run functions to create acf matrix and plot the results
Usage
runACF(
block,
model,
store = FALSE,
save = F,
suppress.printout = TRUE,
maxlag = NULL,
printplot = TRUE
)
Arguments
- block
Vector of blocks that identify data points that are correlated
- model
Fitted model object (glm or gam)
- store
(
default=FALSE
). Logical stating whether a list of the matrix of correlations is stored (output fromacffunc
.)- save
(
default=FALSE
). Logical stating whether plot should be saved into working directory.- suppress.printout
(
default=TRUE
. Logical stating whether to show a printout of block numbers to assess progress.FALSE
will show printout.- maxlag
(
default=NULL
). Numeric entry to allow the restriction of the maximum lag on the plots. IfNULL
then the length of the longest panel is used as the maximum plotted lag.
Value
Plot of lag vs correlation. Each grey line is the correlation for each individual block in block
. The red line is the mean values for each lag.
If store=TRUE
then the matrix of correlations (nblocks x length_max_block) is returned and plotacf
may be used to plot the acf.
Examples
# load data
data(ns.data.re)
model<-gamMRSea(birds ~ observationhour + as.factor(floodebb) + as.factor(impact),
family='quasipoisson', data=ns.data.re)
ns.data.re$blockid<-paste(ns.data.re$GridCode, ns.data.re$Year, ns.data.re$MonthOfYear,
ns.data.re$DayOfMonth, sep='')
ns.data.re$blockid<-as.factor(ns.data.re$blockid)
runACF(ns.data.re$blockid, model, suppress.printout=TRUE)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:igraph':
#>
#> as_data_frame, groups, union
#> The following objects are masked from 'package:raster':
#>
#> intersect, select, union
#> The following object is masked from 'package:MRSea':
#>
#> contains
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#> Error in ggplot(): could not find function "ggplot"
# storing the output and then plotting
acfoutput <- runACF(ns.data.re$blockid, model, suppress.printout=TRUE, store=TRUE)
plotacf(acfoutput$acfmat)
#> Error in ggplot(): could not find function "ggplot"