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## AER::ivreg
getSummary.ivreg <- function(obj, alpha = 0.05, ...) {
## extract summary object
s <- summary(obj)
## coefficient matrix and confidence interval
cf <- cbind(s$coefficients, confint(obj, level = 1 - alpha))
colnames(cf) <- c("est", "se", "stat", "p", "lwr", "upr")
# Patch ME 20.08.2017
dn <- c(
dimnames(cf),
names(obj$model)[1]
)
dim(cf) <- c(dim(cf)[1],dim(cf)[2],1)
dimnames(cf) <- dn
## further summary statistics
sstat <- c("sigma" = s$sigma,
"r.squared" = s$r.squared, "adj.r.squared" = s$adj.r.squared,
"Wald" = s$waldtest[1], "numdf" = s$waldtest[3], "dendf" = s$waldtest[4],
"p" = s$waldtest[2], "N" = nobs(obj))
## return everything
return(list(
coef = cf,
sumstat = sstat,
contrasts = obj$contrasts,
xlevels = obj$xlevels,
call = obj$call
))
}
setSummaryTemplate("ivreg" = c(
"R-squared" = "($r.squared:f#)",
"adj. R-squared" = "($adj.r.squared:f#)",
"sigma" = "($sigma:#)",
"Wald" = "($Wald:f#)",
"p" = "($p:f#)",
"N" = "($N:d)"
))
.SummaryStats$ivreg <- c("adj. R-squared","N")
## AER::tobit
getSummary.tobit <- function(obj, alpha = 0.05, ...) {
## extract summary object
s <- summary(obj)
## coefficient matrix and confidence interval
## compute confidence interval manually to include Log(scale)
cf <- cbind(s$coefficients,
s$coefficients[, 1] + qnorm(alpha/2) * s$coefficients[, 2],
s$coefficients[, 1] + qnorm(1 - alpha/2) * s$coefficients[, 2])
colnames(cf) <- c("est", "se", "stat", "p", "lwr", "upr")
## Improvement by ME: deal with log-scale parameter
sp.row <- match("Log(scale)",rownames(cf))
sp <- cf[sp.row,,drop=FALSE]
cf <- cf[-sp.row,,drop=FALSE]
# Patch ME 20.08.2017
dn <- c(
dimnames(cf),
rownames(attr(obj$terms,"factors"))[1]
)
dim(cf) <- c(dim(cf)[1],dim(cf)[2],1)
dimnames(cf) <- dn
## further summary statistics
sstat <- c(
"scale" = s$scale,
"Wald" = s$wald,
"numdf" = sum(s$df) - s$idf,
"p" = pchisq(s$wald, sum(s$df) - s$idf, lower.tail = FALSE),
"N" = nobs(obj),
"logLik" = as.vector(logLik(obj)),
"AIC" = AIC(obj),
"BIC" = BIC(obj))
## return everything
return(list(
coef=cf,
scale=sp,
sumstat = sstat,
contrasts = obj$contrasts,
xlevels = obj$xlevels,
call = obj$call
))
}
setSummaryTemplate("tobit" = c(
"Scale" = "($scale:#)",
"Wald" = "($Wald:f#)",
"p" = "($p:f#)",
"Log-likelihood" = "($logLik:f#)",
"AIC" = "($AIC:f#)",
"BIC" = "($BIC:f#)",
"N" = "($N:d)"
))
## pscl:::hurdle, pscl:::zeroinfl
getSummary.hurdle <- getSummary.zeroinfl <- function(obj, alpha = 0.05, ...) {
## extract coefficient summary
cf <- summary(obj)$coefficients
## augment with confidence intervals
cval <- qnorm(1 - alpha/2)
for(i in seq_along(cf)) cf[[i]] <- cbind(cf[[i]],
cf[[i]][, 1] - cval * cf[[i]][, 2],
cf[[i]][, 1] + cval * cf[[i]][, 2])
## collect in array
nam <- unique(unlist(lapply(cf, rownames)))
acf <- array(dim = c(length(nam), 6, length(cf)),
