Description Usage Arguments Details Value Author(s) References See Also Examples
Estimates Huggin's closed population capturerecapture models with individual, time, and individualtime varying covariates using the "regression" parameterization of Amstrup et al (2006, Ch 9). For live recaptures only. A logistic link function is used to relate probability of capture to external covariates.
1 2  F.huggins.estim(capture, recapture=NULL, histories, remove=FALSE, cap.init, recap.init,
nhat.v.meth=1, df=NA, link="logit", control=mra.control())

capture 
Formula specifying covariates to included in the initial
capture probability model. Must be a formula object without
a response. Specify ~, followed by the names of 2D arrays of covariates to relate to
initial capture probability.
For example: 'capture = ~ age + sex', where age and sex are matrices of size NAN X NS
containing the age and sex covariate values. NAN = number of individuals = number of rows in

recapture 
Formula specifying covariates to included in the
recapture probability model, or NULL. Should be specified the same way as the

histories 
A NAN X NS = (number of individuals) X (number of capture occasions) matrix containing capture histories. Capture histories are comprised of 0's and 1's only. 0 in cell (i,j) of this matrix means individual i was not captured on occasion j, 1 in cell (i,j) means individual i was captured on occasion j and released live back into the population. Because the population being sampled is assumed closed, deaths on capture (known removals) are not allowed. If deaths on capture occurred and an estimate of N at the beginning of the study is sought, remove the entire history, estimate N using this routine from the remaining histories, and add back the number of deleted histories. 
remove 
A logical scalar, or vector of logical values, specifying which

cap.init 
(optional) Vector of initial values for coefficients in the initial
capture model. One element
per covariate in 
recap.init 
(optional) Vector of initial values for coefficients in the
recapture model. One element
per covariate in 
nhat.v.meth 
Integer specifying method for computing variance estimate
for the population size estimate. Currently, only 
df 
External (override) model degrees of freedom to use during estimation.
If 
link 
The link function to be used. The link function converts linear predictors in the
range (infinity, infinity) to probabilities in the range (0,1). Valid values for the link
function are "logit" (default), "sine", and "hazard". (see Examples in help for

control 
A list containing named control parameters for the minimization and estimation process.
Control parameters include number of iterations, covariance estimation method, etc.
Although the default values work in the vast majority of cases, changes to these
variables can effect speed and performance for illbehaved models. See

