lmls {lmls} | R Documentation |
Gaussian location-scale regression
Description
The location-scale regression model assumes a normally distributed response variable with one linear predictor for the mean (= the location) and one for the standard deviation (= the scale). The standard deviation is mapped to the linear predictor through a log link.
This function sets up the model object and estimates it with maximum likelihood.
Usage
lmls(
location,
scale = ~1,
data = environment(location),
light = TRUE,
maxit = 100,
reltol = sqrt(.Machine$double.eps)
)
Arguments
location |
A two-sided formula with the response variable on the LHS and the predictor for the mean on the RHS. |
scale |
A one-sided formula with the predictor for the standard deviation on the RHS. |
data |
A data frame (or list or environment) in which to evaluate
the |
light |
If |
maxit |
The maximum number of iterations of the Fisher scoring algorithm. |
reltol |
The relative convergence tolerance of the Fisher scoring algorithm. |
Value
A fitted linear model for location and scale as an lmls
S3 object.
The object has at least the following entries:
-
y
: the response vector -
nobs
: the number of observations -
df
: the degrees of freedom -
df.residual
: the residual degrees of freedom -
coefficients
: the regression coefficients as a list with the nameslocation
andscale
-
fitted.values
: the fitted values as a list with the nameslocation
andscale
-
residuals
: the response residuals -
coefficients
: the variance-covariance matrices of the regression coefficients as a list with the nameslocation
andscale
-
iterations
: the number of iterations the Fisher scoring algorithm took to converge
Examples
library(lmls)
m <- lmls(y ~ poly(x, 2), ~ x, data = abdom)
summary(m)
plot(m)
qqnorm(m)