[Rd] as.data.frame() methods for model objects
SOEIRO Thomas
Thom@@@SOEIRO @end|ng |rom @p-hm@|r
Fri Jan 17 21:12:10 CET 2025
Thank you very much Martin.
Below is a patch implementing that.
Two newbie questions:
- should I add row.names = NULL, optional = FALSE to match the arguments of the generic? (this is not the case for e.g. as.data.frame.table but I thought it was needed: https://cloud.r-project.org/doc/manuals/r-devel/R-exts.html#Generic-functions-and-methods)
- shouldn't we use match.fun(transFUN)?
diff --git a/src/library/stats/R/lm.R b/src/library/stats/R/lm.R
index 13a458797b..2ce6b16f6e 100644
--- a/src/library/stats/R/lm.R
+++ b/src/library/stats/R/lm.R
@@ -982,3 +982,18 @@ labels.lm <- function(object, ...)
asgn <- object$assign[qr.lm(object)$pivot[1L:object$rank]]
tl[unique(asgn)]
}
+
+as.data.frame.lm <- function(x, ..., level = 0.95, transFUN = NULL)
+{
+ cf <- x |> summary() |> coef()
+ ci <- confint(x, level = level)
+ if(!is.null(transFUN)) {
+ stopifnot(is.function(transFUN))
+ cf[, "Estimate"] <- transFUN(cf[, "Estimate"])
+ ci <- transFUN(ci)
+ }
+ df <- data.frame(row.names(cf), cf, ci, row.names = NULL)
+ names(df) <- c("term", "estimate", "std.error", "statistic", "p.value",
+ "conf.low", "conf.high")
+ df
+}
diff --git a/src/library/stats/man/lm.Rd b/src/library/stats/man/lm.Rd
index ff05afabff..b54373dff4 100644
--- a/src/library/stats/man/lm.Rd
+++ b/src/library/stats/man/lm.Rd
@@ -21,6 +21,8 @@ lm(formula, data, subset, weights, na.action,
singular.ok = TRUE, contrasts = NULL, offset, \dots)
\S3method{print}{lm}(x, digits = max(3L, getOption("digits") - 3L), \dots)
+
+\S3method{as.data.frame}{lm}(x, ..., level = 0.95, transFUN = NULL)
}
\arguments{
\item{formula}{an object of class \code{"\link{formula}"} (or one that
@@ -81,6 +83,10 @@ lm(formula, data, subset, weights, na.action,
\item{digits}{the number of \emph{significant} digits to be
passed to \code{\link{format}(\link{coef}(x), .)} when
\I{\code{\link{print}()}ing}.}
+ %% as.data.frame.lm():
+ \item{level}{the confidence level required.}
+ \item{transFUN}{a function to transform \code{estimate}, \code{conf.low} and
+ \code{conf.high}.}
}
\details{
Models for \code{lm} are specified symbolically. A typical model has
@@ -168,6 +174,10 @@ lm(formula, data, subset, weights, na.action,
\code{effects} and (unless not requested) \code{qr} relating to the linear
fit, for use by extractor functions such as \code{summary} and
\code{\link{effects}}.
+
+ \code{as.data.frame} returns a data frame with statistics as provided by
+ \code{coef(summary(.))} and confidence intervals for model
+ estimates.
}
\section{Using time series}{
Considerable care is needed when using \code{lm} with time series.
De : Martin Maechler [mailto:maechler using stat.math.ethz.ch]
Envoyé : vendredi 17 janvier 2025 17:04
À : SOEIRO Thomas
Cc : r-devel using r-project.org
Objet : Re: [Rd] as.data.frame() methods for model objects
>>>>> SOEIRO Thomas via R-devel
>>>>> on Fri, 17 Jan 2025 14:19:31 +0000 writes:
> Following Duncan Murdoch's off-list comments (thanks again!), here is a more complete/flexible version:
>
> as.data.frame.lm <- function(x, ..., level = 0.95, exp = FALSE) {
> cf <- x |> summary() |> stats::coef()
> ci <- stats::confint(x, level = level)
> if (exp) {
> cf[, "Estimate"] <- exp(cf[, "Estimate"])
> ci <- exp(ci)
> }
> df <- data.frame(row.names(cf), cf, ci, row.names = NULL)
> names(df) <- c("term", "estimate", "std.error", "statistic", "p.value", "conf.low", "conf.high")
> df
> }
Indeed, using level is much better already.
