Nothing
summary.cesEst <- function( object, rSquaredLog = object$multErr,
ela = TRUE, ... ) {
# number of observations
nObs <- length( residuals( object ) )
# square root of the estimated variance of the random error
object$sigma <- sqrt( object$rss / nObs )
# R-squared value
fitVal <- fitted( object ) # the returned fitted values are never logged
if( rSquaredLog ) {
if( object$multErr ) {
yVal <- log( fitVal ) + residuals( object ) # log( y )
} else {
yVal <- log( fitVal + residuals( object ) ) # log( y )
}
res <- yVal - log( fitVal )
} else {
if( object$multErr ) {
yVal <- fitVal * exp( residuals( object ) ) # y
} else {
yVal <- fitVal + residuals( object ) # y
}
res <- yVal - fitVal
}
object$r.squared <- rSquared( y = yVal, resid = res )
# covariance matrix of the estimated coefficients/parameters
if( is.null( object$vcov ) ) {
object$vcov <- object$sigma^2 * object$cov.unscaled
}
if( ela && !is.null( object$ela ) ) {
# (co)variances of the elasticities of substitution
elaGrad <- matrix( 0, nrow = length( object$ela ),
ncol = length( coef( object ) ),
dimnames = list( names( object$ela ), names( coef( object ) ) ) )
elaGrad[ nrow( elaGrad ), "rho" ] <- - 1 / ( 1 + coef( object )[ "rho" ] )^2
if( nrow( elaGrad ) >= 2 ) {
elaGrad[ 1, "rho_1" ] <- - 1 / ( 1 + coef( object )[ "rho_1" ] )^2
}
if( nrow( elaGrad ) == 3 ) {
elaGrad[ 2, "rho_2" ] <- - 1 / ( 1 + coef( object )[ "rho_2" ] )^2
}
object$elaCov <- elaGrad %*% object$vcov %*% t( elaGrad )
object$elaCov[ is.na( object$ela ), ] <- NA
object$elaCov[ , is.na( object$ela ) ] <- NA
}
object$coefficients <- coefTable( coef( object ),
diag( object$vcov )^0.5, df = Inf )
if( ela && !is.null( object$elaCov ) ) {
object$ela <- coefTable( object$ela, diag( object$elaCov )^0.5, df = Inf )
} else {
object$ela <- NULL
}
class( object ) <- c( "summary.cesEst", class( object ) )
return( object )
}
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