"poisson"Predict responses for new data using fitted models.
# S3 method for poisson
predict(object, newdata, lambda.idx = NULL, p.pred.idx = NULL,
type = "response", s = NULL, newoffset = NULL, ...)Return type depends on type:
"response" (default): numeric matrix of predicted Poisson means
\(\hat{\mu} = \exp(o_{new} + \hat{\beta}_0 + X \hat{\beta})\).
"link": numeric matrix of log-means
\(o_{new} + \hat{\beta}_0 + X \hat{\beta}\).
"nonzero": list of integer vectors of nonzero coefficient indices,
one element per selected lambda.
Rows correspond to observations; columns correspond to lambda.idx (or
s values when s is specified).
An object with S3 class "poisson".
Nonempty finite numeric matrix of new observations for prediction (\(n_{new} \times d\)) with the same number of columns as the fitted design.
Positive integer indices of regularization parameters along the solution path used for prediction. By default, at most the first three fitted path points are used.
Optional row indices to subset returned predictions. NULL returns
every prediction row.
Type of prediction. "response" (default) returns predicted Poisson
means; "link" returns the log-mean; "nonzero" returns
nonzero variable indices.
Optional nonempty vector of finite non-negative lambda values; see
predict.gaussian.
Optional finite numeric vector with one offset per row of newdata.
It is required for response or link prediction when object was
fitted with offset; type = "nonzero" does not use it.
For a model fitted without offset, the default is a vector of zeros.
Arguments to be passed to methods.
Jason Ge, Xingguo Li, Haoming Jiang, Mengdi Wang, Tong Zhang, Han Liu and Tuo Zhao
Maintainer: Tuo Zhao <tourzhao@gatech.edu>
predict.poisson returns predicted Poisson means for newdata using fitted coefficients from object:
$$
\hat{\mu} = e^{o_{new} + \hat{\beta}_0 + X_{new} \hat{\beta}}.
$$
picasso and picasso-package.