stepreg {glmnetr} | R Documentation |
Fit the steps of a stepwise regression.
Description
Fit the steps of a stepwise regression.
Usage
stepreg(
xs_st,
start_time_st = NULL,
y_st,
event_st,
steps_n = 0,
method = "loglik",
family = NULL,
track = 0
)
Arguments
xs_st |
predictor input - an n by p matrix, where n (rows) is sample size, and p (columns) the number of predictors. Must be in matrix form for complete data, no NA's, no Inf's, etc., and not a data frame. |
start_time_st |
start time, Cox model only - class numeric of length same as number of patients (n) |
y_st |
output vector: time, or stop time for Cox model, y_st 0 or 1 for binomal (logistic), numeric for gaussian. Must be a vector of length same as number of sample size. |
event_st |
event_st indicator, 1 for event, 0 for census, Cox model only. Must be a numeric vector of length same as sample size. |
steps_n |
number of steps done in stepwise regression fitting |
method |
method for choosing model in stepwise procedure, "loglik" or "concordance". Other procedures use the "loglik". |
family |
model family, "cox", "binomial" or "gaussian" |
track |
1 to output stepwise fit program, 0 (default) to suppress |
Value
does a stepwise regression of depth maximum depth steps_n
See Also
summary.stepreg
, aicreg
, cv.stepreg
, nested.glmnetr
Examples
set.seed(18306296)
sim.data=glmnetr.simdata(nrows=100, ncols=100, beta=c(0,1,1))
# this gives a more intersting case but takes longer to run
xs=sim.data$xs
# this will work numerically
xs=sim.data$xs[,c(2,3,50:55)]
y_=sim.data$yt
event=sim.data$event
# for a Cox model
cox.step.fit = stepreg(xs, NULL, y_, event, family="cox", steps_n=40)
# ... and for a linear model
y_=sim.data$yt
norm.step.fit = stepreg(xs, NULL, y_, NULL, family="gaussian", steps_n=40)