Package 'GenEst'

Description

Extract AICc values from pkm, pkmSet, pkmSetSize, cpm, cpmSet, and cpmSetSize.

Usage

aicc(x, ...)

Arguments

Value

list of models sorted by AICc

Description

S3 function for generating AIC for cpm objects

Usage

## S3 method for class 'cpm'
aicc(x, ...)

Arguments

Value

AIC, AICc vector

Examples

data(wind_RP)
  mod <- cpm(formula_l = l ~ Season, formula_s = s ~ Season,
           data = wind_RP$CP, left = "LastPresent", right = "FirstAbsent")
 aicc(mod)

Description

S3 function to generate model comparison tables based on AICc values for a set of CP models generated by cpmSet

Usage

## S3 method for class 'cpmSet'
aicc(x, ..., quiet = FALSE, app = FALSE)

Arguments

Value

AICc table

Examples

data(wind_RP)
  mod <- cpmSet(formula_l = l ~ Season * Visibility, formula_s = s ~ Season,
           data = wind_RP$CP, left = "LastPresent", right = "FirstAbsent")
 aicc(mod)

Description

S3 function to generate model comparison tables for lists of of sets of CP models of class cpmSetSize

Usage

## S3 method for class 'cpmSetSize'
aicc(x, ...)

Arguments

Value

AICc table

Examples

cpmods <- cpm(formula_l = l ~ Visibility, data = wind_RP$CP,
   left = "LastPresent", right = "FirstAbsent", sizeCol = "Size",
   allCombos = TRUE)
 aicc(cpmods)

Description

extract AICc value from pkm object

Usage

## S3 method for class 'pkm'
aicc(x, ...)

Arguments

Value

Data frame with the formulas for p and k and the AICc of the model

Description

Generates model comparison tables based on AICc values for a set of pk models generated by pkmSet

Usage

## S3 method for class 'pkmSet'
aicc(x, ..., quiet = FALSE, app = FALSE)

Arguments

Value

AICc table

Examples

data(wind_RP)
  mod <- pkmSet(formula_p = p ~ Season, formula_k = k ~ Season, data = wind_RP$SE)
 aicc(mod)

Description

Generates model comparison tables based on AICc values for a set of pk models generated by pkm with allCombos = TRUE and a non-NULL sizeCol.

Usage

## S3 method for class 'pkmSetSize'
aicc(x, ...)

Arguments

Value

AICc table

Examples

data(wind_RP)
  mod <- pkmSet(formula_p = p ~ Season, formula_k = k ~ Season, data = wind_RP$SE)
 aicc(mod)

Description

Generates model comparison tables based on AICc values for a set of pk models generated by pkm with allCombos = FALSE and a non-NULL sizeCol.

Usage

## S3 method for class 'pkmSize'
aicc(x, ...)

Arguments

Value

AICc table

Examples

data(wind_RP)
  mod <- pkmSet(formula_p = p ~ Season, formula_k = k ~ Season, data = wind_RP$SE)
 aicc(mod)

Description

HTML generators for app information and content

disclaimerUSGS creates the text for the USGS disclaimer.

Usage

createvtext(type = "Full")

gettingStartedContent()

aboutContent()

GenEstAuthors()

GenEstGUIauthors()

GenEstLicense()

GenEstAcknowledgements()

GenEstLogos()

disclaimersContent(appType = "base")

disclaimerUSGS()

disclaimerWEST(appType)

Arguments

Value

Panels and text for displaying general information about GenEst

Description

Handle downloading of app data and figures

Usage

downloadCPFig(rv)

downloadCPmod(rv, input)

downloadSEFig(rv)

downloadSEmod(rv, input)

downloadgFig(rv, sc)

downloadMres(rv, input)

downloadMFig(rv, split = TRUE)

downloadTable(filename, tablename, csvformat)

downloadgres(rv, input)

Arguments

Value

download handler functions

Description

lists of messages used in the app

Usage

msgList()

clearNotifications(msgs = msgList(), clear = TRUE)

msgModRun(msgs, modelType, clear = TRUE)

msgModDone(msgs, rv, type = "SE", clear = TRUE)

msgModPartialFail(mods, type = "SE")

msgSampleSize(mods)

msgModWarning(mods, type = "SE", rv = NULL)

msgModSENobs(rv)

msgModFail(mods, type = "SE", special = NULL)

msgSSavgFail(msgs, rv, clear = TRUE)

msgSSinputFail(msgs, rv, clear = TRUE)

msgSplitFail(type = NULL)

msgFracNote(fracNote)

Arguments

Description

app utilities for formatting text, tables, figs, etc. for display

Usage

classText(rv, type = "SE")

estText(rv, type = "SE")

setNotSuspending(output, dontSuspend)

reNULL(x, toNULL)

initialOutput(rv, output)

setFigW(modelSet)

setFigH(modelSet, modType = "SE")

Arguments

Description

functions for formatting and displaying app panels

Usage

dataTabPanel(dataType)

selectedDataPanel(modType)

modelOutputPanel(outType)

Arguments

Description

This suite of functions defines the server-side program for the GenEst user interface (UI). See the "GenEst Graphic User Interface" vignette for a more complete detailing of the codebase underlying the GenEst UI.

GenEstServer: main server function expressed within the application.

Usage

GenEstServer(input, output, session)

reaction(eventName)

reactionMessageRun(eventName)

reactionMessageDone(eventName)

eventReaction(eventName, rv, input, output, session)

Arguments

Details

GenEstServer is used as the main server function, and is therefore included in the server.R script of the app. This function is not used in a standard R function sense, in that it does not return a value and is not used on its own to have side effects. The code of the function has two parts:

1. preamble that defines all the necessary variables and options

2. observeEvent calls, one for each event in the application. Each call to observeEvent includes the eventExpr (event expression) as the first argument and the handlerExpr (handler expression) as the second argument, which is an evaluated (via eval) block of code returned from reaction for the specific event, as well as any other control switch arguments needed (such as ignoreNULL).

Description

This suite of functions in app_ui.R create the HTML code underlying the GenEst user interface (UI). See the "GenEst Graphic User Interface" vignette for a more complete detailing of the codebase underlying the GenEst UI.

GenEstUI: whole application. Calls dataInputPanel, analysisPanel, and helpPanel.

Usage

GenEstUI(appType = "base")

dataInputPanel()

dataInputSidebar()

loadedDataPanel()

analysisPanel()

GeneralInputsPanel()

GeneralInputSidebar()

SEPanel()

SESidebar()

SEMainPanel()

CPPanel()

CPSidebar()

CPMainPanel()

MPanel()

MSidebar()

MMainPanel()

gPanel()

gSidebar()

gMainPanel()

helpPanel(appType = "base")

gettingStartedPanel()

downloadsPanel()

aboutPanel()

disclaimersPanel(appType = "base")

Arguments

Details

Currently there are few differences between the local and deployed versions of GenEst, and the appType toggle is only included as an argument for functions that can produce different versions of the HTML. At this point, the only content that is different is the disclaimer text on the Help panel.

