使用我自己的数据集我的系数太多了。 我只是想在没有(或部分)打印系数的情况下打印摘要。
示例脚本:
lm.fit <- lm(iris$Sepal.Length ~ iris$Petal.Length)
summary(lm.fit)
输出:
> summary(lm.fit)
Call:
lm(formula = iris$Sepal.Length ~ iris$Petal.Length)
Residuals:
Min 1Q Median 3Q Max
-1.24675 -0.29657 -0.01515 0.27676 1.00269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.30660 0.07839 54.94 <2e-16 ***
iris$Petal.Length 0.40892 0.01889 21.65 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4071 on 148 degrees of freedom
Multiple R-squared: 0.76, Adjusted R-squared: 0.7583
F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16
期望的输出:
Call:
lm(formula = iris$Sepal.Length ~ iris$Petal.Length)
Residuals:
Min 1Q Median 3Q Max
-1.24675 -0.29657 -0.01515 0.27676 1.00269
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4071 on 148 degrees of freedom
Multiple R-squared: 0.76, Adjusted R-squared: 0.7583
F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16
我知道我可以使用从下面提到的summary(lm.fit)$names
中选择的names
拨打电话。但我需要的恰恰相反。摘要没有系数。
> names(summary(lm.fit))
[1] "call" "terms" "residuals" "coefficients"
[5] "aliased" "sigma" "df" "r.squared"
[9] "adj.r.squared" "fstatistic" "cov.unscaled"
这可能吗?如果是这样,怎么样?
编辑:我实际上需要来自RcppArmadillo
包的fastLm。
我只是希望lm()
和fastLm()
的输出相同。 @LyzandeR提供的代码非常适合lm()
。
使用@ LyzandeR的示例我使用Google搜索并创建了以下代码段,该代码段替换了RcppArmadillo软件包提供的打印摘要。
print.summary.fastLm <- function(x, ...) {
## Alternate print summary
## Prints without coefficients
cat("\nCall:\n")
print(x$call)
cat("\nResiduals:\n")
print(x$residSum)
cat("\n")
#printCoefmat(x$coefficients, P.values=TRUE, has.Pvalue=TRUE)
digits <- max(3, getOption("digits") - 3)
cat("\nResidual standard error: ", formatC(x$sigma, digits=digits), " on ",
formatC(x$df), " degrees of freedom\n", sep="")
cat("Multiple R-squared: ", formatC(x$r.squared, digits=digits),
",\tAdjusted R-squared: ",formatC(x$adj.r.squared, digits=digits),
"\n", sep="")
invisible(x)
}
答案 0 :(得分:8)
我略微更改了print.summary.lm
函数,只是注释掉了打印系数的部分(注释的代码行可以在下面看到):
print.sum2 <-
function (x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$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$coefficients
if (!is.null(aliased <- x$aliased) && any(aliased)) {
cn <- names(aliased)
coefs <- matrix(NA, length(aliased), 4, dimnames = list(cn,
colnames(coefs)))
coefs[!aliased, ] <- x$coefficients
}
#printCoefmat(coefs, digits = digits, signif.stars = signif.stars, na.print = "NA", ...)
}
cat("\nResidual standard error:", format(signif(x$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$correlation
if (!is.null(correl)) {
p <- NCOL(correl)
if (p > 1L) {
cat("\nCorrelation of Coefficients:\n")
if (is.logical(symbolic.cor) && symbolic.cor) {
print(symnum(correl, abbr.colnames = NULL))
}
else {
correl <- format(round(correl, 2), nsmall = 2,
digits = digits)
correl[!lower.tri(correl)] <- ""
print(correl[-1, -p, drop = FALSE], quote = FALSE)
}
}
}
cat("\n")
invisible(x)
}
现在如果你跑:
> print.sum2(summary(lm.fit))
Call:
lm(formula = iris$Sepal.Length ~ iris$Petal.Length)
Residuals:
Min 1Q Median 3Q Max
-1.24675 -0.29657 -0.01515 0.27676 1.00269
Residual standard error: 0.4071 on 148 degrees of freedom
Multiple R-squared: 0.76, Adjusted R-squared: 0.7583
F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16
它完全按照您的意愿运作。