tests/testthat/test-significancetest.R
In Displayr/flipFormat: Formatting of R outputs
context("significancetest")
data <- structure(list(Q9_F_2 = structure(c(10, 4.5, 0, 0, 0, 10, 0,
1, 0, 0, 0, 0, 0.028, 0.5, 0, 0, 0, 0, 0.028, 0, 1, 10, 0, 0,
0, 0.25, 0.25, 0.25, 0, 0.028, 0.028, 2.5, 0, 0, 0, 0, 10, 2.5,
10, 0, 0.25, 0, 10, 0, 0, 0.25, 0.25, 0, 0, 0.077, 0, 10, 2.5,
0, 0, 0, 2.5, 0.5, 0.028, 0, 0, 0, 1, 0.028, 2.5, 0.5, 1, 0,
10, 0, 1, 0.5, 0.077, 0.077, 0, 0.25, 0.5, 0, 0.5, 0.028, 0.5,
0.25, 0, 0, 0, 0.5, 0, 2.5, 0.077, 0.25, 0.5, 0.028, 1, 0.077,
0.028, 0.5, 0.028, 0.077, 0.077, 0, 0.077, 0.028, 0, 0.077, 0.25,
0.028, 0.5, 0.25, 4.5, 0.25, 0, 0.25, 1, 0, 0, 0, 0.028, 0.5,
0, 0, 0, 0.028, 0, 0, 0.25, 0, 0.25, 0, 0.077, 0.077, 0.25, 10,
10, 0.028, 0.25, 0.028, 0.5, 0, 4.5, 0, 0.077, 0, 2.5, 1, 0.25,
0.077, 1, 0, 0, 0, 2.5, 1, 0, 2.5, 0.077, 0.028, 0, 0.25, 0,
1, 4.5, 0.028, 2.5, 0, 0.077, 0, 0, 0, 0.25, 0, 1, 0.5, 0, 2.5,
0.5, 10, 0.5, 1, 0.028, 0, 0.077, 0, 0.25, 0, 0.077, 0, 1, 0.25,
0, 0.028, 0, 0, 0, 0.028, 0, 0.25, 0, 0.028, 2.5, 0.25, 0.028,
10, 0, 0, 0.077, 0, 0.25, 0.077, 0, 10, 0.077, 0, 1, 0, 0.077,
0.5, 0.028, 0, 0.077, 0.25, 0, 0.077, 0.028, 0, 0.5, 0.25, 2.5,
2.5, 0, 0, 1, 4.5, 0, 4.5, 0, 0, 0.25, 0, 0, 0.028, 0, 0.25,
0, 0, 0.077, 0, 0.077, 2.5, 0, 0, 0.077, 0, 4.5, 2.5, 0.077,
10, 0, 0.028, 0, 0.028, 10, 4.5, 0.077, 0.028, 0.077, 0.25, 0.028,
0, 4.5, 0.25, 0, 0, 0.028, 0, 0.5, 0, 0, 0, 0, 1, 1, 1, 2.5,
0.028, 10, 0.028, 0, 0.25, 0, 0, 1, 0.028, 0.028, 0.25, 4.5,
0.5, 0.25, 0, 0.028, 0, 0.028, 0.077, 0, 10, 0.5, 0.077, 0, 10,
0.028, 0.077, 0.5, 0.077, 0, 0.077, 2.5, 4.5, 0, 0, 0, 0.077,
0, 0, 0, 0, 0.028, 4.5, 0.077), questiontype = "NumberMulti", name = "Q9_F_2", label = "Pepsi Max", question = "Q9. Cola per week"),
Q4_A = structure(c(9L, 8L, 7L, 8L, 9L, 9L, 5L, 7L, 5L, 7L,
8L, 6L, 5L, 6L, 9L, 8L, 5L, 7L, 7L, 4L, 6L, 9L, 9L, 5L, 9L,
9L, 7L, 9L, 6L, 5L, 5L, 7L, 7L, 9L, 5L, 9L, 9L, 7L, 9L, 9L,
9L, 6L, 9L, 8L, 7L, 7L, 7L, 5L, 9L, 9L, 9L, 9L, 9L, 7L, 6L,
6L, 9L, 7L, 6L, 8L, 9L, 4L, 8L, 7L, 8L, 8L, 6L, 9L, 9L, 6L,
7L, 9L, 4L, 7L, 9L, 8L, 9L, 5L, 8L, 8L, 9L, 4L, 4L, 6L, 6L,
9L, 6L, 7L, 9L, 4L, 6L, 8L, 7L, 6L, 7L, 5L, 6L, 7L, 9L, 9L,
9L, 9L, 8L, 4L, 5L, 4L, 9L, 5L, 8L, 4L, 5L, 9L, 8L, 4L, 5L,
8L, 9L, 6L, 7L, 7L, 6L, 9L, 7L, 5L, 4L, 4L, 9L, 5L, 9L, 8L,
5L, 9L, 9L, 9L, 8L, 9L, 7L, 7L, 7L, 9L, 5L, 5L, 8L, 5L, 7L,
8L, 7L, 9L, 7L, 9L, 7L, 6L, 5L, 7L, 6L, 8L, 8L, 9L, 9L, 6L,
9L, 9L, 7L, 9L, 7L, 7L, 7L, 5L, 9L, 6L, 8L, 5L, 6L, 9L, 5L,
9L, 4L, 9L, 9L, 7L, 9L, 7L, 7L, 6L, 5L, 7L, 8L, 7L, 4L, 6L,
4L, 4L, 9L, 6L, 7L, 6L, 6L, 5L, 7L, 6L, 9L, 9L, 4L, 7L, 4L,
7L, 9L, 7L, 4L, 9L, 6L, 6L, 9L, 6L, 4L, 5L, 7L, 7L, 8L, 7L,
5L, 9L, 6L, 8L, 8L, 7L, 8L, 6L, 6L, 4L, 7L, 7L, 5L, 8L, 9L,
7L, 8L, 7L, 7L, 9L, 9L, 6L, 6L, 7L, 6L, 9L, 6L, 7L, 9L, 6L,
8L, 9L, 8L, 7L, 6L, 9L, 6L, 6L, 9L, 9L, 9L, 9L, 7L, 7L, 9L,
7L, 9L, 6L, 8L, 9L, 9L, 9L, 7L, 6L, 6L, 7L, 7L, 9L, 4L, 7L,
6L, 5L, 8L, 4L, 9L, 6L, 9L, 7L, 7L, 4L, 6L, 6L, 9L, 8L, 9L,
7L, 4L, 5L, 7L, 7L, 9L, 8L, 9L, 9L, 6L, 7L, 9L, 9L, 9L, 8L,
9L, 6L, 6L, 5L, 9L, 8L, 4L, 6L, 4L, 9L, 8L, 7L, 7L, 9L, 7L,
6L, 4L), class = "factor", .Label = c("Never", "Once or twice a year",