Value

Each function returns a string of HTML code, either as a "shiny.tag.list" object (in the case of GenEstUI) or a "shiny.tag" object (in the case of the other functions).

GenEstUI: Full GenEst user interface.

Description

utility functions for simple HTML tasks

Usage

navbar()

style(...)

ol(...)

ul(...)

li(...)

b(...)

u(...)

small(...)

big(text = NULL)

center(text = NULL)

GenEstInlineCSS(...)

GenEstShinyJS(...)

cButtonStyle(buttonType = "single")

Arguments

Description

utility functions for simple app rv management

Usage

initialReactiveValues()

reVal(rv, toReVal)

setkNeed(rv)

updateColNames_size(rv)

selectData(data, cols)

modNameSplit(modNames, pos)

prepSizeclassText(sizeclasses)

modNamePaste(parts, type = "SE", tab = FALSE)

plotNA(type = "model")

updateSizeclasses(data, sizeCol)

pickSizeclass(sizeclasses, choice)

updatesizeCol(sizeCol, colNames_size)

Arguments

Description

This is a generalized function for creating a data input widget used in the GenEst GUI, based on the data type (dataType). Included within the widget is a conditional panel that allows removal of the specific data file (and clearing of all downstream models) once it has been loaded.

Usage

dataInputWidget(dataType)

dataDownloadWidget(set)

modelInputWidget(inType)

widgetMaker(Condition, Name, Fun, Label, Args)

modelRunWidget(modType)

preTextMaker(modType)

modelOutputWidget(modType)

splitButtonWidget()

modelSelectionWidget(mods, modType)

modelSelectionWidgetHeader(mods)

modelSelectionWidgetRow(mods, modType, sci)

kFixedWidget(sizeclasses)

kFixedWidgetHeader(sizeclasses)

kFixedWidgetRow(sizeclasses, sci)

Arguments

Value

HTML for the data input widget.

Description

Given a multi-unit Search Schedule data table, produce an average search schedule for use in generic detection probability estimation.

Usage

averageSS(data_SS, SSdate = NULL)

Arguments

Value

vector of the average search schedule

Examples

data(mock)
  avgSS <- averageSS(mock$SS)

Description

Calculate detection probability (g) given SE and CP parameters and a search schedule.

The g given by calcg is a generic aggregate detection probability and represents the probability of detecting a carcass that arrives at a (uniform) random time during the time spanned by the search schedule for the the given SE and CP parameters. This differs from the GenEst estimation of g when the purpose is to estimate total mortality (M), in which case the detection probability varies with carcass arrival interval and is difficult to summarize statistically. calcg provides a useful "big picture" summary of detection probability, but would be difficult to work with for estimating M with precision.

Usage

calcg(days, param_SE, param_CP, dist)

Arguments

Value

a vector of detection probabilities for each

Description

A carcass that is observed in a given search may have arrived at any time prior to that search, so carcass discovery time is often not a reliable estimate of carcass arrival time. For each observed carcass, calcRate takes into account the estimated probability of arrival in each possible arrival interval, adjusts by detection probability, and sums to estimate the estimated number of carcass arrivals in every search interval.

Usage

calcRate(M, Aj, days = NULL, searches_carcass = NULL, data_SS = NULL)

Arguments

Value

Numeric array (nsim x nsearch) of estimated fatalities in each search interval. NOTE: The search at time t = 0 does not correspond to an interval, and all carcasses found at that time are assumed to have arrived prior to the monitoring period and are not included in mortality estimates so nsearch = length(days) - 1.

Description

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.

Usage

calcSplits(
  M,
  split_CO = NULL,
  data_CO = NULL,
  split_SS = NULL,
  data_SS = NULL,
  split_time = NULL,
  ...
)

Arguments

Details

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.

Value

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.

Examples

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)

Description

calcTsplit() is a lower-level function that requires the output of calcRate as input. See calcSplits for a more powerful, convenient, and flexible alternative.

Usage

calcTsplit(rate, days, tsplit)

Arguments

Value

A numeric array with dimensions dim = c(length(tsplit) - 1, nsim) giving the estimated number of fatalities that occured in each time interval.

Description

Check if all component terms and interactions are included in a formula. Terms are automatically alphabatized within interactions.

Usage

checkComponents(formula)

Arguments

Value

a logical regarding complete set of terms and interactions

Description

Checks whether the dates are in a standard format and sensible. If so, function returns the dates converted to ISO 8601 yyyy-mm-dd format. Acceptable formats are yyyy-mm-dd, yyyy/mm/dd, mm/dd/yyyy, and dd/mm/yyyy. If format is mm/dd/yyyy or dd/mm/yyyy, the dates must be interpretable unambiguously. Also, dates must be later than 1900-01-01. This additional check provides some protection against common data entry errors like entering a year as 0217 or 1017 instead of 2017.

Usage

checkDate(testdate)

Arguments

Value

dates formatted as yyyy-mm-dd (if possible) or NULL (if some value is not interpretable as a date after 1900-01-01).

Examples

checkDate("02/20/2018")
 checkDate("10/08/2018")

Description

Make sure it's available and good, update the name for usage

Usage

checkSpecificModelCP(modelSet, specificModel)

Arguments

Value

updated name of the model to use

Description

Make sure it's available and good, update the name for usage

Usage

checkSpecificModelSE(modelSet, specificModel)

Arguments

Value

updated name of the model to use

Description

Calculate the conditional probability of observing a carcass at search oi as a function arrival interval (assuming carcass is not removed by scavengers before the time of the final search)

Usage

CO_DWP(dwpsim, data_CO, unitCol, sizeCol = NULL)

Arguments

Value

numeric DWP array

Description

Create a table of combinations of factor levels for the the predictors in a searcher efficiency or carcass persistence analysis. This is a utility function called by pkm0 and cpm0.

Usage

combinePreds(preds, data)

Arguments

Value

Data frame with columns for each predictor in preds and rows for each factor level combination among the predictors. In addition there is a column with CellNames, which are the combinations of predictor levels separated by periods ( . ).

Description

Create a table of factor combinations of predictors in given searcher efficiency and carcass persistence models. This is a utility function called by estgGeneric and governs the cells for which detection probabilities are calculated.

Usage

combinePredsAcrossModels(preds_CP, preds_SE, data_CP, data_SE)

Arguments

Value

Data frame with columns for each predictor in preds and rows for each factor level combination among the predictors. In addition there are column with CellNames, CellNames_CP, and CellNames_SE, which are the combinations of predictor levels for all predictors, CP predictors, and SE predictors (respectively), separated by periods ( . ).

