post_stratification: Apply post-stratification to classifiers.
In autoMrP: Improving MrP with Ensemble Learning

post_stratification

R Documentation

Apply post-stratification to classifiers.

Description

Apply post-stratification to classifiers.

Usage

post_stratification(
  y,
  L1.x,
  L2.x,
  L2.unit,
  L2.reg,
  best.subset.opt,
  lasso.opt,
  lasso.L2.x,
  pca.opt,
  gb.opt,
  svm.opt,
  svm.L2.reg,
  svm.L2.unit,
  svm.L2.x,
  mrp.include,
  n.minobsinnode,
  L2.unit.include,
  L2.reg.include,
  kernel,
  mrp.L2.x,
  data,
  ebma.fold,
  census,
  verbose,
  deep.mrp,
  deep.L2.x,
  deep.L2.reg,
  deep.splines
)

Arguments

`y`	Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in `survey`.
`L1.x`	Individual-level covariates. A character vector containing the column names of the individual-level variables in `survey` and `census` used to predict outcome `y`. Note that geographic unit is specified in argument `L2.unit`.
`L2.x`	Context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` used to predict outcome `y`. To exclude context-level variables, set `L2.x = NULL`.
`L2.unit`	Geographic unit. A character scalar containing the column name of the geographic unit in `survey` and `census` at which outcomes should be aggregated.
`L2.reg`	Geographic region. A character scalar containing the column name of the geographic region in `survey` and `census` by which geographic units are grouped (`L2.unit` must be nested within `L2.reg`). Default is `NULL`.
`best.subset.opt`	Optimal tuning parameters from best subset selection classifier. A list returned by `run_best_subset()`.
`lasso.opt`	Optimal tuning parameters from lasso classifier A list returned by `run_lasso()`.
`lasso.L2.x`	Lasso context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the lasso classifier. If `NULL` and `lasso` is set to `TRUE`, then lasso uses the variables specified in `L2.x`. Default is `NULL`.
`pca.opt`	Optimal tuning parameters from best subset selection with principal components classifier A list returned by `run_pca()`.
`gb.opt`	Optimal tuning parameters from gradient tree boosting classifier A list returned by `run_gb()`.
`svm.opt`	Optimal tuning parameters from support vector machine classifier A list returned by `run_svm()`.
`svm.L2.reg`	SVM L2.reg. A logical argument indicating whether `L2.reg` should be included in the SVM classifier. Default is `FALSE`.
`svm.L2.unit`	SVM L2.unit. A logical argument indicating whether `L2.unit` should be included in the SVM classifier. Default is `FALSE`.
`svm.L2.x`	SVM context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the SVM classifier. If `NULL` and `svm` is set to `TRUE`, then SVM uses the variables specified in `L2.x`. Default is `NULL`.
`mrp.include`	Whether to run MRP classifier. A logical argument indicating whether the standard MRP classifier should be used for predicting outcome `y`. Passed from `autoMrP()` argument `mrp`.
`n.minobsinnode`	GB minimum number of observations in the terminal nodes. An integer-valued scalar specifying the minimum number of observations that each terminal node of the trees must contain. Passed from `autoMrP()` argument `gb.n.minobsinnode`.
`L2.unit.include`	GB L2.unit. A logical argument indicating whether `L2.unit` should be included in the GB classifier. Passed from `autoMrP()` argument `gb.L2.unit`.
`L2.reg.include`	A logical argument indicating whether `L2.reg` should be included in the GB classifier. Passed from `autoMrP()` argument `GB L2.reg`.
`kernel`	SVM kernel. A character-valued scalar specifying the kernel to be used by SVM. The possible values are `linear`, `polynomial`, `radial`, and `sigmoid`. Passed from `autoMrP()` argument `svm.kernel`.
`mrp.L2.x`	MRP context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the MRP classifier. The character vector empty if no context-level variables should be used by the MRP classifier. If `NULL` and `mrp` is set to `TRUE`, then MRP uses the variables specified in `L2.x`. Default is `NULL`. Note: For the empty MrP model, set `L2.x = NULL` and `mrp.L2.x = ""`.
`data`	A data.frame containing the survey data used in classifier training.
`ebma.fold`	A data.frame containing the data not used in classifier training.
`census`	Census data. A `data.frame` whose column names include `L1.x`, `L2.x`, `L2.unit`, if specified, `L2.reg` and `pcs`, and either `bin.proportion` or `bin.size`.
`verbose`	Verbose output. A logical argument indicating whether or not verbose output should be printed. Default is `FALSE`.
`deep.mrp`	Deep MRP classifier. A logical argument indicating whether the deep MRP classifier should be used for predicting outcome `y`. Default is `FALSE`.
`deep.L2.x`	Deep MRP context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the deep MRP classifier. If `NULL` and `deep.mrp` is set to `TRUE`, then deep MRP uses the variables specified in `L2.x`. Default is `NULL`.
`deep.L2.reg`	Deep MRP L2.reg. A logical argument indicating whether `L2.reg` should be included in the deep MRP classifier. Default is `TRUE`.
`deep.splines`	Deep MRP splines. A logical argument indicating whether splines should be used in the deep MRP classifier. Default is `TRUE`.