Description Usage Arguments Value References See Also Examples

Calculates a regression tree estimator for a finite population mean or total based on sample data collected from a complex sampling design and auxiliary population data.

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`y` |
A numeric vector of the sampled response variable. |

`x_sample` |
A data frame of the auxiliary data in the sample. |

`x_pop` |
A data frame of population level auxiliary information. It must contain the same names as x_sample. |

`pi` |
A numeric vector of inclusion probabilities for each sampled unit in y. If NULL, then simple random sampling without replacement is assumed. |

`pi2` |
A square matrix of the joint inclusion probabilities. Needed for the "lin_HT" variance estimator. |

`var_est` |
A logical indicating whether or not to compute a variance estimator. Default is FALSE. |

`var_method` |
The method to use when computing the variance estimator. Options are a Taylor linearized technique: "lin_HB"= Hajek-Berger estimator, "lin_HH" = Hansen-Hurwitz estimator, "lin_HTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "lin_HT" = Horvitz-Thompson estimator or a resampling technique: "bootstrap_SRS" = bootstrap variance estimator under simple random sampling without replacement. The default is "lin_HB". |

`B` |
The number of bootstrap samples if computing the bootstrap variance estimator. Default is 1000. |

`p_value` |
Designated p-value level to reject null hypothesis in permutation test used to fit the regression tree. Default value is 0.05. |

`perm_reps` |
An integer specifying the number of permutations for each permutation test run to fit the regression tree. Default value is 500. |

`bin_size` |
A integer specifying the minimum number of observations in each node. |

`strata` |
A factor vector of the stratum membership. If NULL, all units are put into the same stratum. Must have same length as y. |

A list of output containing:

pop_total: Estimate of population total

pop_mean: Estimate of the population mean

pop_total_var: Estimated variance of population total estimate

pop_mean_var: Estimated variance of population mean estimate

weights: Survey weights produced by regression tree

tree: rpms object

mcc17bmase

`greg`

for a linear or logistic regression model.

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