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Analyze and report several dependent variables at once

Source: R/pipeline.R
report_all.Rd

Runs analyze_and_report() for each dependent variable (e.g., all questionnaire scales of a study) and additionally returns a summary table of the omnibus tests with Holm-adjusted p-values across the dependent variables, plus – when patchwork is installed – a combined figure.

Usage

report_all(
  data,
  dvs,
  iv,
  design = c("between", "within"),
  labels = NULL,
  xlabels = NULL,
  plotType = "boxviolin",
  sink_dir = NULL,
  subject = NULL
)

Arguments

data

the data frame

dvs

character vector of dependent variable column names

iv

the independent variable (column name as string)

design

"between" (default) or "within"

labels

optional named character vector mapping a dv name to its axis label, e.g. c(tlx_mental = "Mental Demand")

xlabels

optional labels for the x-axis, passed to every plot

plotType

either "box", "violin", or "boxviolin" (default)

sink_dir

optional directory; each dv's sentences are written to <sink_dir>/<dv>.tex so a manuscript can \input{} them

subject

the participant-ID column (a string); required for design = "within". See analyze_and_report().

Value

Invisibly returns a list with components results (named list of analyze_and_report() results), summary (data frame with one row per dv: method, statistic, p.value, and Holm-adjusted p.holm), and combined_plot (a patchwork figure, or NULL when patchwork is not installed).

Details

Each dependent variable is analysed on its own complete cases (see analyze_and_report()), so in a within-subjects design a participant missing one rating is left out of that dependent variable only. The p.holm column corrects the omnibus p-values for the number of dependent variables; the per-dv sentences report the uncorrected ones.

Examples

# \donttest{
out <- report_all(mtcars, dvs = c("mpg", "disp"), iv = "cyl")
#> Shapiro--Wilk tests in each of the 3 groups of cyl (Holm-corrected for 3 tests) indicated no significant deviation from normality (smallest $p$: $W = 0.91$, $p_{\mathrm{Holm}} = 0.782$ for group 4); therefore, parametric tests were used. Brown-Forsythe test (median-centred Levene's test) indicated unequal variances ($F(2, 29) = 5.51$, $p = 0.009$); Welch-corrected statistics were used where applicable.
#> A One-way analysis of means (not assuming equal variances) found a significant effect of \cyl{} on mpg (\F{2}{18.03}{31.62}, \pminor{0.001}, $\omega^{2}$ = 0.74). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 4 was significantly higher (\m{26.66}, \sd{4.51}) in terms of mpg compared to 6 (\m{19.74}, \sd{1.45}; \padj{0.001}). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 4 was significantly higher (\m{26.66}, \sd{4.51}) in terms of mpg compared to 8 (\m{15.10}, \sd{2.56}; \padjminor{0.001}). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 6 was significantly higher (\m{19.74}, \sd{1.45}) in terms of mpg compared to 8 (\m{15.10}, \sd{2.56}; \padjminor{0.001}). 
#> Shapiro--Wilk tests in each of the 3 groups of cyl (Holm-corrected for 3 tests) indicated no significant deviation from normality (smallest $p$: $W = 0.80$, $p_{\mathrm{Holm}} = 0.129$ for group 6); therefore, parametric tests were used. Brown-Forsythe test (median-centred Levene's test) did not indicate unequal variances ($F(2, 29) = 3.29$, $p = 0.052$).
#> A One-way analysis of means (not assuming equal variances) found a significant effect of \cyl{} on disp (\F{2}{14.89}{76.89}, \pminor{0.001}, $\omega^{2}$ = 0.89). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 6 was significantly higher (\m{183.31}, \sd{41.56}) in terms of disp compared to 4 (\m{105.14}, \sd{26.87}; \padj{0.004}). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 8 was significantly higher (\m{353.10}, \sd{67.77}) in terms of disp compared to 4 (\m{105.14}, \sd{26.87}; \padjminor{0.001}). 
#> A Games-Howell post-hoc test (Holm-adjusted) found that 8 was significantly higher (\m{353.10}, \sd{67.77}) in terms of disp compared to 6 (\m{183.31}, \sd{41.56}; \padjminor{0.001}). 
out$summary
#>     dv                                                   method statistic
#> 1  mpg One-way analysis of means (not assuming equal variances)  31.62424
#> 2 disp One-way analysis of means (not assuming equal variances)  76.89164
#>        p.value       p.holm
#> 1 1.270809e-06 1.270809e-06
#> 2 1.422939e-08 2.845878e-08
# }