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Ideas and example functions that extend OpenMx, encapsulate tedious work, and make scripts easier to write or more compact.

You will probably define helper functions, especially for summarising the output of model you use frequently.

If you have questions not answers, then add those here: That's how a wiki works. Please add material here as you learn...

readLowerTriangle <- function(file, nrows, fill=TRUE) { xvector <- scan(file) X <- matrix(NA, nrows, nrows) i <- 1 for(row in 1:nrows) { for(col in 1:nrows) { if(col>row) next X[row,col] <- xvector[i] i <- i + 1 if (fill) X[col,row] <- X[row,col] } } return(X) }

An alternative using matrix indexing would be:

read.lower.triangle <- function(file, nrows) { X <- matrix(NA, ncol=nrows, nrow=nrows) X[upper.tri(X, diag=TRUE)] <- scan(file) X[lower.tri(X, diag=FALSE)] <- t(X)[lower.tri(X, diag=FALSE)] return(X) }

See also read.moments() in http://cran.r-project.org/web/packages/sem/sem.pdf

require(sem) # install.packages("sem", dep=T)

read.moments(file = "", diag = TRUE,

names = as.character(paste("X", 1:n, sep = "")))

If you are reanalysing published data, you may only have a correlation matrix and the SD for each variable. You can upconvert this to a covariance matrix with cor2cov(matrix, sd) from the MBESS package

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