formula
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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.
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data
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an optional data frame in which to interpret the variables occurring
in formula .
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weights
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optional case weights in fitting.
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subset
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expression saying which subset of the rows of the data should be used
in the fit. All observations are included by default.
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na.action
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a function to filter missing data.
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contrasts
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a list of contrasts to be used for some or all of
the factors appearing as variables in the model formula.
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Hess
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logical for whether the Hessian (the observed information matrix)
should be returned.
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summ
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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.
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censored
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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.
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...
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additional arguments for nnet
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