| multinom {nnet} | R Documentation |
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, ...)
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 numeric matrix with K columns
if the response is either a matrix with K columns or a factor with K > 2
classes, or a numeric vector for a response 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/expected 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.
|
model |
logical. If true, the model frame is saved as component model
of the returned object.
|
... |
additional arguments for nnet
|
multinom calls 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.
A nnet object 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 Hess is true).
|
model |
(if model is true).
|
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))
## Not run: 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
## End(Not run)