Description Usage Arguments Value See Also Examples
View source: R/cmat.star.BinOrdNN.R
The function computes the correlations of intermediate multivariate normal data prior to subsequent dichotomization (for binary variables), ordinalization (for ordinal variables), and transformation (for continuous variables)
1 | cmat.star.BinOrdNN(plist, skew.vec, kurto.vec, no.bin, no.ord, no.NN, CorrMat)
|
plist |
A list of probability vectors corresponding to each binary/ordinal variable. The i-th element of |
skew.vec |
The skewness vector for continuous variables. |
kurto.vec |
The kurtosis vector for continuous variables. |
no.bin |
Number of binary variables. |
no.ord |
Number of ordinal variables. |
no.NN |
Number of continuous variables. |
CorrMat |
The target correlation matrix which must be positive definite and within the valid limits. |
An intermediate correlation of size (no.bin + no.ord + no.NN)*(no.bin + no.ord + no.NN)
validate.target.cormat.BinOrdNN
, IntermediateNonNor
, IntermediateONN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ## Not run:
no.bin <- 1
no.ord <- 2
no.NN <- 4
q <- no.bin + no.ord + no.NN
set.seed(54321)
Sigma <- diag(q)
Sigma[lower.tri(Sigma)] <- runif((q*(q-1)/2),-0.4,0.4)
Sigma <- Sigma + t(Sigma)
diag(Sigma) <- 1
marginal <- list(0.3, cumsum(c(0.30, 0.40) ), cumsum(c(0.4, 0.2, 0.3) ) )
cmat.star <- cmat.star.BinOrdNN(plist=marginal, skew.vec=c(1,2,2,3),
kurto.vec=c(2,7,25,25),no.bin=1, no.ord=2, no.NN=4, CorrMat=Sigma)
## End(Not run)
|
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