predict.gtReg {binGroup2} | R Documentation |
Obtains predictions for individual observations and
optionally estimates standard errors of those predictions from
objects of class "gtReg" returned by gtReg
.
## S3 method for class 'gtReg' predict( object, newdata, type = c("link", "response"), se.fit = FALSE, conf.level = NULL, na.action = na.pass, ... )
object |
a fitted object of class "gtReg". |
newdata |
an optional data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. |
type |
the type of prediction required. The "link" option is on the scale of the linear predictors. The "response" option is on the scale of the response variable. Thus, for the logit model, the "link" predictions are of log-odds (probabilities on the logit scale) and type = "response" gives the predicted probabilities. |
se.fit |
a logical value indicating whether standard errors are required. |
conf.level |
the confidence level of the interval for the predicted values. |
na.action |
a function determining what should be done with missing values in newdata. The default is to predict NA. |
... |
currently not used. |
If newdata is omitted, the predictions are based on the data used for the fit. When newdata is present and contains missing values, how the missing values will be dealt with is determined by the na.action argument. In this case, if na.action=na.omit, omitted cases will not appear, whereas if na.action = na.exclude, omitted cases will appear (in predictions and standard errors) with value NA.
If se = FALSE, a vector or matrix of predictions. If se = TRUE, a list containing:
fit |
predictions. |
se.fit |
estimated standard errors. |
lower |
the lower bound of the confidence interval, if calculated. |
upper |
the upper bound of the confidence interval, if calculated. |
Boan Zhang
data(hivsurv) fit1 <- gtReg(formula = groupres ~ AGE + EDUC., data = hivsurv, groupn = gnum, sens = 0.9, spec = 0.9, linkf = "logit", method = "V") pred.data <- data.frame(AGE = c(15, 25, 30), EDUC. = c(1, 3, 2)) predict(object = fit1, newdata = pred.data, type = "link", se.fit = TRUE) predict(object = fit1, newdata = pred.data, type = "response", se.fit = TRUE, conf.level = 0.9) predict(object = fit1, type = "response", se.fit = TRUE, conf.level = 0.9)