***This package is experimental and not regularly maintained. It is recommended to use {glmnet} instead!

Overview

logitr is an R package for linear and logistic regression with optional ridge and bridge regularization penalties. A (possibly incomplete) list of functions contained in the package can be found below:

  • logisticr() computes the coefficient estimates for logistic regression (ridge and bridge regularization optional)
  • linearr() computes the linear regression coefficient estimates (ridge regularization and weights optional)
  • predict.logisticr() generates predictions and loss metrics for logistic regression
  • predict.linearr() generates predictions and loss metric for linear regression

See vignette or manual.

Installation

If there are any issues/bugs, please let me know: github. You can also contact me via my website. Pull requests are welcome!

Usage

library(logitr)

#we will use the iris data set
X = dplyr::select(iris, -c(Species, Sepal.Length))
y = dplyr::select(iris, Sepal.Length)
y_class = ifelse(dplyr::select(iris, Species) == "setosa", 1, 0)

#ridge regression (use CV for optimal lambda)
linearr(X, y, penalty = "ridge")
## 
## Call: linearr(X = X, y = y, penalty = "ridge")
## 
## Tuning parameters:
##       lam  alpha
## [1,]  0.2    NaN
## 
## MSE:
## [1] 0.09634343
## 
## Coefficients:
##               [,1]
## intercept  1.89913
##            0.64175
##            0.69573
##           -0.52729
## 
## Call: logisticr(X = X, y = y_class, penalty = "ridge")
## 
## Iterations:
## [1] 18
## 
## Tuning parameters:
##       lam  alpha
## [1,]    0    NaN
## 
## MSE:
## [1] 8.0815e-14
## 
## logloss:
## [1] 6.929591e-06
## 
## misclassification:
## [1] 0
## 
## Coefficients:
##                [,1]
## intercept   7.88535
##             8.89485
##            -7.54863
##           -18.60425
#ridge logistic regression (MM)
logisticr(X, y_class, lam = 0.1, penalty = "ridge", method = "MM")
## 
## Call: logisticr(X = X, y = y_class, lam = 0.1, penalty = "ridge", method = "MM")
## 
## Iterations:
## [1] 5459
## 
## Tuning parameters:
##       lam  alpha
## [1,]  0.1    NaN
## 
## MSE:
## [1] 5.134801e-05
## 
## logloss:
## [1] 0.3956563
## 
## misclassification:
## [1] 0
## 
## Coefficients:
##               [,1]
## intercept  6.27623
##            1.54082
##           -3.64177
##           -1.63050
#bridge logistic regression (MM)
fit = logisticr(X, y_class, lam = 0.1, alpha = 1.2, penalty = "bridge")
## [1] "using MM algorithm..."
## 
## Call: logisticr(X = X, y = y_class, lam = 0.1, alpha = 1.2, penalty = "bridge")
## 
## Iterations:
## [1] 26021
## 
## Tuning parameters:
##       lam  alpha
## [1,]  0.1    1.2
## 
## MSE:
## [1] 2.749702e-05
## 
## logloss:
## [1] 0.1878654
## 
## misclassification:
## [1] 0
## 
## Coefficients:
##               [,1]
## intercept 13.08031
##            0.61701
##           -5.71684
##           -0.23004
## $fitted.values
##           [,1]
## [1,] 0.9992467
## [2,] 0.9989747
## [3,] 0.9994881
## 
## $class
##      [,1]
## [1,]    1
## [2,]    1
## [3,]    1
## 
## $MSE
## [1] 6.269174e-07
## 
## $log.loss
## [1] 0.002291457
## 
## $misclassification
## [1] 0