# Calculate Standardized Pearson Residuals for MRCV Data

### Description

The `residuals.genloglin`

method function calculates standardized Pearson residuals for the model specified in the `genloglin`

function. It offers an asymptotic approximation and a bootstrap approximation for estimating the variance of the residuals.

### Usage

1 2 |

### Arguments

`object` |
An object of class |

`...` |
Additional arguments passed to or from other methods. |

### Details

The bootstrap results are only available when `boot = TRUE`

in the call to the `genloglin`

function.

The `residuals.genloglin`

function uses `tabular`

(package tables) to display the results for the two MRCV case.

See Bilder and Loughin (2007) for additional details about calculating the residuals.

### Value

— A list containing at least `std.pearson.res.asymp.var`

. For the two MRCV case, the object is a 2Ix2J table of class `'tabular'`

containing the standardized Pearson residuals based on the estimated asymptotic variance. For the three MRCV case, the object is a data frame containing the 2Ix2Jx2K residuals.

— For `boot = TRUE`

in the call to the `genloglin`

function, the list additionally includes:

`B.use`

: The number of bootstrap resamples used.`B.discard`

: The number of bootstrap resamples discarded due to having at least one item with all positive or negative responses.`std.pearson.res.boot.var`

: For the two MRCV case, a 2Ix2J table of class`'tabular'`

containing the standardized Pearson residuals based on the bootstrap variance. For the three MRCV case, a data frame containing the 2Ix2Jx2K residuals.

### References

Bilder, C. and Loughin, T. (2007) Modeling association between two or more categorical variables that allow for multiple category choices. *Communications in Statistics–Theory and Methods*, **36**, 433–451.

### Examples

1 | ```
## For examples see help(genloglin).
``` |

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