regress(m, x, y,digits=2)
m |
a matrix of correlations |
x |
the column numbers of the x set (e.g., c(1,3,5) |
y |
the column numbers of the y set (e.g., c(2,4,6) |
digits |
round the answer to digits |
Although it is more common to calculate multiple regression from raw data, it is, of course, possible to do so from a set of correlations. The input to the function is a square covariance or correlation matrix, as well as the column numbers of the x (predictor) and y (criterion) variables.
The output is a set of multiple correlations, one for each dependent variable in the y set.
A typical use in the SAPA project is to form item composites by clustering or factoring (see ICLUST
, principal
), extract the clusters from these results (factor2cluster
), and then form the composite correlation matrix using cluster.cor
. The variables in this reduced matrix may then be used in multiple R procedures using mat.regress.
Although the overall matrix can have missing correlations, the correlations in the subset of the matrix used for prediction must exist.
beta |
the beta weights for each variable in X for each variable in Y |
R |
The multiple R for each equation (the amount of change a unit in the predictor set leads to in the criterion set). |
R2 |
The multiple R2 (% variance acounted for) for each equation |
William Revelle
Maintainer: William Revelle <revelle@northwestern.edu>
cluster.cor
, factor2cluster
,principal
,ICLUST
## Not run: test.data <- Harman74.cor$cov #24 mental variables #choose 3 of them to regress against another 4 -- arbitrary choice of variables print(mat.regress(test.data,c(1,2,3),c(4,5,10,12)),digits=2) ## End(Not run) #gives this output #print(mat.regress(test.data,c(1,2,3),c(4,5,10,12)),digits=2) #$beta # Flags GeneralInformation Addition CountingDots #VisualPerception 0.40 0.22 0.16 0.30 #Cubes 0.06 0.18 0.06 0.05 #PaperFormBoard 0.12 0.10 -0.16 0.00 # #$R # Flags GeneralInformation Addition CountingDots # 0.49 0.38 0.18 0.32 # #$R2 # Flags GeneralInformation Addition CountingDots # 0.24 0.15 0.03 0.10 # #