tsnarmimp: Remove and Impute Missing Values in a Multivariate Time...

In dfms: Dynamic Factor Models

Remove and Impute Missing Values in a Multivariate Time Series

Description

This function imputes missing values in a stationary multivariate time series using various methods, and removes cases with too many missing values.

Usage

tsnarmimp(
  X,
  max.missing = 0.8,
  na.rm.method = c("LE", "all"),
  na.impute = c("median.ma.spline", "median.ma", "median", "rnorm"),
  ma.terms = 3L
)

Arguments

X	a T x n numeric data matrix (incl. ts or xts objects) or data frame of stationary time series.
max.missing	numeric. Proportion of series missing for a case to be considered missing.
na.rm.method	character. Method to apply concerning missing cases selected through max.missing: "LE" only removes cases at the beginning or end of the sample, whereas "all" always removes missing cases.
na.impute	character. Method to impute missing values for the PCA estimates used to initialize the EM algorithm. Note that data are standardized (scaled and centered) beforehand. Available options are: "median" simple series-wise median imputation. "rnorm" imputation with random numbers drawn from a standard normal distribution. "median.ma" values are initially imputed with the median, but then a moving average is applied to smooth the estimates. "median.ma.spline" "internal" missing values (not at the beginning or end of the sample) are imputed using a cubic spline, whereas missing values at the beginning and end are imputed with the median of the series and smoothed with a moving average.
ma.terms	the order of the (2-sided) moving average applied in na.impute methods "median.ma" and "median.ma.spline".

Value

The imputed matrix X_imp, with attributes:

"missing"	a missingness matrix W matching the dimensions of X_imp.
"rm.rows"	and a vector of indices of rows (cases) with too many missing values that were removed.

Examples

library(xts)
str(tsnarmimp(BM14_M))

dfms documentation built on June 22, 2024, 10:31 a.m.