Form a single
capthist object from two or more compatible
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one or more
logical, if TRUE assigns new composite individual ID
list of vectors of session indices or names
logical, if TRUE the output is checked with
MS.capthist concatenates the sessions in the input objects as
one multi-session capthist object. Each session may use a different
detector array (traps) and a different number of sampling
occasions. Session names are derived implicitly from the inputs, or
may be given explicitly (see Examples); if any name is duplicated, all
will be replaced with sequential integers. The ... argument may include
lists of single-session capthist objects.
rbind.capthist is used to pool capture data from more than one
session into a single session. The number of rows in the output
session is the sum of the number of rows in the input sessions
(i.e. each animal appears in only one session).
rbind.capthist, the ... argument may be
A series of single-session capthist objects, which are pooled to form one new single-session object, or
One multi-session capthist object, when the components of
‘pool’ are used to define combinations of old sessions; e.g.
list(A=1:3, B=4:5) produces an object with two sessions (named ‘A’
and ‘B’) from 5 old ones. If
pool = NULL (the default) then all the
sessions are pooled to form one single-session capthist object.
Sessions to be pooled must have the same number of capture occasions and use the same detectors (traps). At present there is no function to pool capthist data from different detector arrays. For this it is recommended that you merge the input files and rebuild the capthist object from scratch.
The names of arguments other than ... should be given in full. If
renumber = TRUE (the default), the session name will be prepended
to the animal ID before pooling: animals 1, 2 and 3 in Session A will
become A.1, A.2 and A.3, while those in Session B become B.1, B.2 and
B.3. This ensures that each animal has a unique ID. If
FALSE, the animal IDs will not change.
Other attributes (xy, signal) are handled appropriately. If the signal threshold (attribute ‘cutval’) differs among sessions, the maximum is used and detections of lower signal strength are discarded.
The use of
rbind.capthist to concatenate sessions is now deprecated:
MS.capthist looks like an S3 method, it isn't. The full function name must be used.
rbind.capthist became an S3 method in secr 3.1, so it is called as
MS.capthist, a multi-session object of class ‘capthist’ with
number of sessions equal to the number of sessions in the objects in
rbind.capthist, either an object of class ‘capthist’ with one
session formed by pooling the sessions in the input objects, or a
capthist object with more than one session, each formed by pooling
groups of sessions defined by the ‘pool’ argument. Covariate columns that appear in
all input sessions are retained in the output.
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## extend a multi-session object ## we fake the 2010 data by copying from 2005 ## note how we name the appended session fakeCH <- ovenCH[["2005"]] MS.capthist(ovenCH, "2010" = fakeCH) ## simulate sessions for 2-part mixture temptrap <- make.grid(nx = 8, ny = 8) temp1 <- sim.capthist(temptrap, detectpar = list(g0 = 0.1, sigma = 40)) temp2 <- sim.capthist(temptrap, detectpar = list(g0 = 0.2, sigma = 20)) ## concatenate sessions temp3 <- MS.capthist(large.range = temp1, small.range = temp2) summary(temp3) ## session-specific movement statistic RPSV(temp3) ## pool sessions temp4 <- rbind(temp1, temp2) summary(temp4) RPSV(temp4) ## compare mixture to sum of components ## note `detectors visited' is not additive for 'multi' detector ## nor is `detectors used' (summary(temp1)$counts + summary(temp2)$counts) - summary(temp4)$counts ## Not run: ## compare two different model fits tempfit3 <- secr.fit(temp3, CL = TRUE, buffer = 150, model = list (g0 ~ session, sigma ~ session), trace = FALSE) predict(tempfit3) ## if we can tell which animals had large ranges... covariates(temp4) <- data.frame(range.size = rep(c("large", "small"), c(nrow(temp1), nrow(temp2)))) tempfit4 <- secr.fit(temp4, CL = TRUE, buffer = 150, model = list (g0 ~ range.size, sigma ~ range.size), trace = FALSE) predict(tempfit4, newdata = data.frame(range.size = c("large", "small"))) ## End(Not run)
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