derive_timeseries | R Documentation |

Derive the time series used in the AR1 model.

derive_timeseries(model, AR.start = NULL)

`model` |
GAMM model that includes an AR1 model. |

`AR.start` |
Vector with AR.start information, necessary for the AR1 model. Optional, defaults to NULL. |

A vector with time series indication based on the AR1 model.

Jacolien van Rij

Other functions for model criticism:
`acf_n_plots()`

,
`acf_plot()`

,
`acf_resid()`

,
`resid_gam()`

,
`start_event()`

,
`start_value_rho()`

data(simdat) # add missing values to simdat: simdat[sample(nrow(simdat), 15),]$Y <- NA simdat <- start_event(simdat, event=c('Subject', 'Trial')) ## Not run: # Run GAMM model: m1 <- bam(Y ~ te(Time, Trial)+s(Subject, bs='re'), data=simdat, rho=.5, AR.start=simdat$start.event) simdat$Event <- NA simdat[!is.na(simdat$Y),]$Event <- derive_timeseries(m1) acf_resid(m1, split_pred=list(Event=simdat$Event)) # And this works too: simdat$Event <- derive_timeseries(simdat$start.event) acf_resid(m1, split_pred=list(Event=simdat$Event)) # Note that acf_resid automatically makes use of derive_timeseries: acf_resid(m1, split_pred='AR.start') ## End(Not run)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.