RobustAFT (version 1.4-7)

TML.noncensored.control: Control Parameters for Truncated Maximum Likelihood Regression Without Censored Observations

Description

Control parameters for TML.noncensored. Typically only used internally by TML.noncensored, but may be used to construct a control argument. This function provides default values.

Usage

TML.noncensored.control(iv = 1, nrep = 0, gam = 0.1, nitmon = FALSE, 
                maxit = 200, tol = 1e-04, fastS = FALSE, seed=1313)

Value

A list with components named as the arguments.

Arguments

iv

• 0: use and do not change the initial estimate of scale.

• 1: compute a truncated maximum likelihood estimate of scale.

nrep

• Number of subsamples to be used in the computation of the S-estimate.

• 0: exhaustive sampling if the observation number is not too large.

gam

Relaxation factor for the IRLS algorithm of final estimate. Set 0 < gam <= 1.

nitmon

Set to TRUE if iteration monitoring in IRLS algorithm for the final estimate is desired. Default=FALSE.

maxit

Maximum number of iterations in IRLS algorithm for the final estimate.

tol

Relative tolerance in IRLS algorithm.

fastS

• "TRUE" : the initial S-estimate is computed using lmrob.S from the robustbase package. The control parameters are taken from lmrob.control.

• "FALSE" : the initial S-estimate is computed using hysest from the robeth package.

seed

Seed for the random number generator in the resampling algorithm for the initial S-estimate.

See Also

TML.noncensored

Examples

     ### In the example(TML.noncensored), the control argument can be built 
     ### using this function:
if (FALSE) {
     data(D243)
     Cost <- D243$Cost                             # Cost (Swiss francs)
     LOS  <- D243$LOS                              # Length of stay (days)
     Adm  <- D243$Typadm; Adm <- (Adm==" Urg")*1   # Type of admission 
                                                   # (0=on notification, 1=Emergency)
     Ass  <- D243$Typass; Ass <- (Ass=="P"   )*1   # Type of insurance 
                                                   # (0=usual, 1=private)
     Age  <- D243$age                              # Age (years)
     Dst  <- D243$dest;   Dst <- (Dst=="DOMI")*1   # Destination 
                                                   # (1=Home, 0=another hospital)
     Sex  <- D243$Sexe;   Sex <- (Sex=="M"   )*1   # Sex (1=Male, 0=Female)

     # Truncated maximum likelihood regression with Gaussian errors

     ctrol <- TML.noncensored.control(iv=1, nrep=0, gam=0.2, fastS=TRUE, nitmon=FALSE)
     z     <- TML.noncensored(log(Cost)~log(LOS)+Adm+Ass+Age+Dst+Sex, otp="adaptive")
     summary(z)
}