calcRE: Calculate Relative Efficiency (RE)

In ksauby/ACS: Adaptive Cluster Sampling

Calculate Relative Efficiency (RE)

Description

Calculate efficiency of sampling design, relative to WHAT.

Usage

calcRE(
  MSE_ComparisonSamplingDesign,
  popdata,
  popvar,
  samplesizevar,
  rvar,
  ovar
)

Arguments

MSE_ComparisonSamplingDesign	Sampling design for which relative efficiency (RE) should be calculated.
popdata	Dataframe of population data.
popvar	Categorical variable used to identify different populations.
samplesizevar	Name of column in population data (?) containing the variable indicating variation in sample size.
rvar	Vector of ratio variables. The total vector of variables (c(avar, ovar, rvar)) should be a length of at least 1.
ovar	Vector of occupancy variables. The total vector of variables (c(avar, ovar, rvar)) should be a length of at least 1.

Value

Dataframe including original data and RE estimates.

Examples

# dimensions of populations
#  library(magrittr)
#  library(dplyr)
# WHERE IS POP DATA FROM?
# dimensions <- popdata %>% 
# 	group_by(popvar) %>%
#	summarise(N = n())
# variances of populations
# 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 = "Island", 
# 	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")
# )
# simulation_data_summary_table_re = calcSamplingBias(
# 	population_data_summary	= patch_data_summary_wide, 
# 	simulation_data		= simdata_all_re, 
# 	sampgroupvar	= sampgroupvar, 
# 	popvar = popvar,
# 	ovar			= ovar, 
# 	rvar				= rvar
# )
# RE_values <- calcRE(
# 	population_data = patch_data_summary_wide,
# 	MSE_ComparisonSamplingDesign = simulation_data_summary_table_re,
# 	popvar = "population",
# 	samplesizevar = "N.Total.plots_mean",
# 	ovar = ovar,
# 	rvar = rvar
# )

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