Nothing
## ------------------------------------------------------------------------
library("msaenet")
## ------------------------------------------------------------------------
dat <- msaenet.sim.gaussian(
n = 150, p = 500, rho = 0.5,
coef = rep(1, 10), snr = 5, p.train = 0.7,
seed = 1001
)
## ------------------------------------------------------------------------
msaenet.fit <- msaenet(
dat$x.tr, dat$y.tr,
alphas = seq(0.1, 0.9, 0.1),
nsteps = 10L, tune.nsteps = "ebic",
seed = 1005
)
## ---- eval=FALSE---------------------------------------------------------
# library("doParallel")
# registerDoParallel(detectCores())
## ------------------------------------------------------------------------
msaenet.fit$best.step
msaenet.nzv(msaenet.fit)
msaenet.nzv.all(msaenet.fit)
msaenet.fp(msaenet.fit, 1:10)
msaenet.tp(msaenet.fit, 1:10)
## ------------------------------------------------------------------------
msaenet.pred <- predict(msaenet.fit, dat$x.te)
msaenet.rmse(dat$y.te, msaenet.pred)
msaenet.mae(dat$y.te, msaenet.pred)
## ---- fig.width = 10, fig.height = 8, out.width = 750, out.height = 600, fig.retina = 2----
plot(msaenet.fit, label = TRUE)
## ---- fig.width = 10, fig.height = 6, out.width = 750, out.height = 450, fig.retina = 2----
plot(msaenet.fit, type = "criterion")
## ---- fig.width = 10, fig.height = 8, out.width = 750, out.height = 600, fig.retina = 2----
plot(msaenet.fit, type = "dotplot", label = TRUE, label.cex = 1)
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