error_checks: Catches user input errors
In autoMrP: Improving MrP with Ensemble Learning

error_checks

R Documentation

Catches user input errors

Description

error_checks() checks for incorrect data entry in autoMrP() call.

Usage

error_checks(
  y,
  L1.x,
  L2.x,
  L2.unit,
  L2.reg,
  L2.x.scale,
  pcs,
  folds,
  bin.proportion,
  bin.size,
  survey,
  census,
  ebma.size,
  k.folds,
  cv.sampling,
  loss.unit,
  loss.fun,
  best.subset,
  lasso,
  pca,
  gb,
  svm,
  mrp,
  best.subset.L2.x,
  lasso.L2.x,
  gb.L2.x,
  svm.L2.x,
  mrp.L2.x,
  gb.L2.unit,
  gb.L2.reg,
  lasso.lambda,
  lasso.n.iter,
  uncertainty,
  boot.iter
)

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`.
`L2.x.scale`	Scale context-level covariates. A logical argument indicating whether the context-level covariates should be normalized. Default is `TRUE`. Note that if set to `FALSE`, then the context-level covariates should be normalized prior to calling `auto_MrP()`.
`pcs`	Principal components. A character vector containing the column names of the principal components of the context-level variables in `survey` and `census`. Default is `NULL`.
`folds`	EBMA and cross-validation folds. A character scalar containing the column name of the variable in `survey` that specifies the fold to which an observation is allocated. The variable should contain integers running from `1` to `k + 1`, where `k` is the number of cross-validation folds. Value `k + 1` refers to the EBMA fold. Default is `NULL`. Note: if `folds` is `NULL`, then `ebma.size`, `k.folds`, and `cv.sampling` must be specified.
`bin.proportion`	Proportion of ideal types. A character scalar containing the column name of the variable in `census` that indicates the proportion of individuals by ideal type and geographic unit. Default is `NULL`. Note: if `bin.proportion` is `NULL`, then `bin.size` must be specified.
`bin.size`	Bin size of ideal types. A character scalar containing the column name of the variable in `census` that indicates the bin size of ideal types by geographic unit. Default is `NULL`. Note: ignored if `bin.proportion` is provided, but must be specified otherwise.
`survey`	Survey data. A `data.frame` whose column names include `y`, `L1.x`, `L2.x`, `L2.unit`, and, if specified, `L2.reg`, `pcs`, and `folds`.
`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`.
`ebma.size`	EBMA fold size. A number in the open unit interval indicating the proportion of respondents to be allocated to the EBMA fold. Default is `1/3`. Note: ignored if `folds` is provided, but must be specified otherwise.
`k.folds`	Number of cross-validation folds. An integer-valued scalar indicating the number of folds to be used in cross-validation. Default is `5`. Note: ignored if `folds` is provided, but must be specified otherwise.
`cv.sampling`	Cross-validation sampling method. A character-valued scalar indicating whether cross-validation folds should be created by sampling individual respondents (`individuals`) or geographic units (`L2 units`). Default is `L2 units`. Note: ignored if `folds` is provided, but must be specified otherwise.
`loss.unit`	Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (`individuals`), geographic units (`L2 units`) or at both levels. Default is `c("individuals", "L2 units")`. With multiple loss units, parameters are ranked for each loss unit and the loss unit with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.
`loss.fun`	Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (`MSE`), the mean absolute error (`MAE`), binary cross-entropy (`cross-entropy`), mean squared false error (`msfe`), the f1 score (`f1`), or a combination thereof. Default is `c("MSE", "cross-entropy","msfe", "f1")`. With multiple loss functions, parameters are ranked for each loss function and the parameter combination with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.
`best.subset`	Best subset classifier. A logical argument indicating whether the best subset classifier should be used for predicting outcome `y`. Default is `TRUE`.
`lasso`	Lasso classifier. A logical argument indicating whether the lasso classifier should be used for predicting outcome `y`. Default is `TRUE`.
`pca`	PCA classifier. A logical argument indicating whether the PCA classifier should be used for predicting outcome `y`. Default is `TRUE`.
`gb`	GB classifier. A logical argument indicating whether the GB classifier should be used for predicting outcome `y`. Default is `TRUE`.
`svm`	SVM classifier. A logical argument indicating whether the SVM classifier should be used for predicting outcome `y`. Default is `TRUE`.
`mrp`	MRP classifier. A logical argument indicating whether the standard MRP classifier should be used for predicting outcome `y`. Default is `FALSE`.
`best.subset.L2.x`	Best subset context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the best subset classifier. If `NULL` and `best.subset` is set to `TRUE`, then best subset uses the variables specified in `L2.x`. Default is `NULL`.
`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`.
`gb.L2.x`	GB context-level covariates. A character vector containing the column names of the context-level variables in `survey` and `census` to be used by the GB classifier. If `NULL` and `gb` is set to `TRUE`, then GB uses the variables specified in `L2.x`. Default is `NULL`.
`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.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 = ""`.
`gb.L2.unit`	GB L2.unit. A logical argument indicating whether `L2.unit` should be included in the GB classifier. Default is `FALSE`.
`gb.L2.reg`	GB L2.reg. A logical argument indicating whether `L2.reg` should be included in the GB classifier. Default is `FALSE`.
`lasso.lambda`	Lasso penalty parameter. A numeric `vector` of non-negative values. The penalty parameter controls the shrinkage of the context-level variables in the lasso model. Default is a sequence with minimum 0.1 and maximum 250 that is equally spaced on the log-scale. The number of values is controlled by the `lasso.n.iter` parameter.
`lasso.n.iter`	Lasso number of lambda values. An integer-valued scalar specifying the number of lambda values to search over. Default is `100`. Note: Is ignored if a vector of `lasso.lambda` values is provided.
`uncertainty`	Uncertainty estimates. A logical argument indicating whether uncertainty estimates should be computed. Default is `FALSE`.
`boot.iter`	Number of bootstrap iterations. An integer argument indicating the number of bootstrap iterations to be computed. Will be ignored unless `uncertainty = TRUE`. Default is `200` if `uncertainty = TRUE` and `NULL` if `uncertainty = FALSE`.