Function that allows you to run a linear model on a covariance matrix using lavaan

covlm.r

# see
# http://stackoverflow.com/questions/20203485/why-is-source-gist-function-in-r-devtools-package-not-working

covlm <- function(dv, ivs, n, cov) {
    # Assumes lavaan package
    # library(lavaan)
    # dv: charcter vector of length 1 with name of outcome variable
    # ivs: character vector of names of predictors
    # n: numeric vector of length 1: sample size
    # cov: covariance matrix where row and column names 
    #       correspond to dv and ivs
    # Return
    #      list with lavaan model fit
    #      and various other features of the model
    
    results <- list()
    eq <- paste(dv, "~", paste(ivs, collapse = " + "))
    results$fit <- lavaan::sem(eq, sample.cov = cov,
                       sample.nobs = n)
    
    # unstandardised coefficients
    ufit <- parameterestimates(results$fit) 
    ufit <- ufit[ufit$op == "~", ]
    results$coef <- ufit$est
    names(results$coef) <- ufit$rhs
    
    sfit <- standardizedsolution(results$fit) 
    sfit <- sfit[sfit$op == "~", ]
    results$standardizedcoef <- sfit$est.std
    names(results$standardizedcoef) <- sfit$rhs

    # use unclass to not limit r2 to 3 decimals
    results$r.squared <- as.numeric(unclass(inspect(results$fit, 'r2'))) # r-squared
    
    # adjusted r-squared
    adjr2 <- function(rsquared, n, p) 1 - (1-rsquared)  * ((n-1)/(n-p-1))
    results$adj.r.squared <- as.numeric(adjr2(unclass(inspect(results$fit, 'r2')), 
                                    n = n, p = length(ivs)) )
    results
}