# coefvlm: Extract Model Coefficients In VGAM: Vector Generalized Linear and Additive Models

## Description

Extracts the estimated coefficients from VLM objects such as VGLMs.

## Usage

 `1` ```coefvlm(object, matrix.out = FALSE, label = TRUE, colon = FALSE) ```

## Arguments

 `object` An object for which the extraction of coefficients is meaningful. This will usually be a `vglm` object. `matrix.out` Logical. If `TRUE` then a matrix is returned. The explanatory variables are the rows. The linear/additive predictors are the columns. The constraint matrices are used to compute this matrix. `label` Logical. If `FALSE` then the `names` of the vector of coefficients are set to `NULL`. `colon` Logical. Explanatory variables which appear in more than one linear/additive predictor are labelled with a colon, e.g., `age:1`, `age:2`. However, if it only appears in one linear/additive predictor then the `:1` is omitted by default. Then setting `colon = TRUE` will add the `:1`.

## Details

This function works in a similar way to applying `coef()` to a `lm` or `glm` object. However, for VGLMs, there are more options available.

## Value

A vector usually. A matrix if `matrix.out = TRUE`.

Thomas W. Yee

## References

Yee, T. W. and Hastie, T. J. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.

`vglm`, `coefvgam`, `coef`.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```zdata <- data.frame(x2 = runif(nn <- 200)) zdata <- transform(zdata, pstr0 = logitlink(-0.5 + 1*x2, inverse = TRUE), lambda = loglink( 0.5 + 2*x2, inverse = TRUE)) zdata <- transform(zdata, y2 = rzipois(nn, lambda, pstr0 = pstr0)) fit2 <- vglm(y2 ~ x2, zipoisson(zero = 1), data = zdata, trace = TRUE) coef(fit2, matrix = TRUE) # Always a good idea coef(fit2) coef(fit2, colon = TRUE) ```

### Example output

```Loading required package: stats4
VGLM    linear loop  1 :  loglikelihood = -329.26758
VGLM    linear loop  2 :  loglikelihood = -322.91825
VGLM    linear loop  3 :  loglikelihood = -322.86076
VGLM    linear loop  4 :  loglikelihood = -322.8607
VGLM    linear loop  5 :  loglikelihood = -322.8607