Builds a designTreatmentsN treatment plan and a data frame prepared from dframe that is "cross" in the sense each row is treated using a treatment plan built from a subset of dframe disjoint from the given row. The goal is to try to and supply a method of breaking nested model bias other than splitting into calibration, training, test sets.

mkCrossFrameNExperiment(
  dframe,
  varlist,
  outcomename,
  ...,
  weights = c(),
  minFraction = 0.02,
  smFactor = 0,
  rareCount = 0,
  rareSig = 1,
  collarProb = 0,
  codeRestriction = NULL,
  customCoders = NULL,
  scale = FALSE,
  doCollar = FALSE,
  splitFunction = NULL,
  ncross = 3,
  forceSplit = FALSE,
  verbose = TRUE,
  parallelCluster = NULL,
  use_parallel = TRUE,
  missingness_imputation = NULL,
  imputation_map = NULL
)

Arguments

dframe

Data frame to learn treatments from (training data), must have at least 1 row.

varlist

Names of columns to treat (effective variables).

outcomename

Name of column holding outcome variable. dframe[[outcomename]] must be only finite non-missing values and there must be a cut such that dframe[[outcomename]] is both above the cut at least twice and below the cut at least twice.

...

no additional arguments, declared to forced named binding of later arguments

weights

optional training weights for each row

minFraction

optional minimum frequency a categorical level must have to be converted to an indicator column.

smFactor

optional smoothing factor for impact coding models.

rareCount

optional integer, allow levels with this count or below to be pooled into a shared rare-level. Defaults to 0 or off.

rareSig

optional numeric, suppress levels from pooling at this significance value greater. Defaults to NULL or off.

collarProb

what fraction of the data (pseudo-probability) to collar data at if doCollar is set during prepare.treatmentplan.

codeRestriction

what types of variables to produce (character array of level codes, NULL means no restriction).

customCoders

map from code names to custom categorical variable encoding functions (please see https://github.com/WinVector/vtreat/blob/master/extras/CustomLevelCoders.md).

scale

optional if TRUE replace numeric variables with regression ("move to outcome-scale").

doCollar

optional if TRUE collar numeric variables by cutting off after a tail-probability specified by collarProb during treatment design.

splitFunction

(optional) see vtreat::buildEvalSets .

ncross

optional scalar>=2 number of cross-validation rounds to design.

forceSplit

logical, if TRUE force cross-validated significance calculations on all variables.

verbose

if TRUE print progress.

parallelCluster

(optional) a cluster object created by package parallel or package snow.

use_parallel

logical, if TRUE use parallel methods.

missingness_imputation

function of signature f(values: numeric, weights: numeric), simple missing value imputer.

imputation_map

map from column names to functions of signature f(values: numeric, weights: numeric), simple missing value imputers.

Value

named list containing: treatments, crossFrame, crossWeights, method, and evalSets

See also

Examples

# numeric example set.seed(23525) # we set up our raw training and application data dTrainN <- data.frame( x = c('a', 'a', 'a', 'a', 'b', 'b', NA, NA), z = c(1, 2, 3, 4, 5, NA, 7, NA), y = c(0, 0, 0, 1, 0, 1, 1, 1)) dTestN <- data.frame( x = c('a', 'b', 'c', NA), z = c(10, 20, 30, NA)) # we perform a vtreat cross frame experiment # and unpack the results into treatmentsN # and dTrainNTreated unpack[ treatmentsN = treatments, dTrainNTreated = crossFrame ] <- mkCrossFrameNExperiment( dframe = dTrainN, varlist = setdiff(colnames(dTrainN), 'y'), outcomename = 'y', verbose = FALSE) # the treatments include a score frame relating new # derived variables to original columns treatmentsN$scoreFrame[, c('origName', 'varName', 'code', 'rsq', 'sig', 'extraModelDegrees')] %.>% print(.)
#> origName varName code rsq sig extraModelDegrees #> 1 x x_catP catP 4.047085e-01 0.08994062 2 #> 2 x x_catN catN 2.822908e-01 0.17539581 2 #> 3 x x_catD catD 2.096931e-02 0.73225708 2 #> 4 z z clean 2.880952e-01 0.17018920 0 #> 5 z z_isBAD isBAD 3.333333e-01 0.13397460 0 #> 6 x x_lev_NA lev 3.333333e-01 0.13397460 0 #> 7 x x_lev_x_a lev 2.500000e-01 0.20703125 0 #> 8 x x_lev_x_b lev 1.110223e-16 0.99999998 0
# the treated frame is a "cross frame" which # is a transform of the training data built # as if the treatment were learned on a different # disjoint training set to avoid nested model # bias and over-fit. dTrainNTreated %.>% head(.) %.>% print(.)
#> x_catN x_catD z z_isBAD x_lev_NA x_lev_x_a x_lev_x_b x_catP y #> 1 -0.26666667 0.5000000 1 0 0 1 0 0.6 0 #> 2 -0.50000000 0.0000000 2 0 0 1 0 0.5 0 #> 3 -0.06666667 0.5000000 3 0 0 1 0 0.6 0 #> 4 -0.50000000 0.0000000 4 0 0 1 0 0.5 1 #> 5 0.40000000 0.7071068 5 0 0 0 1 0.2 0 #> 6 -0.40000000 0.7071068 3 1 0 0 1 0.2 1
# Any future application data is prepared with # the prepare method. dTestNTreated <- prepare(treatmentsN, dTestN, pruneSig=NULL) dTestNTreated %.>% head(.) %.>% print(.)
#> x_catP x_catN x_catD z z_isBAD x_lev_NA x_lev_x_a x_lev_x_b #> 1 0.5000 -0.25 0.5000000 10.000000 0 0 1 0 #> 2 0.2500 0.00 0.7071068 20.000000 0 0 0 1 #> 3 0.0625 0.00 0.7071068 30.000000 0 0 0 0 #> 4 0.2500 0.50 0.0000000 3.666667 1 1 0 0