Nothing
tests/testthat/test-aov_car-bugs.R
In afex: Analysis of Factorial Experiments
context("ANOVAs: known bugs")
test_that("aov does not throw 'Error() model is singular' warning for missing values", {
data(md_12.1)
md_12.1b <- md_12.1[-1,]
expect_warning(aov_ez("id", "rt", md_12.1b, within = c("angle", "noise")), "Missing values", all = TRUE)
})
test_that("regex works correctly in aov_car when also having within factors outside the Error term", {
data(obk.long)
expect_is(aov_car(value ~ treatment * gender*phase*hour + Error(id/phase*hour), data = obk.long), "afex_aov")
})
test_that("another label bug (May 2014)", {
data("sk2011.1")
levels(sk2011.1$inference) <- c("A+:D-", "A+:D+", "A-:D+", "A- : D-")
expect_is(aov_ez("id", "response", sk2011.1, between = "instruction", within = c("type", "inference"), return = "Anova", fun_aggregate = mean), "Anova.mlm")
})
test_that("orig label bug", {
data(obk.long)
obk2 <- obk.long
levels(obk2$phase) <- c("fup test", "post-hans", "pre tenetious")
expect_is(aov_car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk2, factorize=FALSE, return = "Anova"), "Anova.mlm")
})
test_that("ANCOVA check bug (reported by Gang Chen), January 2013", {
dat <- read.table(header=TRUE, text = "ID Group Gender ROI Value Propdd00 GAS0 MAD0 CPD0
2016 AE M 05_06 1.581 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 07_08 1.521 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 09_10 1.623 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 03_04 1.569 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 11_12 1.719 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 01_02 1.509 0.543 1.908 0.439999999999998 -0.5335
2031 HC F 09_10 1.739 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 01_02 1.763 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 03_04 1.8 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 11_12 1.793 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 05_06 1.765 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 07_08 1.654 -0.014 0.0480000000000018 -2.347 1.9665
2063 AE F 11_12 1.742 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 01_02 1.634 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 03_04 1.638 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 07_08 1.604 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 09_10 1.654 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 05_06 1.625 -0.027 2.348 -8.88 -0.0335000000000001
2042 HC M 05_06 1.649 -0.014 2.058 -3.497 -0.8635
2042 HC M 07_08 1.565 -0.014 2.058 -3.497 -0.8635
2042 HC M 09_10 1.765 -0.014 2.058 -3.497 -0.8635
2042 HC M 03_04 1.677 -0.014 2.058 -3.497 -0.8635
2042 HC M 11_12 1.706 -0.014 2.058 -3.497 -0.8635
2042 HC M 01_02 1.618 -0.014 2.058 -3.497 -0.8635
2071 AE M 05_06 1.712 -0.317 -0.802 6.74 1.9665
2071 AE M 07_08 1.64 -0.317 -0.802 6.74 1.9665
2071 AE M 09_10 1.791 -0.317 -0.802 6.74 1.9665
2071 AE M 03_04 1.725 -0.317 -0.802 6.74 1.9665
2071 AE M 11_12 1.782 -0.317 -0.802 6.74 1.9665
2071 AE M 01_02 1.712 -0.317 -0.802 6.74 1.9665
2134 HC M 05_06 1.672 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 07_08 1.657 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 09_10 1.791 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 03_04 1.633 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 11_12 1.859 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 01_02 1.653 -0.014 0.347999999999999 -5.807 -2.5335
