Convert the output of cond_indirect_effects() to a data frame

[sfcheung]

Users may want to get the output of cond_indirect_effects() as a data frame such that it can be used by other functions.

# Adapted from https://sfcheung.github.io/manymome/articles/mome_lm.html

library(manymome)
dat <- data_med_mod_a
lm_m <- lm(m ~ x*w + c1 + c2, dat)
lm_y <- lm(y ~ m + x + c1 + c2, dat)
fit_lm <- lm2list(lm_m, lm_y)
boot_out_lm <- do_boot(fit_lm,
                       R = 5000,
                       seed = 54532,
                       ncores = 4)
#> 4 processes started to run bootstrapping.
out_xmy_on_w <- cond_indirect_effects(wlevels = "w",
                                      x = "x",
                                      y = "y",
                                      m = "m",
                                      fit = fit_lm,
                                      boot_ci = TRUE,
                                      boot_out = boot_out_lm)
out_xmy_on_w
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m -> y
#>  Conditional on moderator(s): w
#>  Moderator(s) represented by: w
#> 
#>       [w]   (w)   ind  CI.lo CI.hi Sig   m~x   y~m
#> 1 M+1.0SD 3.164 3.060  1.914 3.979 Sig 3.192 0.959
#> 2 Mean    2.179 2.136  1.282 2.966 Sig 2.228 0.959
#> 3 M-1.0SD 1.194 1.212 -0.102 2.476     1.265 0.959
#> 
#>  - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#>    nonparametric bootstrapping with 5000 samples.
#>  - The 'ind' column shows the indirect effects.
#>  - 'm~x','y~m' is/are the path coefficient(s) along the path conditional
#>    on the moderators.

For now, the output of cond_indirect_effects() has methods such as coef() and confint(), such that other packages that make use of these methods can be used. E.g.,

confint(out_xmy_on_w)
#>                2.5 %   97.5 %
#>w: M+1.0SD  1.9144062 3.978543
#>w: Mean     1.2816511 2.965547
#>w: M-1.0SD -0.1016396 2.475522
coef(out_xmy_on_w)   
#>w: M+1.0SD    w: Mean w: M-1.0SD 
#>  3.060021   2.136191   1.212360

However, there are cases in which users would like to have a data frame. If only the default results are needed (no standard errors, no p-values), then as.data.frame() will work. E.g.,

tmp <- as.data.frame(out_xmy_on_w)
tmp
#>       [w]      (w)      ind      CI.lo    CI.hi      m~x       y~m
#> 1 M+1.0SD 3.164128 3.060021  1.9144062 3.978543 3.191953 0.9586673
#> 2    Mean 2.179279 2.136191  1.2816511 2.965547 2.228292 0.9586673
#> 3 M-1.0SD 1.194429 1.212360 -0.1016396 2.475522 1.264631 0.9586673

The results can be used and manipulated as a usual data frame. E.g.,

tmp[, 1:5]
#>       [w]      (w)      ind      CI.lo    CI.hi
#> 1 M+1.0SD 3.164128 3.060021  1.9144062 3.978543
#> 2    Mean 2.179279 2.136191  1.2816511 2.965547
#> 3 M-1.0SD 1.194429 1.212360 -0.1016396 2.475522
tmp[c(1, 2), 1:5]
#>       [w]      (w)      ind    CI.lo    CI.hi
#> 1 M+1.0SD 3.164128 3.060021 1.914406 3.978543
#> 2    Mean 2.179279 2.136191 1.281651 2.965547

However, if SEs and/or p-values are needed, more need to be done. I do not yet have an idea on how to implement this appropriately. These columns could have been added in the table. However, I would avoid changing the structure (columns) of the output unless really necessary, to avoid breaking other functions that manipulate the output. Therefore, for now, SEs and p-values are stored internally but they are printed only if requested. This is done by the print methods.

