## Written by Mercedeh Movassagh <mercedeh@ds.dfci.harvard.edu>, Aug 2020
#' @importFrom stats sd glm gaussian
NULL
#' Model functions for GLM with Gaussian model.
#'
#' Implements standardized functions to fit the glm with
#' Gaussian family and to obtain coefficients, pvalues, etc.
#'
#' @return structure containing functions \code{fit}, \code{coefficients},
#' \code{aic}, \code{data}, \code{pterm}, \code{pmodel}, and a
#' character string "glm_gaussian" in \code{model}.
#' @export
#' @examples
#' x <- glm_gaussian()
glm_gaussian <- function() {
model_name <- "glm_gaussian"
transform_ <- function(x, data, zscore) {
mf <- modelframe_(x, data) # extract model data.frame
if (identical(zscore, TRUE)) {
mf[, 1] <- (mf[, 1] - mean(mf[, 1])) / sd(mf[, 1])
}
attr(mf, "zscore") <- zscore # add 'zscore' as attribute to data.frame
return(mf)
}
fit_ <- function(x, data) {
tryCatch(
{
g <- glm(x, data = data, family = gaussian())
if (!is.null(g)) {
attr(g, "model") <- model_name
}
return(g)
},
warning = function(e, ...) {
# warning(e)
return(NULL)
},
error = function(e, ...) {
# warning(e)
return(NULL)
}
)
}
# fit
fit <- function(x, data, zscore = FALSE, ...) {
data <- transform_(x, data, zscore)
return(fit_(x, data))
}
pterm <- function(model) {
nm <- termlabels_(model$formula)
ret <- coefficients_(model)[, "Pr(>|t|)", drop = TRUE]
names(ret) <- nm
return(ret)
}
structure(list(
fit = fit, coefficients = coefficients_, aic = aic_, data = data_,
pterm = pterm, pmodel = pmodel_, model = model_name
))
}
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