Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/ictregBayesHier.R

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

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`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.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.

`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:

`fit` |
vector or matrix as above |

`se.fit` |
standard error of prediction |

Graeme Blair, UCLA, graeme.blair@ucla.edu and Kosuke Imai, Princeton University, kimai@princeton.edu

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
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)
## End(Not run)
``` |

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