## ------------------------------------------------------------------------
library(mfe)
## Extract all measures using formula
iris.info <- metafeatures(Species ~ ., iris)
## Extract all measures using data frame
iris.info <- metafeatures(iris[,1:4], iris[,5])
## Extract general, statistical and information-theoretic measures
iris.info <- metafeatures(Species ~ ., iris,
groups=c("general", "statistical", "infotheo"))
## ------------------------------------------------------------------------
## Compute all measures using min, median and max
iris.info <- metafeatures(Species ~ ., iris, summary=c("min", "median", "max"))
## Compute all measures using quantile
iris.info <- metafeatures(Species ~ ., iris, summary="quantile")
## ------------------------------------------------------------------------
## Extract two information theoretical measures
stat.iris <- infotheo(Species ~ ., iris,
features=c("attrEnt", "jointEnt"))
## Extract three statistical measures
disc.iris <- statistical(Species ~ ., iris,
features=c("cancor", "cor", "iqr"))
## Extract the histogram for the correlation measure
hist.iris <- statistical(Species ~ ., iris,
features="cor", summary="hist")
## ------------------------------------------------------------------------
## Show the the available groups
ls.metafeatures()
## ------------------------------------------------------------------------
## Show the the available general measures
ls.general()
## Extract all general measures
general.iris <- general(Species ~ ., iris)
## Extract two general measures
general(Species ~ ., iris, features=c("nrAttr", "nrClass"))
## ------------------------------------------------------------------------
## Extract two general measures
general(Species ~ ., iris, features="freqClass", summary=c("min", "max", "sd"))
## ------------------------------------------------------------------------
## Show the the available statistical measures
ls.statistical()
## Extract all statistical measures
stat.iris <- statistical(Species ~ ., iris)
## Extract two statistical measures
statistical(Species ~ ., iris, features=c("cor", "skewness"))
## ------------------------------------------------------------------------
## Extract correlation using instances by classes
statistical(Species ~ ., iris, features="cor", by.class=TRUE)
## Ignore the class attributes
aux <- cbind(class=iris$Species, iris)
statistical(Species ~ ., aux, transform=FALSE)
## ------------------------------------------------------------------------
## Show the the available information theoretical measures
ls.infotheo()
## Extract all information theoretical measures
inf.iris <- infotheo(Species ~ ., iris)
## Extract two information theoretical measures
infotheo(Species ~ ., iris, features=c("normClassEnt", "mutInf"))
## ------------------------------------------------------------------------
## Ignore the discretization process
aux <- cbind(class=iris$Species, iris)
infotheo(Species ~ ., aux, transform=FALSE)
## ------------------------------------------------------------------------
## Show the the available model.based measures
ls.model.based()
## Extract all model.based measures
land.iris <- model.based(Species ~ ., iris)
## Extract three model.based measures
model.based(Species ~ ., iris, features=c("leaves", "nodes"))
## ------------------------------------------------------------------------
## Show the the available landmarking measures
ls.landmarking()
## Extract all landmarking measures
land.iris <- landmarking(Species ~ ., iris)
## Extract two landmarking measures
landmarking(Species ~ ., iris, features=c("naiveBayes", "oneNN"))
## ------------------------------------------------------------------------
## Extract one landmarking measures with folds=2
landmarking(Species ~ ., iris, features="naiveBayes", folds=2)
## Extract one landmarking measures with folds=2
landmarking(Species ~ ., iris, features="naiveBayes", score="kappa")
## ------------------------------------------------------------------------
## Apply several statistical measures as post processing
statistical(Species ~ ., iris, "cor",
summary=c("kurtosis", "max", "mean", "median", "min", "sd",
"skewness", "var"))
## Apply quantile as post processing method
statistical(Species ~ ., iris, "cor", summary="quantile")
## Get the default values without summarize them
statistical(Species ~ ., iris, "cor", summary=c())
## ------------------------------------------------------------------------
## Apply histogram as post processing method
statistical(Species ~ ., iris, "cor", summary="hist")
## Apply histogram as post processing method and customize it
statistical(Species ~ ., iris, "cor", summary="hist", bins=5, min=0, max=1)
## Extract all correlation values
statistical(Species ~ ., iris, "cor", summary="non.aggregated")
## ------------------------------------------------------------------------
## Compute the absolute difference between the mean and the median
my.method <- function(x, ...) abs(mean(x) - median(x))
## Using the user defined post processing method
statistical(Species ~ ., iris, "cor", summary="my.method")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.