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 lmgce model object.

call

Boolean value. if TRUE, the call used is returned. The default is model = TRUE.

correlation

Boolean value. if TRUE, the correlation matrix of the estimated parameters is returned and printed.

symbolic.cor

Boolean value. if TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.

ci.level

the confidence level (0,1) required to compute the confidence interval. The default is ci.level = NULL which results in the omission of the confidence interval.

ci.method

method used to compute a confidence interval. One of c("z","percentile", "basic"). The default is ci.method = "z".

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 method = c("percentile", "basic") and when object was created with boot.B = 0. The default is boot.B = 100 when the object has no previous sampling information and boot.B = object$boot.B otherwise, which corresponds to the boot.B given to lmgce when the object was created.

boot.method

Method used for bootstrapping. One of c("residuals", "cases", "wild") which corresponds to resampling on residuals, on individual cases or on residuals multiplied by a N(0,1) variable, respectively. The default is boot.method = object$boot.method.

...

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 p \times 4 matrix, where p is the number of non-aliased coefficients, with columns for the estimated coefficient, its standard error, z-statistic and corresponding (two-sided) p-value. Aliased coefficients are omitted.

support

a p \times 3 matrix with columns for the normalized entropy (NormEnt), and lower (LL) and upper (UL) limits for each of the K+1 support spaces.

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 (p, n - p) the first being the number of non-aliased coefficients, the last being the p minus the number of included individuals n.

r.squared

R^2, the ‘fraction of variance explained by the model’

adj.r.squared

the above R^2 statistic ‘adjusted’, penalizing for higher p.

cov.unscaled

a p \times p matrix of covariances of the \hat \beta

support.stdUL

when applicable, the upper limit of the standardized support chosen, when support.method = "standardized" or the factor used when support.method = "ridge".

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 correlation = TRUE is specified.

symbolic.cor

(only if correlation = TRUE) The value of the argument symbolic.cor.

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


[Package GCEstim version 0.1.0 Index]