coef.lmvar: Extracts coefficients from an 'lmvar' object.
In lmvar: Linear Regression with Non-Constant Variances

Description Usage Arguments Details Value See Also Examples

Extracts maximum-likelihood estimators for β_μ and β_σ from an 'lmvar' object.

1 2	## S3 method for class 'lmvar' coef(object, mu = TRUE, sigma = TRUE, ...)

`object`	Object of class 'lmvar'
`mu`	Boolean, specifies whether or not to return the maximum-likelihood estimator for β_μ
`sigma`	Boolean, specifies whether or not to return the maximum-likelihood estimator for β_σ
`...`	For compatibility with `coef` generic

When both mu = TRUE and sigma = TRUE, the names of the coefficients in β_σ are adapted to distinguish them from the names in β_μ, if needed.

When mu = TRUE and sigma = TRUE, a named numeric vector with the elements of β_μ, followed by the elements of β_σ.

When mu = TRUE and sigma = FALSE, a named numeric vector with the elements of β_μ.

When mu = FALSE and sigma = TRUE, a named numeric vector with the elements of β_σ.

beta_sigma_names for the adaptation of the names of the coefficients in β_σ.

confint for the calculation of confidence intervals of β_μ and β_σ.

# As example we use the dataset 'attenu' from the library 'datasets'. The dataset contains
# the response variable 'accel' and two explanatory variables 'mag'  and 'dist'.
library(datasets)

# Create the model matrix for the expected values
X = cbind(attenu$mag, attenu$dist, attenu$mag + attenu$dist)
colnames(X) = c("mag", "dist", "mag+dist")

# Create the model matrix for the standard deviations.
X_s = cbind(attenu$mag, 1 / attenu$dist)
colnames(X_s) = c("mag", "dist_inv")

# Carry out the fit
fit = lmvar(attenu$accel, X, X_s)

# Extract all coefficients
coef(fit)

# Extract only the coefficients corresponding to the (non-aliased)
# columns in the model matrix for the expected values
coef(fit, sigma = FALSE)

# Extract only the coefficients corresponding to the (non-aliased)
# columns in the model matrix for standard deviations
coef(fit, mu = FALSE)