Description

Count the minimum number of carcasses in all of the cells within a _SetSize model complex

Usage

countCarcs(mods)

Arguments

Value

the minimum number of carcasses in the cells

Description

Produce a named vector of standard CP plot colors

Usage

CPcols()

Description

Simply make the named list for the disributions in the CP model

Usage

CPdistOptions()

Value

list with named elements of the distributions

Description

The function used to calculate the negative-loglikelihood of a given carcass persistence model (cpm) with a given data set

Usage

cpLogLik(t1, t2, beta, nbeta_l, cellByCarc, cellMM, dataMM, dist)

Arguments

Value

Negative log likelihood of the observations, given the parameters.

Description

Carcass persistence is modeled as survival function where the one or both parameter(s) can depend on any number of covariates. Format and usage parallel that of common R functions such as lm, glm, and gam. However, the input data (data) are structured differently to accommodate the survival model approach (see "Details"), and model formulas may be entered for both l ("location") and s ("scale").

Usage

cpm(
  formula_l,
  formula_s = NULL,
  data,
  left,
  right,
  dist = "weibull",
  allCombos = FALSE,
  sizeCol = NULL,
  CL = 0.9,
  quiet = FALSE
)

cpm0(
  formula_l,
  formula_s = NULL,
  data = NULL,
  left = NULL,
  right = NULL,
  dist = "weibull",
  CL = 0.9,
  quiet = FALSE
)

cpmSet(
  formula_l,
  formula_s = NULL,
  data,
  left,
  right,
  dist = c("exponential", "weibull", "lognormal", "loglogistic"),
  CL = 0.9,
  quiet = FALSE
)

cpmSize(
  formula_l,
  formula_s = NULL,
  data,
  left,
  right,
  dist = c("exponential", "weibull", "lognormal", "loglogistic"),
  sizeCol = NULL,
  allCombos = FALSE,
  CL = 0.9,
  quiet = FALSE
)

Arguments

Details

The probability of a carcass persisting to a particular time is dictated by the specific distribution chosen and its underlying location (l) and scale (s) parameters (for all models except the exponential, which only has a location parameter). Both l and s may depend on covariates such as ground cover, season, species, etc., and a separate model format (formula_l and formula_s) may be entered for each. The models are entered as they would be in the familiar lm or glm functions in R. For example, l might vary with A, B, and C, while k varies only with A. A user might then enter p ~ A + B + C for formula_l and k ~ A for formula_s. 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.

Carcass persistence data must be entered in a data frame with data in each row giving the fate of a single carcass in the trials. There must be a column for each of the last time the carcass was observed present and the first time the carcass was observed absent (or NA if the carcass was always present). Additional columns with values for categorical covariates (e.g., visibility = E, M, or D) may also be included.

Value

an object of an object of class cpm, cpmSet, cpmSize, or cpmSetSize.

cpm0()

returns a cpm object, which is a description of a single, fitted pk model. Due to the large number and complexity of components of acpm 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 'cpm Components' section.

cpmSet()

returns a list of cpm objects, one for each of the submodels, as described with parameter allCombos = TRUE.

cpmSize()

returns a list of cpmSet objects (one for each 'size') if allCombos = T, or a list of cpm objects (one for each 'size') if allCombos = T

cpm

returns a cpm, cpmSet, cpmSize, or cpmSetSize object:

• cpm object if allCombos = FALSE, sizeCol = NULL

• cpmSet object if allCombos = TRUE, sizeCol = NULL

• cpmSize object if allCombos = FALSE, sizeCol != NULL

• cpmSetSize object if allCombos = TRUE, sizeCol != NULL

cpm Components

The following components of a cpm object are displayed automatically:

call

the function call to fit the model

formula_l

the model formula for the p parameter

formula_s

the model formula for the k parameter

distribution

distribution used

predictors

list of covariates of l and/or s

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_ls

summary statistics for estimated cellwise l and s, including the medians and upper & lower bounds on CIs for each parameter, indexed by cell (or combination of covariate levels).

cell_ab

summary statistics for estimated cellwise pda and pdb, including the medians and upper & lower bounds on CIs for each parameter, indexed by cell (or combination of covariate levels).

cell_desc

Descriptive statistics for estimated cellwise median persistence time and rI for search intervals of 1, 3, 7 14, and 28 days, where rI is the probability of that carcass that arrives at a uniform random time in within a search interval of I days persists until the first search after arrival.

The following components are not printed automatically but can be accessed via the $ operator:

data

the data used to fit the model

betahat_l

parameter estimates for the terms in the regression model for for l

betahat_s

parameter estimates for the terms in the regression model for for s. If dist = "exponential", s is set at 1 and not calculated.

varbeta

the variance-covariance matrix of the estimators for c(betahat_l, betahat_s.

cellMM_l

a cellwise model (design) matrix for covariate structure of l_formula

cellMM_s

a cellwise model(design) matrix for covariate structure of s_formula

levels_l

all levels of each covariate of l

levels_s

all levels of each covariate of s

nbeta_l

number of parameters fit for l

nbeta_s

number of parameters fit for s

cells

cell structure of the cp-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_l

list of covariates of l

predictors_s

list of covariates of s

observations

observations used to fit the model

carcCells

the cell to which each carcass belongs

AIC

the AIC value for the fitted model

CL

the input CL

Advanced

cpmSize may also be used to fit a single model for each size class if allCombos = FALSE. To do so, formula_l, formula_s, and dist be named lists with names matching the sizes listed in unique(data[, sizeCol]). The return value is then a list of cpm objects, one for each size.

Examples

head(data(wind_RP))
 mod1 <- cpm(formula_l = l ~ Season, formula_s = s ~ 1, data = wind_RP$CP,
   left = "LastPresent", right = "FirstAbsent")
 class(mod1)
 mod2 <- cpm(formula_l = l ~ Season, formula_s = s ~ 1, data = wind_RP$CP,
   left = "LastPresent", right = "FirstAbsent", allCombos = TRUE)
 class(mod2)
 names(mod2)
 class(mod2[[1]])
 mod3 <- cpm(formula_l = l ~ Season, formula_s = s ~ 1, data = wind_RP$CP,
   left = "LastPresent", right = "FirstAbsent",
   allCombos = TRUE, sizeCol = "Size")
 class(mod3)
 names(mod3)
 class(mod3[[1]])
 class(mod3[[1]][[1]])

Description

Produce the figure panel for a specific cell (factor level combination) including the specific fitted decay curves.

