Skip to contents

Control parameters for outcome model

Source: R/control_outcome.R
control_out.Rd

control_out constructs a list with all necessary control parameters for outcome model.

Usage

control_out(
  epsilon = 1e-04,
  maxit = 100,
  trace = FALSE,
  k = 5,
  penalty = c("SCAD", "lasso", "MCP"),
  a_SCAD = 3.7,
  a_MCP = 3,
  lambda_min = 0.001,
  nlambda = 100,
  nfolds = 10,
  treetype = c("kd", "rp", "ball"),
  searchtype = c("standard", "priority"),
  pmm_match_type = 1,
  pmm_weights = c("none", "dist"),
  pmm_k_choice = c("none", "min_var"),
  pmm_reg_engine = c("glm", "loess"),
  npar_loess = stats::loess.control(surface = "direct", trace.hat = "approximate")
)

Arguments

epsilon

Tolerance for fitting algorithms. Default is 1e-6.

maxit

Maximum number of iterations.

trace

logical value. If TRUE trace steps of the fitting algorithms. Default is FALSE.

k

The k parameter in the RANN::nn2() function. Default is 5.

penalty

penalty algorithm for variable selection. Default is SCAD

a_SCAD

The tuning parameter of the SCAD penalty for outcome model. Default is 3.7.

a_MCP

The tuning parameter of the MCP penalty for outcome model. Default is 3.

lambda_min

The smallest value for lambda, as a fraction of lambda.max. Default is .001.

nlambda

The number of lambda values. Default is 100.

nfolds

The number of folds during cross-validation for variables selection model.

treetype

Type of tree for nearest neighbour imputation (for the NN and PMM estimator) passed to RANN::nn2() function.

searchtype

Type of search for nearest neighbour imputation (for the NN and PMM estimator) passed to RANN::nn2() function.

pmm_match_type

(Only for the PMM Estimator) Indicates how to select 'closest' unit from nonprobability sample for each unit in probability sample. Either 1 (default) or 2 where 2 is matching by minimizing distance between y_i for i S_A and y_j for j S_B and 1 is matching by minimizing distance between y_i for i S_A and y_i for i S_A.

pmm_weights

(Only for the PMM Estimator) Indicate how to weight k nearest neighbours in S_B to create imputed value for units in S_A. The default value "none" indicates that mean of k nearest y's from S_B should be used whereas "prop_dist" results in weighted mean of these k values where weights are inversely proportional to distance between matched values.

pmm_k_choice

(Only for the PMM Estimator) Character value indicating how k hyper-parameter should be chosen, by default "none" meaning k provided in control_outcome argument will be used. For now the only other option "min_var" means that k will be chosen by minimizing estimated variance of estimator for mean. Parameter k provided in this control list will be chosen as starting point.

pmm_reg_engine

(Only for the PMM Estimator) whether to use parametric ("glm") or non-parametric ("loess") regression model for the outcome. The default is "glm".

npar_loess

control parameters for the stats::loess via the stats::loess.control function.

Value

List with selected parameters.

See also

nonprob() – for fitting procedure with non-probability samples.