summary.prm: Summary of a prm model

In sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

Description

Summarizing models of class prm.

Usage

## S3 method for class 'prm'
summary(object, ...)
## S3 method for class 'prm'
print(x, ...)

Arguments

object, x	object of class prm
...	optional arguments for internal print function.

Value

summary prints model parameters and explained variances.

print prints model parameters.

Author(s)

Irene Hoffmann

References

Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.

Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.

See Also

prms

Examples

set.seed(5023)
U <- c(rep(2,20), rep(5,30))
X <- replicate(6, U+rnorm(50))
beta <- c(rep(1, 3), rep(-1,3))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
d <- as.data.frame(X)
d$y <- y
mod <- prms(y~., data=d, a=2, fun="Hampel")
summary(mod)

Example output

Loading required package: ggplot2
Partial M-robust regression 
 Number of components:  2
 weight function:  Hampel with cutoff 0.95 0.975 0.999

Percentage of explained variance
                              X        y
With  1  component(s): 55.03667 24.25559
With  2  component(s): 75.00504 69.97580

sprm documentation built on May 2, 2019, 9:57 a.m.