Nothing
context("Graphing functions")
test_that("graph_SCD works for design = 'TR'", {
skip_if_not_installed("ggplot2")
Spriggs <- read.csv("../testdata/BC-SMD-Calculations.csv")
# Clean data
dat <- preprocess_SCD(design = "TR",
case = Participant,
phase = Phase,
session = Session,
outcome = PercentageCorrect,
round_session = FALSE,
treatment_name = "B",
data = Spriggs)
# Fit the model
fit_RML <- lme(fixed = PercentageCorrect ~ 1 + trt,
random = ~ 1 | Participant,
correlation = corAR1(0.01, ~ Session | Participant),
data = dat,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
# Calculate effect size with g_mlm()
p_const <- c(0,1)
r_const <- c(1,0,1)
ES_RML <- g_mlm(fit_RML, p_const = p_const, r_const = r_const, infotype = "expected")
# Graph
p <- graph_SCD(design = "TR",
case = Participant, phase = Phase,
session = Session, outcome = PercentageCorrect, model_fit = fit_RML,
data = dat)
expect_s3_class(p, "ggplot")
expect_invisible(print(p))
})
test_that("graph_SCD works for design = 'TR'", {
skip_if_not_installed("ggplot2")
data("Anglesea")
Ang_graph1 <- graph_SCD(case=case, phase=condition, session=session, outcome=outcome,
design="TR", treatment_name = NULL, model_fit=NULL, data=Anglesea)
expect_s3_class(Ang_graph1, "ggplot")
expect_invisible(print(Ang_graph1))
Ang_graph2 <- graph_SCD(case=case, phase=condition, session=session, outcome=outcome,
design="TR", treatment_name = "treatment", model_fit=NULL, data=Anglesea)
expect_s3_class(Ang_graph2, "ggplot")
expect_invisible(print(Ang_graph2))
Ang_graph3 <- graph_SCD(case=Anglesea$case, phase=Anglesea$condition, session=Anglesea$session,
outcome=Anglesea$outcome, design="TR", treatment_name = "treatment", model_fit=NULL)
expect_s3_class(Ang_graph3, "ggplot")
expect_invisible(print(Ang_graph3))
Ang_graph4 <- graph_SCD(case=Anglesea$case, phase=Anglesea$condition, session=Anglesea$session,
outcome=Anglesea$outcome, design="TR", model_fit=NULL)
expect_s3_class(Ang_graph4, "ggplot")
expect_invisible(print(Ang_graph4))
Ang_case <- Anglesea$case
Ang_condition <- Anglesea$condition
Ang_session <- Anglesea$session
Ang_outcome <- Anglesea$outcome
Ang_graph5 <- graph_SCD(case=Ang_case, phase=Ang_condition, session=Ang_session,
outcome=Ang_outcome, design="TR")
expect_s3_class(Ang_graph5, "ggplot")
expect_invisible(print(Ang_graph5))
expect_equivalent(Ang_graph1$data, Ang_graph2$data)
keys <- c("scales","theme","coordinates")
expect_equal(Ang_graph1[keys], Ang_graph3[keys])
expect_equal(Ang_graph1[keys], Ang_graph4[keys])
expect_equal(Ang_graph1[keys], Ang_graph5[keys])
})
test_that("graph_SCD works for design = 'MBP'", {
skip_if_not_installed("ggplot2")
data("Laski")
Laski_RML <- lme(fixed = outcome ~ 1 + treatment,
random = ~ 1 | case,
correlation = corAR1(0, ~ time | case),
data = Laski)
Laski_graph1 <- graph_SCD(case=case, phase=treatment, session=time, outcome=outcome,
design="MBP", treatment_name = "treatment", model_fit=Laski_RML, data=Laski)
expect_s3_class(Laski_graph1, "ggplot")
expect_invisible(print(Laski_graph1))
Laski_graph2 <- graph_SCD(case=case, phase=treatment, session=time, outcome=outcome,
design="MBP", treatment_name = "treatment", model_fit=Laski_RML)
expect_s3_class(Laski_graph2, "ggplot")
expect_invisible(print(Laski_graph2))
keys <- setdiff(names(Laski_graph1), c("data","plot_env", "labels","layers", "layout"))
expect_equal(Laski_graph1[keys], Laski_graph2[keys])
Laski_clean <- preprocess_SCD(design = "MBP", case=case, phase=treatment, session=time, outcome=outcome, data = Laski)
Laski_trend <- lme(fixed = outcome ~ 1 + time_trt,
random = ~ 1 | case,
correlation = corAR1(0, ~ time | case),