2009 AE F 09_10 1.672 -0.027 1.058 3.36 11.1365
2009 AE F 03_04 1.723 -0.027 1.058 3.36 11.1365
2009 AE F 05_06 1.676 -0.027 1.058 3.36 11.1365
2009 AE F 07_08 1.622 -0.027 1.058 3.36 11.1365
2009 AE F 01_02 1.633 -0.027 1.058 3.36 11.1365
2009 AE F 11_12 1.853 -0.027 1.058 3.36 11.1365
2132 HC M 05_06 1.758 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 07_08 1.623 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 09_10 1.843 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 03_04 1.773 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 11_12 1.806 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 01_02 1.708 -0.014 -1.082 -2.857 -0.0335000000000001
2127 HC F 11_12 1.824 -0.014 0.628 6.223 -0.5335
2127 HC F 09_10 1.871 -0.014 0.628 6.223 -0.5335
2127 HC F 01_02 1.687 -0.014 0.628 6.223 -0.5335
2127 HC F 03_04 1.699 -0.014 0.628 6.223 -0.5335
2127 HC F 07_08 1.646 -0.014 0.628 6.223 -0.5335
2127 HC F 05_06 1.738 -0.014 0.628 6.223 -0.5335
2081 AE M 09_10 1.807 -0.027 -2.082 2.43 -1.5335
2081 AE M 11_12 1.917 -0.027 -2.082 2.43 -1.5335
2081 AE M 03_04 1.767 -0.027 -2.082 2.43 -1.5335
2081 AE M 05_06 1.776 -0.027 -2.082 2.43 -1.5335
2081 AE M 07_08 1.733 -0.027 -2.082 2.43 -1.5335
2081 AE M 01_02 1.775 -0.027 -2.082 2.43 -1.5335
2086 AE F 11_12 1.768 -0.457 -1.082 -1.76 6.9665
2086 AE F 09_10 1.769 -0.457 -1.082 -1.76 6.9665
2086 AE F 01_02 1.752 -0.457 -1.082 -1.76 6.9665
2086 AE F 03_04 1.769 -0.457 -1.082 -1.76 6.9665
2086 AE F 05_06 1.751 -0.457 -1.082 -1.76 6.9665
2086 AE F 07_08 1.728 -0.457 -1.082 -1.76 6.9665
2033 HC M 05_06 1.804 0.126 2.768 7.083 -2.2035
2033 HC M 07_08 1.784 0.126 2.768 7.083 -2.2035
2033 HC M 09_10 1.948 0.126 2.768 7.083 -2.2035
2033 HC M 03_04 1.821 0.126 2.768 7.083 -2.2035
2033 HC M 11_12 2.143 0.126 2.768 7.083 -2.2035
2033 HC M 01_02 1.824 0.126 2.768 7.083 -2.2035
2007 AE M 07_08 1.554 -0.027 0.488 -6.05 -0.5335
2007 AE M 05_06 1.643 -0.027 0.488 -6.05 -0.5335
2007 AE M 09_10 1.674 -0.027 0.488 -6.05 -0.5335
2007 AE M 03_04 1.593 -0.027 0.488 -6.05 -0.5335
2007 AE M 11_12 1.726 -0.027 0.488 -6.05 -0.5335
2007 AE M 01_02 1.517 -0.027 0.488 -6.05 -0.5335
6062 HC M 05_06 1.911 -0.014 -3.802 4.093 -3.5335
6062 HC M 07_08 1.887 -0.014 -3.802 4.093 -3.5335
6062 HC M 09_10 1.951 -0.014 -3.802 4.093 -3.5335
6062 HC M 03_04 1.798 -0.014 -3.802 4.093 -3.5335
6062 HC M 11_12 1.953 -0.014 -3.802 4.093 -3.5335
6062 HC M 01_02 1.772 -0.014 -3.802 4.093 -3.5335
2072 AE M 05_06 1.667 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 07_08 1.587 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 09_10 1.739 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 03_04 1.638 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 11_12 1.784 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 01_02 1.662 0.253 1.908 0.289999999999999 -1.0335
2008 HC F 05_06 1.623 -0.014 -1.372 -2.317 2.1365
2008 HC F 07_08 1.6 -0.014 -1.372 -2.317 2.1365
2008 HC F 09_10 1.688 -0.014 -1.372 -2.317 2.1365
2008 HC F 03_04 1.624 -0.014 -1.372 -2.317 2.1365
2008 HC F 11_12 1.772 -0.014 -1.372 -2.317 2.1365
2008 HC F 01_02 1.656 -0.014 -1.372 -2.317 2.1365
