stepreg {glmnetr}

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)