Description Usage Arguments Value Examples
Computes the coefficient estimates for logistic regression. ridge regularization and bridge regularization optional.
1 2 3 4 |
X |
matrix or data frame |
y |
matrix or vector of response values 0,1 |
lam |
optional tuning parameter(s) for ridge regularization term. If passing a list of values, the function will choose optimal value based on K-fold cross validation. Defaults to 'lam = seq(0, 2, 0.1)' |
alpha |
optional tuning parameter for bridge regularization term. If passing a list of values, the function will choose the optimal value based on K-fold cross validation. Defaults to 'alpha = 1.5' |
penalty |
choose from c('none', 'ridge', 'bridge'). Defaults to 'none' |
intercept |
Defaults to TRUE |
method |
optimization algorithm. Choose from 'IRLS' or 'MM'. Defaults to 'IRLS' |
tol |
tolerance - used to determine algorithm convergence. Defaults to 10^-5 |
maxit |
maximum iterations. Defaults to 10^5 |
vec |
optional vector to specify which coefficients will be penalized |
init |
optional initialization for MM algorithm |
criteria |
specify the criteria for cross validation. Choose from c('mse', 'logloss', 'misclass'). Defauls to 'logloss' |
K |
specify number of folds for cross validation, if necessary |
returns selected tuning parameters, beta estimates (includes intercept), MSE, log loss, misclassification rate, total iterations, and gradients.
1 2 3 4 5 6 7 8 9 10 11 | library(dplyr)
X = dplyr::select(iris, -Species)
y = dplyr::select(iris, Species)
y$Species = ifelse(y$Species == 'setosa', 1, 0)
logisticr(X, y)
# ridge Logistic Regression with IRLS
logisticr(X, y, lam = 0.1, penalty = 'ridge')
# ridge Logistic Regression with MM
logisticr(X, y, lam = 0.1, penalty = 'ridge', method = 'MM')
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