residuals.ctmm  R Documentation 
These functions calculate the residuals of a CTMM or UERE calibration model, which should be standardized and IID if the model correctly specified.
A correlogram method is also provided to assess autocorrelation.
This function is analogous to acf
, but can handle missing data and multiple dimensions.
Finally, mag
calculates residual magnitudes, which is useful for comparing against potential covariates.
## S3 method for class 'ctmm'
residuals(object,data,...)
## S3 method for class 'telemetry'
residuals(object,CTMM=NULL,...)
correlogram(data,dt=NULL,fast=TRUE,res=1,axes=c("x","y"),trace=TRUE)
mag(x,...)
## S3 method for class 'telemetry'
mag(x,axes=c('x','y'),...)
object 

data 

CTMM 

... 
Unused arguments. 
dt 
Lag bin width. An ordered array will yield a progressive coarsening of the lags. Defaults to the median sampling interval. 
fast 
Use the lagweighted algorithm if 
res 
Increase the discretization resolution for irregularly sampled data with 
axes 
Array of axes for which to calculate residual correlogram or magnitudes. 
trace 
Display a progress bar if 
x 

Given a telemetry
dataset and ctmm
model, residuals
calculates the standardized residuals of the Kalman filter, which can be tested for independence. The residuals object can then be plotted with plot
or fed into the correlogram
method to test independence. Output of the correlogram can then be plotted as well, though zoom
is much more useful.
When calculating correlograms, minimizing bias is more important than producing a overall smooth estimate. If fast=TRUE
, then res
needs to be large enough to resolve variability in the sampling interval (missing data is permitted). E.g., if the sampling interval is set to 15 minutes, but can be off by a minute or two, then res=15
is a good choice.
residuals
return a residual object (class telemetry
, but flagged as residual) and correlogram
returns a correlogram object (class variogram
, but flagged as an ACF).
If the sampling schedule is irregular, permitting gaps, then the correlogram may not look good even if the model is correctly specified. In this case the correlogram of the residuals should be compared to the correlogram of simulated residuals, using "data" simulated from the fit model and with the same sampling schedule.
C. H. Fleming
C. H. Fleming, D. Sheldon, E. Gurarie, W. F. Fagan, S. LaPoint, J. M. Calabrese, “Kálmán filters for continuoustime movement models”, Ecological Informatics, 40, 821 (2017) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecoinf.2017.04.008")}.
plot.variogram
, variogram
.
# Load package and data
library(ctmm)
data(buffalo)
Cilla < buffalo$Cilla
# fit a model
GUESS < ctmm.guess(Cilla,interactive=FALSE)
FIT < ctmm.fit(Cilla,GUESS)
# calculate residuals
RES < residuals(Cilla,FIT)
# scatter plot of residuals with 50%, 95%, and 99.9% quantiles
plot(RES,col.DF=NA,level.UD=c(.50,.95,0.999))
# calculate correlogram of residuals
# increase the res argument to account for sampling variability
ACF < correlogram(RES,res=10)
# plot 4 day's worth of lags
plot(ACF[ACF$lag<=4 %#% 'day',],fraction=1)
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