View source: R/control_outcome.R
control_out | R Documentation |
control_out
constructs a list with all necessary control parameters
for outcome model.
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")
)
epsilon |
Tolerance for fitting algorithms. Default is |
maxit |
Maximum number of iterations. |
trace |
logical value. If |
k |
The k parameter in the |
penalty |
penalty algorithm for variable selection. Default is |
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 |
searchtype |
Type of search for nearest neighbour imputation (for the NN and PMM estimator) passed to |
pmm_match_type |
(Only for the PMM Estimator)
Indicates how to select 'closest' unit from non-probability sample for each
unit in probability sample. Either |
pmm_weights |
(Only for the PMM Estimator)
Indicate how to weight |
pmm_k_choice |
(Only for the PMM Estimator) Character value indicating how |
pmm_reg_engine |
(Only for the PMM Estimator) whether to use parametric ( |
npar_loess |
control parameters for the stats::loess via the stats::loess.control function. |
List with selected parameters.
nonprob()
– for fitting procedure with non-probability samples.
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