# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English verison of the page.

Class: GeneralizedLinearModel

Add terms to generalized linear model

## Syntax

```mdl1 = addTerms(mdl,terms) ```

## Description

`mdl1 = addTerms(mdl,terms)` returns a generalized linear model the same as `mdl` but with additional terms.

## Input Arguments

 `mdl` Generalized linear model, as constructed by `fitglm` or `stepwiseglm`. `terms` Terms to add to the `mdl` regression model. Specify as either a: Text representing one or more terms to add. For details, see Wilkinson Notation.Row or rows in the terms matrix (see `modelspec` in `fitglm`). For example, if there are three variables `A`, `B`, and `C`:```[0 0 0] represents a constant term or intercept [0 1 0] represents B; equivalently, A^0 * B^1 * C^0 [1 0 1] represents A*C [2 0 0] represents A^2 [0 1 2] represents B*(C^2)```

## Output Arguments

 `mdl1` Generalized linear model, the same as `mdl` but with additional terms given in `terms`. You can set `mdl1` equal to `mdl` to overwrite `mdl`.

## Examples

expand all

Create a model using just one predictor, then add a second.

Generate artificial data for the model, Poisson random numbers with two underlying predictors `X(1)` and `X(2)`.

```rng default % for reproducibility rndvars = randn(100,2); X = [2+rndvars(:,1),rndvars(:,2)]; mu = exp(1 + X*[1;2]); y = poissrnd(mu); ```

Create a generalized linear regression model of Poisson data. Use just the first predictor in the model.

```mdl = fitglm(X,y,... 'y ~ x1','distr','poisson') ```
```mdl = Generalized linear regression model: log(y) ~ 1 + x1 Distribution = Poisson Estimated Coefficients: Estimate SE tStat pValue ________ _________ ______ ______ (Intercept) 2.7784 0.014043 197.85 0 x1 1.1732 0.0033653 348.6 0 100 observations, 98 error degrees of freedom Dispersion: 1 Chi^2-statistic vs. constant model: 1.25e+05, p-value = 0 ```

Add the second predictor to the model.

```mdl1 = addTerms(mdl,'x2') ```
```mdl1 = Generalized linear regression model: log(y) ~ 1 + x1 + x2 Distribution = Poisson Estimated Coefficients: Estimate SE tStat pValue ________ _________ ______ ______ (Intercept) 1.0405 0.022122 47.034 0 x1 0.9968 0.003362 296.49 0 x2 1.987 0.0063433 313.24 0 100 observations, 97 error degrees of freedom Dispersion: 1 Chi^2-statistic vs. constant model: 2.95e+05, p-value = 0 ```

expand all

## Alternatives

`step` adds or removes terms from a model using a greedy one-step algorithm.

## References

[1] Wilkinson, G. N., and C. E. Rogers. Symbolic description of factorial models for analysis of variance. J. Royal Statistics Society 22, pp. 392–399, 1973.