"Once every 3 months", "Once a month", "Once every 2 weeks",
"Once a week", "2 to 3 days a week", "4 to 5 days a week",
"Every or nearly every day"), questiontype = "PickOneMulti", name = "Q4_A", label = "Colas (e.g., Coca Cola, Pepsi Max)?", question = "Q4. Frequency of drinking"),
subset = structure(c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE,
TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE,
TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE,
TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE,
FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, FALSE, FALSE), name = "autobinTEVBT_1", label = "Male")),
.Names = c("Q9_F_2", "Q4_A", "subset"), row.names = c(NA, -327L), class = "data.frame")
filter <- structure(c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE,
FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE,
TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, FALSE, FALSE), name = "autobinTEVBT_1", label = "Male")
weight <- structure(c(0.971899352948101, 0.539944084971167, 0.215977633988467,
0.539944084971167, 0.0107988816994233, 1.51184343791927, 0.539944084971167,
1.51184343791927, 0.971899352948101, 1.51184343791927, 0.539944084971167,
0.215977633988467, 0.539944084971167, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.539944084971167, 0.539944084971167, 0, 0.971899352948101,
0.539944084971167, 0.0107988816994233, 0.215977633988467, 0.0496748558173474,
0.107988816994233, 0.539944084971167, 0.0107988816994233, 0.539944084971167,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.215977633988467,
0.539944084971167, 1.51184343791927, 0, 0.539944084971167, 0.107988816994233,
0.539944084971167, 0.539944084971167, 0, 0.215977633988467, 0.539944084971167,
1.51184343791927, 0.215977633988467, 0.107988816994233, 0.0496748558173474,
0.0496748558173474, 0.539944084971167, 0.0496748558173474, 1.51184343791927,
0, 0.539944084971167, 0.539944084971167, 0.107988816994233, 0.0496748558173474,
0, 0.539944084971167, 0.539944084971167, 0.539944084971167, 0.971899352948101,
0.539944084971167, 1.51184343791927, 0.215977633988467, 0.539944084971167,
0.971899352948101, 0.215977633988467, 1.51184343791927, 0.971899352948101,
1.51184343791927, 0.539944084971167, 0.539944084971167, 0.539944084971167,
0.0496748558173474, 0.539944084971167, 0.0107988816994233, 1.51184343791927,
0.107988816994233, 0.971899352948101, 0.539944084971167, 1.51184343791927,
0.539944084971167, 0.0496748558173474, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.215977633988467, 0.0496748558173474, 0.215977633988467,
0.971899352948101, 0.0107988816994233, 0.971899352948101, 0.0107988816994233,
0.539944084971167, 0.539944084971167, 0.539944084971167, 0.971899352948101,
0.215977633988467, 1.51184343791927, 0.0107988816994233, 0.0107988816994233,
0.971899352948101, 0.215977633988467, 0.539944084971167, 0.215977633988467,
0.0107988816994233, 0.0107988816994233, 0.0107988816994233, 0.539944084971167,
0.0107988816994233, 1.51184343791927, 1.51184343791927, 0.539944084971167,
0.0107988816994233, 0.0496748558173474, 0, 0.539944084971167,
