estim_diff determines point estimate, SD and SE, 95% Credibility Intervals,
and interval width, for both differences in raw means and Cohen's d's for
multiple sample sizes
estim_diff(data, vars_of_interest, sample_size, k = 50, name = "")tbl_select returns a tibble::tibble() containing estimates of the
difference in raw means and of Cohen's d with associated SD, SE, 95% CI, and
width of the 95% CI (lower, upper) for five different sample sizes (starting
with the minimum sample size, then 1/5th parts of the total dataset).
fig_diff returns a scatterplot for the difference in raw means, where for
the five different sample sizes, 10 out of the total number of HDCI's
computed are displayed (in light blue). The average estimate with credible
interval summarizing the total number of HDCIs for each sample size are
plotted in reddish purple
fig_nozero returns a barplot where for each of the five sample sizes the
proportion of permutations not containing zero is displayed for the
difference in raw means
fig_cohens_d returns a scatterplot for Cohen's d, where for the five
different sample sizes, 10 out of the total number of HDCI's computed are
displayed (in light blue). The average estimate with credible interval
summarizing the total number of HDCIs for each sample size are plotted in
reddish purple
fig_d_nozero returns a barplot where for each of the five sample sizes the
proportion of permutations not containing zero is displayed for Cohen's d
tbl_total returns a tibble::tibble() containing estimates of the difference
in raw means and of Cohen's d with associated SD, SE, 95% CI, and width of
the 95% CI (lower, upper) for all sample sizes, including the permutation
number.
Dataframe with the data to be analyzed
Vector containing the names of the variables to be
compared on their means: c("var1", "var2")
The range of sample size to be used min:max
The number of permutations to be used for each sample size. Defaults to 50
The title of the dataset or variables to be displayed with the
figure. Defaults to ""
data_feedback <- feedback
estim_diff(data_feedback,
c("mfg_learning", "mfg_application"), 20:71,
10, "Feedback middle frontal gyrus")
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