# residualsvglm: Residuals for a VGLM fit In VGAM: Vector Generalized Linear and Additive Models

## Description

Residuals for a vector generalized linear model (VGLM) object.

## Usage

 ```1 2 3``` ```residualsvglm(object, type = c("working", "pearson", "response", "deviance", "ldot", "stdres"), matrix.arg = TRUE) ```

## Arguments

 `object` Object of class `"vglm"`, i.e., a `vglm` fit. `type` The value of this argument can be abbreviated. The type of residuals to be returned. The default is the first one: working residuals corresponding to the IRLS algorithm. These should be defined for all models. They are sometimes be added to VGAM plots of estimated component functions (see `plotvgam`). Pearson residuals for GLMs, when squared and summed over the data set, total to the Pearson chi-squared statistic. For VGLMs, Pearson residuals involve the working weight matrices and the score vectors. Under certain limiting conditions, Pearson residuals have 0 means and identity matrix as the variance-covariance matrix. Response residuals are simply the difference between the observed values and the fitted values. Both have to be of the same dimension, hence not all families have response residuals defined. Deviance residuals are only defined for models with a deviance function. They tend to GLMs mainly. This function returns a `NULL` for those models whose deviance is undefined. The choice `"ldot"` should not be used currently. Standardized residuals are currently only defined for 2 types of models: (i) GLMs (`poissonff`, `binomialff`); (ii) those fitted to a two-way table of counts, e.g., `cumulative`, `acat`, `multinomial`, `sratio`, `cratio`. For (ii), they are defined in Section 2.4.5 of Agresti (2018) and are also the output from the `"stdres"` component of `chisq.test`. For the test of independence they are a useful type of residual. Their formula is `(observed - expected) / sqrt(V)`, where `V` is the residual cell variance (also see Agresti, 2007, section 2.4.5). When an independence null hypothesis is true, each standardized residual (corresponding to a cell in the table) has a a large-sample standard normal distribution. Currently this function merely extracts the table of counts from `object` and then computes the standardized residuals like `chisq.test`. `matrix.arg` Logical, which applies when if the pre-processed answer is a vector or a 1-column matrix. If `TRUE` then the value returned will be a matrix, else a vector.

## Details

This function returns various kinds of residuals, sometimes depending on the specific type of model having been fitted. Section 3.7 of Yee (2015) gives some details on several types of residuals defined for the VGLM class.

Standardized residuals for GLMs are described in Section 4.5.6 of Agresti (2013) as the ratio of the raw (response) residuals divided by their standard error. They involve the generalized hat matrix evaluated at the final IRLS iteration. When applied to the LM, standardized residuals for GLMs simplify to `rstandard`. For GLMs they are basically the Pearson residual divided by the square root of 1 minus the leverage.

## Value

If that residual type is undefined or inappropriate then `NULL` is returned, otherwise a matrix or vector of residuals is returned.

## Warning

This function may change in the future, especially those whose definitions may change.

## References

Agresti, A. (2007). An Introduction to Categorical Data Analysis, 2nd ed., New York: John Wiley & Sons. Page 38.

Agresti, A. (2013). Categorical Data Analysis, 3rd ed., New York: John Wiley & Sons.

Agresti, A. (2018). An Introduction to Categorical Data Analysis, 3rd ed., New York: John Wiley & Sons.

`resid`, `vglm`, `chisq.test`, `hatvalues`.

## Examples

 ```1 2 3 4 5 6``` ```pneumo <- transform(pneumo, let = log(exposure.time)) fit <- vglm(cbind(normal, mild, severe) ~ let, propodds, data = pneumo) resid(fit) # Same as having type = "working" (the default) resid(fit, type = "response") resid(fit, type = "pearson") resid(fit, type = "stdres") # Test for independence ```

### Example output

```Loading required package: stats4
1      -1.0060427611        -1.00245033
2      -0.1745467344        -0.34684069
3       0.4314586013         0.02457508
4       0.0809229590         0.41936185
5       0.0361646292        -0.07565599
6      -0.2930059007        -0.26666152
7       0.0215024659         0.05210247
8       0.0001832234         0.16583611
normal         mild       severe
1  5.992263e-03 -0.003560995 -0.002431267
2  1.081598e-02 -0.001396793 -0.009419182
3 -5.600275e-02  0.054440914  0.001561834
4 -1.539087e-02 -0.029468460  0.044859333
5 -8.373983e-03  0.019924341 -0.011550358
6  7.294547e-02 -0.021372077 -0.051573393
7 -5.281530e-03 -0.006498208  0.011779738
8 -4.240084e-05 -0.040198822  0.040241222
1         -0.7006808         -0.3159829
2         -0.1779045         -0.3864521
3          1.2478945         -0.3182214
4         -0.1713918          1.0444171
5          0.2553741         -0.3025368
6         -0.7714393         -0.5047617
7          0.0103928          0.1352679
8         -0.1168063          0.3307779
normal        mild    severe
1  6.1471905 -3.89841752 -4.233223
2  3.1702107 -1.71442237 -2.460790
3  0.1970160  0.85353945 -1.053302
4 -0.8913183  0.04262976  1.103923
5 -2.8080105  2.37507146  1.376388
6 -2.7239929  1.75504571  1.850026
7 -4.6473611  2.03026670  4.060336
8 -3.3701809  0.88159772  3.498457
```

VGAM documentation built on Jan. 16, 2021, 5:21 p.m.