View source: R/searcher_efficiency_functions.R
pkm | R Documentation |
Searcher efficiency is modeled as a function of the number of
times a carcass has been missed in previous searches and any number of
covariates. Format and usage parallel that of common R
functions
lm
, glm
, and gam
. However, the input data
(data
) is structured differently to accommodate the
multiple-search searcher efficiency trials (see Details), and model
formulas may be entered for both p
(akin to an intercept) and
k
(akin to a slope).
pkm(
formula_p,
formula_k = NULL,
data,
obsCol = NULL,
kFixed = NULL,
allCombos = FALSE,
sizeCol = NULL,
CL = 0.9,
kInit = 0.7,
quiet = FALSE,
...
)
pkm0(
formula_p,
formula_k = NULL,
data,
obsCol = NULL,
kFixed = NULL,
kInit = 0.7,
CL = 0.9,
quiet = FALSE
)
pkmSet(
formula_p,
formula_k = NULL,
data,
obsCol = NULL,
kFixed = NULL,
kInit = 0.7,
CL = 0.9,
quiet = FALSE
)
pkmSize(
formula_p,
formula_k = NULL,
data,
kFixed = NULL,
obsCol = NULL,
sizeCol = NULL,
allCombos = FALSE,
kInit = 0.7,
CL = 0.9,
quiet = FALSE
)
formula_p |
Formula for p; an object of class |
formula_k |
Formula for k; an object of class |
data |
Data frame with results from searcher efficiency trials and any
covariates included in |
obsCol |
Vector of names of columns in |
kFixed |
Parameter for user-specified |
allCombos |
logical. If |
sizeCol |
character string. The name of the column in |
CL |
numeric value in (0, 1). confidence level |
kInit |
numeric value in (0, 1). Initial value used for numerical
optimization of |
quiet |
Logical indicator of whether or not to print messsages |
... |
additional arguments passed to subfunctions |
The probability of finding a carcass that is present at the time of
search is p
on the first search after carcass arrival and is
assumed to decrease by a factor of k
each time the carcass is
missed in searches. Both p
and k
may depend on covariates
such as ground cover, season, species, etc., and a separate model format
(formula_p
and formula_k
) may be entered for each. The
models are entered as they would be in the familiar lm
or
glm
functions in R. For example, p
might vary with
A
and B
, while k
varies
only with A
. A user might then enter p ~ A + B
for formula_p
and k ~ A
for
formula_k
. Other R conventions for defining formulas may also be
used, with A:B
for the interaction between covariates
A and B and A * B
as short-hand for A + B + A:B
.
Search trial data
must be entered in a data frame with data in
each row giving the fate of a single carcass in the field trials. There
must be a column for each search occassion, with 0, 1, or NA depending on
whether the carcass was missed, found, or not available (typically
because it was found and removed on a previous search, had been earlier
removed by scavengers, or was not searched for) on the given search
occasion. Additional columns with values for categorical covariates
(e.g., visibility = E, M, or D) may also be included.
When all trial carcasses are either found on the first search or
are missed on the first search after carcass placement, pkm effects a
necessary adjustment to the for accuracy; otherwise, the model would not be
able to determine the uncertainty and would substantially over-estimate the
variance of the parameter estimates, giving \hat{p}
essentially equal
to 0 or 1 with approximately equal probability. The adjustment is to fit the
model on an adjusted data set with duplicated copies of the original data
(2n
observations) but with one carcass having the opposite fate of the
others. For example, in field trials with very high searcher efficiency and
n = 10
carcasses, all of which are found in the first search after
carcass placement, the original data set would have a carcass observation
column consisting of 1s (rep(1, 10)
). The adjusted data set would
have an observation column consisting of 2n - 1
1s and one 0. In this
case, the point estimate of p
is 1/(2n)
with distribution that
closely resembling the Bayesian posterior distributions of p
with a
uniform or a Jeffreys prior. The adjustment is applied on a cellwise basis
in full cell models (e.g., 1, A, B, A * B). In the additive model with two
predictors (A + B), the adjustment is made only when a full level of
covariate A or B is all 0s or 1s.
an object of an object of class pkm
, pkmSet
,
pkmSize
, or pkmSetSize
.
