# morey_plot: Plot out power sensitivity plots for t or F tests In Superpower: Simulation-Based Power Analysis for Factorial Designs

 morey_plot.ttest R Documentation

## Plot out power sensitivity plots for t or F tests

### Description

Plot out power sensitivity plots for t or F tests

### Usage

```morey_plot.ttest(
es = seq(0, 1, 0.05),
n = NULL,
type = c("two.sample", "one.sample", "paired"),
alternative = c("two.sided", "one.sided"),
alpha_level = Superpower_options("alpha_level")
)

morey_plot.ftest(
es = seq(0, 1, 0.05),
num_df = 1,
den_df = NULL,
alpha_level = Superpower_options("alpha_level"),
liberal_lambda = Superpower_options("liberal_lambda")
)
```

### Arguments

 `es` Effect size magnitudes to include on the plot; either cohen's f or cohen's d depending on whether it is an F-test or t-test `n` Sample size (t-test only) per group (two sample), total number of pairs (paired samples), or total observations (one-sample); only applies to t-test `type` string specifying the type of t test. Can be abbreviated. (t-test only) `alternative` one- or two-sided test. Can be abbreviated. (t-test only) `alpha_level` vector of alpha levels; default is 0.05 `num_df` Numerator degrees of freedom for an F-test. `den_df` Denominator degrees of freedom for an F-test. `liberal_lambda` Logical indicator of whether to use the liberal (cohen_f^2\*(num_df+den_df)) or conservative (cohen_f^2\*den_df) calculation of the noncentrality (lambda) parameter estimate. Default is FALSE.

### Value

Returns plots of effect size (x-axis)

### Functions

• `morey_plot.ttest`: Power-sensitivity plot for t-tests

• `morey_plot.ftest`: Power-sensitivity plot for F-tests

### References

Morey, R.D. (2020). Power and precision Why the push for replacing “power” with “precision” is misguided. Retrieved from: https://richarddmorey.medium.com/power-and-precision-47f644ddea5e

### Examples

```## Not run:
# t-test example ------
# Sensitivity for cohen's d from .1 to .5
# sample sizes of 10 and 20
# alpha levels .05 and .075
# type will be paired and one sided
# Set effect sizes with seq function (?seq)

morey_plot.ttest(es = seq(.1,.5,.01),
n = c(10,20),
alpha_level = c(.05,.075),
type = "paired",
alternative = "one.sided")

## End(Not run)
```

Superpower documentation built on May 17, 2022, 5:08 p.m.