0.0107988816994233, 0.215977633988467, 1.51184343791927, 0.539944084971167,
0.0107988816994233, 0.0107988816994233, 1.51184343791927, 0,
0.539944084971167, 0.0107988816994233, 0.0107988816994233, 0,
0.215977633988467, 1.51184343791927, 0.215977633988467, 0.215977633988467,
0.215977633988467, 0.539944084971167, 0.215977633988467, 0.215977633988467,
0.0107988816994233, 0.215977633988467, 0.215977633988467, 0.0107988816994233,
0.0107988816994233, 0.539944084971167, 0.0107988816994233, 0.215977633988467,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.0107988816994233, 0.539944084971167, 0.215977633988467, 1.51184343791927,
0.539944084971167, 0.0107988816994233, 0.539944084971167, 0,
0.0107988816994233, 1.51184343791927, 0.0107988816994233, 0.0107988816994233,
1.51184343791927, 0.0107988816994233, 0.215977633988467, 0.971899352948101,
0.539944084971167, 0.0107988816994233, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.0107988816994233, 0.215977633988467, 1.51184343791927, 1.51184343791927,
0.0107988816994233, 0.539944084971167, 0, 0.539944084971167,
0.0107988816994233, 0.107988816994233, 1.51184343791927, 1.51184343791927,
0.0496748558173474, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.215977633988467, 0.215977633988467, 0.215977633988467, 0.539944084971167,
0.107988816994233, 1.51184343791927, 0.539944084971167, 0.971899352948101,
0.107988816994233, 0.0107988816994233, 0, 1.51184343791927, 0.971899352948101,
0.971899352948101, 0.539944084971167, 0.0107988816994233, 0.0496748558173474,
0.0107988816994233, 0.0496748558173474, 1.51184343791927, 0.215977633988467,
0, 0.971899352948101, 0, 0.971899352948101, 0, 0.0496748558173474,
0.539944084971167, 0.0107988816994233, 0, 0.215977633988467,
0.539944084971167, 0.107988816994233, 0.215977633988467, 0.539944084971167,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.971899352948101,
1.51184343791927, 0.107988816994233, 0, 0.971899352948101, 1.51184343791927,
0.215977633988467, 1.51184343791927, 0.0496748558173474, 0, 0.215977633988467,
0, 0.539944084971167, 0.539944084971167, 0, 0.215977633988467,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.0496748558173474,
1.51184343791927, 0.0107988816994233, 0, 0.0107988816994233,
0.215977633988467, 0.0107988816994233, 1.51184343791927, 0.0496748558173474,
0, 0.0107988816994233, 0.0107988816994233, 0.971899352948101,
0.215977633988467, 0.0107988816994233, 0.0496748558173474, 0.0496748558173474,
0.0107988816994233, 0.0107988816994233, 0.0496748558173474, 0.215977633988467,
0.107988816994233, 0.971899352948101, 0.971899352948101, 1.51184343791927,
0.0496748558173474, 0.0107988816994233, 0.215977633988467, 0.539944084971167,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.0107988816994233,
0.215977633988467, 0.539944084971167, 0.539944084971167, 1.51184343791927,
0.539944084971167, 0.539944084971167, 0, 0, 0.215977633988467,
1.51184343791927, 0, 0.0107988816994233, 0, 0.215977633988467,
1.51184343791927, 0.539944084971167, 0, 0.971899352948101, 0,
0.539944084971167, 0.215977633988467, 0.107988816994233, 0.0107988816994233,
0.0107988816994233, 0.971899352948101, 0.539944084971167, 0.0496748558173474,
