process_data: Data transforms

In forvis/PdM: R package for predictive maintenance in industry 4.0 concept

Description

The function for data transformation. For more details about the parameter arguments, use ?transfrom_data

Usage

process_data(train_df, test_df, method = "range")

Arguments

train_df	the training dataframe containing multiple multivariate time series formatted using the specific Table Schema, use showDF() to display schema specification details.
test_df	the test dataframe containing multiple multivariate time series formatted using the specific Table Schema, use showDF() to display schema specification details.
method	The transform nethod. Possible methods are: BoxCox: apply a Box-Cox transform, values must be non-zero and positive. YeoJohnson: apply a Yeo-Johnson transform, like a BoxCox, but values can be negative. expoTrans: apply a power transform like BoxCox and YeoJohnson. zv: remove attributes with a zero variance (all the same value). nzv: remove attributes with a near zero variance (close to the same value). center: divide values by standard deviation. scale: subtract mean from values. range: normalize values. pca: transform data to the principal components. ica: transform data to the independent components. spatialSign: project data onto a unit circle. c("center", "scale"): standardize data

Value

Returns the transformed training and test datasets

Author(s)

Cuong Sai and Maxim Shcherbakov.

See Also

showDF, validate_data,summarize_data

Examples

train

forvis/PdM documentation built on Dec. 5, 2020, 8:54 p.m.