MSPE_McJack_Benchmark: McJack MSPE estimator of benchmarked OBP
In rohosen/OBPSAE: Observed best predictors for small area estimation

Description Usage Arguments Details Value References

This function computes the McJack MSPE estimator of the benchmarked Observed Best Predictor.

1 2	MSPE_McJack_Benchmark(formula, data, errorvar, weight, A.BPE, K = 1000, returnMcSpline = TRUE)

`formula`	an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The variables included in formula must have a length equal to the number of small areas. More about the model specification are given under Details.
`data`	optional data frame containing the variable names in `formula`.
`errorvar`	vector containing the variances of the random errors for all small areas.
`weight`	vector containing the sampling weights of small areas. If sum of the weights is not 1, the weights are normalized.
`A.BPE`	optional BPE estimate of random effects variance.
`K`	number of Monte Carlo simulations. Default is 1000.
`returnMcSpline`	logical. Returns McSpline MSPE benchmark estimators with McJack (default).

formula is specified in the form response ~ predictor where the predictor is univariate. formula has an implied intercept term. To remove the intercept term, use either y ~ x - 1 or y ~ 0 + x.

If A.BPE is missing, the function computes the BPE from data. User can also provide the true A instead if that is known.

The function will return a list with the following objects.

`McJack_Adj_bench`	McJack MSPE estimator of the adjusted OBP.
`McJack_Aug_bench`	McJack MSPE estimator of the augmented OBP.
`McSpline_Adj_bench`	McSpline MSPE estimator of the adjusted OBP. This is returned by default. To turn this feature off, set `returnMcSpline = FALSE`
`McSpline_Aug_bench`	McSpline MSPE estimator of the augmented OBP. This is returned by default. To turn this feature off, set `returnMcSpline = FALSE`