Usage

cpmCPCellPlot(model, specificCell, col, axis_y = TRUE, axis_x = TRUE)

Arguments

Description

Run a check the arg is a well-fit cpm object

Usage

cpmFail(cpmod)

Arguments

Value

logical value indicating a failed fit (TRUE) or successful (FALSE)

Description

Run a check on each model within a cpmSet object to determine if it failed or not

Usage

cpmSetFail(cpmSetToCheck)

Arguments

Value

A vector of logical values indicating if each of the models failed

Remove failed cpm models from a cpmSet object

Description

Remove all failed models within a cpmSet object

Usage

cpmSetFailRemove(cpmSetToTidy)

Arguments

Value

A cpmSet object with failed models removed

Description

Run a check on each model within a cpmSetSize object to determine if they all failed or not

Usage

cpmSetSizeFail(cpmSetSizeToCheck)

Arguments

Value

A list of vectors of logical values indicating if each of the models failed

Description

Remove failed models from a cpmSetSize object

Usage

cpmSetSizeFailRemove(cpmSetSizeToTidy)

Arguments

Value

A list of cpmSet objects with failed models removed

Description

Produce the figure panel for a specific cell (factor level combination) including the specific fitted decay curves.

Usage

cpmSetSpecCPCellPlot(modelSet, specificModel, specificCell, cols, axes)

Arguments

Value

a specific cell plot panel

Description

Simple function to facilitate selection of date columns from a data table

Usage

dateCols(data)

Arguments

Value

column names of columns that can be coerced to dates

Description

Convert calendar date to integer day from a reference date (ref).

Usage

dateToDay(date, ref = NULL)

Arguments

Value

Numeric value(s) of days from ref.

Examples

x <- c("2018-01-01", "2018-02-01")
  dateToDay(x, x[1])

Description

If a unit column is not explicitly defined by user in the arg list to estM or estg, then defineUnitCol parses the CO, DWP, and SS files to extract the unit column if possible.

Criteria that a column must meet to be a unit column are that it is found in both data_CO and data_DWP, all units in data_CO must also be included among units in data_DWP, all units in both data_CO and data_DWP must be included among the column names in data_SS. If data_DWP = NULL, then the unit column must be included in data_CO and all its units must be included among the column names of data_SS.

Usage

defineUnitCol(data_CO, data_SS = NULL, data_DWP = NULL)

Arguments

Value

name of unit column (unitCol), if a unique unit column can be identified. If no unit column is present or there is more than one unit column, defineUnitCol stops with an error.

Description

Given a cpm object, calculate convenient descriptive statistics, including the median CP, specified rI statistics, and pda and pdb statistics for the fitted model (EoA parameterization), and location and scale parameters for the fitted model (survival package parameterization) along with estimated CIs.

Usage

desc(model_CP, Ir = c(1, 3, 7, 14, 28), CL = 0.9, nsim = 10000)

Arguments

Details

The CIs for the r statistics (and the medianCP for the Weibull) ara based on simulation of the pda and pdb parameters, calculation of the statistics, and taking the empirical distribution of the simulated values. Other CIs are based on the assumed bivariate normal distributions of the appropriately transformed l and s parameters in the fitted model using beta_hat and varbeta.

NOTE: rI is the probability that a carcass that arrives at a uniform random time in an interval of I days will persist until the first search after arrival.

Value

Matrix of point and interval estimates for the median CP and the r statistics for the specified intervals. The matrix is assigned to class descCP that is simply a matrix with dimensions ncell x (1 + 3*(5 + length(Ir))), column names that give the number of observations in each cell, statistic name and upper and lower bounds (in triplets), and row names giving the names of the cells. CL, nsim, and the name of the fitted model (model_CP) are included as object attributes.

See Also

cpm, rcp, ppersist

Description

Format a user-friendly version of the parameter table from a Searcher Efficiency model, based on confidence level of interest

Usage

dlModTabSE(modTab, CL = 0.9)

Arguments

Value

download version of the SE model table

Description

Simple function to facilitate selection of columns that could be DWP values from a data table

Usage

DWPCols(data)

Arguments

Value

column names of columns that can be DWP values

Description

Carcass density is modeled as a function of distance from turbine. Format and usage parallel that of common R functions lm, glm, and gam and the GenEst functions pkm and cpm.

Usage

dwpm(data_DWP, type = "data", unitCol = NULL, dwpCols = NULL)

Arguments

Details

The fraction of carcasses falling in the area searched at a turbine may be a function of carcass class (e.g., large or small) and/or direction from the turbine. Data may be provided for fitting a distance model(s) or, alternatively, simulated turbine-wise DWP data from custom-fitted models may be provided. If pre-fit, pre-simulated data are used, then glm returns a dwpm object with type = data.

To fit a model, data_DWP should be a data frame with a row for each carcass and columns giving (at a minimum) unique carcass IDs, turbine ID, distance from turbine, and fraction of area searched at the given distance at the given turbine. Optional columns may include carcass class, covariates that may influence detection probability (e.g., visibility class), and direction. If covariates are to be included in the model, then the fraction of area column would give the fraction of the area in the given covariate level at that distance. Alternatively, prefab data may be provided in a dataframe, with structure depending on data type. The simplest case would be that point estimates only are provided. In that case, if there are no distinctions among carcass classes (e.g., size), then data_DWP should be a dataframe with one column giving the unit (e.g., turbine) and one column with the DWP at each unit; if distinctions are made among carcass classes, then data_DWP would be a data frame with a unit column and a DWP column for each carcass class. If the DWP estimates incorporate uncertainties, then data_DWP should be an array with n_unit * nsim rows and with colunms for units and DWPs for each carcass class.

Value

an object of an object of class dwpm, which is a list with model type (currently only type = data is supported) and model, which gives the simulated DWP values as an array (if there's only a single carcass class) or a list of arrays (if there are more than one carcass classes).

Description

Estimate g values and arrival intervals for a set of carcasses from fitted pk and cp models and search data

Usage

estg(
  data_CO,
  COdate,
  data_SS,
  SSdate = NULL,
  model_SE,
  model_CP,
  model_DWP = NULL,
  sizeCol = NULL,
  unitCol = NULL,
  IDcol = NULL,
  nsim = 1000,
  max_intervals = 8
)

Arguments

Value

list of [1] g estimates (ghat) and [2] arrival interval estimates (Aj) for each of the carcasses. The row names of the Aj matrix are the units at which carcasses were found. Row names of ghat are the carcass IDs (in data_CO).

Examples

data(mock)
 model_SE <- pkm(formula_p = p ~ HabitatType, formula_k = k ~ 1,
              data = mock$SE)
 model_CP <- cpm(formula_l = l ~ Visibility, formula_s = s ~ Visibility, 
               data = mock$CP, dist = "weibull",
               left = "LastPresentDecimalDays", 
               right = "FirstAbsentDecimalDays"
             )
 ghat <- estg(data_CO = mock$CO, COdate = "DateFound",  data_SS = mock$SS,
      model_SE = model_SE, model_CP = model_CP, unitCol = "Unit", nsim = 100)

Description

Generic g estimation by simulation from given SE model and CP models under a specific search schedule.

