[Rd] as.data.frame() methods for model objects

[SOEIRO Thomas]

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 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