Open iago-pssjd opened 3 years ago
Note that parameters
only returns details contained in the underlying object.
It returns group means for Welch two-sample test because the underlying object contains them.
library(parameters)
x <- c(1.83, 0.50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
mod1 <- t.test(x, y, paired = TRUE)
mod1
#>
#> Paired t-test
#>
#> data: x and y
#> t = 3.0354, df = 8, p-value = 0.01618
#> alternative hypothesis: true difference in means is not equal to 0
#> 95 percent confidence interval:
#> 0.1037787 0.7599991
#> sample estimates:
#> mean of the differences
#> 0.4318889
parameters(mod1)
#> Paired t-test
#>
#> Parameter | Group | Difference | t(8) | p | 95% CI
#> ------------------------------------------------------------
#> x | y | 0.43 | 3.04 | 0.016 | [0.10, 0.76]
But it doesn't do so for Wilcoxon rank sum test because the object doesn't contain them:
mod2 <- wilcox.test(x, y, paired = TRUE)
mod2
#>
#> Wilcoxon signed rank exact test
#>
#> data: x and y
#> V = 40, p-value = 0.03906
#> alternative hypothesis: true location shift is not equal to 0
parameters(mod2)
#> Wilcoxon signed rank exact test
#>
#> Parameter1 | Parameter2 | W | p
#> ---------------------------------------
#> x | y | 40.00 | 0.039
You can always add effect size information for these object though:
parameters(mod1, hedges_g = TRUE)
#> Paired t-test
#>
#> Parameter | Group | Difference | 95% CI | t(8) | Hedges_g | g 95% CI | p
#> --------------------------------------------------------------------------------------
#> x | y | 0.43 | [0.10, 0.76] | 3.04 | 0.91 | [0.17, 1.73] | 0.016
parameters(mod2, rank_biserial = TRUE)
#> Wilcoxon signed rank exact test
#>
#> Parameter1 | Parameter2 | W | r_rank_biserial | rank_biserial 95% CI | p
#> --------------------------------------------------------------------------------
#> x | y | 40.00 | 0.78 | [0.33, 1.00] | 0.039
Created on 2021-05-13 by the reprex package (v2.0.0)
Reopening in light of https://github.com/easystats/report/issues/189
This will depend on whether we can recover data from the htest
object, which is not always guaranteed.
Something along these lines:
x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
## wilcox.test ----
model <- wilcox.test(x, y)
data <- insight::get_data(model)
desc <- NULL
if (!is.null(data)) {
desc <- as.data.frame(t(sapply(data, function(d) {
data.frame(Median = median(d, na.rm = TRUE),
MAD = mad(d, na.rm = TRUE))
})))
}
desc
#> Median MAD
#> x 1.415 0.630105
#> y 0.9 0.237216
## wilcox.test ----
model <- t.test(x, y)
data <- insight::get_data(model)
desc <- NULL
if (!is.null(data)) {
desc <- as.data.frame(t(sapply(data, function(d) {
data.frame(Mean = mean(d, na.rm = TRUE),
SD = sd(d, na.rm = TRUE))
})))
}
desc
#> Mean SD
#> x 1.313 0.4412117
#> y 0.976 0.1973069
Created on 2021-06-17 by the reprex package (v2.0.0)
As
model_parameters
shows group means for Welch two-sample test, it could also show group medians for Wilcoxon rank sum test.