R/predict.augSIMEX.R

In augSIMEX: Analysis of Data with Mixed Measurement Error and Misclassification in Covariates

predict.augSIMEX<-function(object, newdata = NULL, type = c("link", "response", 
                                           "terms"), se.fit = FALSE, dispersion = NULL, terms = NULL, 
          na.action = na.pass, ...) 
{
  type <- match.arg(type)
  na.act <- object$na.action
  object$na.action <- NULL
  class(object)<-c(class(object),"lm")
  if (!se.fit) {
    if (missing(newdata)) {
      pred <- switch(type, link = object$linear.predictors, 
                     response = object$fitted.values, terms = predict.lm(object, 
                                                                         se.fit = se.fit, scale = 1, type = "terms", 
                                                                         terms = terms))
      if (!is.null(na.act)) 
        pred <- napredict(na.act, pred)
    }
    else {
      pred <- predict.lm(object, newdata, se.fit, scale = 1, 
                         type = ifelse(type == "link", "response", type), 
                         terms = terms, na.action = na.action)
      switch(type, response = {
        pred <- family(object)$linkinv(pred)
      }, link = , terms = )
    }
  }
  else {
    if (is.null(dispersion) || dispersion == 0) 
      dispersion <- summary(object, dispersion = dispersion)$dispersion
    residual.scale <- as.vector(sqrt(dispersion))
    pred <- predict.lm(object, newdata, se.fit, scale = residual.scale, 
                       type = ifelse(type == "link", "response", type), 
                       terms = terms, na.action = na.action)
    fit <- pred$fit
    se.fit <- pred$se.fit
    switch(type, response = {
      se.fit <- se.fit * abs(family(object)$mu.eta(fit))
      fit <- family(object)$linkinv(fit)
    }, link = , terms = )
    if (missing(newdata) && !is.null(na.act)) {
      fit <- napredict(na.act, fit)
      se.fit <- napredict(na.act, se.fit)
    }
    pred <- list(fit = fit, se.fit = se.fit, residual.scale = residual.scale)
  }
  pred
}