Fit Multinomial Log-linear Models
Usage
multinom(formula, data=sys.parent(), weights, subset, na.action,
contrasts=NULL, Hess=FALSE, summ=0, censored=FALSE, ...)
Arguments
formula
|
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 coefficients zero for the first
class. An offset can be included: it should be a matrix with K columns
if the response is a matrix with K columns or a factor with K > 2
classes, or a vector for a factor with 2 levels.
See the documentation of formula for other details.
|
data
|
an optional data frame in which to interpret the variables occurring
in formula.
|
weights
|
optional case weights in fitting.
|
subset
|
expression saying which subset of the rows of the data should be used
in the fit. All observations are included by default.
|
na.action
|
a function to filter missing data.
|
contrasts
|
a list of contrasts to be used for some or all of
the factors appearing as variables in the model formula.
|
Hess
|
logical for whether the Hessian (the observed information matrix)
should be returned.
|
summ
|
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.
|
censored
|
If Y is a matrix with K > 2 columns, interpret the entries as one
for possible classes, zero for impossible classes, rather than as
counts.
|
...
|
additional arguments for nnet
|
Description
Fits multinomial log-linear models via neural networks.Value
A nnet object with additional structure.
deviance
|
the residual deviance.
|
edf
|
the (effective) number of degrees of freedom used by the model
|
AIC
|
the AIC for this fit.
|
Hessian
|
(if Hess is true).
|
See Also
nnetExamples
options(contrasts = c("contr.treatment", "contr.poly"))
library(MASS)
example(birthwt)
bwt.mu <- multinom(low ~ ., bwt)
bwt.mu
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