R/lav_partable_from_lm.R

In lavaan: Latent Variable Analysis

# build a bare-bones parameter table from a fitted lm object
lav_partable_from_lm <- function(object, est = FALSE, label = FALSE,
                                 as.data.frame. = FALSE) {

    # sanity check
    if(!inherits(object, "lm")) {
        stop("object must be of class lm")
    }

    objectTerms <- terms(object)

    responseIndex <- attr(objectTerms, "response")
    varNames <- as.character(attr(objectTerms, "variables"))[-1]
    responseName <- varNames[responseIndex]

    predCoef  <- lav_object_inspect_coef(object, type = "free",
                                         add.labels = TRUE)
    predNames <- names(predCoef)

    lhs <- rep(responseName, length(predNames))
     op <- rep("~", length(predNames))
    rhs <- predNames

    # intercept?
    if(attr(objectTerms, "intercept")) {
        int.idx <- which(rhs == "(Intercept)")
        op[int.idx] <- "~1"
        rhs[int.idx] <- ""
    }

    # always add residual variance?
    #lhs <- c(lhs, responseName)
    # op <- c(op, "~~")
    #rhs <- c(rhs, responseName)

    # construct minimal partable
    partable <- list(lhs = lhs, op = op, rhs = rhs)

    # include 'est' column?
    if(est) {
        #partable$est <- c(as.numeric(predCoef),
        #                  sum(resid(object)^2) / object$df.residual)
        partable$est <- as.numeric(predCoef)
    }

    # include 'label' column?
    if(label) {
        # partable$label <- c(predNames, responseName)
        partable$label <- predNames

        # convert all ':' to '.'
        partable$label <- gsub("[:()]", ".", partable$label)
    }

    # convert to data.frame?
    if(as.data.frame.) {
        partable <- as.data.frame(partable, stringsAsFactors = FALSE)
    }

    partable
}