eivlm: eivlm (Errors in variables regression model)

In bolimorales/RegUtils: Tools For Model Regressions

Description

etreg fits a regression models when one or more of the independent variables are measured with additive noise.

Usage

eivlm <- function(formula, data, subset, weights, na.action,
                  model = TRUE, x = FALSE, method = NULL, y = FALSE,
                  singular.ok = TRUE, contrasts = NULL, rel = NULL, offset, ...)

Arguments

formula	a symbolic description for the model to be estimated,
data	a data.frame,
subset	see lm for "alm", a character or numeric vector indicaing asubset of the table of coefficient to be printed for "print.summary.alm",
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
weights	an optional vector of weights to be used in the fitting process.
model, x, y	logicals. If TRUE the corresponding components of the fit (the model frame, the model matrices , the response) are returned.
...	further arguments.

Details

Standard regression would underestimate the effect of the variable if it is measure with aditive noise. The regression can be adjusted for the biases if the reliability is known:

reliability = 1 - (noise variance)/(total variance)

Value

object of class 'eivlm' which inherits from class 'lm'. Components are identical to those of class 'lm', see lm.

References

Draper, N., and H. Smith. (1998). Applied Regression Analysis. 3rd ed. New York. Wiley.

See Also

See Also lm

Examples

#Assuming speed information was measured with a reliability of 0.85
fit1 = eivlm(dist~speed, data=cars, rel = c(0.85))
summary(fit1)

bolimorales/RegUtils documentation built on May 12, 2019, 11:33 p.m.