summary.lmgce {GCEstim} | R Documentation |
Summarise a linear regression model via generalized cross entropy fit
Description
summary method for class lmgce
. Function used to produce
summary information from a fitted linear regression model via generalized
cross entropy as represented by object
of class lmgce
.
Usage
## S3 method for class 'lmgce'
summary(
object,
call = TRUE,
correlation = FALSE,
symbolic.cor = FALSE,
ci.level = NULL,
ci.method = c("z", "percentile", "basic"),
boot.B = ifelse(object$boot.B == 0, 100, object$boot.B),
boot.method = object$boot.method,
...
)
Arguments
object |
Fitted |
call |
Boolean value. if |
correlation |
Boolean value. if |
symbolic.cor |
Boolean value. if |
ci.level |
the confidence level (0,1) required to compute the confidence
interval. The default is |
ci.method |
method used to compute a confidence interval. One of
c("z","percentile", "basic"). The default is |
boot.B |
A single positive integer greater or equal to 10 for the number
of bootstrap replicates for the computation of the bootstrap confidence
interval(s), to be used when |
boot.method |
Method used for bootstrapping. One of
|
... |
additional arguments. |
Value
The function summary.lmgce
computes and returns a list of
summary statistics of the fitted lmgce
linear model given in
object
, using the components (list elements) "call" and "terms" from
its argument, plus
residuals |
the residuals, that is response minus fitted values. |
coefficients |
a |
support |
a |
aliased |
named logical vector showing if the original coefficients are aliased. |
sigma |
the square root of the estimated variance of the random error. |
df |
degrees of freedom, a 3-vector |
r.squared |
|
adj.r.squared |
the above |
cov.unscaled |
a |
support.stdUL |
when applicable, the upper limit of the standardized
support chosen, when |
support.method |
method chosen for the support's limits |
nep |
the normalized entropy of the model. |
nep.cv.mean |
the cross-validation normalized entropy of the model. |
nep.cv.sd |
the standard deviation of the cross-validation normalized entropy of the model. |
error |
the error measure chosen |
error.which |
which criterion/standardized/factor support was used |
error.measure |
the value of the error measure |
error.measure.cv.mean |
the cross-validation value of the error measure |
error.measure.cv.sd |
the standard deviation of the cross-validation value of the error measure |
correlation |
the correlation matrix corresponding to the above
cov.unscaled, if |
symbolic.cor |
(only if |
na.action |
from object, if present there. |
Author(s)
Jorge Cabral, jorgecabral@ua.pt
Examples
res_gce_package <-
lmgce(y ~ .,
data = dataGCE,
boot.B = 50,
seed = 230676)
sm_res_gce_package <- summary(res_gce_package)
str(sm_res_gce_package)
sm_res_gce_package$coefficients