calcSamplingBias: Calculate Simulation Data Sampling Bias

In ksauby/ACS: Adaptive Cluster Sampling

Calculate Simulation Data Sampling Bias

Description

Calculate the sampling bias of different sampling designs from simulation data.

Usage

calcSamplingBias(
  popdatasummary,
  simdata,
  popvar,
  samplinggroupvar,
  ovar,
  orvar,
  rvar
)

Arguments

popdatasummary	Summary statistics on the species patch realizations of patches (created by calculateRealizationSummaryStatistics function).
simdata	Simulation data on sampling of the multiple patch realizations.
popvar	Categorical variable used to identify different populations.
samplinggroupvar	Categorical variable used to identify different samples.
ovar	Vector of occupancy variables. The total vector of variables (c(avar, ovar, rvar)) should be a length of at least 1.
orvar	Vector of variables for which secondary variables should be estimated. Can be identical to ovar or a subset.
rvar	Vector of ratio variables. The total vector of variables (c(avar, ovar, rvar)) should be a length of at least 1.

Value

Dataframe including simulation data summary statistics, including relative bias and mean squared error (MSE) of the mean and variance.

Examples

# Create realizations
x_start = 1
x_end = 30
y_start = 1
y_end = 30
n.networks = c(5, 15, 10, 20, 30, 40)
n.realizations = 1
SpeciesInfo = PlotSurveys_season1
Species.Fields = c("Stricta", "Pusilla", "Cactus")
cactus.realizations = createRealizations(x_start, x_end,
	y_start, y_end, buffer=5, n.networks, n.realizations, SpeciesInfo, 
	start.seed=1, Species.Fields, yvar="Cactus")

# Sample from the realizations
simulations=1
n1_vec=c(5,10,20,40)
population <- createPop(x_start = 1, x_end = 30, y_start = 1, 
	y_end = 30)
abundance.variables = NULL
occupancy.variables = c(
	"Stricta",
	"Pusilla",
	"Cactus",
	"CACA_on_Pusilla",
	"CACA_on_Stricta",
	"MEPR_on_Pusilla",
	"MEPR_on_Stricta",
	"Old_Moth_Evidence_Pusilla",
	"Old_Moth_Evidence_Stricta"
	# "Percent_Cover_Pusilla", # how do I do these? they are occupancy nor abundance
	# "Percent_Cover_Stricta",
	# "Height_Pusilla",
	# "Height_Stricta"
)		
patch_data = cactus.realizations
# simdata <- sampleSpeciesPatchRealizations(patch_data, simulations, 
#	n1_vec, population, abundance.variables, occupancy.variables)

# summary.variables = occupancy.variables
# grouping.variables = c("n.networks", "realization")
# dataset = cactus.realizations
# patch_data_summary <- calculateRealizationSummaryStatistics(dataset, 
# 	summary.variables, grouping.variables)
sims=200
n1=c(5,10,20,40)
population <- createPop(x_start = 1, x_end = 30, y_start = 1, y_end = 30)
avar = NULL
ovar = c(
"Stricta",
"Pusilla",
"Cactus",
"CACA_on_Pusilla",
"CACA_on_Stricta",
"MEPR_on_Pusilla",
"MEPR_on_Stricta",
"Old_Moth_Evidence_Pusilla",
"Old_Moth_Evidence_Stricta"
# "Percent_Cover_Pusilla", # how do I do these? they are occupancy nor abundance
# "Percent_Cover_Stricta",
# "Height_Pusilla",
# "Height_Stricta",
)		
popdata = cactus.realizations
#simulation_data <- sampleRealizations(popdata, sims, 
#n1, population, avar, ovar)
#CactusRealizationSummary <- calcPopSummaryStats(
#	popdata = CactusRealizations, 
#	summaryvar = c("Stricta", "Pusilla", "Cactus",
#		"MEPR_on_Stricta", "CACA_on_Stricta", "Percent_Cover_Stricta", 
#		"Height_Stricta", "Old_Moth_Evidence_Stricta"), 
#	popvar = "population", 
#	rvar = c("MEPR_on_Stricta", "CACA_on_Stricta", 
#		"Percent_Cover_Stricta", "Height_Stricta", 
#		"Old_Moth_Evidence_Stricta"),
#	nrow=30,
#	ncol=30
#)
#patch_data_summary_wide <- createWidePopSummaryStats(
#	popsummarystats = CactusRealizationSummary,
#	ovar = "Stricta",
#	rvar = c("MEPR_on_Stricta", "CACA_on_Stricta", "Percent_Cover_Stricta", 
#		"Height_Stricta", "Old_Moth_Evidence_Stricta")
#)
#simdata_summary_table_re = calcSamplingBias(
#	popdatasummary	= patch_data_summary_wide, 
#	simdata		= simdata_all_re, 
#	sampgroupvar	= sampgroupvar, 
#	popvar = popvar,
#	ovar			= ovar, 
#	rvar				= rvar
#)
# avar = NULL

ksauby/ACS documentation built on Aug. 18, 2022, 3:33 a.m.