2070 AE F 05_06 1.657 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 07_08 1.579 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 09_10 1.75 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 03_04 1.808 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 11_12 1.777 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 01_02 1.702 0.113 -1.372 -0.140000000000001 -5.5335
2064 AE F 11_12 1.781 -0.027 -5.512 3.57 -3.5335
2064 AE F 09_10 1.724 -0.027 -5.512 3.57 -3.5335
2064 AE F 01_02 1.631 -0.027 -5.512 3.57 -3.5335
2064 AE F 03_04 1.607 -0.027 -5.512 3.57 -3.5335
2064 AE F 05_06 1.577 -0.027 -5.512 3.57 -3.5335
2064 AE F 07_08 1.546 -0.027 -5.512 3.57 -3.5335
2039 HC M 09_10 1.879 -0.014 2.628 -1.867 -5.5335
2039 HC M 11_12 1.918 -0.014 2.628 -1.867 -5.5335
2039 HC M 03_04 1.794 -0.014 2.628 -1.867 -5.5335
2039 HC M 05_06 1.787 -0.014 2.628 -1.867 -5.5335
2039 HC M 07_08 1.687 -0.014 2.628 -1.867 -5.5335
2039 HC M 01_02 1.774 -0.014 2.628 -1.867 -5.5335
2117 HC F 09_10 1.712 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 11_12 1.75 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 03_04 1.717 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 07_08 1.587 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 05_06 1.667 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 01_02 1.663 -0.014 0.917999999999999 1.293 3.7965
")
dat$ID <- as.factor(dat$ID)
fm <- aov_car(Value ~ Propdd00 + Group + Gender + GAS0 + MAD0 + CPD0 + Error(ID/ROI), data=dat, factorize=FALSE, return = "Anova")
fm0 <- aov_car(Value ~ MAD0 + CPD0 + Error(ID/ROI), data=dat, factorize=FALSE, return='afex_aov')
expect_is(fm, "Anova.mlm")
expect_is(fm0, "afex_aov")
})
test_that("ANOVA: ids in multiple between.subjects conditions", {
species<- c("a","b","c","c","b","c","b","b","a","b","c","c","a","a","b","b","a","a","b","c")
habitat<- c("x","x","x","y","y","y","x","x","y","z","y","y","z","z","x","x","y","y","z","z")
mvt.rate<-c(6,5,7,8,9,4,3,5,6,9,3,6,6,7,8,9,5,6,7,8)
ind<-as.factor(c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4))
data1<-data.frame(species, habitat, mvt.rate, ind)
# should give an error
expect_error(aov_ez("ind", "mvt.rate", data1, within = "habitat", between = "species"), "Following ids are in more than one between subjects condition:")
})
test_that("empty factors are not causing aov.cat to choke", {
data(sleepstudy) #Example data in lme4
sleepstudy$Days<-factor(sleepstudy$Days)
#Works with all factors
expect_is(aov_ez("Subject","Reaction",sleepstudy, within="Days", return = "Anova"), "Anova.mlm")
#If you remove a factor it fails...
expect_is(aov_ez("Subject","Reaction",sleepstudy[sleepstudy$Days!=9,], within="Days", return = "Anova"), "Anova.mlm")
})
test_that("factors have more than one level", {
data(obk.long)
expect_error(aov_car(value ~ treatment+ Error(id/phase), data = obk.long[ obk.long$treatment == "control",]), "one level only.")
expect_error(aov_car(value ~ treatment+ Error(id/phase), data = obk.long[ obk.long$phase == "pre",]), "one level only.")
})
test_that("variable names longer", {
data(obk.long)
obk.long$gender2 <- obk.long$treatment
orig <- aov_car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender")
v1 <- aov_car(value ~ gender2 * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender")
v2 <- aov_car(value ~ gender2 * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender2")
expect_equivalent(orig$anova_table, v1$anova_table)
expect_identical(nice(orig)[,-1], nice(v1)[,-1])
expect_identical(nice(orig)[,c("df", "MSE", "F", "p.value")], nice(v2)[,c("df", "MSE", "F", "p.value")])
