# R/mtable.R In crch: Censored Regression with Conditional Heteroscedasticity

#### Documented in getSummary.crch

```getSummary.crch <- function(obj, alpha = 0.05, ...)
{
## internally force setting of the summary template (so that
## memisc does not have to be registered in the NAMESPACE already)
memisc::setSummaryTemplate("crch" = c(
"Log-likelihood" = "(\$logLik:f#)",
"AIC" = "(\$AIC:f#)",
"BIC" = "(\$BIC:f#)",
"N" = "(\$N:d)"
))

## extract coefficient summary
cf <- summary(obj)\$coefficients
## augment with confidence intervals
cval <- qnorm(1 - alpha/2)
for(i in seq_along(cf)) cf[[i]] <- cbind(cf[[i]],
cf[[i]][, 1] - cval * cf[[i]][, 2],
cf[[i]][, 1] + cval * cf[[i]][, 2])
## collect in array
nam <- unique(unlist(lapply(cf, rownames)))
acf <- array(dim = c(length(nam), 6, length(cf)),
dimnames = list(nam, c("est", "se", "stat", "p", "lwr", "upr"), names(cf)))
for(i in seq_along(cf)) acf[rownames(cf[[i]]), , i] <- cf[[i]]

## contrasts (omitting duplicates between location and scale part) and factor levels
ctr <- c(obj\$contrasts\$location, obj\$contrasts\$scale)
ctr <- ctr[!duplicated(names(ctr))]
xlev <- obj\$levels\$full

## return everything
return(list(
coef = acf,
sumstat = c(
"N" = nobs(obj),
"logLik" = as.vector(logLik(obj)),
"AIC" = AIC(obj),
"BIC" = BIC(obj)
),
contrasts = ctr,
xlevels = xlev,
call = obj\$call
))
}
```

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crch documentation built on Sept. 10, 2022, 1:06 a.m.