Until the team has a decision, if SEs and/or p-values are needed, the following function can be used as a tentative solution:

cond_indirect_effects_to_data_frame <- function(x,
                                                pvalue = FALSE,
                                                se = FALSE) {
    out <- as.data.frame(x)
    full_output <- attr(x, "full_output")
    if (se) {
        se <- sapply(full_output, function(x) {
            if (!is.null(x$boot_se)) return(x$boot_se)
            if (!is.null(x$mc_se)) return(x$mc_se)
            return(NA)
          })
        out$SE <- se
      }
    if (pvalue) {
        pv <- sapply(full_output, function(x) {
            if (!is.null(x$boot_p)) return(x$boot_p)
            if (!is.null(x$mc_p)) return(x$mc_p)
            return(NA)
          })
        out$pvalue <- pv
      }
    return(out)
  }

This is how it works:

tmp <- cond_indirect_effects_to_data_frame(out_xmy_on_w, pvalue = TRUE)
tmp
#>       [w]      (w)      ind      CI.lo    CI.hi      m~x       y~m pvalue
#> 1 M+1.0SD 3.164128 3.060021  1.9144062 3.978543 3.191953 0.9586673 0.0004
#> 2    Mean 2.179279 2.136191  1.2816511 2.965547 2.228292 0.9586673 0.0000
#> 3 M-1.0SD 1.194429 1.212360 -0.1016396 2.475522 1.264631 0.9586673 0.0736

tmp <- cond_indirect_effects_to_data_frame(out_xmy_on_w, se = TRUE)
tmp
#>       [w]      (w)      ind      CI.lo    CI.hi      m~x       y~m        SE
#> 1 M+1.0SD 3.164128 3.060021  1.9144062 3.978543 3.191953 0.9586673 0.5230653
#> 2    Mean 2.179279 2.136191  1.2816511 2.965547 2.228292 0.9586673 0.4283923
#> 3 M-1.0SD 1.194429 1.212360 -0.1016396 2.475522 1.264631 0.9586673 0.6569414

tmp <- cond_indirect_effects_to_data_frame(out_xmy_on_w, pvalue = TRUE, se = TRUE)
tmp
#>       [w]      (w)      ind      CI.lo    CI.hi      m~x       y~m        SE
#> 1 M+1.0SD 3.164128 3.060021  1.9144062 3.978543 3.191953 0.9586673 0.5230653
#> 2    Mean 2.179279 2.136191  1.2816511 2.965547 2.228292 0.9586673 0.4283923
#> 3 M-1.0SD 1.194429 1.212360 -0.1016396 2.475522 1.264631 0.9586673 0.6569414
#>   pvalue
#> 1 0.0004
#> 2 0.0000
#> 3 0.0736

This is not good solution, I admit. Nevertheless, this works for now.

Users who want the results as data frames can just use as.data.frame() or the function cond_indirect_effects_to_data_frame(). Because the output is just a usual data frame, they can be manipulated as data frames (e..g, columns reordered or some rows deleted). E.g.,

tmp <- cond_indirect_effects_to_data_frame(out_xmy_on_w, pvalue = TRUE, se =$
tmp
#>     [w]      (w)      ind      CI.lo    CI.hi      m~x       y~m        SE
#> M+1.0SD 3.164128 3.060021  1.9144062 3.978543 3.191953 0.9586673 0.5230653
#>    Mean 2.179279 2.136191  1.2816511 2.965547 2.228292 0.9586673 0.4283923
#> M-1.0SD 1.194429 1.212360 -0.1016396 2.475522 1.264631 0.9586673 0.6569414
#> pvalue
#> 0.0004
#> 0.0000
#> 0.0736
tmp[, c("[w]", "ind", "SE", "pvalue")]
#>     [w]      ind        SE pvalue
#> M+1.0SD 3.060021 0.5230653 0.0004
#>    Mean 2.136191 0.4283923 0.0000
#> M-1.0SD 1.212360 0.6569414 0.0736

I opened this issue to remind me to work on it.

Comments and suggests are welcomed.