predict.gt {binGroup} | R Documentation |

## Predict Method for Group Testing Model Fits

### Description

Obtains predictions for individual observations and optionally estimates standard errors of those predictions from objects of class `"gt"` or `"gt.mp"` returned by `gtreg` and `gtreg.mp`, respectively.

### Usage

```
## S3 method for class 'gt'
predict(object, newdata, type = c("link", "response"),
se.fit = FALSE, conf.level = NULL, na.action = na.pass, ...)
```

### Arguments

`object` |
a fitted object of class |

`newdata` |
optionally, a 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 option |

`se.fit` |
logical switch indicating if standard errors are required. |

`conf.level` |
confidence level of the interval of the predicted values. |

`na.action` |
function determining what should be done with missing values in |

`...` |
currently not used |

### Details

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` they will appear (in predictions and standard errors), with value `NA`. See also `napredict`.

### Value

If `se = FALSE`, a vector or matrix of predictions. If `se = TRUE`, a list with components

`fit` |
Predictions |

`se.fit` |
Estimated standard errors |

`lower` |
Lower bound of the confidence interval if calculated |

`upper` |
Upper bound of the confidence interval if calculated |

### Author(s)

Boan Zhang

### Examples

```
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)
```

*binGroup*version 2.2-1 Index]