#  File src/library/stats/R/logLik.R
#  Part of the R package, http://www.R-project.org
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#
#  This program is free software; you can redistribute it and/or modify
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#  GNU General Public License for more details.
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### originally from package nlne.


logLik <- function(object, ...) UseMethod("logLik")

print.logLik <- function(x, digits = getOption("digits"), ...)
{
    cat("'log Lik.' ", paste(format(c(x), digits = digits), collapse = ", "),
        " (df=", format(attr(x,"df")), ")\n", sep = "")
    invisible(x)
}

str.logLik <- function(object, digits = max(2L, getOption("digits") - 3L),
                       vec.len = getOption("str")$vec.len, ...)
{
    cl <- oldClass(object)
    len <- length(co <- c(object))
    cutl <- len > vec.len
    cat("Class", if (length(cl) > 1L) "es",
	" '", paste(cl, collapse = "', '"), "' : ",
	paste
        (format(co[seq_len(min(len,vec.len))], digits = digits),
	      collapse = ", "), if(cutl) ", ...",
	" (df=", format(attr(object,"df")), ")\n", sep = "")
}

## rather silly (but potentially used in pkg nlme):
as.data.frame.logLik <- function (x, ...)
    as.data.frame(c(x), ...)

## >> logLik.nls() in nls.R

## from package:nlme

## log-likelihood for glm objects
logLik.glm <- function(object, ...)
{
    if(!missing(...)) warning("extra arguments discarded")
    fam <- family(object)$family
    p <- object$rank
    ## allow for estimated dispersion
    if(fam %in% c("gaussian", "Gamma", "inverse.gaussian")) p <- p + 1
    val <- p - object$aic / 2
    ## Note: zero prior weights have NA working residuals.
    attr(val, "nobs") <- sum(!is.na(object$residuals))
    attr(val, "df") <- p
    class(val) <- "logLik"
    val
}

## log-likelihood for lm objects
logLik.lm <- function(object, REML = FALSE, ...)
{
    if(inherits(object, "mlm"))
        stop("'logLik.lm' does not support multiple responses")
    res <- object$residuals # not resid(object) because of NA methods
    p <- object$rank
    N <- length(res)
    if(is.null(w <- object$weights)) {
        w <- rep.int(1, N)
    } else {
        ## this is OK as both resids and weights are for the cases used
        excl <- w == 0			# eliminating zero weights
        if (any(excl)) {
            res <- res[!excl]
            N <- length(res)
            w <- w[!excl]
        }
    }
    N0 <- N
    if(REML) N <- N - p
    val <- .5* (sum(log(w)) - N * (log(2 * pi) + 1 - log(N) +
                                   log(sum(w*res^2))))
    if(REML) val <- val - sum(log(abs(diag(object$qr$qr)[1L:p])))
    attr(val, "nall") <- N0 # NB, still omits zero weights
    attr(val, "nobs") <- N
    attr(val, "df") <- p + 1
    class(val) <- "logLik"
    val
}

## Since AIC, BIC call logLik on everything
logLik.logLik <- function(object, ...) object

nobs <- function(object, ...) UseMethod("nobs")

## also used for mlm fits
nobs.lm <- function(object, ...)
    if(!is.null(w <- object$weights)) sum(w != 0) else NROW(object$residuals)

nobs.glm <- function(object, ...)
    if(!is.null(w <- object$prior.weights)) sum(w != 0) else length(object$residuals)

nobs.logLik <- function(object, ...) {
    res <- attr(object, "nobs")
    if (is.null(res)) stop("no \"nobs\" attribute is available")
    res
}

nobs.nls <- function(object, ...)
    if (is.null(w <- object$weights)) length(object$m$resid()) else sum(w != 0)

## it is probably too unsafe to use residuals generally, not least
## because of e.g. weighted fits.
nobs.default <- function(object, use.fallback = FALSE, ...)
{
    ## MASS::loglm  and MASS::polr fits have an 'nobs' component
    if(is.list(object) && !is.null(n <- object[["nobs"]])) n
    else if(use.fallback) {
        if(!is.null(w <- object[["weights"]])) sum(w != 0)
        else if("residuals" %in% names(object))
            NROW(object$residuals) # and not residuals(object)
            ## perhaps sum(!is.na(object$residuals)) ?
        else {
            warning("no 'nobs' method is available")
            0L # which is what object$residuals used to give.
        }
    } else stop("no 'nobs' method is available") # or maybe NA_integer_
}