Total mortality can be split into sub-categories, according to
various splitting covariates such as species, visibility class, season,
site, unit, etc. Given the carcass search data, estimated mortalities,
and splitting covariates, calcSplits() gives the "splits" or
summaries the estimated mortalities by levels of the splitting
covariates. For example, user may specify "season" and
"species" as splitting variables to see estimated mortalities by
season and species. Input would be arrays of estimated mortalities and
arrival intervals when ncarc carcass have been discovered and
uncertainty in mortality estimates is captured via simulation with
nsim simulation draws.
calcSplits(M, split_CO = NULL, data_CO = NULL, split_SS = NULL,
data_SS = NULL, split_time = NULL, ...)estM object, containing numeric array (ncarc x nsim)
of estimated mortalities and other pieces
Character vector of names of splitting covariates to be
found in the data_CO data frame. No more than two split_CO
variables are allowed. Use split_CO = NULL if no CO splits are
desired.
data frame that summarizes the carcass search data and must
include columns specified by the split_CO arg. Each row includes
search and discovery parameters associated with a single observed
carcass. Columns include carcass ID, carcass discovery date, unit, and
any number of covariates. data_CO is required if and only if
split_CO is non-NULL.
Character string giving the name of a splitting covariate
in the data_SS list, with data_SS[[split_SS]] describing
characteristics of the search intervals (e.g., "season"). Note that
length(data_SS[[split_SS]] must equal
length(data_SS$days) - 1 because no inference is made about
carcass arrivals prior to time t = 0, and the "interval" prior to t = 0
is not taken as a "search interval." If no split_SS split is
desired, use split_SS = NULL.
Search schedule data
Numeric vector that defines time intervals for splits.
Times must be numeric, strictly increasing, and span the monitoring period
[0, max(data_SS$days)]. If no split_time is desired, use
split_time = NULL. If split_time is NULL and split_SS
is not NULL, data_SS is required.
arguments to be passed down
An object of class splitFull is returned. If one splitting
covariate is given, then the output will be an array of estimated
mortality in each level of the splitting covariate, with one row for each
covariate level and one column for each simulation draw. If two splitting
covariates are given, output will be a list of arrays. Each array gives
the estimated mortalities for one level of the second splitting covariate
and all levels of the first splitting covariate.
Objects of class splitFull have attributes vars (which gives
the name of the splitting covariate(s)) and type (which specifies
whether the covariate(s) are of type split_CO, split_SS, or
split_time). A summary of a resulting splitFull object
is returned from the S3 function summary(splits, CL = 0.90, ...),
which gives the mean and a 5-number summary for each level of each
covariate. The 5-number summary includes the alpha/2, 0.25, 0.5, 0.75,
and 1 - alpha/2 quantiles, where alpha = 1 - CL. A graph summarizing the
results can be drawn using plot(splits, CL, ...), which gives
a graphical representation of the summary.
Arrival intervals (Aj) are given as integers, j, that
indicate which search interval the given carcass (indexed by row) arrived
in the given simulation draw (indexed by column). Arrival interval indices
(j) are relative to indexed carcasses' search schedules.
No more than two splitting variables (split_CO, split_SS,
and split_time) in total may be used. split_CO variables
describe qualitative characteristics of the observed carcasses or where
they were found. Some examples include searcher (DHD, JPS, MMH), carcass
size (S, M, L), species, age (fresh/dry or immature/mature), unit,
visibility class (easy, moderate, difficult), etc.
split_SS variables describe characteristics of the search intervals,
such as season (spring, summer, fall, winter) or treatment
(pre- or post-minimization). Each search interval is assigned a level of
the split_SS variable. For example, for a search schedule with
5 searches (including a search at t = 0), and the split_SS
variable would have values for each of the 4 search intervals. The
levels of the split_SS must be in contiguous blocks. For example,
season = c("S", "S", "F", "F") would be acceptable, but
season = c("S", "F", "S", "F") would not be.
split_time variables are numeric vectors that split the monitoring
period into distinct time intervals. For example,
split_time = c(0, 30, 60, 90, 120) would split the 120 monitoring
period into 30-day intervals, and calcSplits() would return
mortality estimates for each of the intervals.
# NOT RUN {
# }
# NOT RUN {
model_SE <- pkm(p ~ 1, k ~ 1, data = wind_RPbat$SE)
model_CP <- cpm(l ~ 1, s ~ 1, data = wind_RPbat$CP, dist = "weibull",
left = "LastPresent", right = "FirstAbsent")
Mhat <- estM(nsim = 1000, data_CO = wind_RPbat$CO,
data_SS = wind_RPbat$SS, data_DWP = wind_RPbat$DWP,
model_SE = model_SE, model_CP = model_CP,
unitCol = "Turbine", COdate = "DateFound")
M_spp <- calcSplits(M = Mhat, split_CO = "Species",
data_CO = wind_RPbat$CO)
summary(M_spp)
plot(M_spp)
# }
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