Fit Multinomial Log-linear Models
Fits multinomial log-linear models via neural networks.
multinom(formula, data, weights, subset, na.action, contrasts = NULL, Hess = FALSE, summ = 0, censored = FALSE, model = FALSE, ...)
- a formula expression as for regression models, of the form
response ~ predictors. The response should be a factor or a matrix with K columns, which will be interpreted as counts for each of K classes. A log-linear model is fitted, with coeffi
- an optional data frame in which to interpret the variables occurring
- optional case weights in fitting.
- expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
- a function to filter missing data.
- a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
- logical for whether the Hessian (the observed/expected information matrix) should be returned.
- integer; if non-zero summarize by deleting duplicate rows and adjust weights.
Methods 1 and 2 differ in speed (2 uses
C); method 3 also combines rows with the same X and different Y, which changes the baseline for the deviance.
- If Y is a matrix with
K > 2columns, interpret the entries as one for possible classes, zero for impossible classes, rather than as counts.
- logical. If true, the model frame is saved as component
modelof the returned object.
- additional arguments for
nnet. The variables on the rhs of
the formula should be roughly scaled to [0,1] or the fit will be slow
or may not converge at all.
nnetobject with additional components:
deviance the residual deviance, compared to the full saturated model (that explains individual observations exactly). Also, minus twice log-likelihood. edf the (effective) number of degrees of freedom used by the model AIC the AIC for this fit. Hessian (if
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
options(contrasts = c("contr.treatment", "contr.poly")) library(MASS) example(birthwt) (bwt.mu <- multinom(low ~ ., bwt)) Call: multinom(formula = low ~ ., data = bwt) Coefficients: (Intercept) age lwt raceblack raceother 0.823477 -0.03724311 -0.01565475 1.192371 0.7406606 smoke ptd ht ui ftv1 ftv2+ 0.7555234 1.343648 1.913213 0.6802007 -0.4363238 0.1789888 Residual Deviance: 195.4755 AIC: 217.4755