0.539944084971167, 0.971899352948101, 0.971899352948101, 0.215977633988467,
0.539944084971167, 0.539944084971167, 1.51184343791927, 0, 0.215977633988467,
0.0107988816994233, 1.51184343791927, 1.51184343791927, 1.51184343791927,
0.539944084971167, 0.215977633988467, 0.215977633988467, 0.0107988816994233,
0.107988816994233, 0.539944084971167, 0.539944084971167, 0.215977633988467,
1.51184343791927), name = "Q28_2", label = "Q28. Exercise frequency per week")
test.obj <- kruskal.test(Q9_F_2 ~ Q4_A, data = data)
test_that("Kruskal-Wallis Rank Sum Test", {
expect_error(SignificanceTest(test.obj,
"Kruskal-Wallis Rank Sum Test",
vars = list(data$Q9_F_2, data$Q4_A),
filter = filter,
weight = weight), NA)})
shapiro <- structure(list(statistic = structure(0.898542697405643, .Names = "W"),
p.value = 5.66119463036052e-14, method = "Shapiro-Wilk normality test",
data.name = "resid(linear)"), .Names = c("statistic", "p.value",
"method", "data.name"), class = "htest")
test_that("Test of Residual Normality (Shapiro-Wilk)", {
expect_error(SignificanceTest(shapiro,
"Test of Residual Normality (Shapiro-Wilk)",
vars = NULL,
filter = NULL,
weight = NULL,
reg.name = "linear",
reg.sample.description = "n = 327 cases used in estimation;"), NA)})
Displayr/flipFormat documentation built on Feb. 26, 2024, 12:37 a.m.
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context("significancetest")
data <- structure(list(Q9_F_2 = structure(c(10, 4.5, 0, 0, 0, 10, 0,
1, 0, 0, 0, 0, 0.028, 0.5, 0, 0, 0, 0, 0.028, 0, 1, 10, 0, 0,
0, 0.25, 0.25, 0.25, 0, 0.028, 0.028, 2.5, 0, 0, 0, 0, 10, 2.5,
10, 0, 0.25, 0, 10, 0, 0, 0.25, 0.25, 0, 0, 0.077, 0, 10, 2.5,
0, 0, 0, 2.5, 0.5, 0.028, 0, 0, 0, 1, 0.028, 2.5, 0.5, 1, 0,
10, 0, 1, 0.5, 0.077, 0.077, 0, 0.25, 0.5, 0, 0.5, 0.028, 0.5,
0.25, 0, 0, 0, 0.5, 0, 2.5, 0.077, 0.25, 0.5, 0.028, 1, 0.077,
0.028, 0.5, 0.028, 0.077, 0.077, 0, 0.077, 0.028, 0, 0.077, 0.25,
0.028, 0.5, 0.25, 4.5, 0.25, 0, 0.25, 1, 0, 0, 0, 0.028, 0.5,
0, 0, 0, 0.028, 0, 0, 0.25, 0, 0.25, 0, 0.077, 0.077, 0.25, 10,
10, 0.028, 0.25, 0.028, 0.5, 0, 4.5, 0, 0.077, 0, 2.5, 1, 0.25,
0.077, 1, 0, 0, 0, 2.5, 1, 0, 2.5, 0.077, 0.028, 0, 0.25, 0,
1, 4.5, 0.028, 2.5, 0, 0.077, 0, 0, 0, 0.25, 0, 1, 0.5, 0, 2.5,
0.5, 10, 0.5, 1, 0.028, 0, 0.077, 0, 0.25, 0, 0.077, 0, 1, 0.25,
0, 0.028, 0, 0, 0, 0.028, 0, 0.25, 0, 0.028, 2.5, 0.25, 0.028,
10, 0, 0, 0.077, 0, 0.25, 0.077, 0, 10, 0.077, 0, 1, 0, 0.077,
0.5, 0.028, 0, 0.077, 0.25, 0, 0.077, 0.028, 0, 0.5, 0.25, 2.5,
2.5, 0, 0, 1, 4.5, 0, 4.5, 0, 0, 0.25, 0, 0, 0.028, 0, 0.25,
0, 0, 0.077, 0, 0.077, 2.5, 0, 0, 0.077, 0, 4.5, 2.5, 0.077,
10, 0, 0.028, 0, 0.028, 10, 4.5, 0.077, 0.028, 0.077, 0.25, 0.028,
0, 4.5, 0.25, 0, 0, 0.028, 0, 0.5, 0, 0, 0, 0, 1, 1, 1, 2.5,
0.028, 10, 0.028, 0, 0.25, 0, 0, 1, 0.028, 0.028, 0.25, 4.5,
0.5, 0.25, 0, 0.028, 0, 0.028, 0.077, 0, 10, 0.5, 0.077, 0, 10,
0.028, 0.077, 0.5, 0.077, 0, 0.077, 2.5, 4.5, 0, 0, 0, 0.077,
0, 0, 0, 0, 0.028, 4.5, 0.077), questiontype = "NumberMulti", name = "Q9_F_2", label = "Pepsi Max", question = "Q9. Cola per week"),
Q4_A = structure(c(9L, 8L, 7L, 8L, 9L, 9L, 5L, 7L, 5L, 7L,
