Revision of helper-functions from Thu, 11/05/2009 - 11:44

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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.

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Read a Lower triangle file

```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)
}```

require(sem) # install.packages("sem", dep=T)
read.moments(file = "", diag = TRUE,
names = as.character(paste("X", 1:n, sep = "")))

Converting a correlation matrix to a covariance matrix

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