ctmm.boot | R Documentation |
This function allows the point estimates and confidence intervals of an initial estimated movement model to be improved by parametric boostrap, as described in Fleming et al (2019).
ctmm.boot(data,CTMM,method=CTMM$method,AICc=FALSE,iterate=FALSE,robust=FALSE,error=0.01, cores=1,trace=TRUE,...)
data |
Timeseries data represented as a |
CTMM |
A |
method |
Fitting method to use: |
AICc |
Run dual set of simulations to approximate AICc values via Kullback–Leibler divergence. Otherwise, only the AIC is updated. |
iterate |
Iteratively solve for the parameters such that the average estimate (of |
robust |
Uses robust estimates of the average and covariation for debiasing. Useful when parameters are near boundaries. |
error |
Relative standard error target for bootstrap ensemble estimates and nonlinear iterations. |
cores |
Number of simulations to run in parallel. |
trace |
Report progress updates. Can be among |
... |
Further arguments passed to |
A model fit object with relatively unbiased estimates of location covariance, and autocorrelation timescales (and more accurate CIs than ctmm.fit
). If AICc=TRUE
, then, in addition to an updated AICc
slot, the model fit object will also contain a VAR.AICc
slot quantifying the numerical variance in the AICc
estimate. This variance can be decreased by decreasing argument error
.
C. H. Fleming.
C. H. Fleming, M. J. Noonan, E. P. Medici, J. M. Calabrese, “Overcoming the challenge of small effective sample sizes in home-range estimation”, Methods in Ecology and Evolution 10:10, 1679-1689 (2019) doi: 10.1111/2041-210X.13270.
ctmm.fit
.
# Load package and data library(ctmm) data(gazelle) DATA <- gazelle[[3]] GUESS <- ctmm.guess(DATA,interactive=FALSE) FIT <- ctmm.select(DATA,GUESS) # some human-readable information summary(FIT) # in general, you will want to set iterate=TRUE,trace=TRUE BOOT <- ctmm.boot(DATA,FIT,iterate=FALSE,trace=FALSE) # compare to the previous estimate summary(BOOT)
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