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#### S3 methods for Qlm
vcov.Qlm <- function(object, ...) object$covar
summary.Qlm <- function(object, correlation = FALSE, symbolic.cor = FALSE, ...){
z <- object
p <- z$rank
rdf <- z$df.residual
if (p == 0) {
r <- z$residuals
n <- length(r)
w <- z$weights
if (is.null(w)){rss <- sum(r^2)
}
else {
rss <- sum(w * r^2)
r <- sqrt(w) * r
}
resvar <- rss/rdf
ans <- z[c("call", "terms", if (!is.null(z$weights)) "weights")]
class(ans) <- "summary.Qlm"
ans$aliased <- is.na(coef(object))
ans$residuals <- r
ans$df <- c(0L, n, length(ans$aliased))
ans$coefficients <- matrix(NA, 0L, 4L)
dimnames(ans$coefficients) <- list(NULL, c("Estimate", "Std. Error", "t value", "Pr(>|t|)"))
ans$sigma <- sqrt(resvar)
ans$r.squared <- ans$adj.r.squared <- 0
return(ans)
}
if (is.null(z$terms)) stop("invalid 'Qlm' object: no 'terms' component")
if (!inherits(object, "Qlm")) warning("calling summary.lm(<fake-lm-object>) ...")
Qr <- z$qr
n <- NROW(Qr$qr)
if (is.na(z$df.residual) || n - p != z$df.residual)
warning("residual degrees of freedom in object suggest this is not an \"Qlm\" fit")
r <- z$residuals
f <- z$fitted.values
w <- z$weights
if (is.null(w)) {
mss <- if (attr(z$terms, "intercept")) sum((f - mean(f))^2) else sum(f^2)
rss <- sum(r^2)
}
else {
mss <- if (attr(z$terms, "intercept")) {
m <- sum(w * f/sum(w))
sum(w * (f - m)^2)
}
else sum(w * f^2)
rss <- sum(w * r^2)
r <- sqrt(w) * r
}
resvar <- z$dispersion
if (is.finite(resvar) && resvar < (mean(f)^2 + var(f)) * 1e-30)
warning("essentially perfect fit: summary may be unreliable")
p1 <- 1L:p
R <- z$covar
se <- z$std.errs
est <- z$coefficients
tval <- est/se
ans <- z[c("call", "terms", if (!is.null(z$weights)) "weights")]
ans$residuals <- r
ans$coefficients <- cbind(Estimate = est, `Std. Error` = se, `t value` = tval, `Pr(>|t|)` = 2 * pt(abs(tval), rdf, lower.tail = FALSE))
ans$aliased <- is.na(z$coefficients)
ans$sigma <- sqrt(resvar)
ans$df <- c(p, rdf, NCOL(Qr$qr))
if (p != attr(z$terms, "intercept")) {
df.int <- if (attr(z$terms, "intercept")) 1L else 0L
ans$r.squared <- mss/(mss + rss)
ans$adj.r.squared <- 1 - (1 - ans$r.squared) * ((n - df.int)/rdf)
ans$fstatistic <- c(value = (mss/(p - df.int))/resvar, numdf = p - df.int, dendf = rdf)
}
else ans$r.squared <- ans$adj.r.squared <- 0
ans$cov.unscaled <- R
if (correlation) {
ans$correlation <- R/outer(se, se)
ans$symbolic.cor <- symbolic.cor
}
if (!is.null(z$na.action)) ans$na.action <- z$na.action
class(ans) <- "summary.Qlm"
ans
}
print.summary.Qlm <- function (x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...){
cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"), "\n\n", sep = "")
resid <- x$residuals
df <- x$df
rdf <- df[2L]
cat(if (!is.null(x$weights) && diff(range(x$weights))) "Weighted ", "Residuals:\n", sep = "")
if (rdf > 5L) {
nam <- c("Min", "1Q", "Median", "3Q", "Max")
rq <- if (length(dim(resid)) == 2L)
structure(apply(t(resid), 1L, quantile), dimnames = list(nam, dimnames(resid)[[2L]]))
else {
zz <- zapsmall(quantile(resid), digits + 1L)
structure(zz, names = nam)
}
print(rq, digits = digits, ...)
}
else if (rdf > 0L) {
print(resid, digits = digits, ...)
}
else {
cat("ALL", df[1L], "residuals are 0: no residual degrees of freedom!")
cat("\n")
}
if (length(x$aliased) == 0L) {
cat("\nNo Coefficients\n")
}
else {
if (nsingular <- df[3L] - df[1L])
cat("\nCoefficients: (", nsingular, " not defined because of singularities)\n", sep = "")
else cat("\nCoefficients:\n")
coefs <- x$coefficients
printCoefmat(coefs, digits = digits, signif.stars = signif.stars, na.print = "NA", ...)
}
cat("\nResidual standard error:", format(signif(x$sigma, digits)), "on", rdf, "degrees of freedom")
cat("\n")
if (nzchar(mess <- naprint(x$na.action))) cat(" (", mess, ")\n", sep = "")
if (!is.null(x$fstatistic)) {
cat("Multiple R-squared: ", formatC(x$r.squared, digits = digits))
cat(",\tAdjusted R-squared: ", formatC(x$adj.r.squared, digits = digits),
"\nF-statistic:", formatC(x$fstatistic[1L], digits = digits), "on", x$fstatistic[2L], "and",
x$fstatistic[3L], "DF, p-value:", format.pval(pf(x$fstatistic[1L], x$fstatistic[2L],
x$fstatistic[3L], lower.tail = FALSE), digits = digits))
cat("\n")
}
correl <- x$correlation
if (!is.null(correl)) {
p <- NCOL(correl)
if (p > 1L) {
cat("\nCorrelation of Coefficients:\n")
if (is.logical(symbolic.cor) && symbolic.cor) {
print(symnum(correl, abbr.colnames = NULL))
}
else {
correl <- format(round(correl, 2), nsmall = 2, digits = digits)
correl[!lower.tri(correl)] <- ""
print(correl[-1, -p, drop = FALSE], quote = FALSE)
}
}
}
cat("\n")
invisible(x)
}
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