power_analysis: Power analysis for fitted models.

In neuropsychology/psycho.R: Efficient and Publishing-Oriented Workflow for Psychological Science

Description

Compute the n models based on n sampling of data.

Usage

power_analysis(
  fit,
  n_max,
  n_min = NULL,
  step = 1,
  n_batch = 1,
  groups = NULL,
  verbose = TRUE,
  CI = 90
)

Arguments

fit	A lm or stanreg model.
n_max	Max sample size.
n_min	Min sample size. If null, take current nrow.
step	Increment of the sequence.
n_batch	Number of iterations at each sample size.
groups	Grouping variable name (string) to preserve proportions. Can be a list of strings.
verbose	Print progress.
CI	Confidence level.

Value

A dataframe containing the summary of all models for all iterations.

Author(s)

Dominique Makowski

Examples

## Not run: 
library(dplyr)
library(psycho)

fit <- lm(Sepal.Length ~ Sepal.Width, data = iris)

results <- power_analysis(fit, n_max = 300, n_min = 100, step = 5, n_batch = 20)

results %>%
  filter(Variable == "Sepal.Width") %>%
  select(n, p) %>%
  group_by(n) %>%
  summarise(
    p_median = median(p),
    p_mad = mad(p)
  )

## End(Not run)

neuropsychology/psycho.R documentation built on Jan. 25, 2021, 7:59 a.m.