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

For each stratum ,and for the population as a whole, approximate design variances are calculated.

1 2 3 |

`popfile` |
dataframe containing information on all plots in the population. |

`nrefs` |
vector containing the sample size of each stratum. |

`desvars` |
vector containing the names of the design variables. |

`yvars` |
character vector containing the name of each variable of interest (dependent variable) for which design variances are required. |

`kvalue` |
scalar specifying the value of k for the k-nn imputation. |

`B` |
number of re-samples used to calculate the design variances. |

`zvars` |
character vector containing the name/s of the predictor variables. |

`training` |
dataframe containing the data needed to determine the predictor variable. Must contain the necessary yvars and xvars. If missing, predictor variables are supplied by the user (zvars) |

`xvars` |
character vector containing the name/s of the predictor variables. |

`pool` |
logical value - should strata be pooled prior to fitting regression model? |

Approximate design variances are calculated using a re-sampling procedure in conjunction with a predictor variable. The predictor variable can be user-supplied or determined by the program using random forest regression based on a set of training data. The regression model can be fitted separately for each strata (pool=F), the default, or based on pooled training data with stratum included in the regression model as a factor.

A dataframe containing the design variances for each stratum and for the whole population.

G. Melville

1 2 | ```
## DesVar(popfile, nrefs, desvars, yvars, B=1000, zvars=NULL,
## training=NULL, xvars=NULL, pool=F)
``` |

NCSampling documentation built on June 27, 2017, 9:01 a.m.

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