estim_diff: Estimate differences (unstandardized and Cohen's d)

In neuroUp: Plan Sample Size for Task fMRI Research using Bayesian Updating

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: c("var1", "var2")
sample_size	The range of sample size to be used min:max
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 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.

Examples

data_feedback <- feedback
estim_diff(data_feedback,
  c("mfg_learning", "mfg_application"), 20:71,
  10, "Feedback middle frontal gyrus")

neuroUp documentation built on Sept. 11, 2024, 8:22 p.m.