This routine compiles all the covariate matrices, then calls a Fortran routine to maximize the Huggins closed population likelihood. Socalled heterogeneous models that utilize mixture distributions for probability of capture cannot be fitted via this routine.
If remove=FALSE
(default) the models for initial capture and
subsequent recapture are,
p[i,j] = B0 + B1*x1[i,j] + ... + Ba*xa[i,j]
and
c[i,j] = B0 + B1*x1[i,j] + ... + Ba*xa[i,j] + G0 + G1*z1[i,j] + ... + Gb*zb[i,j]
where the x's and z's are covariate values specified in capture
and recapture
, respectively, and the B's and
G's are estimated coefficients. (For brevity,
'a' has been substituted for NX, 'b' for NY.) In other words, by default
all effects in the capture model also appear in the recapture model
with the same estimated coefficients. This is done so that
capture and recapture probabilities can be constrained to equal one another.
If capture=~1
and recapture=NULL
, capture and recapture
probabilities are constant and equal to one another.
If capture=~x1
and recapture=NULL
, capture and recapture
probabilities are equal, and both are the exact same function of
covariate x1
. A simple additive
behavioral (trap happy or trap shy) effect is fitted by specifying an
interceptonly model for recaptures, i.e.,
capture=~x1+x2+...+xp
and recapture=~1
.
When a Huggin's model object is printed using the default
print method (print.hug
), a "C" (for "capture") appears next to coefficients
in the recapture model that are also in the initial capture model. These
coefficients are fixed in the recapture model. A "B" (for "behavioral")
appears next to free coefficients in the recapture model that do not
appear in the initial capture model.
If remove
is something other than FALSE, it is extended to have length
NX, and if element i equals TRUE, the ith
effect in the capture model is removed from the recapture model. If
remove=c(FALSE, TRUE, FALSE)
, capture=~x1+x2
, and
recapture=~x1+x3
, the models for initial capture and subsequent
recapture are,
p[i,j] = B0 + B1*x1[i,j] + B2*x2[i,j]
and
c[i,j] = B0 + B2*x2[i,j] + G0 + G1*x1[i,j] + G2*x3[i,j].
Note that x1
appears in the recapture equation, but with a
different estimated coefficient. If remove=TRUE
, all capture effects
are removed from the recapture model and the models are completely separate.
The ability to remove terms from the recapture model adds modeling flexibility.
For example, if initial captures were hypothesized to depend on the variable
sex
, but recaptures were hypothesized to be constant (no sex
effect),
the arguments to fit this model would be capture=~sex
, recapture=~1
,
and remove=TRUE
. A pure timevarying model with different time
effects in the initial and subsequent capture models can be fitted using
capture=~tvar(1:ns,nan)
, recapture=~tvar(1:ns,nan)
,
and remove=TRUE
. In this case, the same model, but parameterized differently,
can be fitted with remove=FALSE
.
See Details of help(F.cjs.estim)
for ways that 2d matrices, 1d
vectors, and 1d factors can be specified in the capture and recapture
models.
If argument trace
in a call to mra.control
is set to something
other than 0, a log file named mra.log
is written to the current directory.
See mra.control
for actions associated with values of trace
.
CAREFUL: mra.log
is overwritten each run.
Values in 2d Matrix Covariates: Even though covariate matrices are required to be NAN x NS (same size as capture histories), there are not that many recapture parameters. Recapture parameters for the first occasion are not defined. For all covariates in the recapture model, only values in columns 2:ncol(histories) are used. See examples for demonstration.
An object (list) of class c("hug","cr") with many components.
Use print.hug
to print
it nicely. Use names(fit)
, where the call was fit < F.huggins.estim(...)
,
to see names of all returned components. To see values of individual components,
issue commands like fit\$n.hat, fit\$se.n.hat, etc.
Components of the returned object are as follows:
histories 
The input capture history matrix. Size NAN x NS 
aux 
Auxiliary information, mostly stored input values. This is a list containing: \$call, \$nan=number of individuals, \$ns=number of samples, \$nx=number of coefficients in the initial capture model, \$ny=number of coefficients in recapture model, \$cov.name=names of the covariates in both models (initial capture covariates first, then recapture covariates), \$ic.name=name of capture history matrix, \$mra.version=version number of this package, \$R.version=R version used, \$run.date=date the model was estimated. 
loglik 
Value of the Huggins log likelihood at it's maximum. 
deviance 
Model deviance = 2* 
aic 
AIC for the model = 
aicc 
AIC with small sample correction = AIC + (2* 
capcoef 
Vector of estimated coefficients in the initial capture model. Length NX. 
se.capcoef 
Vector of estimated standard errors for coefficients in initial capture model. Length NX. 
recapcoef 
Vector of estimated coefficients in the recapture model. Length NY. 
se.surcoef 
Vector of standard errors for coefficients in recapture model. Length NY. 
covariance 
Variancecovariance matrix for the estimated model coefficients. Size (NX+NY) X (NX+NY). 
p.hat 
Matrix of estimated initial capture probabilities computed from the model. Size of this matrix is NAN x NS. Cell (i,j) is estimated probability of first capture for individual i during capture occasion j. 
se.p.hat 
Matrix of standard errors for estimated initial capture probabilities. Size NAN x NS. 
c.hat 
Matrix of estimated recapture probabilities computed from the model. Size NAN x NS. Cell (i,j) is estimated probability of capturing individual i during occasion j given that it was initially captured prior to j. 
se.c.hat 
Matrix of standard errors for estimated recapture probabilities. Size NAN X NS. 
df 
Number of estimable parameters in the model. 
message 
A string indicating whether the maximization routine converged. 
exit.code 
Exit code from the maximization routine.
Interpretation for 
cov.code 
A code indicating the method used to compute the covariance matrix. 
cov.meth 
String indicating method used to compute covariance matrix.
Interprets 
n.hat 
The Huggins estimate of population size. This estimate is
sum( 1/ pstar(i) ), where pstar(i) is probability of observing individual i,
which equals 1  p.hat[i,1]*p.hat[i,2]* ... *p.hat[i,NS], where p.hat is the
returned value of 
se.n.hat 
Estimated standard error of 
n.hat.lower 
Lower limit of log based confidence interval for