expect_equivalent(orig$anova_table[,c("num Df", "den Df", "MSE", "F", "Pr(>F)")], v2$anova_table[c("num Df", "den Df", "MSE", "F", "Pr(>F)")])
})
test_that("works with dplyr data.frames (see https://github.com/singmann/afex/issues/6):", {
if (getRversion() >= "3.1.2") {
skip_if_not_installed("dplyr")
data(md_12.1)
md2 <- dplyr::as_tibble(md_12.1)
expect_is(aov_ez("id", "rt", md2, within = c("angle", "noise"),
anova_table=list(correction = "none", es = "none")),
"afex_aov")
}
})
test_that("return='nice' works", {
data(md_12.1)
expect_is(aov_ez("id", "rt", md_12.1, within = c("angle", "noise"), return = "nice"), "data.frame")
})
test_that("aov_car works with column names containing spaces: https://github.com/singmann/afex/issues/22", {
data <- list("dependent" = rnorm(100), "RM Factor 1" = factor(rep(c("Level 1", "Level 2"), 50)), "subject" = factor(rep(1:50, each = 2)))
attr(data, 'row.names') <- seq_len(length(data[[1]]))
attr(data, 'class') <- 'data.frame'
expect_is(aov_car(dependent ~ `RM Factor 1` + Error(subject/(`RM Factor 1`)), data), "afex_aov")
expect_is(aov_4(dependent ~ `RM Factor 1` + (`RM Factor 1`|subject), data), "afex_aov")
expect_is(aov_ez("subject", "dependent", data, within = "RM Factor 1"), "afex_aov")
})
test_that("aov_car works with column names containing spaces for between factors", {
data <- list("dependent" = rnorm(100), "RM Factor 1" = factor(rep(c("Level 1", "Level 2"), 50)), "subject" = factor(rep(1:100)))
attr(data, 'row.names') <- seq_len(length(data[[1]]))
attr(data, 'class') <- 'data.frame'
expect_is(aov_car(dependent ~ `RM Factor 1` + Error(subject), data), "afex_aov")
expect_is(aov_4(dependent ~ `RM Factor 1` + (1|subject), data), "afex_aov")
expect_is(aov_ez("subject", "dependent", data, between = "RM Factor 1"), "afex_aov")
})
test_that("aov_ez works with multiple covariates", {
skip_if_not_installed("psychTools")
require(psychTools)
data(msq)
msq2 <- msq[!is.na(msq$Extraversion),]
msq2 <- droplevels(msq2[msq2$ID != "18",])
msq2$TOD <- msq2$TOD-mean(msq2$TOD)
msq2$MSQ_Time <- msq2$MSQ_Time-mean(msq2$MSQ_Time)
msq2$condition <- msq2$condition-mean(msq2$condition) # that is somewhat stupid
mulcov <- aov_ez(data=msq2, dv="Extraversion", id = "ID",
between = "condition",
covariate=c("TOD", "MSQ_Time"),
factorize=FALSE, fun_aggregate = mean)
expect_is(mulcov, "afex_aov")
})
test_that("aov_car works with p.val adjustment == NA for HF as well as GG", {
# see: https://github.com/singmann/afex/issues/36
skip_on_cran() ## takes rather long
load("anova_hf_error.rda")
#load("tests/testthat/anova_hf_error.rda")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"))),
"nice_table")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"),
anova_table = list(correction = "GG"))),
"nice_table")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"),
anova_table = list(correction = "HF"))),
"nice_table")
})
test_that("aov_car: character variables and factorize = FALSE", {
data(obk.long)
obk2 <- obk.long
obk2$treatment <- as.character(obk2$treatment)
a1 <- aov_car(value ~ treatment * gender + Error(id), data = obk.long,
fun_aggregate = mean)
a2 <- aov_car(value ~ treatment * gender + Error(id), data = obk2,
fun_aggregate = mean)
a3 <- aov_car(value ~ treatment * gender + Error(id), data = obk2,
fun_aggregate = mean, factorize = FALSE)
