Linear Regression

Computes the linear regression coefficient estimates (ridge-penalization and weights, optional)

linearr(X, y, lam = seq(0, 2, 0.1), alpha = 1.5, penalty = c("none",
  "ridge", "bridge"), weights = NULL, intercept = TRUE, kernel = FALSE,
  method = c("SVD", "MM"), tol = 1e-05, maxit = 1e+05, vec = NULL,
  init = 1, K = 5)

Arguments

X	matrix or data frame
y	matrix or data frame of response values
lam	optional tuning parameter for ridge regularization term. If passing a list of values, the function will choose the 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'
weights	optional vector of weights for weighted least squares
intercept	add column of ones if not already present. Defaults to TRUE
kernel	use linear kernel to compute ridge regression coefficeients. Defaults to TRUE when p >> n (for 'SVD')
method	optimization algorithm. Choose from 'SVD' or 'MM'. Defaults to 'SVD'
tol	tolerance - used to determine algorithm convergence for 'MM'. Defaults to 10^-5
maxit	maximum iterations for 'MM'. Defaults to 10^5
vec	optional vector to specify which coefficients will be penalized
init	optional initialization for MM algorithm
K	specify number of folds for cross validation, if necessary

Value

returns the selected tuning parameters, coefficient estimates, MSE, and gradients

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
X = dplyr::select(iris, -c(Species, Sepal.Length))
y = dplyr::select(iris, Sepal.Length)
linearr(X, y, lam = 0.1, penalty = 'ridge')
#> 
#> Call: linearr(X = X, y = y, lam = 0.1, penalty = "ridge")
#> 
#> Tuning parameters:
#>       lam  alpha
#> [1,]  0.1    NaN
#> 
#> MSE:
#> [1] 0.09631325
#> 
#> Coefficients:
#>               [,1]
#> intercept  1.87785
#>            0.64624
#>            0.70231
#>           -0.54160