Documentation

coefCI

Confidence intervals of coefficient estimates of generalized linear model

Syntax

ci = coefCI(mdl)
ci = coefCI(mdl,alpha)

Description

ci = coefCI(mdl) returns confidence intervals for the coefficients in mdl.

ci = coefCI(mdl,alpha) returns confidence intervals with confidence level 1 - alpha.

Input Arguments

 mdl Generalized linear model, specified as a full GeneralizedLinearModel object constructed using fitglm or stepwiseglm, or a compacted CompactGeneralizedLinearModel object constructed using compact. alpha Scalar from 0 to 1, the probability that the confidence interval does not contain the true value. Default: 0.05

Output Arguments

 ci k-by-2 matrix of confidence intervals. The jth row of ci is the confidence interval of coefficient j of mdl. The name of coefficient j of mdl is in mdl.CoefficientNames.

Examples

expand all

Find confidence intervals for the coefficients of a fitted generalized nonlinear model.

Generate artificial data for the model using 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

Find 95% (default) confidence intervals on the coefficients of the model.

ci = coefCI(mdl)
ci = 3×2

0.9966    1.0844
0.9901    1.0035
1.9744    1.9996

Find 99% confidence intervals on the coefficients.

alpha = .01;
ci = coefCI(mdl,alpha)
ci = 3×2

0.9824    1.0986
0.9880    1.0056
1.9703    2.0036