expect_equal(a1$anova_table, a2$anova_table)
expect_equal(a1$anova_table, a3$anova_table)
})
test_that("additive design works without interaction and corresponding data", {
dat <- read.csv("skf_issue.csv")
dat[["ID"]] <- factor(1:nrow(dat))
aov1 <- aov_car(formula = Y ~ A + B + C + D + E + F + G + Error(ID),
data = dat, type="3")
expect_is(aov1, "afex_aov")
datFac <- dat
for(i in 1:7)
datFac[[LETTERS[i]]] <- factor(ifelse(dat[[LETTERS[i]]] == -1, "a", "b"))
aov2 <- aov_car(formula = Y ~ A + B + C + D + E + F + G + Error(ID),
data = datFac, type="3")
expect_is(aov2, "afex_aov")
})
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context("ANOVAs: known bugs")
test_that("aov does not throw 'Error() model is singular' warning for missing values", {
data(md_12.1)
md_12.1b <- md_12.1[-1,]
expect_warning(aov_ez("id", "rt", md_12.1b, within = c("angle", "noise")), "Missing values", all = TRUE)
})
test_that("regex works correctly in aov_car when also having within factors outside the Error term", {
data(obk.long)
expect_is(aov_car(value ~ treatment * gender*phase*hour + Error(id/phase*hour), data = obk.long), "afex_aov")
})
test_that("another label bug (May 2014)", {
data("sk2011.1")
levels(sk2011.1$inference) <- c("A+:D-", "A+:D+", "A-:D+", "A- : D-")
expect_is(aov_ez("id", "response", sk2011.1, between = "instruction", within = c("type", "inference"), return = "Anova", fun_aggregate = mean), "Anova.mlm")
})
test_that("orig label bug", {
data(obk.long)
obk2 <- obk.long
levels(obk2$phase) <- c("fup test", "post-hans", "pre tenetious")
expect_is(aov_car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk2, factorize=FALSE, return = "Anova"), "Anova.mlm")
})
test_that("ANCOVA check bug (reported by Gang Chen), January 2013", {
dat <- read.table(header=TRUE, text = "ID Group Gender ROI Value Propdd00 GAS0 MAD0 CPD0
2016 AE M 05_06 1.581 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 07_08 1.521 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 09_10 1.623 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 03_04 1.569 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 11_12 1.719 0.543 1.908 0.439999999999998 -0.5335
2016 AE M 01_02 1.509 0.543 1.908 0.439999999999998 -0.5335
2031 HC F 09_10 1.739 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 01_02 1.763 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 03_04 1.8 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 11_12 1.793 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 05_06 1.765 -0.014 0.0480000000000018 -2.347 1.9665
2031 HC F 07_08 1.654 -0.014 0.0480000000000018 -2.347 1.9665
2063 AE F 11_12 1.742 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 01_02 1.634 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 03_04 1.638 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 07_08 1.604 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 09_10 1.654 -0.027 2.348 -8.88 -0.0335000000000001
2063 AE F 05_06 1.625 -0.027 2.348 -8.88 -0.0335000000000001
2042 HC M 05_06 1.649 -0.014 2.058 -3.497 -0.8635
2042 HC M 07_08 1.565 -0.014 2.058 -3.497 -0.8635
2042 HC M 09_10 1.765 -0.014 2.058 -3.497 -0.8635
2042 HC M 03_04 1.677 -0.014 2.058 -3.497 -0.8635
2042 HC M 11_12 1.706 -0.014 2.058 -3.497 -0.8635
2042 HC M 01_02 1.618 -0.014 2.058 -3.497 -0.8635
2071 AE M 05_06 1.712 -0.317 -0.802 6.74 1.9665
2071 AE M 07_08 1.64 -0.317 -0.802 6.74 1.9665
