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Class: LinearModel

Remove terms from linear model


mdl1 = removeTerms(mdl,terms)


mdl1 = removeTerms(mdl,terms) returns a linear regression model mdl1 that is the same as the input model mdl, but with terms removed as specified by terms.

Input Arguments

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Full, fitted linear regression model, specified as a LinearModel object constructed using fitlm or stepwiselm.

Terms to remove from the regression model mdl, specified as one of the following:

  • Formula representing one or more terms to remove. For details, see Wilkinson Notation.

  • Row or rows in the terms matrix (see the modelspec argument description in the fitting function fitlm). 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

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Regression model with fewer terms, returned as a LinearModel object. mdl1 is the same as mdl but with terms removed. To overwrite mdl, set mdl1 equal to mdl.


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Construct a default linear model of the Hald data. Remove terms with high -values.

Load the data.

load hald
X = ingredients; % predictor variables
y = heat; % response

Fit a default linear model to the data.

mdl = fitlm(X,y)
mdl = 

Linear regression model:
    y ~ 1 + x1 + x2 + x3 + x4

Estimated Coefficients:
                   Estimate      SE        tStat       pValue 
                   ________    _______    ________    ________

    (Intercept)      62.405     70.071      0.8906     0.39913
    x1               1.5511    0.74477      2.0827    0.070822
    x2              0.51017    0.72379     0.70486      0.5009
    x3              0.10191    0.75471     0.13503     0.89592
    x4             -0.14406    0.70905    -0.20317     0.84407

Number of observations: 13, Error degrees of freedom: 8
Root Mean Squared Error: 2.45
R-squared: 0.982,  Adjusted R-Squared 0.974
F-statistic vs. constant model: 111, p-value = 4.76e-07

Remove the x3 and x4 terms because their -values are so high.

terms = 'x3 + x4'; % terms to remove
mdl1 = removeTerms(mdl, terms)
mdl1 = 

Linear regression model:
    y ~ 1 + x1 + x2

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)     52.577       2.2862    22.998    5.4566e-10
    x1              1.4683       0.1213    12.105    2.6922e-07
    x2             0.66225     0.045855    14.442     5.029e-08

Number of observations: 13, Error degrees of freedom: 10
Root Mean Squared Error: 2.41
R-squared: 0.979,  Adjusted R-Squared 0.974
F-statistic vs. constant model: 230, p-value = 4.41e-09

The new model has the same adjusted R-Squared value (0.974) as the previous model, meaning it is about as good a fit. All the terms in the new model have extremely low -values.


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Use stepwiselm to select a model from a starting model, continuing until no single step is beneficial.

Use addTerms to add particular terms.

Use step to optimally improve the model by adding or removing terms.


[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.

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