Package 'fastLogisticRegressionWrap'

Title: Fast Logistic Regression Wrapper
Description: Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the 'RcppNumerical' package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.
Authors: Adam Kapelner [aut, cre] , Beau Walker [rev, dtc] , Gabriel Mayer [fnd, dtc]
Maintainer: Adam Kapelner <[email protected]>
License: GPL-3
Version: 1.2.1
Built: 2025-01-06 03:16:17 UTC
Source: https://github.com/kapelner/fastlogisticregressionwrap

Help Index

Asymmetric Cost Explorer

Description

Given a set of desired proportions of predicted outcomes, what is the error rate for each of those models?

Usage

asymmetric_cost_explorer(
  phat,
  ybin,
  steps = seq(from = 0.001, to = 0.999, by = 0.001),
  outcome_of_analysis = 0,
  proportions_desired = seq(from = 0.1, to = 0.9, by = 0.1),
  proportion_tolerance = 0.01
)

Arguments

phat

The vector of probability estimates to be thresholded to make a binary decision
ybin

The true binary responses
steps

All possibile thresholds which must be a vector of numbers in (0, 1). Default is seq(from = 0.001, to = 0.999, by = 0.001).
outcome_of_analysis

Which class do you care about performance? Either 0 or 1 for the negative class or positive class. Default is 0.
proportions_desired

Which proportions of outcome_of_analysis class do you wish to understand performance for?
proportion_tolerance

If the model cannot match the proportion_desired within this amount, it does not return that model's performance. Default is 0.01.
K_folds

If not NULL, this indicates that we wish to fit the phat thresholds out of sample using this number of folds. Default is NULL for in-sample fitting.

Value

A table with column 1: proportions_desired, column 2: actual proportions (as close as possible), column 3: error rate, column 4: probability threshold.

Author(s)

Adam Kapelner

Asymmetric Cost Explorer

Description

Given a set of desired proportions of predicted outcomes, what is the error rate for each of those models?

Usage

asymmetric_cost_explorer_cross_validated(phat, ybin, K_CV = 5, ...)

Arguments

phat

The vector of probability estimates to be thresholded to make a binary decision
ybin

The true binary responses
K_CV

We wish to fit the phat thresholds out of sample using this number of folds. Default is 5.
...

Other parameters to be passed into the asymmetric_cost_explorer function

Value

A table with column 1: proportions_desired, column 2: actual proportions (as close as possible), column 3: error rate, column 4: probability threshold.

Author(s)

Adam Kapelner

Binary Confusion Table and Errors

Description

Provides a binary confusion table and error metrics

Usage

confusion_results(yhat, ybin, skip_argument_checks = FALSE)

Arguments

yhat

The binary predictions
ybin

The true binary responses
skip_argument_checks

If TRUE it does not check this function's arguments for appropriateness. It is not recommended unless you truly need speed and thus the default is FALSE.

Value

A list of raw results

Examples

library(MASS); data(Pima.te)
ybin = as.numeric(Pima.te$type == "Yes")
flr = fast_logistic_regression(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = ybin
)
phat = predict(flr, model.matrix(~ . - type, Pima.te))
confusion_results(phat > 0.5, ybin)

Compute Single Value of the Diagonal of a Symmetric Matrix's Inverse

Description

Via the eigen package's conjugate gradient descent algorithm.

Usage

eigen_compute_single_entry_of_diagonal_matrix(M, j, num_cores = 1)

Arguments

M

The symmetric matrix which to invert (and then extract one element of its diagonal)
j

The diagonal entry of M's inverse
num_cores

The number of cores to use. Default is 1.

Value

The value of m^-1_j,j

Author(s)

Adam Kapelner

Examples

n = 500
	X = matrix(rnorm(n^2), nrow = n, ncol = n)
	M = t(X) %*% X
	j = 137
	eigen_compute_single_entry_of_diagonal_matrix(M, j)
	solve(M)[j, j] #to ensure it's the same value

A fast det(X) function

Description

Via the eigen package

Usage

eigen_det(X, num_cores = 1)

Arguments

X

A numeric matrix of size p x p
num_cores

The number of cores to use. Unless p is large, keep to the default of 1.