The g estimated by estgGeneric is a generic aggregate detection probability and represents the probability of detecting a carcass that arrives at a (uniform) random time during the period monitored, for each of the possible cell combinations, given the SE and CP models. This is somethat different from the GenEst estimation of g when the purpose is to estimate total mortality (M), in which case the detection probability varies with carcass arrival interval and is difficult to summarize statistically. The estgGeneric estimate is a useful "big picture" summary of detection probability, but would be difficult to work with for estimating M with precision.

Usage

estgGeneric(days, model_SE, model_CP, nsim = 1000)

Arguments

Value

gGeneric object that is a list of [1] a list of g estimates, with one element in the list corresponding to each of the cells from the cross-model combination and [2] a table of predictors and cell names associated with the gs

Examples

data(mock)
  model_SE <- pkm(formula_p = p ~ HabitatType, formula_k = k ~ 1,
                data = mock$SE)
  model_CP <- cpm(formula_l = l ~ Visibility, formula_s = s ~ Visibility, 
                data = mock$CP, left = "LastPresentDecimalDays", 
                right = "FirstAbsentDecimalDays")
  avgSS <- averageSS(mock$SS)
  ghatsGeneric <- estgGeneric(days = avgSS, model_SE = model_SE,
   model_CP = model_CP)

Description

Generic g estimation for a combination of SE model and CP model under a given search schedule

The g estimated by estgGenericSize is a generic aggregate detection probability and represents the probability of detecting a carcass that arrives at a (uniform) random time during the period monitored, for each of the possible cell combinations, given the SE and CP models. This is somethat different from the GenEst estimation of g when the purpose is to estimate total mortality (M), in which case the detection probability varies with carcass arrival interval and is difficult to summarize statistically. The estgGeneric estimate is a useful "big picture" summary of detection probability, but would be difficult to work with for estimating M with precision.

Usage

estgGenericSize(
  days,
  modelSetSize_SE,
  modelSetSize_CP,
  modelSizeSelections_SE,
  modelSizeSelections_CP,
  nsim = 1000
)

Arguments

Value

list of g estimates, with one element in the list corresponding to each of the cells from the cross-model combination

Examples

data(mock)
  pkmModsSize <- pkm(formula_p = p ~ HabitatType,
                   formula_k = k ~ HabitatType, data = mock$SE,
                   obsCol = c("Search1", "Search2", "Search3", "Search4"),
                   sizeCol = "Size", allCombos = TRUE)
  cpmModsSize <- cpm(formula_l = l ~ Visibility,
                   formula_s = s ~ Visibility, data = mock$CP,
                   left = "LastPresentDecimalDays",
                   right = "FirstAbsentDecimalDays",
                   dist = c("exponential", "lognormal"),
                   sizeCol = "Size", allCombos = TRUE)

  pkMods <- c("S" = "p ~ 1; k ~ 1", "L" = "p ~ 1; k ~ 1",
             "M" = "p ~ 1; k ~ 1", "XL" = "p ~ 1; k ~ 1"
            )
  cpMods <- c("S" = "dist: exponential; l ~ 1; NULL", 
              "L" = "dist: exponential; l ~ 1; NULL",
              "M" = "dist: exponential; l ~ 1; NULL",
              "XL" = "dist: exponential; l ~ 1; NULL"
            )
  avgSS <- averageSS(mock$SS)
  gsGeneric <- estgGenericSize(nsim = 1000, days = avgSS,
                 modelSetSize_SE = pkmModsSize,
                 modelSetSize_CP = cpmModsSize,
                 modelSizeSelections_SE = pkMods,
                 modelSizeSelections_CP = cpMods
               )

Description

Given given fitted Searcher Efficiency and Carcass Persistence models; Search Schedule, Density Weighted Proportion, and Carcass Observation data; and information about the fraction of the the facility that was surveyed.

Usage

estM(
  data_CO,
  data_SS,
  data_DWP = NULL,
  frac = 1,
  COdate = "DateFound",
  model_SE,
  model_CP,
  model_DWP = NULL,
  unitCol = NULL,
  SSdate = NULL,
  sizeCol = NULL,
  IDcol = NULL,
  DWPCol = NULL,
  nsim = 1000,
  max_intervals = 8
)

Arguments

Value

list of Mhat, Aj, ghat, DWP (by carcass), and Xtot = total number of carcasses observe

Examples

## Not run: 
 data(mock)
 model_SE <- pkm(formula_p = p ~ HabitatType, formula_k = k ~ 1,
              data = mock$SE
             )
 model_CP <- cpm(formula_l = l ~ Visibility, formula_s = s ~ Visibility, 
               data = mock$CP, dist = "weibull",
               left = "LastPresentDecimalDays", 
               right = "FirstAbsentDecimalDays"
             )
 eM <- estM(nsim = 1000, data_CO = mock$CO, data_SS = mock$SS, 
         data_DWP = mock$DWP, frac = 1, model_SE = model_SE, 
         model_CP = model_CP, COdate = "DateFound",
         DWPCol = "S", sizeCol = NULL
       )
 
## End(Not run)

Description

Expand the exponential models across the other distributions

Usage

expandModelSetCP(modelSet)

Arguments

Value

updated model set

Description

This package is designed to analyze searcher efficiency, carcass persistence, search schedule, and carcass observation data for the estimation of bird and bat mortality at wind and solar power facilities.

Information

browseVignettes("GenEst")
packageDescription("GenEst")
disclaimerUSGS()
disclaimerWEST()

Data sets

mock
wind_cleared
wind_RP
wind_RPbat
solar_powerTower
solar_PV
solar_trough

Main command-line functions

pkm, cpm, dwpm

estimate searcher efficiency (pk), carcass persistence (cp), and (dwp) parameters

estM

estimate mortality given pkm, cpm and data

calcSplits

split mortality estimates by subcategories

plot

S3 function for pkm, pkmSet, cpm, cpmSet, estM, splitFull, splitSummary, gGeneric, and gGenericSize objects

transposeSplits

transpose 2-d splits

summary

S3 function for estM, splitFull, gGeneric, gGenericSize objects

aicc

S3 function for extracting models' AICc values from pkm, pkmSet, pkmSize, pkmSetSize, cpm, cpmSet, cpmSize, and cpmSetSize objects

desc

Calculate descriptive statistics for a fitted CP model

estgGeneric

estimate detection probability (g) for given searcher efficiency and carcass persistence model

runGenEst()

start the GUI

Potentially useful calculation functions

rpk, qpk, rcp, rdwp
estg, calcg
ppersist, SEsi
alogit, logit
pkLogLik, cpLogLik
calcRate, calcTsplit, ltranspose
refMod
countCarcs
simpleMplot

Potentially useful editing functions

estgGenericSize
prepSS
averageSS
tidyModelSetCP
tidyModelSetSE
checkDate
dateCols
dateToDay
defineUnitCol
dlModTabSE
prettyModTabCP
prettyModTabSE
prettySplitTab

