context("trend analysis")
#
# Section for data input
#
# Read the data frame containing survey design and analysis variables
load(system.file("extdata", "NLA_IN.rda", package = "spsurvey"))
# Create a population size data frame
popsize <- data.frame(
LAKE_ORGN = c("MAN_MADE", "NATURAL"),
Total = c(6000, 14000)
)
# Create finite population correction factor objects
fpc1 <- 20000
fpc2a <- list(
Urban = 5000,
"Non-Urban" = 15000
)
fpc2b <- list(
MAN_MADE = 6000,
NATURAL = 14000
)
fpc3 <- c(
Ncluster = 200,
clusterID_1 = 100,
clusterID_2 = 100,
clusterID_3 = 100,
clusterID_4 = 100
)
fpc4a <- list(
Urban = c(
Ncluster = 75,
clusterID_1 = 50,
clusterID_2 = 50,
clusterID_3 = 50,
clusterID_4 = 50
),
"Non-Urban" = c(
Ncluster = 125,
clusterID_1 = 50,
clusterID_2 = 50,
clusterID_3 = 50,
clusterID_4 = 50
)
)
fpc4b <- list(
NATURAL = c(
Ncluster = 130,
clusterID_1 = 50,
clusterID_2 = 50,
clusterID_3 = 50,
clusterID_4 = 50
),
MAN_MADE = c(
Ncluster = 70,
clusterID_1 = 50,
clusterID_2 = 50,
clusterID_3 = 50,
clusterID_4 = 50
)
)
#
# Section for the trend data analysis function
#
# Create a year variable for trend estimation
NLA_IN$year <- NLA_IN$YEAR - 2007
# Assign response variable names to the vars_cat and vars_cont vectors
vars_cat <- c("BENT_MMI_COND_2017")
vars_cont <- c("ContVar")
# Assign subpopulation variable names to the subpops vector
subpops <- c("All_Sites", "LAKE_ORGN")
# Perform tests
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD"
)
test_that("Trend: Unstratified single-stage analysis", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
popsize = popsize
)
test_that("Trend: with known population sizes", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
fpc = fpc1
)
test_that("Trend: with finite population correction factor", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN"
)
test_that("Trend: Stratified single-stage analysis", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN", fpc = fpc2b
)
test_that("Trend: with finite population correction factor", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
clusterID = "clusterID", weight1 = "weight1", xcoord1 = "xcoord1",
ycoord1 = "ycoord1", vartype = "SRS"
)
test_that("Trend: Unstratified two-stage analysis", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
clusterID = "clusterID", weight1 = "weight1", xcoord1 = "xcoord1",
ycoord1 = "ycoord1", popsize = popsize, vartype = "SRS"
)
test_that("Trend: with known population sizes", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
clusterID = "clusterID", weight1 = "weight1", xcoord1 = "xcoord1",
ycoord1 = "ycoord1", fpc = fpc3, vartype = "SRS"
)
test_that("Trend: with finite population correction factor", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN", clusterID = "clusterID", weight1 = "weight1",
xcoord1 = "xcoord1", ycoord1 = "ycoord1", vartype = "SRS"
)
test_that("Trend: Stratified two-stage analysis", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN", clusterID = "clusterID", weight1 = "weight1",
xcoord1 = "xcoord1", ycoord1 = "ycoord1", popsize = popsize, vartype = "SRS"
)
test_that("Trend: with known population sizes", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
Trend_Estimates <- trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "WGT_TP", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN", clusterID = "clusterID", weight1 = "weight1",
xcoord1 = "xcoord1", ycoord1 = "ycoord1", fpc = fpc4b, vartype = "SRS"
)
test_that("Trend: with finite population correction factor", {
expect_true(exists("Trend_Estimates"))
expect_equal(attributes(Trend_Estimates$catsum)$class, "data.frame")
expect_equal(attributes(Trend_Estimates$contsum)$class, "data.frame")
expect_equal(nrow(Trend_Estimates$catsum), 6)
expect_equal(nrow(Trend_Estimates$contsum), 3)
})
test_that("A warning (in message form) is produced", {
expect_message(expect_error(trend_analysis(
dframe = NLA_IN, vars_cat = vars_cat,
vars_cont = vars_cont, model_cont = "SLR", subpops = subpops,
siteID = "UNIQUE_ID", weight = "XYZ", xcoord = "XCOORD", ycoord = "YCOORD",
stratumID = "LAKE_ORGN", clusterID = "clusterID", weight1 = "weight1",
xcoord1 = "xcoord1", ycoord1 = "ycoord1", fpc = fpc4b, vartype = "SRS"
)))
})
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