Function to calculate predictions and uncertainties of predictions from estimates from hierarchical multivariate regression analysis of survey data with the item count technique.

# S3 method for ictregBayesHier predict(object, newdata, se.fit = FALSE, interval = c("none", "confidence"), level = 0.95, sensitive.item, ...)

object | Object of class inheriting from "ictregBayes" or "ictregBayesMulti" |
---|---|

newdata | An optional data frame containing data that will be used to make predictions from. If omitted, the data used to fit the regression are used. |

se.fit | A switch indicating if standard errors are required. |

interval | Type of interval calculation. |

level | Significance level for confidence intervals. |

sensitive.item | For the multiple sensitive item design, the integer indicating which sensitive item coefficients will be used for prediction. |

... | further arguments to be passed to or from other methods. |

`predict.ictreg`

produces a vector of predictions or a matrix
of predictions and bounds with column names fit, lwr, and upr if interval is
set. If se.fit is TRUE, a list with the following components is returned:

vector or matrix as above

standard error of prediction

`predict.ictregBayesHier`

produces predicted values, obtained by
evaluating the regression function in the frame newdata (which defaults to
`model.frame(object)`

. If the logical `se.fit`

is `TRUE`

,
standard errors of the predictions are calculated. Setting `interval`

specifies computation of confidence intervals at the specified level or no
intervals.

The mean prediction across all observations in the dataset is calculated,
and if the `se.fit`

option is set to `TRUE`

a standard error for
this mean estimate will be provided. The `interval`

option will output
confidence intervals instead of only the point estimate if set to
`TRUE`

.

In the multiple sensitive item design, prediction can only be based on the
coefficients from one of the sensitive item fits. The `sensitive.item`

option allows you to specify which is used, using integers from 1 to the
number of sensitive items.

Blair, Graeme and Kosuke Imai. (2012) ``Statistical Analysis of List Experiments." Political Analysis, Vol. 20, No 1 (Winter). available at http://imai.princeton.edu/research/listP.html

Imai, Kosuke. (2011) ``Multivariate Regression Analysis for the Item Count Technique.'' Journal of the American Statistical Association, Vol. 106, No. 494 (June), pp. 407-416. available at http://imai.princeton.edu/research/list.html

`ictreg`

for model fitting

data(race)# NOT RUN { mle.estimates.multi <- ictreg(y ~ male + college, data = multi, constrained = TRUE) draws <- mvrnorm(n = 3, mu = coef(mle.estimates.multi), Sigma = vcov(mle.estimates.multi) * 9) bayes.fit <- ictregBayesHier(y ~ male + college, formula.level.2 = ~ 1, delta.start.level.1 = list(draws[1, 8:9], draws[1, 2:3], draws[1, 5:6]), data = multi, treat = "treat", delta.tune = list(rep(0.005, 2), rep(0.05, 2), rep(0.05, 2)), alpha.tune = rep(0.001, length(unique(multi$state))), J = 3, group.level.2 = "state", n.draws = 100, burnin = 10, thin = 1) bayes.predict <- predict(bayes.fit, interval = "confidence", se.fit = TRUE) # }