Summary Method for Linear Models

DESCRIPTION:

Returns a summary of the linear models stored in a fit.models object with virtual class lmfm.

USAGE:

summary.lmfm(object, correlation=T, ...)

REQUIRED ARGUMENTS:

object
a fit.models object with virtual class lmfm that contains the fitted models.

OPTIONAL ARGUMENTS:

correlation
if TRUE, the correlation matrix for the coefficients is included in the summary.

VALUE:

a summary.lmfm object with the following components:
mod.names
the names of the models in object.
calls
a list containing the call of each model in object.
restats
a 2-dimensional array with one row (containing the minimum, the maximum, and the quartiles of the residuals) for each model in object.
coefs
a 3-dimensional array. For each level of the 3rd index (one for each model in object), the 2-dimensional slice contains the coefficient estimates, their standard errors, the t statistic, and the p-value.
df
the residual degrees of freedom for each model in object.
r.squared
the R-squared statistic for each model in object.
sigmas
the residual scale estimate for each model in object.
correlations
a 3-dimensional array. For each level of the 3rd index (one for each model in object), the 2-dimensional slice is the estimated correlation matrix of the coefficients.
biasTest
result of the bias test for robust models. See .

SEE ALSO:

, , , .

EXAMPLES:

stack.lm <- lm(Loss ~ ., data=stack.dat)
stack.lmRob <- lmRob(Loss ~ ., data=stack.dat)
stack.summary <- summary(fit.models(stack.lmRob, stack.lm))
stack.summary