| moving.fit | R Documentation | 
Apply a function to successive multi-session windows from a capthist object. The default function is openCR.fit, but any function may be used whose first argument accepts a capthist object.
moving.fit (..., width = 3, centres = NULL, filestem = NULL, 
    trace = FALSE, FUN = openCR.fit)
extractFocal (ocrlist, ...) 
... | 
 named arguments passed to   | 
width | 
 integer; moving window width (number of primary sessions)  | 
centres | 
 integer; central sessions of windows to consider  | 
filestem | 
 character or NULL; stem used to form filenames for optional intermediate output  | 
trace | 
 logical; if TRUE a status message is given at each call of FUN  | 
FUN | 
 function to be applied to successive capthist objects  | 
ocrlist | 
 openCRlist object returned by   | 
moving.fit applies FUN to successive multi-session subsets
of the data in the capthist argument. width should be an odd integer. 
centres may be used to restrict the range of windows considered; 
the default is to use all complete windows (width%/%2 + 1)...).
If a filestem
is specified then each result is output to a file that may be loaded with
load. This is useful if fitting takes a long time and analyses
may be terminated before completion. 
extractFocal returns the focal-session (central) estimates from a moving.fit 
with FUN = openCR.fit. The ... argument is passed to predict.openCR; 
it may be used, for example, to choose a different alpha level for confidence intervals.
extractFocal is untested for complex models (e.g. finite mixtures).
A list in which each component is the output from FUN applied to one subset. The window width is saved as attribute ‘width’.
openCR.fit
## number of individuals detected
moving.fit(capthist = OVpossumCH, FUN = nrow)
## Not run: 
## if package R2ucare installed
if (requireNamespace("R2ucare"))
    moving.fit(capthist = OVpossumCH, FUN = ucare.cjs, width = 5, tests = "overall_CJS")
## using default FUN = openCR.fit
mf1 <- moving.fit(capthist = OVpossumCH, type = 'JSSAfCL', 
     model = list(p~t, phi~t))
lapply(mf1, predict)
extractFocal(mf1)
     
msk <- make.mask(traps(OVpossumCH[[1]]), nx = 32)
mf2 <- moving.fit(capthist = OVpossumCH, mask = msk, type = 'JSSAsecrfCL')
extractFocal(mf2)
## End(Not run)
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