Value

The determinant as a scalar numeric value

Examples

p = 30
  eigen_det(matrix(rnorm(p^2), nrow = p))

A fast solve(X) function

Description

Via the eigen package

Usage

eigen_inv(X, num_cores = 1)

Arguments

X

A numeric matrix of size p x p
num_cores

The number of cores to use. Unless p is large, keep to the default of 1.

Value

The resulting matrix

Examples

p = 10
  eigen_inv(matrix(rnorm(p^2), nrow = p))

A fast Xt [times] diag(w) [times] X function

Description

Via the eigen package

Usage

eigen_Xt_times_diag_w_times_X(X, w, num_cores = 1)

Arguments

X

A numeric matrix of size n x p
w

A numeric vector of length p
num_cores

The number of cores to use. Unless p is large, keep to the default of 1.

Value

The resulting matrix

Examples

n = 100
  p = 10
  X = matrix(rnorm(n * p), nrow = n, ncol = p)
  w = rnorm(p)
  eigen_Xt_times_diag_w_times_X(t(X), w)

FastLR Wrapper

Description

Returns most of what you get from glm

Usage

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,
  ...
)

Arguments

Xmm

The model.matrix for X (you need to create this yourself before)
ybin

The binary response vector
drop_collinear_variables

Should we drop perfectly collinear variables? Default is FALSE to inform the user of the problem.
lm_fit_tol

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.
do_inference_on_var

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).
Xt_times_diag_w_times_X_fun

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.
sqrt_diag_matrix_inverse_fun

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.
num_cores

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.

Value

A list of raw results

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression(
	 Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = as.numeric(Pima.te$type == "Yes")
)

Rapid Forward Stepwise Logistic Regression

Description

Roughly duplicates the following glm-style code:

Usage

fast_logistic_regression_stepwise_forward(
  Xmm,
  ybin,
  mode = "aic",
  pval_threshold = 0.05,
  use_intercept = TRUE,
  verbose = TRUE,
  drop_collinear_variables = FALSE,
  lm_fit_tol = 1e-07,
  ...
)

Arguments

Xmm

The model.matrix for X (you need to create this yourself before).
ybin

The binary response vector.
mode

"aic" (default, fast) or "pval" (slow, but possibly yields a better model).
pval_threshold

The significance threshold to include a new variable. Default is 0.05. If mode == "aic", this argument is ignored.
use_intercept

Should we automatically begin with an intercept? Default is TRUE.
verbose

Print out messages during the loop? Default is TRUE.
drop_collinear_variables

Parameter used in fast_logistic_regression. Default is FALSE. See documentation there.
lm_fit_tol

Parameter used in fast_logistic_regression. Default is 1e-7. See documentation there.
...

Other arguments to be passed to fastLR. See documentation there.

Details

nullmod = glm(ybin ~ 0, data.frame(Xmm), family = binomial) fullmod = glm(ybin ~ 0 + ., data.frame(Xmm), family = binomial) forwards = step(nullmod, scope = list(lower = formula(nullmod), upper = formula(fullmod)), direction = "forward", trace = 0)

Value

A list of raw results

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression_stepwise_forward(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = as.numeric(Pima.te$type == "Yes")
)

A Wrapper for FastLR

Description

A tool to find many types of a priori experimental designs

Author(s)

Adam Kapelner [email protected]

References

Kapelner, A

General Confusion Table and Errors

Description

Provides a confusion table and error metrics for general factor vectors. There is no need for the same levels in the two vectors.

Usage

general_confusion_results(yhat, yfac, proportions_scaled_by_column = FALSE)

Arguments

yhat

The factor predictions
yfac

The true factor responses
proportions_scaled_by_column

When returning the proportion table, scale by column? Default is FALSE to keep the probabilities unconditional to provide the same values as the function confusion_results. Set to TRUE to understand error probabilities by prediction bucket.