pkm0()
returns a pkm
object, which is a description
of a single, fitted pk model. Due to the large number and complexity of
components of apkm
model, only a subset of them is printed
automatically; the rest can be viewed/accessed via the $
operator
if desired. These are described in detail in the 'pkm
Components'
section.
pkmSet()
returns a list of pkm
objects, one for each
of the submodels, as described with parameter allCombos = TRUE
.
pkmSize()
returns a list of pkmSet
objects (one for
each 'size') if allCombos = T
, or a list of pkm
objects (one
for each 'size') if allCombos = T
pkm
returns a pkm
, pkmSet
, pkmSize
, or
pkmSetSize
object:
pkm
object if allCombos = FALSE, sizeCol = NULL
pkmSet
object if allCombos = TRUE, sizeCol = NULL
pkmSize
object if allCombos = FALSE, sizeCol != NULL
pkmSetSize
object if allCombos = TRUE, sizeCol != NULL
pkm
ComponentsThe following components of a pkm
object are displayed automatically:
call
the function call to fit the model
formula_p
the model formula for the p
parameter
formula_k
the model formula for the k
parameter
predictors
list of covariates of p
and/or k
AICc
the AIC value as corrected for small sample size
convergence
convergence status of the numerical optimization
to find the maximum likelihood estimates of p
and k
. A
value of 0
indicates that the model was fit successfully. For
help in deciphering other values, see optim
.
cell_pk
summary statistics for estimated cellwise estimates
of p
and k
, including the number of carcasses in each cell,
medians and upper & lower bounds on CIs for each parameter, indexed by
cell (or combination of covariate levels).
The following components are not printed automatically but can be accessed
via the $
operator:
data
the data used to fit the model
data0
$data
with NA rows removed
betahat_p, betahat_k
parameter estimates for the terms in the
regression model for for p
and k
(logit scale). If k
is fixed or not provided, then betahat_k
is not calculated.
varbeta
the variance-covariance matrix of the estimators
for c(betahat_p, betahat_k)
.
cellMM_p, cellMM_k
cellwise model (design) matrices for
covariate structures of p_formula
and k_formula
levels_p, levels_k
all levels of each covariate of p
and k
nbeta_p, nbeta_k
number of parameters to fit the p
and k
models
cells
cell structure of the pk-model, i.e., combinations of
all levels for each covariate of p
and k
. For example, if
covar1
has levels "a"
, "b"
, and "c"
, and
covar2
has levels "X"
and "Y"
, then the cells
would consist of a.X
, a.Y
, b.X
, b.Y
,
c.X
, and c.Y
.
ncell
total number of cells
predictors_k, predictors_p
covariates of p
and k
observations
observations used to fit the model
kFixed
the input kFixed
AIC
the AIC value for the fitted model
carcCells
the cell to which each carcass belongs
CL
the input CL
loglik
the log-liklihood for the maximum likelihood estimate
pOnly
a logical value telling whether k
is included in
the model. pOnly = TRUE
if and only if length(obsCol) == 1)
and kFixed = NULL
.
data_adj
data0
as adjusted for the 2n fix to accommodate
scenarios in which all trial carcasses are either found or all are not
found on the first search occasion (uncommon)
fixBadCells
vector giving the names of cells adjusted for the 2n fix
pkmSize
may also be used to fit a single model for each carcass class if
allCombos = FALSE
. To do so, formula_p
and formula_k
must be a named list of formulas with names matching the sizes listed in
unique(data[, sizeCol])
. The return value is then a list of
pkm
objects, one for each size.
rpk
, qpk
, aicc
,
plot.pkm
head(data(wind_RP))
mod1 <- pkm(formula_p = p ~ Season, formula_k = k ~ 1, data = wind_RP$SE)
class(mod1)
mod2 <- pkm(formula_p = p ~ Season, formula_k = k ~ 1, data = wind_RP$SE,
allCombos = TRUE)
class(mod2)
names(mod2)
class(mod2[[1]])
mod3 <- pkm(formula_p = p ~ Season, formula_k = k ~ 1, data = wind_RP$SE,
allCombos = TRUE, sizeCol = "Size")
class(mod3)
names(mod3)
class(mod3[[1]])
class(mod3[[1]][[1]])
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