Nothing
## ----setup, include = FALSE---------------------------------------------------
require(rmarkdown)
require(knitr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
rm(list = ls())
library(GenEst)
vers <- packageVersion("GenEst")
today <- Sys.Date()
set.seed(951)
## -----------------------------------------------------------------------------
library(GenEst)
data(solar_PV)
names(solar_PV)
## ----pk data------------------------------------------------------------------
data_SE <- solar_PV$SE
head(data_SE)
## ----pk one model-------------------------------------------------------------
SE_model <- pkm(p ~ 1, k ~ 1, data = data_SE)
SE_model
## ----pk two models------------------------------------------------------------
SE_model_set <- pkm(p~Season, k~1, data = data_SE, allCombos = TRUE)
class(SE_model_set)
length(SE_model_set)
names(SE_model_set)
class(SE_model_set[[1]])
## ----pk set AICc--------------------------------------------------------------
aicc(SE_model_set)
## ----pk size set--------------------------------------------------------------
SE_size_model <- pkm(p ~ Season,
k ~ 1,
sizeCol = "Size",
data = data_SE)
class(SE_size_model)
names(SE_size_model) # A list is created with a model set per size class.
class(SE_size_model$small)
names(SE_size_model$small) # Each model set contains one model in this case.
## -----------------------------------------------------------------------------
SE_size_model_set <- pkm(p ~ Season,
k ~ 1,
sizeCol = "Size",
data = data_SE, allCombos = TRUE)
aicc(SE_size_model_set)
SE_models <- list()
## -----------------------------------------------------------------------------
SE_models$small <- SE_size_model_set$small[[2]]
## ----pk size Medium-----------------------------------------------------------
SE_models$med <- SE_size_model_set$med[[2]]
## ----pk Size Large------------------------------------------------------------
SE_models$lrg <- SE_size_model_set$lrg[[1]]
## ----cp data------------------------------------------------------------------
data_CP <- solar_PV$CP
head(data_CP)
## ----cp-----------------------------------------------------------------------
cpm(l ~ Season, s ~ 1, data = data_CP,
left = "LastPresent",
right = "FirstAbsent",
dist = "weibull")
## ----cp set-------------------------------------------------------------------
CP_weibull_set <- cpm(l ~ Season, s ~ 1, data = data_CP,
left = "LastPresent",
right = "FirstAbsent",
dist = "weibull", allCombos = TRUE)
class(CP_weibull_set)
aicc(CP_weibull_set)
## ----cp Size Set--------------------------------------------------------------
CP_size_model_set <- cpm(formula_l = l ~ Season,
formula_s = s ~ 1,
left = "LastPresent",
right = "FirstAbsent",
dist = c("exponential", "weibull"),
sizeCol = "Size",
data = data_CP, allCombos = TRUE)
class(CP_size_model_set)
names(CP_size_model_set)
class(CP_size_model_set$small)
length(CP_size_model_set$small)
names(CP_size_model_set$small)
## -----------------------------------------------------------------------------
aicc(CP_size_model_set)
CP_models <- list()
## ----cp Size Small------------------------------------------------------------
CP_models$small <- CP_size_model_set$small[[4]]
## ----cp size Medium-----------------------------------------------------------
CP_models$med <- CP_size_model_set$med[[4]]
## ----Size Large---------------------------------------------------------------
CP_models$lrg <- CP_size_model_set$lrg[[2]]
## ----Load CO SS and DWP-------------------------------------------------------
data_CO <- solar_PV$CO
head(data_CO)
## ----SS Data------------------------------------------------------------------
data_SS <- solar_PV$SS
data_SS[1:5 , 1:10]
## ----DWP data-----------------------------------------------------------------
data_DWP <- solar_PV$DWP
head(data_DWP)
## ----Arrival Times, options---------------------------------------------------
Mest <- estM(
nsim = 100, frac = 1,
data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP,
model_SE = SE_models, model_CP = CP_models,
unitCol = "Unit", sizeCol = "Size",
COdate = "DateFound", SSdate = "DateSearched"
)
## ---- fig.show = "hold", fig.height = 4, fig.width = 6, fig.align = 'center'----
plot(Mest)
## ----Summary - Season---------------------------------------------------------
unique(data_SS[, "Season"])
M_season <- calcSplits(M = Mest,
split_SS = "Season", data_SS = data_SS,
split_CO = NULL, data_CO = data_CO
)
## ----splitFull plot, fig.height = 4, fig.width = 4, fig.align = 'center'------
plot(M_season)
## ----SplitFull Summary--------------------------------------------------------
summary(M_season, CL = 0.95)
## ----Summary - Weekly---------------------------------------------------------
SSdat <- prepSS(data_SS) # Creates an object of type prepSS.
schedule <- seq(from = 0, to = max(SSdat$days), by = 7)
tail(schedule)
## ----Summary - Weekly Part 2, fig.height = 4, fig.width = 7, fig.align = 'center'----
M_week <- calcSplits(M = Mest,
split_time = schedule,
data_SS = SSdat,
data_CO = data_CO
)
plot(x = M_week, rate = TRUE)
## ----Summary - Unit, fig.height = 4, fig.width = 7, fig.align = 'center'------
M_unit <- calcSplits(M = Mest,
split_CO = "Unit",
data_CO = data_CO,
data_SS = data_SS
)
plot(M_unit, rate = FALSE)
## ----individual unit summary--------------------------------------------------
dim(summary(M_unit)) # only 164 arrays had observations.
# A list of the arrays without observed carcasses:
setdiff(paste0("Unit", 1:300), data_CO$Unit)
# Create summaries for arrays Unit12 and Unit100.
whichRow <- rownames(summary(M_unit)) %in% c("Unit12", "Unit100")
summary(M_unit)[whichRow, ]
## ----Summary - season and species, fig.height = 5, fig.width = 3, fig.align = 'center'----
M_unit_and_species <- calcSplits(M = Mest,
split_SS = c("Season"),
split_CO = c("Size"),
data_CO = data_CO,
data_SS = data_SS
)
plot(M_unit_and_species, rate = FALSE)
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