Other functions

trimSetSize
combinePreds
combinePredsAcrossModels
pkmSetSizeFailRemove
pkmSetFailRemove
cpmSetSizeFailRemove
cpmSetSizeFail
cpmSetFailRemove
CO_DWP
CPcols
cpmCPCellPlot
cpmFail
cpmSetFail
cpmSetSpecCPCellPlot
DWPCols
expandModelSetCP
obsCols_SE
pkmFail
pkmSetAllFail
pkmSetFail
pkmSetSizeFail
plotCPCells
plotCPFigure
plotCPHeader
predsCols
SEsi_left
SEsi_right
SEsi0
sizeCols
matchCells
checkComponents
checkSpecificModelCP
checkSpecificModelSE
combinePredsAcrossModels
CPdistOptions
obsCols_fta
obsCols_ltp
pkmParamPlot
pkmSECellPlot
pkmSet
pkmSetSpecParamPlot
pkmSetSpecSECellPlot
plotSEBoxPlots
plotSEBoxTemplate
plotSECells
plotSEFigure
plotSEHeader
prepPredictors
readCSV
removeCols

Internal functions (not exported)

_GenEst_calcRateC
_GenEst_calcTsplitC
calcRateC
calcTsplitC
aboutContent
aboutPanel
analysisPanel
b
big
cButtonStyle
center
classText
clearNotifications
CPMainPanel
CPPanel
downloadCPFig
estText
CPSidebar
initialReactiveValues
createvtext
dataDownloadWidget
dataInputPanel
dataInputSidebar
dataInputWidget
dataTabPanel
disclaimersContent
disclaimersPanel
downloadCPFig
downloadCPMod
downloadCPMres
downloadSEmod
downloadgFig
downloadMFig
downloadSEFig
downloadsPanel
downloadTable
eventReaction
GeneralInputSidebar
GeneralInputsPanel
GenEstAcknowledgements
GenEstAuthors
GenEstGUIauthors
GenEstInlineCSS
GenEstLicense
GenEstLogos
GenEstShinyJS
GenEstUI
GenEstServer
gettingStartedContent
gettingStartedPanel
gMainPanel
gPanel
gSidebar
helpPanel
initialOutput
kFixedWidget
kFixedWidgetHeader
kFixedWidgetRow
li
loadedDataPanel
MMainPanel
modelInputWidget
modelOutputPanel
modelOutputWidget
modelRunWidget
modelSelectionWidget
modelSelectionWidgetHeader
modelSelectionWidgetRow
modelSetCells
modelSetModelCells
modelSetModelPredictors
modelSetPredictors
modNamePaste
modNameSplit
MPanel
msgFracNote
msgList
msgModDone
msgModFail
msgModPartialFail
msgModRun
msgModSENobs
msgModWarning
msgSampleSize
msgSplitFail
msgSSavgFail
msgSSinputFail
MSidebar
navbar
ol
pickSizeclass
plotNA
prepSizeclassText
preTextMaker
reaction
reactionMessageDone
reactionMessageRun
reNULL
reVal
SEboxes
SEcols
selectData
selectedDataPanel
SEMainPanel
SEPanel
SEpanel
SESidebar
setkNeed
setNotSuspending
small
splitButtonWidget
style
trimSetSize
u
ul
update_input
update_output
update_rv
updateColNames_size
updateSizeclasses
updatesizeCol
widgetMaker

Description

Compute the logit or anti-logit

Usage

logit(x)

alogit(x)

Arguments

Value

logit: The logit of x.

alogit: The anti-logit of x.

Examples

logit(0.5)

  alogit(0)

Description

A list of n arrays, each with dimension m x k is redimensioned to a list of m arrays, each with dimension m x k. NOTE: Attributes are not preserved.

Usage

ltranspose(M)

Arguments

Value

a list of m n x k arrays

Description

A template dataset used for testing purposes. Dataset containing SE, CP, SS, DWP, and CO data. Data are mostly random without patterns.

Usage

mock

Format

A list with 5 items:

SE

Searcher efficiency trial data

CP

Carcass persistence trial data

SS

Search schedule data

DWP

Density weighted proportion of area searched data

CO

Carcass observations

Source

mock

Description

model utility functions that are not exported

Usage

matchCells(specific, modelSet)

modelSetModelPredictors(modelSet)

modelSetPredictors(modelSet)

modelSetModelCells(modelSet)

modelSetCells(modelSet)

Arguments

Description

Simple function to facilitate selection of columns that could be First Time Absent observations for a CP model

Usage

obsCols_fta(data)

Arguments

Value

column names of columns that can be observations

Description

Simple function to facilitate selection of columns that could be Last Time Present observations for a CP model

Usage

obsCols_ltp(data)

Arguments

Value

column names of columns that can be observations

Description

Simple function to facilitate selection of columns that could be observations for an SE model

Usage

obsCols_SE(data)

Arguments

Value

column names of columns that can be observations

Description

The function used to calculate the negative-loglikelihood of a given searcher efficiency model (pkm) with a given data set

Usage

pkLogLik(
  misses,
  foundOn,
  beta,
  nbeta_p,
  cellByCarc,
  maxmisses,
  cellMM,
  kFixed = NULL
)

Arguments

Value

Negative log likelihood of the observations, given the parameters.

Description

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).

Usage

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
)

Arguments

Details

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.

Value

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 Components

The 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

Advanced

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.

See Also

rpk, qpk, aicc, plot.pkm

Examples

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]])

Description

Run a check the arg is a well-fit pkm object

Usage

pkmFail(pkmod)

Arguments

Value

logical value indicating a failed fit (TRUE) or successful (FALSE)

Description

Boxplot for pk model cells (soon to be deprecated)

Usage

pkmParamPlot(model, pk = "p", col)

Arguments

Value

a parameter plot panel

Description

Plot cell-specific decay curve for searcher efficiency

Usage

pkmSECellPlot(model, specificCell, col, axis_y = TRUE, axis_x = TRUE)

Arguments

Value

a cell plot panel

Description

Run a check on each model within a pkmSet object to determine if they all failed or not

Usage

pkmSetAllFail(pkmSetToCheck)

Arguments

Value

A logical value indicating if all models failed in the set

Description

Run a check on each model within a pkmSet object to determine if it failed or not

Usage

pkmSetFail(pkmSetToCheck)

Arguments

Value

A vector of logical values indicating if each of the models failed

Description

Remove all failed models within a pkmSet object

Usage

pkmSetFailRemove(pkmSetToTidy)

Arguments

Value

A pkmSet object with failed models removed

Description

Run a check on each model within a pkmSetSize object to determine if they all failed or not

Usage

pkmSetSizeFail(pkmSetSizeToCheck)

Arguments

Value

A list of logical vectors indicating which models failed

Description

Remove failed models from a pkmSetSize object

Usage

pkmSetSizeFailRemove(pkmSetSizeToTidy)

Arguments

Value

A list of pkmSet objects with failed models removed

Description

Plot parameter box plots for each cell within a model for either p or k with comparison to the cellwise model (soor to be deprecated)

