data_reduction: Dimension reduction via principal components
In tylerJPike/OOS: Out-of-Sample Time Series Forecasting
Description
Usage
Arguments
Value
View source: R/data_preparation.R
Description
A function to estimate principal components.
Usage
1
data_reduction(Data, variables = NULL, ncomp, standardize = TRUE)
Arguments
Data
data.frame: data frame of target variable, exogenous variables, and observed date (named 'date')
variables
string: vector of variables to standardize, default is all but 'date' column
ncomp
int: number of factors to create
standardize
boolean: normalize variables (mean zero, variance one) before estimating factors
Value
data.frame with a date column and one column per forecast method selected
tylerJPike/OOS documentation built on March 20, 2021, 3:20 a.m.
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Description Usage Arguments Value
View source: R/data_preparation.R
Description
A function to estimate principal components.
Usage
1 | data_reduction(Data, variables = NULL, ncomp, standardize = TRUE)
|
Arguments
Data |
data.frame: data frame of target variable, exogenous variables, and observed date (named 'date') |
variables |
string: vector of variables to standardize, default is all but 'date' column |
ncomp |
int: number of factors to create |
standardize |
boolean: normalize variables (mean zero, variance one) before estimating factors |
Value
data.frame with a date column and one column per forecast method selected
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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