Tabulation: Tabulation of overall SGB regression results with AIC and...

In SGB: Simplicial Generalized Beta Regression

Description

table.regSGB: Value of the log-likelihood, number of parameters, AIC criterion, optimality tests and iterations counts.
coefmat: regression coefficients in matrix form with significance level.

Usage

table.regSGB(object)
coefmat(object,digits=3)

Arguments

object	an object of class regSGB
digits	number of decimal places for the coefficients

Value

table.regSGB: Data frame with one column, with the overall statistics results.

value	the maximum log-likelihood
n.par	the number of parameters
n.par.fixed	the number of fixed parameters
AIC	the AIC criterion
Rsquare	total variance of estimated over total variance of observed compositions
convergence	the convergence code (0: converged, others, see auglag).
kkt1	the first Karush-Kuhn-Tucker conditions (1=TRUE, 0=FALSE), see auglag.
kkt2	the second Karush-Kuhn-Tucker conditions (1=TRUE, 0=FALSE), see auglag.
counts.function	number of times the log-likelihood was evaluated.
counts.gradient	number of times the gradient was evaluated.

coefmat: character matrix with the regression coefficients arranged in columns, one for each log-ratio transform. Each ceofficient is followed by the significance level.

See Also

regSGB, oilr, auglag.

Examples

## Overall model statistics
table.regSGB(oilr)
## 
print(coefmat(oilr),quote=FALSE)
## it is a subset of
summary(oilr)

SGB documentation built on March 26, 2020, 8:02 p.m.