2071 AE M 09_10 1.791 -0.317 -0.802 6.74 1.9665
2071 AE M 03_04 1.725 -0.317 -0.802 6.74 1.9665
2071 AE M 11_12 1.782 -0.317 -0.802 6.74 1.9665
2071 AE M 01_02 1.712 -0.317 -0.802 6.74 1.9665
2134 HC M 05_06 1.672 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 07_08 1.657 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 09_10 1.791 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 03_04 1.633 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 11_12 1.859 -0.014 0.347999999999999 -5.807 -2.5335
2134 HC M 01_02 1.653 -0.014 0.347999999999999 -5.807 -2.5335
2009 AE F 09_10 1.672 -0.027 1.058 3.36 11.1365
2009 AE F 03_04 1.723 -0.027 1.058 3.36 11.1365
2009 AE F 05_06 1.676 -0.027 1.058 3.36 11.1365
2009 AE F 07_08 1.622 -0.027 1.058 3.36 11.1365
2009 AE F 01_02 1.633 -0.027 1.058 3.36 11.1365
2009 AE F 11_12 1.853 -0.027 1.058 3.36 11.1365
2132 HC M 05_06 1.758 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 07_08 1.623 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 09_10 1.843 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 03_04 1.773 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 11_12 1.806 -0.014 -1.082 -2.857 -0.0335000000000001
2132 HC M 01_02 1.708 -0.014 -1.082 -2.857 -0.0335000000000001
2127 HC F 11_12 1.824 -0.014 0.628 6.223 -0.5335
2127 HC F 09_10 1.871 -0.014 0.628 6.223 -0.5335
2127 HC F 01_02 1.687 -0.014 0.628 6.223 -0.5335
2127 HC F 03_04 1.699 -0.014 0.628 6.223 -0.5335
2127 HC F 07_08 1.646 -0.014 0.628 6.223 -0.5335
2127 HC F 05_06 1.738 -0.014 0.628 6.223 -0.5335
2081 AE M 09_10 1.807 -0.027 -2.082 2.43 -1.5335
2081 AE M 11_12 1.917 -0.027 -2.082 2.43 -1.5335
2081 AE M 03_04 1.767 -0.027 -2.082 2.43 -1.5335
2081 AE M 05_06 1.776 -0.027 -2.082 2.43 -1.5335
2081 AE M 07_08 1.733 -0.027 -2.082 2.43 -1.5335
2081 AE M 01_02 1.775 -0.027 -2.082 2.43 -1.5335
2086 AE F 11_12 1.768 -0.457 -1.082 -1.76 6.9665
2086 AE F 09_10 1.769 -0.457 -1.082 -1.76 6.9665
2086 AE F 01_02 1.752 -0.457 -1.082 -1.76 6.9665
2086 AE F 03_04 1.769 -0.457 -1.082 -1.76 6.9665
2086 AE F 05_06 1.751 -0.457 -1.082 -1.76 6.9665
2086 AE F 07_08 1.728 -0.457 -1.082 -1.76 6.9665
2033 HC M 05_06 1.804 0.126 2.768 7.083 -2.2035
2033 HC M 07_08 1.784 0.126 2.768 7.083 -2.2035
2033 HC M 09_10 1.948 0.126 2.768 7.083 -2.2035
2033 HC M 03_04 1.821 0.126 2.768 7.083 -2.2035
2033 HC M 11_12 2.143 0.126 2.768 7.083 -2.2035
2033 HC M 01_02 1.824 0.126 2.768 7.083 -2.2035
2007 AE M 07_08 1.554 -0.027 0.488 -6.05 -0.5335
2007 AE M 05_06 1.643 -0.027 0.488 -6.05 -0.5335
2007 AE M 09_10 1.674 -0.027 0.488 -6.05 -0.5335
2007 AE M 03_04 1.593 -0.027 0.488 -6.05 -0.5335
2007 AE M 11_12 1.726 -0.027 0.488 -6.05 -0.5335
2007 AE M 01_02 1.517 -0.027 0.488 -6.05 -0.5335
6062 HC M 05_06 1.911 -0.014 -3.802 4.093 -3.5335
6062 HC M 07_08 1.887 -0.014 -3.802 4.093 -3.5335
6062 HC M 09_10 1.951 -0.014 -3.802 4.093 -3.5335
6062 HC M 03_04 1.798 -0.014 -3.802 4.093 -3.5335
6062 HC M 11_12 1.953 -0.014 -3.802 4.093 -3.5335
6062 HC M 01_02 1.772 -0.014 -3.802 4.093 -3.5335
2072 AE M 05_06 1.667 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 07_08 1.587 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 09_10 1.739 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 03_04 1.638 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 11_12 1.784 0.253 1.908 0.289999999999999 -1.0335
