Linear hypothesis test on nonlinear regression model coefficients
p = coefTest(mdl)
p = coefTest(mdl,H)
p = coefTest(mdl,H,C)
[p,F] = coefTest(mdl,...)
[p,F,r] = coefTest(mdl,...)
Nonlinear regression model, constructed by
Numeric matrix having one column for each coefficient in the
Numeric vector with the same number of rows as
p-value of the F test (see Definitions).
Value of the test statistic for the F test (see Definitions).
Numerator degrees of freedom for the F test
(see Definitions). The F statistic
The p-value, F statistic, and numerator degrees of freedom are valid under these assumptions:
The data comes from a normal distribution.
The entries are independent.
Suppose these assumptions hold. Let β represent the unknown coefficient vector of the linear regression. Suppose H is a full-rank matrix of size r-by-s, where s is the number of terms in β. Let v be a vector the same size as β. The following is a test statistic for the hypothesis that Hβ = v:
Here is the estimate of the coefficient
vector β in
and C is the estimated covariance of the coefficient
mdl.CoefCov. When the hypothesis is
true, the test statistic F has an F Distribution with r and u degrees
Make a nonlinear model of mileage as a function
of the weight from the
carsmall data set. Test
the coefficients to see if all should be zero.
Create an exponential model of car mileage as a function
of weight from the
carsmall data. Scale the weight
by a factor of 1000 so all the variables are roughly equal in size.
load carsmall X = Weight; y = MPG; modelfun = 'y ~ b1 + b2*exp(-b3*x/1000)'; beta0 = [1 1 1]; mdl = fitnlm(X,y,modelfun,beta0);
Test the model for significant differences from a constant model.
p = coefTest(mdl)
p = 1.3708e-36
There is no doubt that the model contains nonzero terms.
The values of commonly used test statistics are available in