You can use the texreg
package for this purpose and customize it for the ugarchfit
function of the rugarch
package:
library(texreg)
#define dependent variable:
y <- x #to generalize your case (y is usually the dependent variable)
extract.rugarch <- function(fit,
include.rsquared = TRUE, include.loglike = TRUE, include.aic = TRUE, include.bic = TRUE) {
# extract coefficient table from fit:
coefnames <- rownames(as.data.frame(fit@fit$coef))
coefs <- fit@fit$coef
se <- as.vector(fit@fit$matcoef[, c(2)])
pvalues <- as.vector(fit@fit$matcoef[, c(4)]) # numeric vector with p-values
# create empty GOF vectors and subsequently add GOF statistics from model:
gof <- numeric()
gof.names <- character()
gof.decimal <- logical()
if (include.rsquared == TRUE) {
r2 <- 1 - (var(fit@fit$residuals) / var(y))
gof <- c(gof, r2)
gof.names <- c(gof.names, "R^2")
gof.decimal <- c(gof.decimal, TRUE)
}
if (include.loglike == TRUE) {
loglike <- fit@fit$LLH
gof <- c(gof, loglike)
gof.names <- c(gof.names, "Log likelihood")
gof.decimal <- c(gof.decimal, TRUE)
}
if (include.aic == TRUE) {
aic <- infocriteria(fit)[c(1)]
gof <- c(gof, aic)
gof.names <- c(gof.names, "AIC")
gof.decimal <- c(gof.decimal, TRUE)
}
if (include.bic == TRUE) {
bic <- infocriteria(fit)[c(2)]
gof <- c(gof, bic)
gof.names <- c(gof.names, "BIC")
gof.decimal <- c(gof.decimal, TRUE)
}
# create texreg object:
tr <- createTexreg(
coef.names = coefnames,
coef = coefs,
se = se,
pvalues = pvalues,
gof.names = gof.names,
gof = gof,
gof.decimal = gof.decimal
)
return(tr)
}
#print table:
texreg(extract.rugarch(fit, include.rsquared = FALSE)) #for latex # as R^2 is zero in this example.
Philip Leifeld, the package author, provided a really good and detailed explanation on how to customize texreg for unsupported packages: Print "pretty" tables for h2o models in R