Instead of the exp = FALSE ,
I'd use transFUN = NULL
and then
if(!is.null(transFUN)) {
stopifnot(is.function(transFUN))
cf[, "Estimate"] <- transFUN(cf[, "Estimate"])
ci <- transFUN(ci)
}
Noting that I'd want "inverse-logit" (*) in some cases, but also
different things for different link functions, hence just
exp = T/F is not enough.
Martin
--
*) "inverse-logit" is simply R's plogis() function; quite a
few people have been re-inventing it, also in their packages ...
> > lm(breaks ~ wool + tension, warpbreaks) |> as.data.frame()
> term estimate std.error statistic p.value conf.low conf.high
> 1 (Intercept) 39.277778 3.161783 12.422667 6.681866e-17 32.92715 45.6284061
> 2 woolB -5.777778 3.161783 -1.827380 7.361367e-02 -12.12841 0.5728505
> 3 tensionM -10.000000 3.872378 -2.582393 1.278683e-02 -17.77790 -2.2221006
> 4 tensionH -14.722222 3.872378 -3.801856 3.913842e-04 -22.50012 -6.9443228
>
> > glm(breaks < 20 ~ wool + tension, data = warpbreaks) |> as.data.frame(exp = TRUE)
> Waiting for profiling to be done...
> term estimate std.error statistic p.value conf.low conf.high
> 1 (Intercept) 1.076887 0.1226144 0.6041221 0.54849393 0.8468381 1.369429
> 2 woolB 1.076887 0.1226144 0.6041221 0.54849393 0.8468381 1.369429
> 3 tensionM 1.248849 0.1501714 1.4797909 0.14520270 0.9304302 1.676239
> 4 tensionH 1.395612 0.1501714 2.2196863 0.03100435 1.0397735 1.873229
>
> Thank you.
>
> Best regards,
> Thomas
>
>
>
> -----Message d'origine-----
> De : SOEIRO Thomas
> Envoyé : jeudi 16 janvier 2025 14:36
> À : r-devel using r-project.org
> Objet : as.data.frame() methods for model objects
>
> Hello all,
>
> Would there be any interest for adding as.data.frame() methods for model objects?
> Of course there is packages (e.g. broom), but I think providing methods would be more discoverable (and the patch would be small).
> It is really useful for exporting model results or for plotting.
>
> e.g.:
>
> as.data.frame.lm <- function(x) { # could get other arguments, e.g. exp = TRUE/FALSE to exponentiate estimate, conf.low, conf.high
> cf <- x |> summary() |> stats::coef()
> ci <- stats::confint(x)
> data.frame(
> term = row.names(cf),
> estimate = cf[, "Estimate"],
> p.value = cf[, 4], # magic number because name changes between lm() and glm(*, family = *)
> conf.low = ci[, "2.5 %"],
> conf.high = ci[, "97.5 %"],
> row.names = NULL
> )
> }
>
> > lm(breaks ~ wool + tension, warpbreaks) |> as.data.frame()
> term estimate p.value conf.low conf.high
> 1 (Intercept) 39.277778 6.681866e-17 32.92715 45.6284061
> 2 woolB -5.777778 7.361367e-02 -12.12841 0.5728505
> 3 tensionM -10.000000 1.278683e-02 -17.77790 -2.2221006
> 4 tensionH -14.722222 3.913842e-04 -22.50012 -6.9443228
>
> > glm(breaks < 20 ~ wool + tension, data = warpbreaks) |> as.data.frame()
> Waiting for profiling to be done...
> term estimate p.value conf.low conf.high
> 1 (Intercept) 0.07407407 0.54849393 -0.16624575 0.3143939
> 2 woolB 0.07407407 0.54849393 -0.16624575 0.3143939
> 3 tensionM 0.22222222 0.14520270 -0.07210825 0.5165527
> 4 tensionH 0.33333333 0.03100435 0.03900286 0.6276638
>
> Thank you.
>
> Best regards,
> Thomas
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