2072 AE M 01_02 1.662 0.253 1.908 0.289999999999999 -1.0335
2008 HC F 05_06 1.623 -0.014 -1.372 -2.317 2.1365
2008 HC F 07_08 1.6 -0.014 -1.372 -2.317 2.1365
2008 HC F 09_10 1.688 -0.014 -1.372 -2.317 2.1365
2008 HC F 03_04 1.624 -0.014 -1.372 -2.317 2.1365
2008 HC F 11_12 1.772 -0.014 -1.372 -2.317 2.1365
2008 HC F 01_02 1.656 -0.014 -1.372 -2.317 2.1365
2070 AE F 05_06 1.657 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 07_08 1.579 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 09_10 1.75 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 03_04 1.808 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 11_12 1.777 0.113 -1.372 -0.140000000000001 -5.5335
2070 AE F 01_02 1.702 0.113 -1.372 -0.140000000000001 -5.5335
2064 AE F 11_12 1.781 -0.027 -5.512 3.57 -3.5335
2064 AE F 09_10 1.724 -0.027 -5.512 3.57 -3.5335
2064 AE F 01_02 1.631 -0.027 -5.512 3.57 -3.5335
2064 AE F 03_04 1.607 -0.027 -5.512 3.57 -3.5335
2064 AE F 05_06 1.577 -0.027 -5.512 3.57 -3.5335
2064 AE F 07_08 1.546 -0.027 -5.512 3.57 -3.5335
2039 HC M 09_10 1.879 -0.014 2.628 -1.867 -5.5335
2039 HC M 11_12 1.918 -0.014 2.628 -1.867 -5.5335
2039 HC M 03_04 1.794 -0.014 2.628 -1.867 -5.5335
2039 HC M 05_06 1.787 -0.014 2.628 -1.867 -5.5335
2039 HC M 07_08 1.687 -0.014 2.628 -1.867 -5.5335
2039 HC M 01_02 1.774 -0.014 2.628 -1.867 -5.5335
2117 HC F 09_10 1.712 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 11_12 1.75 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 03_04 1.717 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 07_08 1.587 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 05_06 1.667 -0.014 0.917999999999999 1.293 3.7965
2117 HC F 01_02 1.663 -0.014 0.917999999999999 1.293 3.7965
")
dat$ID <- as.factor(dat$ID)
fm <- aov_car(Value ~ Propdd00 + Group + Gender + GAS0 + MAD0 + CPD0 + Error(ID/ROI), data=dat, factorize=FALSE, return = "Anova")
fm0 <- aov_car(Value ~ MAD0 + CPD0 + Error(ID/ROI), data=dat, factorize=FALSE, return='afex_aov')
expect_is(fm, "Anova.mlm")
expect_is(fm0, "afex_aov")
})
test_that("ANOVA: ids in multiple between.subjects conditions", {
species<- c("a","b","c","c","b","c","b","b","a","b","c","c","a","a","b","b","a","a","b","c")
habitat<- c("x","x","x","y","y","y","x","x","y","z","y","y","z","z","x","x","y","y","z","z")
mvt.rate<-c(6,5,7,8,9,4,3,5,6,9,3,6,6,7,8,9,5,6,7,8)
ind<-as.factor(c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4))
data1<-data.frame(species, habitat, mvt.rate, ind)
# should give an error
expect_error(aov_ez("ind", "mvt.rate", data1, within = "habitat", between = "species"), "Following ids are in more than one between subjects condition:")
})
test_that("empty factors are not causing aov.cat to choke", {
data(sleepstudy) #Example data in lme4
sleepstudy$Days<-factor(sleepstudy$Days)
#Works with all factors
expect_is(aov_ez("Subject","Reaction",sleepstudy, within="Days", return = "Anova"), "Anova.mlm")
#If you remove a factor it fails...
expect_is(aov_ez("Subject","Reaction",sleepstudy[sleepstudy$Days!=9,], within="Days", return = "Anova"), "Anova.mlm")
})
test_that("factors have more than one level", {
data(obk.long)
expect_error(aov_car(value ~ treatment+ Error(id/phase), data = obk.long[ obk.long$treatment == "control",]), "one level only.")
expect_error(aov_car(value ~ treatment+ Error(id/phase), data = obk.long[ obk.long$phase == "pre",]), "one level only.")