Usage

pkmSetSpecParamPlot(modelSet, specificModel, pk = "p", cols)

Arguments

Value

a specific parameter plot panel

Description

Plot cell-specific decay curve for searcher efficiency for a specific model with comparison to the cellwise model

Usage

pkmSetSpecSECellPlot(modelSet, specificModel, specificCell, cols, axes)

Arguments

Value

a specific cell plot panel

Description

The CDF of the loglogistic distribution

Usage

pllogis(q, pda, pdb)

Arguments

Details

There are several common parameterizations of the loglogistic distribution. The one used here gives the following:

CDF

Pr(X <= x) = 1/(1 + (x/$\beta$)^-$\alpha$)

PDF

Pr(X = x) = ($\alpha$/$\beta$) * (x/$\beta$)^($\alpha$ - 1)/(1 + (x/$\beta$)^$\alpha$)^2

Value

Pr(X <= q | pda, pdb)

Description

Plot a single cpm model

Usage

## S3 method for class 'cpm'
plot(x, col = "black", ...)

Arguments

Examples

data(wind_RP)
  mod <- cpm(formula_l = l ~ Season, formula_s = s ~ Season,  
           data = wind_RP$CP, left = "LastPresent", right = "FirstAbsent")
 plot(mod)

Description

Produce a set of figures for a set of CP models, as fit by cpmSet

Usage

## S3 method for class 'cpmSet'
plot(x, specificModel = NULL, cols = NULL, ...)

Arguments

Examples

data(wind_RP)
  mod <- cpmSet(formula_l = l ~ Season, formula_s = s ~ Season,  
           data = wind_RP$CP, left = "LastPresent", right = "FirstAbsent")
 plot(mod)

Description

plot defined for class estM objects

Usage

## S3 method for class 'estM'
plot(x, ..., CL = 0.9)

Arguments

Examples

## Not run: 
 data(mock)
 model_SE <- pkm(formula_p = p ~ HabitatType, formula_k = k ~ 1,
              data = mock$SE)
 model_CP <- cpm(formula_l = l ~ Visibility, formula_s = s ~ Visibility, 
               data = mock$CP, dist = "weibull",
               left = "LastPresentDecimalDays", 
               right = "FirstAbsentDecimalDays")
 eM <- estM(nsim = 1000, data_CO = mock$CO, data_SS = mock$SS, 
         data_DWP = mock$DWP, frac = 1, model_SE = model_SE, 
         model_CP = model_CP, COdate = "DateFound",
         DWPCol = "S", sizeCol = NULL)
 plot(eM)
 
## End(Not run)

Description

Plot method for a single generic ghat estimation

Usage

## S3 method for class 'gGeneric'
plot(x, CL = 0.9, ...)

Arguments

Value

generic detection probability plot

Examples

data(mock)
  model_SE <- pkm(formula_p = p ~ HabitatType, formula_k = k ~ 1,
                data = mock$SE)
  model_CP <- cpm(formula_l = l ~ Visibility, formula_s = s ~ Visibility, 
                data = mock$CP, left = "LastPresentDecimalDays", 
                right = "FirstAbsentDecimalDays")
  avgSS <- averageSS(mock$SS)
  ghatsGeneric <- estgGeneric(nsim = 1000, avgSS, model_SE, model_CP)
  plot(ghatsGeneric)

Description

Plot method for a size-based generic ghat estimation

Usage

## S3 method for class 'gGenericSize'
plot(x, CL = 0.9, ...)

Arguments

Value

size-based detection probability plot

Examples

data(mock)
  pkmModsSize <- pkm(formula_p = p ~ HabitatType,
                   formula_k = k ~ HabitatType, data = mock$SE,
                   obsCol = c("Search1", "Search2", "Search3", "Search4"),
                   sizeCol = "Size", allCombos = TRUE)
  cpmModsSize <- cpm(formula_l = l ~ Visibility,
                   formula_s = s ~ Visibility, data = mock$CP,
                   left = "LastPresentDecimalDays",
                   right = "FirstAbsentDecimalDays",
                   dist = c("exponential", "lognormal"),
                   sizeCol = "Size", allCombos = TRUE)
  pkMods <- c("S" = "p ~ 1; k ~ 1", "L" = "p ~ 1; k ~ 1",
             "M" = "p ~ 1; k ~ 1", "XL" = "p ~ 1; k ~ 1"
            )
  cpMods <- c("S" = "dist: exponential; l ~ 1; NULL", 
              "L" = "dist: exponential; l ~ 1; NULL",
              "M" = "dist: exponential; l ~ 1; NULL",
              "XL" = "dist: exponential; l ~ 1; NULL"
            )
  avgSS <- averageSS(mock$SS)
  gsGeneric <- estgGenericSize(nsim = 1000, days = avgSS,
                 modelSetSize_SE = pkmModsSize,
                 modelSetSize_CP = cpmModsSize,
                 modelSizeSelections_SE = pkMods,
                 modelSizeSelections_CP = cpMods
               )
 plot(gsGeneric)

Description

Plot a single pkm model

Usage

## S3 method for class 'pkm'
plot(x, col = NULL, CL = NULL, ...)

Arguments

Value

a plot

Examples

data(wind_RP)
  mod <- pkm(formula_p = p ~ Season, formula_k = k ~ 1, data = wind_RP$SE)
  plot(mod)

Description

Produce a set of figures for a set of SE models, as fit by pkmSet

Usage

## S3 method for class 'pkmSet'
plot(x, specificModel = NULL, cols = NULL, CL = NULL, ...)

Arguments

Value

a set of plots

Examples

data(wind_RP)
  mod <- pkmSet(formula_p = p ~ Season, formula_k = k ~ Season,
           data = wind_RP$SE
         )
  plot(mod)

Description

The S3 plot method for splitFull objects constructs boxplots of the mortality estimates for all combinations of splitting covariates summarized in the splits variable. This is a simple wrapper function for creating a splitSummary object by calling summary.splitFull and plotting the result via plot.splitSummary.

Usage

## S3 method for class 'splitFull'
plot(x, rate = FALSE, CL = 0.9, commonScale = FALSE, ...)

Arguments

Description

The S3 plot method for splitSummary objects constructs boxplots of the mortality estimates for all combinations of splitting covariates summarized in the splits variable.

For 1-covariate splits, box plots showing median, IQR, and confidence intervals (for the CL attribute for the splits object). For 2-covariate splits, the box plots are in an array with levels of the temporal split (split_SS or split_time) arranged horizontally (if present) and the levels of the split_CO variable arranged vertically. If no temporal splits are present, then the box plots along the levels of the first split_CO variable are arranged horizontally and the levels of the second variable are are arranged vertically.

