estim_corr {neuroUp} | R Documentation |
Estimate correlations
Description
estim_corr
determines point estimate, SD and SE, 95% Credibility Intervals,
and interval width, for Pearson correlations for multiple sample sizes
Usage
estim_corr(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
correlated: |
sample_size |
The range of sample size to be used: |
k |
The number of permutations to be used for each sample size. Defaults
to |
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 Pearson correlation between two correlated variables 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_corr
returns a scatterplot where for the five different sample sizes, 10 out of the total number of HDCIs computed are displayed (in green). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in orange -
fig_corr_nozero
returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed -
tbl_total
returns atibble::tibble()
containing estimates of the Pearson correlation between two correlated variables with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for all sample sizes, including the permutation number.
Examples
data_gambling <- gambling
estim_corr(data_gambling,
c("lnacc_self_winvsloss", "age"), 20:221,
10, "Gambling NAcc correlation with age")