| Type: | Package |
| Title: | Feature Finder |
| Version: | 1.2 |
| Author: | Richard Davis [aut, cre] |
| Maintainer: | Richard Davis <davisconsulting@gmail.com> |
| Description: | Finds features through a detailed analysis of model residuals using rpart classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model differs from the actual data. |
| Depends: | R (≥ 3.2.0) |
| License: | MIT + file LICENSE |
| LazyData: | true |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Suggests: | knitr,rmarkdown, png |
| VignetteBuilder: | knitr |
| Imports: | rpart, rpart.plot, plyr, grDevices |
| BuildVignettes: | true |
| NeedsCompilation: | no |
| Packaged: | 2024-12-16 21:30:05 UTC; davis |
| Repository: | CRAN |
| Date/Publication: | 2024-12-17 15:00:06 UTC |
data
Description
Sample data based on dataset EuStockMarkets in the datasets package.
Format
A data frame with 1860 rows and 4 variables
Author(s)
Richard Davis richard.davis@cba.com.au
Source
https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html
Examples
data(mycsv)
thismodel=lm(formula=DAX ~ .,data=data)
expectedprob=predict(thismodel,data)
actualprob=data$DAX
residual=actualprob-expectedprob
data=cbind(data,expectedprob, actualprob, residual)
findFeatures
Description
Perform analysis of residuals grouped by factor to identify features which explain the target variable
Usage
findFeatures(
OutputPath,
fcsv,
ExclusionVars,
FactorToNumericList,
treeGenerationMinBucket = 50,
treeSummaryMinBucket = 20,
treeSummaryResidualThreshold = 0,
treeSummaryResidualMagnitudeThreshold = 0,
doAllFactors = TRUE,
maxFactorLevels = 20
)
Arguments
OutputPath |
A string containing the location of the input csv file. Results are also stored in this location. |
fcsv |
A string containing the name of a csv file |
ExclusionVars |
A string consisting of a list of variable names with double quotes around each variable |
FactorToNumericList |
A list of variable names as strings |
treeGenerationMinBucket |
Desired minimum number of data points per leaf (default 50) |
treeSummaryMinBucket |
Minimum number of data points in each leaf for the summary (default 20) |
treeSummaryResidualThreshold |
Minimum residual in the summary (default 0 for positive residuals) |
treeSummaryResidualMagnitudeThreshold |
Minimum residual magnitude in the summary (default 0 i.e. no restriction) |
doAllFactors |
Flag to indicate whether to analyse the levels of all factor variables (default TRUE) |
maxFactorLevels |
(maximum number of levels per factor before it is converted to numeric (default 20) |
Value
Saves residual CART trees and associated highlighted residuals for each to the path provided.
Examples
require(featurefinder)
data(mycsv)
data$SMIfactor=paste("smi",as.matrix(data$SMIfactor),sep="")
nn=floor(length(data$DAX)/2)
# Can we predict the relative movement of DAX and SMI?
data$y=data$DAX*0
data$y[1:(nn-1)]=((data$DAX[2:nn])-(data$DAX[1:(nn-1)]))/
(data$DAX[1:(nn-1)])-(data$SMI[2:nn]-(data$SMI[1:(nn-1)]))/(data$SMI[1:(nn-1)])
thismodel=lm(formula=y ~ .,data=data)
expected=predict(thismodel,data)
actual=data$y
residual=actual-expected
data=cbind(data,expected, actual, residual)
OutputPath=tempdir()
fcsv <- file.path(OutputPath, "mycsv.csv")
write.csv(data[(nn+1):(length(data$y)),], file = fcsv, row.names=FALSE)
ExclusionVars="\"residual\",\"expected\", \"actual\",\"y\""
FactorToNumericList=c()
findFeatures(OutputPath, fcsv, ExclusionVars,FactorToNumericList,
treeGenerationMinBucket=50,
treeSummaryMinBucket=20)
generateResidualCutoffCode
Description
For each tree print a summary of the significant residuals as specified by the user
Usage
generateResidualCutoffCode(data, filename, trees, names, runname, ...)
Arguments
data |
A dataframe |
filename |
A string |
trees |
A list of trees generated by saveTree |
names |
A list of level names |
runname |
A string corresponding to the name of the factor variable being analysed |
... |
and parameters to be passed through |
Value
A list of residuals for each tree provided.
generateTrees
Description
Generate a residual tree for each level of factor mainfac
Usage
generateTrees(data, vars, expr, runname, ...)
Arguments
data |
A dataframe |
vars |
A list of candidate predictors |
expr |
A expression to be modelled by the RPART tree |
runname |
A string corresponding to the name of the variable being modelled |
... |
and parameters to be passed through |
Value
A list of residual trees for each level of the mainfac factor provided
getVarAv
Description
This function generates a residual tree on a subset of the data
Usage
getVarAv(dd, varAv, varString)
Arguments
dd |
A dataframe |
varAv |
A string corresponding to the numeric field to be averaged within each leaf node |
varString |
A string |
Value
An average of the numeric variable varString in the segment
parseSplits
Description
Extract information relating to the paths and volume of data in the leaves of the tree
Usage
parseSplits(thistree)
Arguments
thistree |
A tree |
Value
A list of parsed splits.
printResiduals
Description
This function generates a residual tree on a subset of the data
Usage
printResiduals(
fileConn,
all,
dat,
runname,
levelname,
treeSummaryResidualThreshold,
treeSummaryMinBucket,
treeSummaryResidualMagnitudeThreshold,
...
)
Arguments
fileConn |
A file connection |
all |
A dataframe |
dat |
The dataset |
runname |
A string corresponding to the name of the factor being analysed |
levelname |
A string corresponding to the factor level being analysed |
treeSummaryResidualThreshold |
The minimum residual threshold |
treeSummaryMinBucket |
The minumum volume per leaf |
treeSummaryResidualMagnitudeThreshold |
Minimun residual magnitude |
... |
and parameters to be passed through |
Value
Residuals are printed and also saved in a simplified format.
saveTree
Description
Generate a residual tree on a subset of the data specified by the factor level mainfaclev (main factor level)
Usage
saveTree(
data,
vars,
expr,
i,
varname,
mainfaclev,
treeGenerationMinBucket,
...
)
Arguments
data |
A dataframe containing the residual and some predictors |
vars |
A list of candidate predictors |
expr |
A expression to be modelled by the RPART tree |
i |
An integer corresponding to the factor level |
varname |
A string corresponding to the name of the factor variable being analysed |
mainfaclev |
A level of the mainfac factor |
treeGenerationMinBucket |
Minimum size for tree generation |
... |
and parameters to be passed through |
Value
A tree object