data = Laski_clean)
Laski_graph3 <- graph_SCD(case=case, phase=treatment, session=time, outcome=outcome,
design="MBP", treatment_name = "treatment", model_fit=Laski_trend, data=Laski_clean)
expect_s3_class(Laski_graph3, "ggplot")
expect_invisible(print(Laski_graph3))
Laski_graph4 <- graph_SCD(case=case, phase=treatment, session=time, outcome=outcome,
design="MBP", treatment_name = "treatment", model_fit=Laski_trend)
expect_s3_class(Laski_graph4, "ggplot")
expect_invisible(print(Laski_graph4))
keys <- setdiff(names(Laski_graph1), c("data","plot_env", "labels","layers", "layout"))
expect_equal(Laski_graph3[keys], Laski_graph4[keys])
})
test_that("graph_SCD works for design = 'RMBB'", {
skip_if_not_installed("ggplot2")
data("Thiemann2001")
Thiemann2001_RML <- lme(outcome ~ 1 + time_c + treatment + trt_time,
random = ~ 1 | case / series,
data = Thiemann2001)
# graph using data =
Thiemann_graph1 <- graph_SCD(design = "RMBB",
case = case, series = series,
phase = treatment, session = time, outcome = outcome,
treatment_name = "treatment",
data = Thiemann2001)
expect_s3_class(Thiemann_graph1, "ggplot")
expect_invisible(print(Thiemann_graph1))
# graph using vectors only
Thiemann_graph2 <- graph_SCD(design = "RMBB",
case = Thiemann2001$case, series = Thiemann2001$series,
phase = Thiemann2001$treatment, session = Thiemann2001$time,
outcome = Thiemann2001$outcome,
treatment_name = "treatment", model_fit = Thiemann2001_RML)
expect_s3_class(Thiemann_graph2, "ggplot")
expect_invisible(print(Thiemann_graph2))
keys <- c("scales","theme","coordinates")
expect_equal(Thiemann_graph1[keys], Thiemann_graph2[keys])
# graph with model_fit = Thiemann2001_RML (with data)
Thiemann_graph3 <- graph_SCD(design = "RMBB",
case = case, series = series,
phase = treatment, session = time, outcome = outcome,
treatment_name = "treatment",
data = Thiemann2001, model_fit = Thiemann2001_RML)
expect_s3_class(Thiemann_graph3, "ggplot")
expect_invisible(print(Thiemann_graph3))
# graph with model_fit = Thiemann2001_RML (without data)
Thiemann_graph4 <- graph_SCD(design = "RMBB",
case = case, series = series,
phase = treatment, session = time, outcome = outcome,
treatment_name = "treatment", model_fit = Thiemann2001_RML)
expect_s3_class(Thiemann_graph4, "ggplot")
expect_invisible(print(Thiemann_graph4))
keys <- setdiff(names(Thiemann_graph3), c("data","plot_env", "labels","layers", "layout"))
expect_equal(Thiemann_graph3[keys], Thiemann_graph4[keys])
})
test_that("graph_SCD works for design = 'CMB'", {
skip_if_not_installed("ggplot2")
data("Bryant2018")
Bryant2018_RML <-lme(fixed = outcome ~ treatment,
random = ~ 1 | group / case,
correlation = corAR1(0, ~ session | group / case),
weights = varIdent(form = ~ 1 | treatment),
data = Bryant2018,
na.action = na.omit)
# graph using data =
Bry_graph1 <- graph_SCD(design = "CMB",
cluster = group, case = case,
phase = treatment, session = session, outcome = outcome,
treatment_name = "treatment",
data = Bryant2018)
expect_s3_class(Bry_graph1, "ggplot")
expect_invisible(print(Bry_graph1))
# graph using vectors only
Bry_graph2 <- graph_SCD(design = "CMB",
cluster = Bryant2018$group, case = Bryant2018$case,
phase = Bryant2018$treatment, session = Bryant2018$session,
outcome = Bryant2018$outcome,
treatment_name = "treatment", model_fit = Bryant2018_RML)
expect_s3_class(Bry_graph2, "ggplot")
expect_invisible(print(Bry_graph2))
keys <- c("scales","theme","coordinates")
expect_equal(Bry_graph1[keys], Bry_graph2[keys])
# graph with model_fit (with data)
Bry_graph3 <- graph_SCD(design = "CMB",
cluster = group, case = case,
phase = treatment, session = session, outcome = outcome,
