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

seq_aipe_slr_slope

R Documentation

seq_aipe_slr_slope

Description

Sequential approach to Accuracy in Parameter Estimation for Effect Sizes (AIPE): Simple Linear Regression Slope

Usage

seq_aipe_slr_slope(
  alpha = 0.05,
  omega,
  data = NULL,
  x = NULL,
  y = NULL,
  pilot = FALSE,
  m0 = 4,
  na.rm = TRUE
)

Arguments

`alpha`	The significance level., default is 0.05.
`omega`	omega
`data`	The data for which to calculate the slope.
`x`	The data vector for the independent variable.
`y`	The data vector for the response.
`pilot`	Should a pilot sample be generated.
`m0`	The initial sample size.
`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 calculated slope, and an indicator of if the criterion is satisfied.

Author(s)

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

Examples

pilot_ss <- seq_aipe_slr_slope(alpha=0.05, omega=0.2, pilot=TRUE)
SLS <- matrix( rnorm(pilot_ss[1],mean=0,sd=1),
 rnorm(pilot_ss[1], mean = 0, sd = 1), nrow=20, ncol= 2)
seq_aipe_slr_slope(alpha=0.05, omega=0.2,data = SLS)

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