n.hat.upper 
Upper limit of log based confidence interval for

n.hat.conf 
Confidence level for the interval on 
nhat.v.meth 
Code for method used to compute variance of 
num.caught 
Number of individuals ever captured = number of
rows in the 
n.effective 
Effective sample size = number of observed individuals times number of occasions = NAN * NS 
Trent McDonald, WESTINC, [email protected]
Huggins, R. M. 1989. On the statistical analysis of capture experiments. Biometrika 76:133140.
Amstrup, S. C., T. L. McDonald, and B. F. J. Manly (editors). 2005. Handbook of CaptureRecapture Analysis. Princeton University Press.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  # Fake the data for these examples
set.seed(3425)
ch.mat < matrix( round(runif(30*5)), nrow=30, ncol=5)
ch.mat < ch.mat[ apply(ch.mat,1,sum) > 0, ] # no zero rows allowed
ct < as.factor(1:ncol(ch.mat))
attr(ct,"nan") < nrow(ch.mat) # used to fit time varying factor
sex < round(runif(nrow(ch.mat))) # fake sex
attr(sex,"ns") < ncol(ch.mat)
# Models parallel to the 8 Otis et al. models.
# see Amstrup et al. (2005, p. 77)
# Constant model (model M(0)).
hug.0 < F.huggins.estim( ~1, NULL, ch.mat )
# Time varying model (model M(t))
hug.t < F.huggins.estim( ~tvar(ct), NULL, ch.mat)
# Additive Behavioral model (model M(b))
hug.b < F.huggins.estim( ~1, ~1, ch.mat )
# Time and Behavioral model (model M(tb))
hug.tb < F.huggins.estim( ~tvar(ct), ~1, ch.mat )
# Individual effects (model M(h))
hug.h < F.huggins.estim( ~ivar(sex), NULL, ch.mat )
# Individual and Behavioral effects (model M(bh))
hug.bh < F.huggins.estim( ~ivar(sex), ~1, ch.mat )
# Individual and time effects (model M(th))
hug.th < F.huggins.estim( ~ivar(sex)+tvar(ct), NULL, ch.mat )
# Individual, time, and behavoral effects (model M(tbh))
hug.tbh < F.huggins.estim( ~ivar(sex)+tvar(ct), ~1, ch.mat )
# Time varying initial captures, recaptures are constant and depend on sex.
hug.custom1 < F.huggins.estim( ~tvar(ct), ~ivar(sex), ch.mat, remove=TRUE )
# Compare hug.custom1 to the following: Time varying initial captures with
# time varying recaptures that depend on sex.
hug.custom2 < F.huggins.estim( ~tvar(ct), ~ivar(sex), ch.mat, remove=FALSE )
# Values in first column of recapture covariates do not matter.
# Below, mod.1 and mod.2 are identical.
mod.1 < F.huggins.estim( ~tvar(ct), ~tvar( c( 0,1,2,3,4), nrow(ch.mat)), ch.mat, remove=TRUE)
mod.2 < F.huggins.estim( ~tvar(ct), ~tvar( c(9,1,2,3,4), nrow(ch.mat)), ch.mat, remove=TRUE)

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