run_gb_mc {autoMrP} | R Documentation |
run_gb_mc
is called from within run_gb
. It tunes using
multiple cores.
run_gb_mc( y, L1.x, L2.eval.unit, L2.unit, L2.reg, form, gb_grid, n.minobsinnode, loss.unit, loss.fun, data, cores )
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 |
L1.x |
Individual-level covariates. A character vector containing the
column names of the individual-level variables in |
L2.eval.unit |
Geographic unit. A character scalar containing the column
name of the geographic unit in |
L2.unit |
Geographic unit. A character scalar containing the column
name of the geographic unit in |
L2.reg |
Geographic region. A character scalar containing the column
name of the geographic region in |
form |
Model formula. A two-sided linear formula describing the model to be fit, with the outcome on the LHS and the covariates separated by + operators on the RHS. |
gb_grid |
Search grid. A data.frame object where columns are parameters and rows are search iterations. |
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. Default is 5. |
loss.unit |
Loss function unit. A character-valued scalar indicating
whether performance loss should be evaluated at the level of individual
respondents ( |
loss.fun |
Loss function. A character-valued scalar indicating whether
prediction loss should be measured by the mean squared error ( |
data |
Data for cross-validation. A |
cores |
The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1. |
The tuning parameter combinations and there associated loss function scores. A list.