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
}
> 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@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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