derive_timeseries: Derive the time series used in the AR1 model.

In itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs

Derive the time series used in the AR1 model.

Description

Derive the time series used in the AR1 model.

Usage

derive_timeseries(model, AR.start = NULL)

Arguments

model	GAMM model that includes an AR1 model.
AR.start	Vector with AR.start information, necessary for the AR1 model. Optional, defaults to NULL.

Value

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

Author(s)

Jacolien van Rij

See Also

Other functions for model criticism: acf_n_plots(), acf_plot(), acf_resid(), resid_gam(), start_event(), start_value_rho()

Examples

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)

itsadug documentation built on June 17, 2022, 5:05 p.m.