Returns most of what you get from glm
fast_logistic_regression(
Xmm,
ybin,
drop_collinear_variables = FALSE,
lm_fit_tol = 1e-07,
do_inference_on_var = "none",
Xt_times_diag_w_times_X_fun = NULL,
sqrt_diag_matrix_inverse_fun = NULL,
num_cores = 1,
...
)A list of raw results
The model.matrix for X (you need to create this yourself before)
The binary response vector
Should we drop perfectly collinear variables? Default is FALSE to inform the user of the problem.
When drop_collinear_variables = TRUE, this is the tolerance to detect collinearity among predictors.
We use the default value from base::lm.fit's which is 1e-7. If you fit the logistic regression and
still get p-values near 1 indicating high collinearity, we recommend making this value smaller.
Which variables should we compute approximate standard errors of the coefficients and approximate p-values for the test of
no linear log-odds probability effect? Default is "none" for inference on none (for speed). If not default, then "all"
to indicate inference should be computed for all variables. The final option is to pass one index to indicate the column
number of Xmm where inference is desired. We have a special routine to compute inference for one variable only. It consists of a conjugate
gradient descent which is another approximation atop the coefficient-fitting approximation in RcppNumerical. Note: if you are just comparing
nested models using anova, there is no need to compute inference for coefficients (keep the default of FALSE for speed).
A custom function whose arguments are X (an n x m matrix), w (a vector of length m) and this function's num_cores
argument in that order. The function must return an m x m R matrix class object which is the result of the computing X^T
function is not parallelized, the num_cores argument is ignored. Default is NULL which uses the function
eigen_Xt_times_diag_w_times_X which is implemented with the Eigen C++ package and hence very fast. The only way we know of to beat the default is to use a method that employs
GPUs. See README on github for more information.
A custom function that returns a numeric vector which is square root of the diagonal of the inverse of the inputted matrix. Its arguments are X
(an n x n matrix) and this function's num_cores argument in that order. If your custom function is not parallelized, the num_cores argument is ignored.
The object returned must further have a defined function diag which returns the diagonal of the matrix as a vector. Default is NULL which uses the function
eigen_inv which is implemented with the Eigen C++ package and hence very fast. The only way we know of to beat the default is to use a method that employs
GPUs. See README on github for more information.
Number of cores to use to speed up matrix multiplication and matrix inversion (used only during inference computation). Default is 1.
Unless the number of variables, i.e. ncol(Xmm), is large, there does not seem to be a performance gain in using multiple cores.
Other arguments to be passed to fastLR. See documentation there.
library(MASS); data(Pima.te)
flr = fast_logistic_regression(
Xmm = model.matrix(~ . - type, Pima.te),
ybin = as.numeric(Pima.te$type == "Yes")
)
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