Description Usage Arguments Value Examples
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.
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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. |
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 names
location
and scale
fitted.values
: the fitted values as a list with the names location
and scale
residuals
: the response residuals
coefficients
: the variance-covariance matrices of the regression
coefficients as a list with the names location
and scale
iterations
: the number of iterations the Fisher scoring algorithm
took to converge
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