coefTest

Linear hypothesis test on linear regression model coefficients

Syntax

p = coefTest(mdl)

p = coefTest(mdl,H)

p = coefTest(mdl,H,C)

[p,F] =
coefTest(___)

[p,F,r]
= coefTest(___)

Description

p = coefTest(mdl) computes the p-value for an F-test that all coefficient estimates in mdl, except for the intercept term, are zero.

example

p = coefTest(mdl,H) performs an F-test that H × B = 0, where B represents the coefficient vector. Use H to specify the coefficients to include in the F-test.

example

p = coefTest(mdl,H,C) performs an F-test that H × B = C.

[p,F] = coefTest(___) also returns the F-test statistic F using any of the input argument combinations in previous syntaxes.

example

[p,F,r] = coefTest(___) also returns the numerator degrees of freedom r for the test.

example

Examples

collapse all

Test Significance of Linear Regression Model

Open Live Script

Fit a linear regression model and test the coefficients of the fitted model to see if they are zero.

Load the carsmall data set and create a table in which the Model_Year predictor is categorical.

load carsmall
Model_Year = categorical(Model_Year);
tbl = table(MPG,Weight,Model_Year);

Fit a linear regression model of mileage as a function of the weight, weight squared, and model year.

mdl = fitlm(tbl,'MPG ~ Model_Year + Weight^2')

mdl = 
Linear regression model:
    MPG ~ 1 + Weight + Model_Year + Weight^2

Estimated Coefficients:
                      Estimate         SE         tStat       pValue  
                     __________    __________    _______    __________

    (Intercept)          54.206        4.7117     11.505    2.6648e-19
    Weight            -0.016404     0.0031249    -5.2493    1.0283e-06
    Model_Year_76        2.0887       0.71491     2.9215     0.0044137
    Model_Year_82        8.1864       0.81531     10.041    2.6364e-16
    Weight^2         1.5573e-06    4.9454e-07      3.149     0.0022303


Number of observations: 94, Error degrees of freedom: 89
Root Mean Squared Error: 2.78
R-squared: 0.885,  Adjusted R-Squared: 0.88
F-statistic vs. constant model: 172, p-value = 5.52e-41

The last line of the model display shows the F-statistic value of the regression model and the corresponding p-value. The small p-value indicates that the model fits significantly better than a degenerate model consisting of only an intercept term. You can return these two values by using coefTest.

[p,F] = coefTest(mdl)

p = 
5.5208e-41

F = 
171.8844

Test Significance of Linear Model Coefficient

Open Live Script

Fit a linear regression model and test the significance of a specified coefficient in the fitted model by using coefTest. You can also use anova to test the significance of each predictor in the model.

Load the carsmall data set and create a table in which the Model_Year predictor is categorical.

load carsmall
Model_Year = categorical(Model_Year);
tbl = table(MPG,Acceleration,Weight,Model_Year);

Fit a linear regression model of mileage as a function of the weight, weight squared, and model year.

mdl = fitlm(tbl,'MPG ~ Acceleration + Model_Year + Weight')

mdl = 
Linear regression model:
    MPG ~ 1 + Acceleration + Weight + Model_Year

Estimated Coefficients:
                      Estimate         SE         tStat        pValue  
                     __________    __________    ________    __________

    (Intercept)          40.523        2.5293      16.021    5.8302e-28
    Acceleration      -0.023438       0.11353    -0.20644       0.83692
    Weight           -0.0066799    0.00045796     -14.586    2.5314e-25
    Model_Year_76        1.9898       0.80696      2.4657      0.015591
    Model_Year_82        7.9661       0.89745      8.8763    6.7725e-14


Number of observations: 94, Error degrees of freedom: 89
Root Mean Squared Error: 2.93
R-squared: 0.873,  Adjusted R-Squared: 0.867
F-statistic vs. constant model: 153, p-value = 5.86e-39

The model display includes the p-value for the t-statistic for each coefficient to test the null hypothesis that the corresponding coefficient is zero.

You can examine the significance of the coefficient using coefTest. For example, test the significance of the Acceleration coefficient. According to the model display, Acceleration is the second predictor. Specify the coefficient by using a numeric index vector.

[p_Acceleration,F_Acceleration,r_Acceleration] = coefTest(mdl,[0 1 0 0 0])

p_Acceleration = 
0.8369

F_Acceleration = 
0.0426

r_Acceleration = 
1

p_Acceleration is the p-value corresponding to the F-statistic value F_Acceleration, and r_Acceleration is the numerator degrees of freedom for the F-test. The returned p-value indicates that Acceleration is not statistically significant in the fitted model. Note that p_Acceleration is equal to the p-value of t-statistic (tStat) in the model display, and F_Acceleration is the square of tStat.

Test the significance of the categorical predictor Model_Year. Instead of testing Model_Year_76 and Model_Year_82 separately, you can perform a single test for the categorical predictor Model_Year. Specify Model_Year_76 and Model_Year_82 by using a numeric index matrix.