8L, 6L, 5L, 6L, 9L, 8L, 5L, 7L, 7L, 4L, 6L, 9L, 9L, 5L, 9L,
9L, 7L, 9L, 6L, 5L, 5L, 7L, 7L, 9L, 5L, 9L, 9L, 7L, 9L, 9L,
9L, 6L, 9L, 8L, 7L, 7L, 7L, 5L, 9L, 9L, 9L, 9L, 9L, 7L, 6L,
6L, 9L, 7L, 6L, 8L, 9L, 4L, 8L, 7L, 8L, 8L, 6L, 9L, 9L, 6L,
7L, 9L, 4L, 7L, 9L, 8L, 9L, 5L, 8L, 8L, 9L, 4L, 4L, 6L, 6L,
9L, 6L, 7L, 9L, 4L, 6L, 8L, 7L, 6L, 7L, 5L, 6L, 7L, 9L, 9L,
9L, 9L, 8L, 4L, 5L, 4L, 9L, 5L, 8L, 4L, 5L, 9L, 8L, 4L, 5L,
8L, 9L, 6L, 7L, 7L, 6L, 9L, 7L, 5L, 4L, 4L, 9L, 5L, 9L, 8L,
5L, 9L, 9L, 9L, 8L, 9L, 7L, 7L, 7L, 9L, 5L, 5L, 8L, 5L, 7L,
8L, 7L, 9L, 7L, 9L, 7L, 6L, 5L, 7L, 6L, 8L, 8L, 9L, 9L, 6L,
9L, 9L, 7L, 9L, 7L, 7L, 7L, 5L, 9L, 6L, 8L, 5L, 6L, 9L, 5L,
9L, 4L, 9L, 9L, 7L, 9L, 7L, 7L, 6L, 5L, 7L, 8L, 7L, 4L, 6L,
4L, 4L, 9L, 6L, 7L, 6L, 6L, 5L, 7L, 6L, 9L, 9L, 4L, 7L, 4L,
7L, 9L, 7L, 4L, 9L, 6L, 6L, 9L, 6L, 4L, 5L, 7L, 7L, 8L, 7L,
5L, 9L, 6L, 8L, 8L, 7L, 8L, 6L, 6L, 4L, 7L, 7L, 5L, 8L, 9L,
7L, 8L, 7L, 7L, 9L, 9L, 6L, 6L, 7L, 6L, 9L, 6L, 7L, 9L, 6L,
8L, 9L, 8L, 7L, 6L, 9L, 6L, 6L, 9L, 9L, 9L, 9L, 7L, 7L, 9L,
7L, 9L, 6L, 8L, 9L, 9L, 9L, 7L, 6L, 6L, 7L, 7L, 9L, 4L, 7L,
6L, 5L, 8L, 4L, 9L, 6L, 9L, 7L, 7L, 4L, 6L, 6L, 9L, 8L, 9L,
7L, 4L, 5L, 7L, 7L, 9L, 8L, 9L, 9L, 6L, 7L, 9L, 9L, 9L, 8L,
9L, 6L, 6L, 5L, 9L, 8L, 4L, 6L, 4L, 9L, 8L, 7L, 7L, 9L, 7L,
6L, 4L), class = "factor", .Label = c("Never", "Once or twice a year",
"Once every 3 months", "Once a month", "Once every 2 weeks",
"Once a week", "2 to 3 days a week", "4 to 5 days a week",
"Every or nearly every day"), questiontype = "PickOneMulti", name = "Q4_A", label = "Colas (e.g., Coca Cola, Pepsi Max)?", question = "Q4. Frequency of drinking"),
subset = structure(c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE,
TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE,
TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE,
TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE,
FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, FALSE, FALSE), name = "autobinTEVBT_1", label = "Male")),
.Names = c("Q9_F_2", "Q4_A", "subset"), row.names = c(NA, -327L), class = "data.frame")
filter <- structure(c(FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE,
FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE,
TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE,
FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE,
TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE,
FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE,
TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE,
FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE,
TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE,
TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE,
FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE,
TRUE, FALSE, FALSE), name = "autobinTEVBT_1", label = "Male")
weight <- structure(c(0.971899352948101, 0.539944084971167, 0.215977633988467,
0.539944084971167, 0.0107988816994233, 1.51184343791927, 0.539944084971167,
1.51184343791927, 0.971899352948101, 1.51184343791927, 0.539944084971167,
0.215977633988467, 0.539944084971167, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.539944084971167, 0.539944084971167, 0, 0.971899352948101,
