R/glm_fit.R

Defines functions llik loglik_glm_fit aicloglik_glm_fit do_glm_fit update_glm_fit predict_glm_fit

# helper function: prediction from matrix, coefficients and inverse link
predict_glm_fit <-
function(beta, x, offset, family = NULL) {
	if(is.null(offset)) offset <- 0
	ok <- !is.na(beta)
	beta <- beta[ok]
	x <- x[, ok, drop = FALSE]
	if(inherits(family, "family")) return(family$linkinv(offset + (x %*% beta)))
	return(offset + (x %*% beta))
}

update_glm_fit <-
function(fit, data, weights, offset, nobs = nrow(data), y = NULL) {
    tf <- terms(fit)
    x <- model.matrix(tf, data = data)
    if(is.null(y)) {
        y <- get.response(tf, data)
        if(is.matrix(y)) {
            wts <- rowSums(y)
            y <- y[, 1L] / wts
        } else wts <- rep(1, nobs)
        weights <- weights * wts
    }
    glm.fit(x, y, weights, offset = offset, family = family(fit))
}

do_glm_fit <-
function(tf, data, family, weights, offset, nobs = nrow(data), y = NULL) {
	x <- model.matrix(tf, data = data)
    if(is.null(y)) {
		y <- get.response(tf, data)
        if(is.matrix(y)) {
            wts <- rowSums(y)
            y <- y[, 1L] / wts
        } else wts <- rep(1, nobs)
        weights <- weights * wts
    }
    glm.fit(x, y, weights, offset = offset, family = family)
}

aicloglik_glm_fit <-
function(object, y, x, wt, offset = NULL) {
    fam <- object$family
    nobs <- NROW(x)
    n <- if (NCOL(y) == 1) 
        rep.int(1, nobs) else rowSums(y)
    #mu <- fam$linkinv((x %*% object$coefficients)[, 1L])
	mu <- predict_glm_fit(object$coefficients, x, offset, fam)[, 1L]
    dev <- sum(fam$dev.resids(y, mu, wt))
	rank <- object$rank
    aic <- fam$aic(y, n, mu, wt, dev) + 2 * rank
    p <- rank
    if (fam$family %in% c("gaussian", "Gamma", "inverse.gaussian")) 
        p <- p + 1
    ll <- p - aic/2
    # c(aic = aic, loglik = ll, nobs = nobs, df = p)
    c(aic, ll, p)
}

loglik_glm_fit <-
function(object, aic = object$aic) {
    p <- object$rank
    if (object$family$family %in% c("gaussian", "Gamma", "inverse.gaussian")) 
        p <- p + 1
	structure(p - aic / 2, nobs = length(object$residuals), df = p, class = "logLik")
}


llik <- function(y, mu, fam, n, wt = 1, off = NULL) {
	# wt == fit$prior.weights
	no <- NROW(y)
	ep <- if (fam$family %in% c("gaussian", "Gamma", "inverse.gaussian")) 1 else 0
	dev <- sum(fam$dev.resids(y, mu, wt))
	(fam$aic(y, n, mu, wt, dev) / 2) - ep # +LL
}


# list(coefficients =, family =, rank=)

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MuMIn documentation built on Sept. 1, 2022, 1:08 a.m.