interpolate_missing_samples: Impute missing samples using linear interpolation
In weecology/MATSS-pipeline: Macroecological Analyses of Time Series Structure

Description Usage Arguments Details Value

Impute missing samples using linear interpolation

interpolate_missing_samples(
  data,
  period = NULL,
  tol = 1e-06,
  interpolate_covariates = FALSE
)

`data`	dataset to modify
`period`	period to check the times against (if `NULL`, first check to see if there is a known `period` set in the metadata, otherwise assumes 1)
`tol`	tolerance for the period
`interpolate_covariates`	`TRUE` or `FALSE` (whether to do covariates, too)

First, check if the data are evenly sampled in time. If not, we exit early. Next, apply forecast::na.interp() to each variable that has non-finite values.

the dataset, with interpolated samples

weecology/MATSS-pipeline documentation built on May 16, 2020, 1:54 p.m.