Description Usage Arguments Value Examples
Performs model selection for rcRF model to select the best penalty parameter.
1 2 3 4 5 6 7 8 9 | rcRF.select(data, split.var, test = NULL, N0 = 20, n0 = 5,
efficacy = "y", risk = "r", col.trt = "trt", col.prtx = "prtx",
ntree = 500, lambda.upper = NA, risk.control = TRUE,
risk.threshold = NA, AIPWE = FALSE, ctg = NA,
mtry = max(floor(length(split.var)/3), 1), avoid.nul.tree = FALSE,
max.depth = 15, stabilize.type = c("linear", "rf"), stabilize = TRUE,
verbose = FALSE, use.other.nodes = TRUE, extremeRandomized = FALSE,
importance = FALSE, order.importances = TRUE, max.iter = 10,
risk.tolerance = c(0.995, 1.005))
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data |
data.frame. Data used to construct rcDT model. Must contain efficacy variable (y), risk variable (r), binary treatment indicator coded as 0 / 1 (trt), propensity score (prtx), candidate splitting covariates. |
split.var |
numeric vector. Columns of spliting variables. |
test |
data.frame of testing observations. Should be formatted the same as 'data'. |
N0 |
numeric specifying minimum number of observations required to call a node terminal. Defaults to 20. |
n0 |
numeric specifying minimum number of treatment/control observations needed in a split to declare a node terminal. Defaults to 5. |
efficacy |
char. Efficacy outcome column. Defaults to 'y'. |
risk |
char. Risk outcome column. Defaults to 'r'. |
col.trt |
char. Treatment indicator column name. Should be of form 0/1 or -1/+1. |
col.prtx |
char. Propensity score column name. |
ntree |
numeric. Number of trees to construct. |
lambda.upper |
numeric. Upper bound for risk penalty. An attempt at reasonable selection will be performed automatically. |
risk.control |
logical. Should risk be controlled? Defaults to TRUE. |
risk.threshold |
numeric. Desired level of risk control. |
AIPWE |
logical. Should AIPWE (TRUE) or IPWE (FALSE) be used. Not available yet. |
ctg |
numeric vector corresponding to the categorical input columns. Defaults to NULL. Not available yet. |
mtry |
numeric specifying the number of randomly selected splitting variables to be included. Defaults to the greater of 1 and length(split.var)/3. |
avoid.nul.tree |
logical. Should null trees be discarded? |
max.depth |
numeric specifying maximum depth of the tree. Defaults to 15 levels. |
stabilize.type |
character specifying method used for estimating residuals. Current options are 'linear' for linear model (default) and 'rf' for random forest. |
stabilize |
logical indicating if efficacy should be modeled using residuals. Defaults to TRUE. |
verbose |
logical. Give updates about forest progression? |
use.other.nodes |
logical. Should global estimator of objective function be used. Defaults to TRUE. |
extremeRandomized |
logical. Experimental for randomly selecting cutpoints in a random forest model. Defaults to FALSE and users should change this at their own peril. |
importance |
logical. Indicated if variable importance measures should be estimated and returned. Defaults to FALSE. |
order.importances |
logical. Should importances be ordered (if requested)? |
max.iter |
numeric. Indicates the maximum number of forest iterations to perform. Defaults to 10. |
risk.tolerance |
numeric. Two component vector giving the bound on risk that is acceptable (acceptable risk range is calcuated as risk.threshold * risk.tolerance). Defaults to c(0.995, 1.005), i.e. 0.5% tolerance. |
A summary of the cross validation including optimal penalty parameter and the optimal model.
best.fit |
optimal rcRF model |
lambda |
optimal lambda value selected |
oob.risk |
out-of-bag risk from best model |
converged |
max number of iterations reached? |
importances |
importance measures, if requested |
risks |
vector of risk scores obtained over tuning procedure |
lambdas |
vector of lambda values tried over tuning procedure |
time.elapsed |
elapsed time for model tuning |
1 2 3 4 5 6 | # Grow large tree
set.seed(123)
dat <- generateData()
fit <- rcRF.select(data = dat,
split.var = 1:10,
risk.threshold = 2.75)
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