0.539944084971167, 0.0107988816994233, 0.215977633988467, 0.0496748558173474,
0.107988816994233, 0.539944084971167, 0.0107988816994233, 0.539944084971167,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.215977633988467,
0.539944084971167, 1.51184343791927, 0, 0.539944084971167, 0.107988816994233,
0.539944084971167, 0.539944084971167, 0, 0.215977633988467, 0.539944084971167,
1.51184343791927, 0.215977633988467, 0.107988816994233, 0.0496748558173474,
0.0496748558173474, 0.539944084971167, 0.0496748558173474, 1.51184343791927,
0, 0.539944084971167, 0.539944084971167, 0.107988816994233, 0.0496748558173474,
0, 0.539944084971167, 0.539944084971167, 0.539944084971167, 0.971899352948101,
0.539944084971167, 1.51184343791927, 0.215977633988467, 0.539944084971167,
0.971899352948101, 0.215977633988467, 1.51184343791927, 0.971899352948101,
1.51184343791927, 0.539944084971167, 0.539944084971167, 0.539944084971167,
0.0496748558173474, 0.539944084971167, 0.0107988816994233, 1.51184343791927,
0.107988816994233, 0.971899352948101, 0.539944084971167, 1.51184343791927,
0.539944084971167, 0.0496748558173474, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.215977633988467, 0.0496748558173474, 0.215977633988467,
0.971899352948101, 0.0107988816994233, 0.971899352948101, 0.0107988816994233,
0.539944084971167, 0.539944084971167, 0.539944084971167, 0.971899352948101,
0.215977633988467, 1.51184343791927, 0.0107988816994233, 0.0107988816994233,
0.971899352948101, 0.215977633988467, 0.539944084971167, 0.215977633988467,
0.0107988816994233, 0.0107988816994233, 0.0107988816994233, 0.539944084971167,
0.0107988816994233, 1.51184343791927, 1.51184343791927, 0.539944084971167,
0.0107988816994233, 0.0496748558173474, 0, 0.539944084971167,
0.0107988816994233, 0.215977633988467, 1.51184343791927, 0.539944084971167,
0.0107988816994233, 0.0107988816994233, 1.51184343791927, 0,
0.539944084971167, 0.0107988816994233, 0.0107988816994233, 0,
0.215977633988467, 1.51184343791927, 0.215977633988467, 0.215977633988467,
0.215977633988467, 0.539944084971167, 0.215977633988467, 0.215977633988467,
0.0107988816994233, 0.215977633988467, 0.215977633988467, 0.0107988816994233,
0.0107988816994233, 0.539944084971167, 0.0107988816994233, 0.215977633988467,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.0107988816994233, 0.539944084971167, 0.215977633988467, 1.51184343791927,
0.539944084971167, 0.0107988816994233, 0.539944084971167, 0,
0.0107988816994233, 1.51184343791927, 0.0107988816994233, 0.0107988816994233,
1.51184343791927, 0.0107988816994233, 0.215977633988467, 0.971899352948101,
0.539944084971167, 0.0107988816994233, 0.539944084971167, 1.51184343791927,
0.215977633988467, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.0107988816994233, 0.215977633988467, 1.51184343791927, 1.51184343791927,
0.0107988816994233, 0.539944084971167, 0, 0.539944084971167,
0.0107988816994233, 0.107988816994233, 1.51184343791927, 1.51184343791927,
0.0496748558173474, 0.215977633988467, 0.539944084971167, 0.539944084971167,
0.215977633988467, 0.215977633988467, 0.215977633988467, 0.539944084971167,
0.107988816994233, 1.51184343791927, 0.539944084971167, 0.971899352948101,
