# MultipleANOVAs: 'MultipleANOVAs' In Displayr/flipAnalysisOfVariance: Functions for computing ANOVA, MANOVA, and related methods

## Description

Computes multiple ANOVAs.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```MultipleANOVAs( dependents, independent, subset = NULL, weights = NULL, compare = "To mean", correction = "Table FDR", robust.se = FALSE, alternative = "Two-sided", missing = "Exclude cases with missing data", show.labels = FALSE, seed = 1223, p.cutoff = 0.05, data = NULL, ... ) ```

## Arguments

 `dependents` The outcome variables. `independent` The factor representing the groups. `subset` An optional vector specifying a subset of observations to be used in the fitting process, or, the name of a variable in `data`. It may not be an expression. `subset` may not `weights` An optional vector of sampling weights, or, the name or, the name of a variable in `data`. It may not be an expression. `compare` One of `"To mean", "Pairwise", "To first"` (which implement's Dunnett's C, when combined with 'correction' == 'Tukey Range'), or `"All"` `correction` The multiple comparison adjustment method: ```"Table FDR", "Tukey Range", "None", "False Discovery Rate", "Benjamini & Yekutieli", "Bonferroni", "Free Combinations"```, ```"Hochberg", "Holm", "Hommel", "Single-step"``` `"Shaffer"`, and `"Westfall"`. `robust.se` Computes standard errors that are robust to violations of the assumption of constant variance. This parameter is ignored if weights are applied (as weights already employ a sandwich estimator). `alternative` The alternative hypothesis: "Two sided", "Greater", or "Less". The main application of this is when Compare us set 'To first' (e.g., if testing a new product, where the purpose is to work out of the new product is superior to an existing product, "Greater" would be chosen). `missing` How missing data is to be treated in the ANOVA. Options: `"Error if missing data"`. `"Exclude cases with missing data"`, and `"Imputation (replace missing values with estimates)"`. `show.labels` Shows the variable labels, as opposed to the labels, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). `seed` The random number seed used when evaluating the multivariate t-distribution. `p.cutoff` The alpha level to be used in testing. `data` An optional `data.frame` that can be used as alternative to specifying `dependent`, `independent`, and `weights` for the case when the independent variables are deferring lengths. Must have columns `"dependent", "independent", "weights", and "dependent.names"`. The `"dependent.names"` column contains the character name of the dependent variable for each observation. The `dependent`, `independent`, `weights`, and `subset` arguments are all ignored if `data` is supplied. `...` Other parameters to be passed to `OneWayANOVA`.

## Details

Conducts multiple `OneWayANOVA`s, and puts them in a list. If `correction` is `"Table FDR"`, the false discovery rate correction is applied across the entire table. All other corrections are performed within rows. Additional detail about the other parameters can be found in `OneWayANOVA`.

Displayr/flipAnalysisOfVariance documentation built on Aug. 11, 2021, 12:58 a.m.