acf_n_plots {itsadug} | R Documentation |
Generate N ACF plots of individual or aggregated time series.
Description
Generate N ACF plots of individual or aggregated time series.
Usage
acf_n_plots(
x,
n = 5,
split_by = NULL,
cond = NULL,
max_lag = NULL,
fun = mean,
plot = TRUE,
random = F,
mfrow = NULL,
add = FALSE,
print.summary = getOption("itsadug_print"),
...
)
Arguments
x |
A vector with time series data, typically residuals of a regression model. |
n |
The number of plots to generate. |
split_by |
List of vectors (each with equal length as |
cond |
Named list with a selection of the time series events
specified in |
max_lag |
Maximum lag at which to calculate the acf. Default is the maximum for the longest time series. |
fun |
The function used when aggregating over time series
(depending on the value of |
plot |
Logical: whether or not to produce plot. Default is TRUE. |
random |
Logical: determine randomly which |
mfrow |
A vector of the form c(nr, nc). The figures will be drawn in an nr-by-nc array on the device by rows. |
add |
Logical: whether to add the plots to an exiting plot window or not. Default is FALSE. |
print.summary |
Logical: whether or not to print summary.
Default set to the print info messages option
(see |
... |
Other arguments for plotting, see |
Value
n
ACF plots providing information about the autocorrelation
in x
.
Author(s)
Jacolien van Rij, R. Harald Baayen
See Also
Use acf
for the original ACF function,
and acf_plot
for an ACF that takes into account time series
in the data.
Other functions for model criticism:
acf_plot()
,
acf_resid()
,
derive_timeseries()
,
resid_gam()
,
start_event()
,
start_value_rho()
Examples
data(simdat)
# Separate ACF for each time series:
acf_n_plots(simdat$Y, split_by=list(simdat$Subject, simdat$Trial))
# Average ACF per participant:
acf_n_plots(simdat$Y, split_by=list(simdat$Subject))
## Not run:
# Data treated as single time series. Plot is added to current window.
# Note: 1 time series results in 1 plot.
acf_n_plots(simdat$Y, add=TRUE)
# Plot 4 ACF plots doesn't work without splitting data:
acf_n_plots(simdat$Y, add=TRUE, n=4)
# Plot ACFs of 4 randomly selected time series:
acf_n_plots(simdat$Y, random=TRUE, n=4, add=TRUE,
split_by=list(simdat$Subject, simdat$Trial))
## End(Not run)
#---------------------------------------------
# When using model residuals
#---------------------------------------------
## Not run:
# add missing values to simdat:
simdat[sample(nrow(simdat), 15),]$Y <- NA
# simple linear model:
m1 <- lm(Y ~ Time, data=simdat)
# This will generate an error:
# acf_n_plots(resid(m1), split_by=list(simdat$Subject, simdat$Trial))
# This should work:
el.na <- missing_est(m1)
acf_n_plots(resid(m1),
split_by=list(simdat[-el.na,]$Subject, simdat[-el.na,]$Trial))
# This should also work:
simdat$res <- NA
simdat[!is.na(simdat$Y),]$res <- resid(m1)
acf_n_plots(simdat$res, split_by=list(simdat$Subject, simdat$Trial))
## End(Not run)
# see the vignette for examples:
vignette('acf', package='itsadug')