TS_LG_object: Prepare a time series for a local Gaussian inspection
In LAJordanger/localgaussSpec: Local Gaussian Spectral Analysis

TS_LG_object

R Documentation

Prepare a time series for a local Gaussian inspection

Description

Prepare a time series for a local Gaussian inspection

Usage

TS_LG_object(
  TS_data,
  details = NULL,
  main_dir = c("~", "LG_DATA"),
  save_dir = NULL,
  .remove_ties = TRUE
)

Arguments

`TS_data`	The time series data we will work upon. This can either be an observed or a simulated time series. If `TS_data` are of class "TS_simulated", i.e. it has been generated by `TS_sample`, then the information stored in it will be used to create `save_dir`. Note that `TS_data` can be univariate or multivariate. A univariate time series can be given as a vector, whereas a multivariate must have the observations along the rows and the variables along the columns. (The program terminates if the number of rows are lower than the number of columns.)
`details`	This can be used to add a reminder that will be shown under the interactive investigation later on. The default value `NULL` will imply that no information is shown.
`main_dir`	The main directory into which the information will be stored. Default value `c("~", "LG_DATA")`, i.e. a specially designed directory in the home directory of your file-system. If the proposed default directory does not exists, then `TS_LG_object` will ask for permission to create it, but otherwise it is a requirement that only existing directories can be used. This is done as a precaution against accidentally ending up with unintended data-directories all over the file-system. Note that the default value is given as a vector in order to avoid issues related to operative system dependent values for the file separator. The argument can also be given as a character-string.
`save_dir`	The sub-directory of `main_dir` where all the stuff related to `TS_data` will be saved. Default value `NULL`, but with the following defaults in the code for what to replace it with: When `TS_data` has been created by `TS_sample`, a value for `save_dir` will be created from the information in `TS_data`, and any attempt from the user to create another name will be outright ignored. If no value is given for `save_dir` (and none can be computed from `TS_data`), then the default value from `LG_default` will be used to create `save_dir`. If the user specifies `save_dir` (for a time-series not originating from `TS_sample`), then that name will be used, but only if no previous directories happens to have that name too – if that should be the case, the program will terminate and inform the user about it.
`.remove_ties`	A logical value, default `TRUE`, in which case the presence of ties will trigger a minor perturbation of the data. Note: Whenever this happens, `set.seed(1)` will be used in order to ensure reproducibility.

Details

This function will for a given (sample from a) time series create a directory that will be used to store all the files that occur during the local Gaussian analysis. The function will in addition create a local info-file to take care of the subsequent bookkeeping. Moreover, this function will also maintain a global info-file in the main_dir-directory, where information about the top-level part of the directory-structure is stored – in order to avoid the despicable situation that more than one directory stores information about the exact same set of data. The sample will be nudged a tiny bit (always using 1 as the seed-value) if ties are detected in it. The algorithm ensures that this nudge is of a small order compared to the original values.

Value

This function will take care of some file-handling before it returns a two-component list to the work-flow, containing the following nodes:

TS_done_before: A logical value that reveals whether or not the time series from TS_data already was stored in the folder main_dir.
result: A list whose format depends upon whether or not TS_data was created by TS_sample – and some of the content are only connected to the internal work-flow of this function. The four parts of result that always is present is TS_key (the origin of the time series), TS (the values), N (the number of observations), and save_dir (the path to the save-directory). These four values will be used by the functions that analyses TS based upon Local Gaussian Approximations and Local Gaussian Spectral Densities.