})
test_that("variable names longer", {
data(obk.long)
obk.long$gender2 <- obk.long$treatment
orig <- aov_car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender")
v1 <- aov_car(value ~ gender2 * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender")
v2 <- aov_car(value ~ gender2 * gender + age + Error(id/phase*hour), data = obk.long, factorize=FALSE, observed = "gender2")
expect_equivalent(orig$anova_table, v1$anova_table)
expect_identical(nice(orig)[,-1], nice(v1)[,-1])
expect_identical(nice(orig)[,c("df", "MSE", "F", "p.value")], nice(v2)[,c("df", "MSE", "F", "p.value")])
expect_equivalent(orig$anova_table[,c("num Df", "den Df", "MSE", "F", "Pr(>F)")], v2$anova_table[c("num Df", "den Df", "MSE", "F", "Pr(>F)")])
})
test_that("works with dplyr data.frames (see https://github.com/singmann/afex/issues/6):", {
if (getRversion() >= "3.1.2") {
skip_if_not_installed("dplyr")
data(md_12.1)
md2 <- dplyr::as_tibble(md_12.1)
expect_is(aov_ez("id", "rt", md2, within = c("angle", "noise"),
anova_table=list(correction = "none", es = "none")),
"afex_aov")
}
})
test_that("return='nice' works", {
data(md_12.1)
expect_is(aov_ez("id", "rt", md_12.1, within = c("angle", "noise"), return = "nice"), "data.frame")
})
test_that("aov_car works with column names containing spaces: https://github.com/singmann/afex/issues/22", {
data <- list("dependent" = rnorm(100), "RM Factor 1" = factor(rep(c("Level 1", "Level 2"), 50)), "subject" = factor(rep(1:50, each = 2)))
attr(data, 'row.names') <- seq_len(length(data[[1]]))
attr(data, 'class') <- 'data.frame'
expect_is(aov_car(dependent ~ `RM Factor 1` + Error(subject/(`RM Factor 1`)), data), "afex_aov")
expect_is(aov_4(dependent ~ `RM Factor 1` + (`RM Factor 1`|subject), data), "afex_aov")
expect_is(aov_ez("subject", "dependent", data, within = "RM Factor 1"), "afex_aov")
})
test_that("aov_car works with column names containing spaces for between factors", {
data <- list("dependent" = rnorm(100), "RM Factor 1" = factor(rep(c("Level 1", "Level 2"), 50)), "subject" = factor(rep(1:100)))
attr(data, 'row.names') <- seq_len(length(data[[1]]))
attr(data, 'class') <- 'data.frame'
expect_is(aov_car(dependent ~ `RM Factor 1` + Error(subject), data), "afex_aov")
expect_is(aov_4(dependent ~ `RM Factor 1` + (1|subject), data), "afex_aov")
expect_is(aov_ez("subject", "dependent", data, between = "RM Factor 1"), "afex_aov")
})
test_that("aov_ez works with multiple covariates", {
skip_if_not_installed("psychTools")
require(psychTools)
data(msq)
msq2 <- msq[!is.na(msq$Extraversion),]
msq2 <- droplevels(msq2[msq2$ID != "18",])
msq2$TOD <- msq2$TOD-mean(msq2$TOD)
msq2$MSQ_Time <- msq2$MSQ_Time-mean(msq2$MSQ_Time)
msq2$condition <- msq2$condition-mean(msq2$condition) # that is somewhat stupid
mulcov <- aov_ez(data=msq2, dv="Extraversion", id = "ID",
between = "condition",
covariate=c("TOD", "MSQ_Time"),
factorize=FALSE, fun_aggregate = mean)
expect_is(mulcov, "afex_aov")
})
test_that("aov_car works with p.val adjustment == NA for HF as well as GG", {
# see: https://github.com/singmann/afex/issues/36
skip_on_cran() ## takes rather long
load("anova_hf_error.rda")
#load("tests/testthat/anova_hf_error.rda")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"))),
"nice_table")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"),
anova_table = list(correction = "GG"))),
"nice_table")
expect_is(nice(aov_ez("Snum", "RT", demo, within=c("DistF", "WidthF", "AngleF"),
anova_table = list(correction = "HF"))),
"nice_table")
})
test_that("aov_car: character variables and factorize = FALSE", {
data(obk.long)
obk2 <- obk.long
obk2$treatment <- as.character(obk2$treatment)
a1 <- aov_car(value ~ treatment * gender + Error(id), data = obk.long,
fun_aggregate = mean)
a2 <- aov_car(value ~ treatment * gender + Error(id), data = obk2,
fun_aggregate = mean)
a3 <- aov_car(value ~ treatment * gender + Error(id), data = obk2,
fun_aggregate = mean, factorize = FALSE)
expect_equal(a1$anova_table, a2$anova_table)
expect_equal(a1$anova_table, a3$anova_table)
})
test_that("additive design works without interaction and corresponding data", {
dat <- read.csv("skf_issue.csv")
dat[["ID"]] <- factor(1:nrow(dat))
aov1 <- aov_car(formula = Y ~ A + B + C + D + E + F + G + Error(ID),
data = dat, type="3")
expect_is(aov1, "afex_aov")
datFac <- dat
for(i in 1:7)
datFac[[LETTERS[i]]] <- factor(ifelse(dat[[LETTERS[i]]] == -1, "a", "b"))
aov2 <- aov_car(formula = Y ~ A + B + C + D + E + F + G + Error(ID),
data = datFac, type="3")
expect_is(aov2, "afex_aov")
})
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