[p_Model_Year,F_Model_Year,r_Model_Year] = coefTest(mdl,[0 0 0 1 0; 0 0 0 0 1])

p_Model_Year = 
2.7408e-14

F_Model_Year = 
45.2691

r_Model_Year = 
2

The returned p-value indicates that Model_Year is statistically significant in the fitted model.

You can also return these values by using anova.

anova(mdl)

ans=4×5 table
                     SumSq     DF    MeanSq        F          pValue  
                    _______    __    _______    ________    __________

    Acceleration    0.36613     1    0.36613    0.042618       0.83692
    Weight           1827.7     1     1827.7      212.75    2.5314e-25
    Model_Year       777.81     2      388.9      45.269    2.7408e-14
    Error            764.59    89      8.591

Input Arguments

collapse all

`mdl` — Linear regression model object
`LinearModel` object | `CompactLinearModel` object

Linear regression model object, specified as a LinearModel object created by using fitlm or stepwiselm, or a CompactLinearModel object created by using compact.

`H` — Hypothesis matrix
numeric index matrix

Hypothesis matrix, specified as a full-rank numeric index matrix of size r-by-s, where r is the number of linear combinations of coefficients being tested, and s is the total number of coefficients.

If you specify H, then the output p is the p-value for an F-test that H × B = 0, where B represents the coefficient vector.
If you specify H and C, then the output p is the p-value for an F-test that H × B = C.

Example: [1 0 0 0 0] tests the first coefficient among five coefficients.

Data Types: single | double

`C` — Hypothesized value
numeric vector

Hypothesized value for testing the null hypothesis, specified as a numeric vector with the same number of rows as H.

If you specify H and C, then the output p is the p-value for an F-test that H × B = C, where B represents the coefficient vector.

Data Types: single | double

Output Arguments

collapse all

`p` — p-value for F-test
numeric value in the range [0,1]

p-value for the F-test, returned as a numeric value in the range [0,1].

`F` — Value of test statistic for F-test
numeric value

Value of the test statistic for the F-test, returned as a numeric value.

`r` — Numerator degrees of freedom for F-test
positive integer

Numerator degrees of freedom for the F-test, returned as a positive integer. The F-statistic has r degrees of freedom in the numerator and mdl.DFE degrees of freedom in the denominator.

Algorithms

The p-value, F-statistic, and numerator degrees of freedom are valid under these assumptions:

The data comes from a model represented by the formula in the Formula property of the fitted model.
The observations are independent, conditional on the predictor values.

Under these assumptions, let β represent the (unknown) coefficient vector of the linear regression. Suppose H is a full-rank numeric index matrix of size r-by-s, where r is the number of linear combinations of coefficients being tested, and s is the total number of coefficients. Let c be a column vector with r rows. The following is a test statistic for the hypothesis that Hβ = c:

$F = {(H \hat{β} - c)}^{'} {(H V H^{'})}^{- 1} (H \hat{β} - c) / r .$

Here $\hat{β}$ is the estimate of the coefficient vector β, stored in the Coefficients property, and V is the estimated covariance of the coefficient estimates, stored in the CoefficientCovariance property. When the hypothesis is true, the test statistic F has an F Distribution with r and u degrees of freedom, where u is the degrees of freedom for error, stored in the DFE property.

Alternative Functionality

The values of commonly used test statistics are available in the Coefficients property of a fitted model.
anova provides tests for each model predictor and groups of predictors.

Extended Capabilities

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2012a

coefTest

Syntax

Description

Examples

Test Significance of Linear Regression Model

Test Significance of Linear Model Coefficient

Input Arguments

`mdl` — Linear regression model object
`LinearModel` object | `CompactLinearModel` object

`H` — Hypothesis matrix
numeric index matrix

`C` — Hypothesized value
numeric vector

Output Arguments

`p` — p-value for F-test
numeric value in the range [0,1]

`F` — Value of test statistic for F-test
numeric value

`r` — Numerator degrees of freedom for F-test
positive integer

Algorithms

Alternative Functionality

Extended Capabilities

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

See Also

Topics

coefTest

Syntax

Description

Examples

Test Significance of Linear Regression Model

Test Significance of Linear Model Coefficient

Input Arguments

mdl — Linear regression model object LinearModel object | CompactLinearModel object

H — Hypothesis matrix numeric index matrix

C — Hypothesized value numeric vector

Output Arguments

p — p-value for F-test numeric value in the range [0,1]

F — Value of test statistic for F-test numeric value

r — Numerator degrees of freedom for F-test positive integer

Algorithms

Alternative Functionality

Extended Capabilities

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

See Also

Topics

`mdl` — Linear regression model object
`LinearModel` object | `CompactLinearModel` object

`H` — Hypothesis matrix
numeric index matrix

`C` — Hypothesized value
numeric vector

`p` — p-value for F-test
numeric value in the range [0,1]

`F` — Value of test statistic for F-test
numeric value

`r` — Numerator degrees of freedom for F-test
positive integer

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.