dimnames = list(nam, c("est", "se", "stat", "p", "lwr", "upr"), names(cf)))
for(i in seq_along(cf)) acf[rownames(cf[[i]]), , i] <- cf[[i]]
## return everything
return(list(
coef = acf,
sumstat = c(
"N" = obj$n,
"logLik" = as.vector(logLik(obj)),
"AIC" = AIC(obj),
"BIC" = AIC(obj, k = log(obj$n))
),
contrasts = obj$contrasts,
xlevels = obj$xlevels,
call = obj$call
))
}
setSummaryTemplate("hurdle" = c(
"Log-likelihood" = "($logLik:f#)",
"AIC" = "($AIC:f#)",
"BIC" = "($BIC:f#)",
"N" = "($N:d)"
))
setSummaryTemplate("zeroinfl" = c(
"Log-likelihood" = "($logLik:f#)",
"AIC" = "($AIC:f#)",
"BIC" = "($BIC:f#)",
"N" = "($N:d)"
))
## betareg::betareg
getSummary.betareg <- function(obj, alpha = 0.05, ...) {
## extract coefficient summary
s <- summary(obj)
cf <- s$coefficients
## augment with confidence intervals
cval <- qnorm(1 - alpha/2)
for(i in seq_along(cf)) cf[[i]] <- cbind(cf[[i]],
cf[[i]][, 1] - cval * cf[[i]][, 2],
cf[[i]][, 1] + cval * cf[[i]][, 2])
## collect in array
nam <- unique(unlist(lapply(cf, rownames)))
acf <- array(dim = c(length(nam), 6, length(cf)),
dimnames = list(nam, c("est", "se", "stat", "p", "lwr", "upr"), names(cf)))
for(i in seq_along(cf)) acf[rownames(cf[[i]]), , i] <- cf[[i]]
## return everything
return(list(
coef = acf,
sumstat = c(
"N" = obj$n,
"pseudo.r.squared" = s$pseudo.r.squared,
"logLik" = as.vector(logLik(obj)),
"AIC" = AIC(obj),
"BIC" = AIC(obj, k = log(obj$n))
),
contrasts = obj$contrasts,
xlevels = obj$xlevels,
call = obj$call
))
}
setSummaryTemplate("betareg" = c(
"Pseudo R-sq." = "($pseudo.r.squared:f#)",
"Log-likelihood" = "($logLik:f#)",
"AIC" = "($AIC:f#)",
"BIC" = "($BIC:f#)",
"N" = "($N:d)"
))
getSummary.multinom <- function(obj, alpha = 0.05, ...) {
## extract coefficient summary
s <- summary(obj)
cf <- s$coefficients
## set up array
acf <- array(dim = c(NCOL(cf), 6, NROW(cf)),
dimnames = list(colnames(cf), c("est", "se", "stat", "p", "lwr", "upr"), rownames(cf)))
## coefficients and standard errors
acf[, 1, ] <- t(cf)
acf[, 2, ] <- t(s$standard.errors)
## compute z-statistic and asymptotic p-value
acf[, 3, ] <- acf[, 1, ] / acf[, 2, ]
acf[, 4, ] <- 2 * pnorm(abs(acf[, 3, ]), lower.tail = FALSE)
## asymptotic confidence intervals
cval <- qnorm(1 - alpha/2)
acf[, 5, ] <- acf[, 1, ] - cval * acf[, 2, ]
acf[, 6, ] <- acf[, 1, ] + cval * acf[, 2, ]
## compute number of observations as sum of weights
nobs <- sum(obj$weights)
## return everything
return(list(
coef = acf,
sumstat = c(
"N" = nobs,
"deviance" = obj$deviance,
"logLik" = as.vector(logLik(obj)),
"AIC" = AIC(obj),
"BIC" = AIC(obj, k = log(nobs))
),
contrasts = obj$contrasts,
xlevels = obj$xlevels,
call = obj$call
))
}
setSummaryTemplate("multinom" = c(
"Deviance" = "($deviance:f#)",
"Log-likelihood" = "($logLik:f#)",
"AIC" = "($AIC:f#)",
"BIC" = "($BIC:f#)",
"N" = "($N:d)"
))
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