coef.msaenet: Extract Model Coefficients

In msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

Description

Extract model coefficients from the final model in msaenet model objects.

Usage

## S3 method for class 'msaenet'
coef(object, ...)

Arguments

object	An object of class msaenet produced by aenet, amnet, asnet, msaenet, msamnet, or msasnet.
...	Additional parameters for coef (not used).

Value

A numerical vector of model coefficients.

Author(s)

Nan Xiao <https://nanx.me>

Examples

dat <- msaenet.sim.gaussian(
  n = 150, p = 500, rho = 0.6,
  coef = rep(1, 5), snr = 2, p.train = 0.7,
  seed = 1001
)

msaenet.fit <- msaenet(
  dat$x.tr, dat$y.tr,
  alphas = seq(0.2, 0.8, 0.2),
  nsteps = 3L, seed = 1003
)

coef(msaenet.fit)

Example output

  [1]  0.0000000  1.5676827  0.7983849  1.2047923  0.9912135  0.0000000
  [7]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [13]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [19]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [25]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [31]  0.0000000  0.0000000  0.0000000  0.0000000  0.1679623  0.0000000
 [37]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [43]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [49]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [55]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [61]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [67]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [73]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [79]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [85]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [91]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
 [97]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[103]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[109]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 -0.5600420
[115]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[121]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[127]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[133]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[139]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[145]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[151]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[157]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[163]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[169]  0.0000000  0.0000000  0.6189521  0.0000000  0.0000000  0.0000000
[175]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[181]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[187]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[193]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[199]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[205]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[211]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[217]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[223]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[229]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[235]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[241]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[247]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[253]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[259]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[265]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[271]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[277]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[283]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[289]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[295]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[301]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[307]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 -0.5053576
[313]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[319]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[325]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[331]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[337]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[343]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[349]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[355]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[361]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[367]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[373]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[379] -0.7342114  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[385]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[391]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[397]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[403]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[409]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[415]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[421]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[427]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[433]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[439]  0.0000000  0.0000000 -0.3172814  0.0000000  0.0000000  0.0000000
[445]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[451]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[457]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[463]  0.0000000 -0.3886030  0.0000000  0.0000000  0.0000000  0.0000000
[469]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[475]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[481]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[487]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[493]  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
[499]  0.0000000 -0.1883070

msaenet documentation built on May 18, 2019, 1:03 a.m.