# Documentation

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# removeTerms

Class: GeneralizedLinearModel

Remove terms from generalized linear model

## Syntax

mdl1 = removeTerms(mdl,terms)

## Description

mdl1 = removeTerms(mdl,terms) returns a linear model the same as mdl but with fewer terms.

## Input Arguments

 mdl Generalized linear model, as constructed by fitglm or stepwiseglm. terms Terms to remove from the mdl regression model. Specify as either a: Text representing one or more terms to remove. 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 without the terms given in terms. You can set mdl1 equal to mdl to overwrite mdl.

## Examples

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This example makes a model using two predictors, then removes one.

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.

mdl = fitglm(X,y,'y ~ x1 + x2','distr','poisson')
mdl =

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

Remove the second predictor from the model.

mdl1 = removeTerms(mdl,'x2')
mdl1 =

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

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## 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.