seq_aipe_cor: seq_aipe_cor
In yelleKneK/SMSD: Sequential Methods for Study Design

seq_aipe_cor

R Documentation

seq_aipe_cor

Description

Sequential approach to Accuracy in Parameter Estimation for Effect Sizes (AIPE): Correlation Coefficient

Usage

seq_aipe_cor(
  alpha = 0.05,
  omega,
  data = NULL,
  Group.1 = NULL,
  Group.2 = NULL,
  pilot = FALSE,
  m0 = 4,
  method = c("pearson", "kendall", "spearman"),
  na.rm = TRUE
)

Arguments

`alpha`	The significance level., default is 0.05.
`omega`	omega
`data`	The data set, should have two columns.
`Group.1`	The data for the first group.
`Group.2`	The data for the second group.
`pilot`	Should a pilot sample be generated.
`m0`	The initial sample size.
`method`	The correlation method to be used.
`na.rm`	This parameter controls whether NA values are removed from the data prior to calculation. Default is `TRUE`.

Value

The current sample size, the current correlation and an indicator of if the criterion has been satisfied.

Author(s)

Ken Kelley KKelley@nd.edu, Francis Bilson Darku FBilsonD@nd.edu, Bhargab Chattopadhyay Bhargab@iiitvadodara.ac.in

References

Kelley, K., Darku, F. B., \& Chattopadhyay, B. (2018). Accuracy in parameter estimation for a general class of effect sizes: A sequential approach. Psychological Methods, 23, 226–243.

Kelley, K., Darku, F. B., \& Chattopadhyay, B. (2019). Sequential Accuracy in parameter estimation for population correlation coefficients. Psychological Methods, 24, 492–515.

Examples

pilot_ss <- seq_aipe_cor(alpha=0.05, omega=0.2, pilot=TRUE)
  SLS <- matrix(c(rexp(pilot_ss, rate=0.05),
  rexp(pilot_ss, rate=0.05)), ncol = 2)
seq_aipe_cor(alpha=0.05, omega=0.2,data = SLS)

yelleKneK/SMSD documentation built on Nov. 23, 2022, 6:40 p.m.