Value

A list of raw results

Examples

library(MASS); data(Pima.te)
ybin = as.numeric(Pima.te$type == "Yes")
flr = fast_logistic_regression(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = ybin
)
phat = predict(flr, model.matrix(~ . - type, Pima.te))
yhat = array(NA, length(ybin))
yhat[phat <= 1/3] = "no"
yhat[phat >= 2/3] = "yes"
yhat[is.na(yhat)] = "maybe"
general_confusion_results(factor(yhat, levels = c("no", "yes", "maybe")), factor(ybin)) 
#you want the "no" to align with 0, the "yes" to align with 1 and the "maybe" to be 
#last to align with nothing

FastLR Wrapper Predictions

Description

Predicts returning p-hats

Usage

## S3 method for class 'fast_logistic_regression'
predict(object, newdata, type = "response", ...)

Arguments

object

The object built using the fast_logistic_regression or fast_logistic_regression_stepwise wrapper functions
newdata

A matrix of observations where you wish to predict the binary response.
type

The type of prediction required. The default is "response" which is on the response scale (i.e. probability estimates) and the alternative is "link" which is the linear scale (i.e. log-odds).
...

Further arguments passed to or from other methods

Value

A numeric vector of length nrow(newdata) of estimates of P(Y = 1) for each unit in newdata.

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = as.numeric(Pima.te$type == "Yes")
)
phat = predict(flr, model.matrix(~ . - type, Pima.te))

FastLR Wrapper Predictions

Description

Predicts returning p-hats

Usage

## S3 method for class 'fast_logistic_regression_stepwise'
predict(object, newdata, type = "response", ...)

Arguments

object

The object built using the fast_logistic_regression or fast_logistic_regression_stepwise wrapper functions
newdata

A matrix of observations where you wish to predict the binary response.
type

The type of prediction required. The default is "response" which is on the response scale (i.e. probability estimates) and the alternative is "link" which is the linear scale (i.e. log-odds).
...

Further arguments passed to or from other methods

Value

A numeric vector of length nrow(newdata) of estimates of P(Y = 1) for each unit in newdata.

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression_stepwise_forward(
  Xmm = model.matrix(~ . - type, Pima.te), 
  ybin = as.numeric(Pima.te$type == "Yes")
)
phat = predict(flr, model.matrix(~ . - type, Pima.te))

FastLR Wrapper Print

Description

Returns the summary table a la glm

Usage

## S3 method for class 'fast_logistic_regression'
print(x, ...)

Arguments

x

The object built using the fast_logistic_regression or fast_logistic_regression_stepwise wrapper functions
...

Other arguments to be passed to print

Value

The summary as a data.frame

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression(
	Xmm = model.matrix(~ . - type, Pima.te), 
 ybin = as.numeric(Pima.te$type == "Yes"))
print(flr)

FastLR Wrapper Print

Description

Returns the summary table a la glm

Usage

## S3 method for class 'fast_logistic_regression_stepwise'
print(x, ...)

Arguments

x

The object built using the fast_logistic_regression or fast_logistic_regression_stepwise wrapper functions
...

Other arguments to be passed to print

Value

The summary as a data.frame

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression_stepwise_forward(
	Xmm = model.matrix(~ . - type, Pima.te), 
 ybin = as.numeric(Pima.te$type == "Yes"))
print(flr)

FastLR Wrapper Summary

Description

Returns the summary table a la glm

Usage

## S3 method for class 'fast_logistic_regression'
summary(object, alpha_order = TRUE, ...)

Arguments

object

The object built using the fast_logistic_regression or fast_logistic_regression_stepwise wrapper functions
alpha_order

Should the coefficients be ordered in alphabetical order? Default is TRUE.
...

Other arguments to be passed to summary.

Value

The summary as a data.frame

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression(
	Xmm = model.matrix(~ . - type, Pima.te), 
 ybin = as.numeric(Pima.te$type == "Yes"))
summary(flr)

FastLR Wrapper Summary

Description

Returns the summary table a la glm

Usage

## S3 method for class 'fast_logistic_regression_stepwise'
summary(object, ...)

Arguments

object

The object built using the fast_logistic_regression_stepwise wrapper functions
...

Other arguments to be passed to summary.

Value

The summary as a data.frame

Examples

library(MASS); data(Pima.te)
flr = fast_logistic_regression_stepwise_forward(
	Xmm = model.matrix(~ . - type, Pima.te), 
 ybin = as.numeric(Pima.te$type == "Yes"))
summary(flr)