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,
clamp=0.001,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. |
clamp |
Fix the COV/CoV estimate to the initial COV/CoV estimate, until |
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
.
The bootstrapped COV estimates tend to be far more noisy than the bootstrapped point estimates. clamp
can fix the bootstrapped COV/CoV estimate to the initial COV/CoV estimate until the point estimates obtain higher numerical precision.
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) \Sexpr[results=rd]{tools:::Rd_expr_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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