BANOVA.model {BANOVA} | R Documentation |

## Extract BANOVA models

### Description

`BANOVA.model`

extracts BANOVA models from the package.

### Usage

```
BANOVA.model(model_name, single_level = F)
```

### Arguments

`model_name` |
a character string in c('Normal', 'T', 'Bernoulli', 'Binomial', 'Poisson', 'ordMultinomial', 'Multinomial', 'multiNormal', 'truncNormal') |

`single_level` |
if the model is a single level model, default False |

### Details

The function loads a pre-specified 'Stan' model for the analysis in BANOVA.

`'Normal'`

model: A model suitable for a continuous dependent variable, which follows a Normal distribution.

`'T'`

model: A model suitable for a continuous dependent variable, which might be prone to 'outliers' or fatter tails than the Normal.

`'Bernoulli'`

model: A model suitable for a binary dependent variable, which can take values 0 and 1.

`'Binomial'`

model: A model suitable for a dependent variable, which represents a number of successes in a sequence of B independent Bernoulli experiments.

`'Poisson'`

model: A model suitable for a dependent variable, which represents count data. A Poisson distributed dependent variable can take values 0, 1, 2 ....

`'ordMultinomial'`

model: A model suitable for an ordered categorical (ordinal) dependent variable, which follows an ordered Multinomial distribution. This dependent variable can take values from 1 to K, where possible alternatives are ordered according to some principal.

`'Multinomial'`

model: A model suitable for a categorical (nominal) dependent variable, which follows a Multinomial distribution. This dependent variable can take values from 1 to K, where possible alternatives are unordered.

`'multiNormal'`

model: A model suitable for a Multivariate Normal dependent variable, which represents L possibly correlated Normal dependent variables with shared predictors. The analysis corresponds to the seemingly unrelated regressions (SUR) technique.

`'truncNormal'`

model: A model suitable a dependent variable, which values can only be observed if they lie within a certain range. The variable can be bounded from below, above, or from two sides.

### Value

`BANOVA.model`

returns an object of class `"BANOVA.model"`

. The returned object is a list containing:

`model_code` |
the model code of the extracted model |

`model_name` |
the model name |

`single_level` |
if the model is a single level model |

### Examples

```
model <- BANOVA.model('Poisson', single_level = FALSE)
cat(model$model_code)
```

*BANOVA*version 1.2.1 Index]