Usage

## S3 method for class 'splitSummary'
plot(x, rate = FALSE, commonScale = FALSE, ...)

Arguments

Description

Produce a set of cellwise figures for a specific CP model, as fit by cpmSet

Usage

plotCPCells(modelSet, specificModel, cols)

Arguments

Value

a plot

Description

Produce a figures for a specific CP model, as fit by cpmSet

Usage

plotCPFigure(modelSet, specificModel, cols = CPcols())

Arguments

Value

a plot

Description

Produce the header for a CP plot

Usage

plotCPHeader(modelSet, specificModel, cols = CPcols())

Arguments

Value

a plot

Description

Plot parameter box plots for each cell within a model for both p and k with comparison to the cellwise model (soon to be deprecated)

Usage

plotSEBoxPlots(modelSet, specificModel, cols)

Arguments

Value

a set of parameter plot panels

Description

Plot template box plot (soon to be deprecated)

Usage

plotSEBoxTemplate(modelSet, specificModel, cols)

Arguments

Value

a template box plot

Description

Produce a set of cellwise figures for a specific SE model, as fit by pkmSet (soon to be deprecated)

Usage

plotSECells(modelSet, specificModel, cols)

Arguments

Value

a plot

Description

Produce a figures for a specific SE model, as fit by pkmSet (soon to be deprecated)

Usage

plotSEFigure(modelSet, specificModel, app, cols)

Arguments

Value

a plot

Description

Produce the header for an SE plot (soon to be deprecated)

Usage

plotSEHeader(modelSet, specificModel, app = FALSE, cols = SEcols())

Arguments

Value

a plot

Description

Given a set of CP parameters (of "ppersist" type), calculate the probability of persistence to detection for a carcass.

Usage

ppersist(pda, pdb, dist, t_arrive0, t_arrive1, t_search)

Arguments

Value

Probability of persistence of detection to at t_search, given arrival between t_arrive0 and t_arrive1

Description

Simple function to facilitate selection of columns that could be predictors for SE or CP models from a data table

Usage

predsCols(data)

Arguments

Value

column names of columns that can be predictors

Description

Prepare predictor inputs from the app for use in the model function

Usage

prepPredictors(preds = NULL)

Arguments

Value

prepared predictors (or 1 if no predictors)

Description

Since data_SS columns largely have a specific, required format, the prepSS function can often automatically decipher the data, but the user may specify explicit instructions for parsing the data for safety if desired. If the data are formatted properly, the automatic parsing is reliable in most cases. There are two exceptions. (1) If there is more than one column with possible dates (formatted as formal dates (as class Date, POSIXlt or POSIXct) or character strings or factors that can be unambiguously interpreted as dates (with assumed format "2018-05-15" or "2018/5/15"). In that case, the user must specify the desired dates as dateColumn. (2) If there is a covariate column consisting entirely of 0s and 1s. In that case, the user must specify the column(s) in covars.

Usage

prepSS(data_SS, SSdate = NULL, preds = NULL)

Arguments

Value

prepSS object that can be conveniently used in the splitting functions.

Examples

data(mock)
 prepSS(mock$SS)

Description

Format a reader-friendly version of the parameter table from a carcass persistence model showing CIs for medianCP and for rI's for intervals of Ir

Usage

prettyModTabCP(modTab)

Arguments

Value

pretty version of the CP model table in a data frame with point and interval estimates for medianCP and rI statistics. Output table is ready for rendering in shiny and posting in the GUI

Description

Format reader-friendly versions of results summary tables for searcher efficiency (GUI display and download), carcass persistence, and splits.

Usage

prettyModTabSE(modTab, CL = 0.9)

Arguments

Value

pretty version of the SE model table

Description

Format a reader-friendly version of the split summary table a mortality estimation

Usage

prettySplitTab(splitSummary)

Arguments

Value

split pretty table

Print a cpm model object

Description

Print a cpm model object

Usage

## S3 method for class 'cpm'
print(x, ...)

Arguments

Print a pkm model object

Description

Print a pkm model object

Usage

## S3 method for class 'pkm'
print(x, ...)

Arguments

Description

Calculate quantiles of marginal distributions of $\hat{p}$ and $\hat{k}$ for a pkm model object

Usage

qpk(p, model)

Arguments

Value

either a list of ncell $\times$ length(p) matrices of quantiles for $p and $k for cells defined by the model object (if model$pOnly == FALSE) or a ncell $\times$ length(p) matrix of quantiles for p

See Also

rpk, pkm

Examples

# 90% confidence intervals for \code{p} and \code{k}
  mod <- pkm(formula_p = p ~ Visibility * Season, formula_k = k ~ Season,
   data = wind_cleared$SE)
  qpk(p = c(0.05, 0.95), model = mod)

Description

Simulate parameters from a cpm model object, and format them as either type "survreg" or "ppersist"

Usage

rcp(n, model, type = "survreg")

Arguments

Value

list of two matrices of n simulated l and s (if type = "survreg") or a and b (if type = "ppersist")for cells defined by the model object.

Examples

data(wind_RP)
  mod <- cpm(formula_l = l ~ 1, data = wind_RP$CP, left = "LastPresent",
           right = "FirstAbsent"
         )
  rcp(n = 10, model = mod, type = "survreg")
  rcp(n = 10, model = mod, type = "ppersist")

Description

Simulate parameters from a dwpm model object

Usage

rdwp(n, model)

Arguments

Details

If the model type = data, then the number of simulated columns must be either >=n (in which case the first n colunms are taken as the simulated DWP) or 1 (in which case, DWP is assumed constant).

Value

array of n simulated dwp values for each unit. Dimensions = c(n, number of units).

Description

Handle reading in of a csv that is either comma-decimal or semicolon-comma separation style

Usage

readCSV(path)

Arguments

Value

read in data table

Description

Compares all fitted models in a list and returns the model with the greatest log-likelihood

Usage

refMod(modelSet)

Arguments

Value

The model object with the greatest log-likelihood among the models in modelSet

Description

Simple function to facilitate removal of columns selected

Usage

removeCols(colNames, selCols)

Arguments

Value

column names without selected columns

Description

Simulate parameters from a pkm model object

Usage

rpk(n, model)

Arguments

Value

list of pairs of matrices of n simulated p and k for cells defined by the model object.

See Also

rpk, pkm

Examples

data(wind_RP)
  mod <- pkm(formula_p = p ~ 1, formula_k = k ~ Season, data = wind_RP$SE)
  rpk(n = 10, model = mod)

Description

Launches a local version of the GenEst application by running runApp pointed to the app subdirectory in the local GenEst package folder.

Usage

runGenEst()

Description

Produce a named vectory with standard SE plot colors. soon to be deprecated.

Usage

SEcols()

Description

Each call to SEpanel produces a single panel showing searcher efficiency as a function of number of searches. Includes raw data (found and available) and model fits for a specific model (y_spc) and for the reference model (y_ref) for the pkmSet object from the reference model was extracted. For interal use only, for producing figs for plot.pkmSet.