需要更改的是print.summary.lm函数，而不是summary.lm。这里有一个版本添加了一个“简洁”选项，当concise为false时，注意不要更改任何内容：
print.summary.lm <-
function (x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), concise = FALSE, ...)
{
cat("\nCall:", if(!concise) "\n" else " ", paste(deparse(x$call), sep = "\n", collapse = "\n"),
if (!concise) "\n\n", sep = "")
resid <- x$residuals
df <- x$df
rdf <- df[2L]
if (!concise) {
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)
}
if (!concise) 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$aliased) == 0L) {
cat("\nNo Coefficients\n")
}
else {
if (nsingular <- df[3L] - df[1L])
cat("\nCoefficients: (", nsingular, " not defined because of singularities)\n",
sep = "")
else { cat("\n"); if (!concise) cat("Coefficients:\n") }
coefs <- x$coefficients
if (!is.null(aliased <- x$aliased) && any(aliased)) {
cn <- names(aliased)
coefs <- matrix(NA, length(aliased), 4, dimnames = list(cn,
colnames(coefs)))
coefs[!aliased, ] <- x$coefficients
}
printCoefmat(coefs, digits = digits, signif.stars = signif.stars, signif.legend = (!concise),
na.print = "NA", eps.Pvalue = if (!concise) .Machine$double.eps else 1e-4, ...)
}
cat("\nResidual standard error:", format(signif(x$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, if (!concise) .Machine$double.eps else 1e-4))
cat("\n")
}
correl <- x$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)
}
现在，
开始
。
x <- rnorm(100); y <- rnorm(100)+x
print(summary(lm(y ~ x)))
print(summary(lm(y ~ x)), concise=TRUE)
第一次打印提供标准的 R 打印结果，而后者提供了更好的打印效果。
Call: lm(formula = y ~ x)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.010 0.102 -0.10 0.92
x 1.009 0.112 9.02 <0.0001 ***
Residual standard error: 1.02 on 98 degrees of freedom
Multiple R-squared: 0.454, Adjusted R-squared: 0.448
F-statistic: 81.4 on 1 and 98 DF, p-value: <0.0001
PS: 这个更准确地对真实数据进行统计：单个系数的p值现在被限制为0.0001，而不是机器精度。
PPS: 如果R团队在听，请注意，我认为这应该成为标准的R功能。
stats:::print.summary.lm，但是从列表中删除摘要对象的lm类并选择你想要的内容并打印可能更容易。 - rawr