0.107988816994233, 0.0107988816994233, 0, 1.51184343791927, 0.971899352948101,
0.971899352948101, 0.539944084971167, 0.0107988816994233, 0.0496748558173474,
0.0107988816994233, 0.0496748558173474, 1.51184343791927, 0.215977633988467,
0, 0.971899352948101, 0, 0.971899352948101, 0, 0.0496748558173474,
0.539944084971167, 0.0107988816994233, 0, 0.215977633988467,
0.539944084971167, 0.107988816994233, 0.215977633988467, 0.539944084971167,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.971899352948101,
1.51184343791927, 0.107988816994233, 0, 0.971899352948101, 1.51184343791927,
0.215977633988467, 1.51184343791927, 0.0496748558173474, 0, 0.215977633988467,
0, 0.539944084971167, 0.539944084971167, 0, 0.215977633988467,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.0496748558173474,
1.51184343791927, 0.0107988816994233, 0, 0.0107988816994233,
0.215977633988467, 0.0107988816994233, 1.51184343791927, 0.0496748558173474,
0, 0.0107988816994233, 0.0107988816994233, 0.971899352948101,
0.215977633988467, 0.0107988816994233, 0.0496748558173474, 0.0496748558173474,
0.0107988816994233, 0.0107988816994233, 0.0496748558173474, 0.215977633988467,
0.107988816994233, 0.971899352948101, 0.971899352948101, 1.51184343791927,
0.0496748558173474, 0.0107988816994233, 0.215977633988467, 0.539944084971167,
0.539944084971167, 0.539944084971167, 0.0107988816994233, 0.0107988816994233,
0.215977633988467, 0.539944084971167, 0.539944084971167, 1.51184343791927,
0.539944084971167, 0.539944084971167, 0, 0, 0.215977633988467,
1.51184343791927, 0, 0.0107988816994233, 0, 0.215977633988467,
1.51184343791927, 0.539944084971167, 0, 0.971899352948101, 0,
0.539944084971167, 0.215977633988467, 0.107988816994233, 0.0107988816994233,
0.0107988816994233, 0.971899352948101, 0.539944084971167, 0.0496748558173474,
0.539944084971167, 0.971899352948101, 0.971899352948101, 0.215977633988467,
0.539944084971167, 0.539944084971167, 1.51184343791927, 0, 0.215977633988467,
0.0107988816994233, 1.51184343791927, 1.51184343791927, 1.51184343791927,
0.539944084971167, 0.215977633988467, 0.215977633988467, 0.0107988816994233,
0.107988816994233, 0.539944084971167, 0.539944084971167, 0.215977633988467,
1.51184343791927), name = "Q28_2", label = "Q28. Exercise frequency per week")
test.obj <- kruskal.test(Q9_F_2 ~ Q4_A, data = data)
test_that("Kruskal-Wallis Rank Sum Test", {
expect_error(SignificanceTest(test.obj,
"Kruskal-Wallis Rank Sum Test",
vars = list(data$Q9_F_2, data$Q4_A),
filter = filter,
weight = weight), NA)})
shapiro <- structure(list(statistic = structure(0.898542697405643, .Names = "W"),
p.value = 5.66119463036052e-14, method = "Shapiro-Wilk normality test",
data.name = "resid(linear)"), .Names = c("statistic", "p.value",
"method", "data.name"), class = "htest")
test_that("Test of Residual Normality (Shapiro-Wilk)", {
expect_error(SignificanceTest(shapiro,
"Test of Residual Normality (Shapiro-Wilk)",
vars = NULL,
filter = NULL,
weight = NULL,
reg.name = "linear",
reg.sample.description = "n = 327 cases used in estimation;"), NA)})
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