INDperform (version 0.2.2)

test_tac: Compute (partial) autocorrelation functions and test for significance

Description

test_tac is a helper function for model_gam, model_gamm, and plot_diagnostics to compute the (partial) autocorrelation functions. It also tests whether residuals show temporal autocorrelation (see details).

Usage

test_tac(model_resid)

Arguments

model_resid

A list of residuals from one or many Generalized Additive (Mixed) Models (GAM(M).

Value

The function returns a tibble with one row for each model and three columns:

acf

A list-column with values from the autocorrelation function.

pacf

A list-column with values from the partial autocorrelation function.

tac

logical; if TRUE, temporal autocorrelation was detected

Details

NOTE: if the time series on which the GAM(M) is fitted contains missing values, they need to be accounted for in the residual vector. Observations with one or more NAs in-between will be otherwise considered as having a lag of 1.

The test for temporal autocorrelation is based on the following condition: If any of the acf and any of the pacf values of lag 1 - 5 is greater or lower than 0.4, a TRUE is returned.

See Also

model_gam, model_gamm, plot_diagnostics

Examples

# NOT RUN {
# Using models of the Baltic Sea demo data:
# Get model residuals of GAMs
model_resid <- purrr::map(model_gam_ex$model,
  ~mgcv::residuals.gam(., type = "deviance"))
# test whether model residuals show significant TAC
test_tac(model_resid)

# Works also with GAMMs
model_resid <- purrr::map_if(model_gamm_ex$model,
  !is.na(model_gamm_ex$model),
  ~as.numeric(mgcv::residuals.gam(.$gam, type = "deviance")))
  # (exclude those GAMMs that were not fitted)
# test whether model residuals show significant TAC
test_tac(model_resid)$tac
# }