"logit"Predict responses for new data using fitted models.
# S3 method for logit
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 probabilities
\(\hat{p} = \sigma(o_{new} + \hat{\beta}_0 + X \hat{\beta})\).
"link": numeric matrix of log-odds
\(o_{new} + \hat{\beta}_0 + X \hat{\beta}\).
"class": integer matrix of predicted class labels (0 or 1).
"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 "logit".
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 probabilities;
"link" returns the log-odds; "class" returns 1 when the
link-scale score is positive and 0 otherwise; "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, link, or class 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.logit returns predicted Bernoulli probabilities for newdata using fitted coefficients from object:
$$
\hat{p} = \frac{e^{o_{new} + \hat{\beta}_0 + X_{new} \hat{\beta}}}{1+e^{o_{new} + \hat{\beta}_0 + X_{new} \hat{\beta}}}.
$$
picasso and picasso-package.