RLM: Robust Fitting of Linear Models

In LVSmiRNA: LVS normalization for Agilent miRNA data

Description

Fit a linear model by robust regression using the Huber estimator.

Usage

RLM(formula, maxit=20, k=1.345, data, model=TRUE,na.action,
method=c("joint","rlm"), x=TRUE, y=TRUE,
offset,cov.formula=c("weighted","asymptotic"), start=NULL,...)

Arguments

formula	a formula of the form y ~ x1 + x2 + ...
maxit	the limit on the number of IWLS iterations.
k	tuning constant used for Huber proposal 2 scale estimation.
data	data frame from which variables specified in formula are preferentially to be taken.
model	should the model frame be returned in the object?
na.action	A function to specify the action to be taken if NAs are found. The 'factory-fresh' default action in R is na.omit, and can be changed by options.
method	currently, method="rlm" and "joint" are supported.
x	should the model frame be returned in the object?
y	should the model matrix be returned in the object?
offset	numeric of length n. This can be used to specify an a priori known component to be included in the linear predictor during fitting.
cov.formula	are the methods to compute covariance matrix, currently either weighted or asymptotic.
start	vector containing starting values for the parameters in the predictor.
...	...

Details

Fitting is done by iterated re-weighted least squares (IWLS). This customized version of robust linear model deal with wild ouliers using log link in joint modelling heterogeneous variance of covariates.

Value

An object of class "RLM" inheriting from "lm".

Author(s)

Stefano Calza <[e-mail]>, Suo Chen and Yudi Pawitan.

References

Pawitan, Y. 'In All Likelihood: Statistical Modeling and Inference Using Likelihood', (2001, Oxford University Press); Huber, P. J. , Robust Statistics, (1981. Wiley).

See Also

RLM is modified from "rlm" in the MASS, "rlmFit"

Examples

set.seed(133)
n <- 9 
p <- 3
X <- matrix(rnorm(n * p), n,p) 
y <- rnorm(n)

fit <- RLM(y~X-1) #no intercept

LVSmiRNA documentation built on Oct. 31, 2019, 5:29 a.m.