FSReda_control: Creates an 'FSReda_control' object

In fsdaR: Robust Data Analysis Through Monitoring and Dynamic Visualization

Creates an FSReda_control object

Description

Creates an object of class FSReda_control to be used with the fsreg() function, containing various control parameters.

Usage

FSReda_control(intercept = TRUE, init, nocheck = FALSE, 
    tstat = c("trad", "scal"), conflev = c(0.95, 0.99))

Arguments

intercept	Indicator for constant term. Scalar. If intercept=TRUE, a model with constant term will be fitted (default), else, no constant term will be included.
init	Search initialization, scalar. It specifies the initial subset size to start monitoring exceedances of minimum deletion residual, if init is not specified it set equal to: p+1, if the sample size is smaller than 40 or min(3*p+1,floor(0.5*(n+p+1))), otherwise. For example, if init=100, the procedure starts monitoring from step m=100.
nocheck	Check input arguments, scalar. If nocheck=TRUE no check is performed on matrix y and matrix X. Notice that y and X are left unchanged. In other words the additional column of ones for the intercept is not added. As default nocheck=FALSE.
tstat	The kind of t-statistics which have to be monitored. tstat="trad" implies monitoring of traditional t statistics (out$Tols). In this case the estimate of s2 at step m is based on s2m (notice that s2m<<s2 when m/n is small) tstat="scal" (default) implies monitoring of rescaled t statistics. In this case the estimate of s2 at step m is based on s2m/vartruncnorm(m/n) where vartruncnorm(m/n) is the variance of the truncated normal distribution.
conflev	Confidence level which is used to declare units as outliers. Usually conflev=0.95, 0.975, 0.99 (individual alpha) or conflev=1-0.05/n, 1-0.025/n, 1-0.01/n (simultaneous alpha). Default value is 0.975.

Details

Creates an object of class FSReda_control to be used with the fsreg() function, containing various control parameters.

Value

An object of class "FSReda_control" which is basically a list with components the input arguments of the function mapped accordingly to the corresponding Matlab function.

Author(s)

FSDA team

See Also

See Also as FSR_control, MMreg_control and LXS_control

Examples

## Not run: 
data(hbk, package="robustbase")
(out <- fsreg(Y~., data=hbk, method="FS", monitoring=TRUE, 
    control=FSReda_control(tstat="scal")))

## End(Not run)

fsdaR documentation built on March 31, 2023, 8:18 p.m.