estim_diff {neuroUp} | R Documentation |
Estimate differences (unstandardized and Cohen's d)
Description
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
Usage
estim_diff(data, vars_of_interest, sample_size, k = 50, name = "")
Arguments
data |
Dataframe with the data to be analyzed |
vars_of_interest |
Vector containing the names of the variables to be
compared on their means: |
sample_size |
The range of sample size to be used |
k |
The number of permutations to be used for each sample size. Defaults to 50 |
name |
The title of the dataset or variables to be displayed with the
figure. Defaults to |
Value
-
tbl_select
returns atibble::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 atibble::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.
Examples
data_feedback <- feedback
estim_diff(data_feedback,
c("mfg_learning", "mfg_application"), 20:71,
10, "Feedback middle frontal gyrus")