# ANCOVA_analytic: Power Calculations for Factorial ANCOVAs In Superpower: Simulation-Based Power Analysis for Factorial Designs

 ANCOVA_analytic R Documentation

## Power Calculations for Factorial ANCOVAs

### Description

Complete power analyses for ANCOVA omnibus tests and contrasts. This function does not support within subjects factors.

### Usage

```ANCOVA_analytic(
design,
mu,
n = NULL,
sd,
r2 = NULL,
n_cov,
alpha_level = Superpower_options("alpha_level"),
beta_level = NULL,
cmats = list(),
label_list = NULL,
design_result = NULL,
round_up = TRUE
)
```

### Arguments

 `design` Output from the ANOVA_design function `mu` Vector specifying mean for each condition `n` Sample size in each condition `sd` Standard deviation for all conditions (or a vector specifying the sd for each condition) `r2` Coefficient of Determination of the model with only the covariates `n_cov` Number of covariates `alpha_level` Alpha level used to determine statistical significance `beta_level` Type II error probability (power/100-1) `cmats` List of matrices for specific contrasts of interest `label_list` An optional list to specify the factor names and condition (recommended, if not used factors and levels are indicated by letters and numbers). `design_result` Output from the ANOVA_design function `round_up` Logical indicator (default = TRUE) for whether to round up sample size calculations to nearest whole number

### Value

One, or two, data frames containing the power analysis results from the power analysis for the omnibus ANCOVA (main_results) or contrast tests (contrast_results). In addition, every F-test (aov_list and con_list) is included in a list of power.htest results. Lastly, a (design_param) list containing the design parameters is also included in the results.

### References

Shieh, G. (2020). Power analysis and sample size planning in ANCOVA designs. Psychometrika, 85(1), 101-120.

### Examples

```# Simple 2x3 ANCOVA

ANCOVA_analytic(
design = "2b*3b",
mu = c(400, 450, 500,
400, 500, 600),
n_cov = 3,
sd = 100,
r2 = .25,
alpha_level = .05,
beta_level = .2,
round_up = TRUE
)
```

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