b = glmfit (X, y, distribution) returns a vector b of coefficient estimates for a generalized linear regression model of the responses in y on the predictors in X, using the distribution defined in distribution.

• X is an $n\times p$ numeric matrix of predictor variables with $n$ observations and $p$ predictors.
• y is an $n\times 1$ numeric vector of responses for all supported distributions, except for the ’binomial’ distribution in which case y can be either a numeric or logical $n\times 1$ vector or an $n\times 2$ matrix, where the first column contains the number of successes and the second column contains the number of trials.
• distribution is a character vector specifying the distribution of the response variable. Supported distributions are "normal", "binomial", "poisson", "gamma", and "inverse gaussian".

b = glmfit (…, Name, Value) specifies additional options using Name-Value pair arguments.

[b, dev] = glmfit (…) also returns the deviance of the fit as a numeric value in dev. Deviance is a generalization of the residual sum of squares. It measures the goodness of fit compared to a saturated model.

[b, dev, stats] = glmfit (…) also returns the structure stats, which contains the model statistics in the following fields:

• beta - Coefficient estimates b
• dfe - Degrees of freedom for error
• sfit - Estimated dispersion parameter
• s - Theoretical or estimated dispersion parameter
• estdisp - false when "EstDisp" is "off" and true when "EstDisp" is "on"
• covb - Estimated covariance matrix for b
• se - Vector of standard errors of the coefficient estimates b
• coeffcorr - Correlation matrix for b
• t - $t$ statistics for b
• p - $p$-values for b
• resid - Vector of residuals
• residp - Vector of Pearson residuals
• residd - Vector of deviance residuals
• resida - Vector of Anscombe residuals

See also: glmval

Source Code: glmfit