treatment_name = "treatment",
data = Bryant2018,
model_fit = Bryant2018_RML)
expect_s3_class(Bry_graph3, "ggplot")
expect_invisible(print(Bry_graph3))
# graph with model_fit (without data)
Bry_graph4 <- graph_SCD(design = "CMB",
cluster = group, case = case,
phase = treatment, session = session, outcome = outcome,
treatment_name = "treatment", model_fit = Bryant2018_RML)
expect_s3_class(Bry_graph4, "ggplot")
expect_invisible(print(Bry_graph4))
keys <- setdiff(names(Bry_graph3), c("data","plot_env", "labels","layers", "layout", "guides"))
expect_equal(Bry_graph3[keys], Bry_graph4[keys])
})
test_that("graph_SCD works with hypothetical newdata", {
data("Anglesea")
Anglesea_clean <- preprocess_SCD(design = "TR",
case = case,
phase = phase,
session = session,
outcome = outcome,
data = Anglesea)
# Fit the model
suppressWarnings(
Ang_RML <- lme(fixed = outcome ~ 1 + trt,
random = ~ 1 + trt | case,
correlation = corAR1(0.01, ~ session | case),
data = Anglesea_clean,
control = lmeControl(msMaxIter = 50, apVar = FALSE, returnObject = TRUE))
)
Ang_graph1 <- graph_SCD(case=case, phase=phase, session=session, outcome=outcome,
design="TR", treatment_name = NULL, model_fit=Ang_RML)
expect_s3_class(Ang_graph1, "ggplot")
expect_invisible(print(Ang_graph1))
Anglesea_mod <- Anglesea_clean
Anglesea_mod$phase <- factor(1, levels = levels(Anglesea_clean$phase))
Anglesea_mod$trt <- 0
Ang_graph2 <- graph_SCD(case=case, phase=phase, session=session, outcome=outcome,
design="TR", treatment_name = NULL, newdata = Anglesea_mod, model_fit=Ang_RML)
expect_s3_class(Ang_graph2, "ggplot")
expect_invisible(print(Ang_graph2))
Ang_graph3 <- graph_SCD(case=case, phase=phase, session=session, outcome=outcome,
design="TR", treatment_name = NULL, data = Anglesea_clean, newdata = Anglesea_mod, model_fit=Ang_RML)
expect_s3_class(Ang_graph3, "ggplot")
expect_invisible(print(Ang_graph3))
data("Laski")
Laski_RML <- lme(fixed = outcome ~ 1 + treatment,
random = ~ 1 | case,
correlation = corAR1(0, ~ time | case),
data = Laski)
Laski_mod <- Laski
Laski_mod$treatment <- factor("baseline", levels = levels(Laski$treatment))
Laski_graph <- graph_SCD(case=case, phase=treatment, session=time, outcome=outcome,
design="MBP", treatment_name = "treatment", model_fit=Laski_RML, newdata=Laski_mod)
expect_s3_class(Laski_graph, "ggplot")
expect_invisible(print(Laski_graph))
data("Thiemann2001")
Thiemann2001_RML <- lme(outcome ~ 1 + time_c + treatment + trt_time,
random = ~ 1 | case / series,
data = Thiemann2001)
Thiemann2001_mod <- Thiemann2001
Thiemann2001_mod$treatment <- factor("baseline", levels = levels(Thiemann2001$treatment))
Thiemann2001_mod$trt_time <- 0
Thiemann_graph <- graph_SCD(design = "RMBB",
case = case, series = series,
phase = treatment, session = time, outcome = outcome,
treatment_name = "treatment",
model_fit = Thiemann2001_RML,
newdata = Thiemann2001_mod)
expect_s3_class(Thiemann_graph, "ggplot")
expect_invisible(print(Thiemann_graph))
data("Bryant2018")
Bryant2018_RML <-lme(fixed = outcome ~ treatment,
random = ~ 1 | group / case,
correlation = corAR1(0, ~ session | group / case),
weights = varIdent(form = ~ 1 | treatment),
data = Bryant2018,
na.action = na.omit)
Bryant2018_mod <- Bryant2018
Bryant2018_mod$treatment <- factor("baseline", levels = levels(Bryant2018$treatment))
Bry_graph <- graph_SCD(design = "CMB",
cluster = group, case = case,
phase = treatment, session = session, outcome = outcome,
treatment_name = "treatment",
model_fit = Bryant2018_RML, newdata = Bryant2018_mod)
expect_s3_class(Bry_graph, "ggplot")
expect_invisible(print(Bry_graph))
})
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