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@ddsjoberg
ddsjoberg committed Apr 19, 2024
1 parent 828c43d commit d0abe22
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ Imports:
tibble (>= 3.2.1),
tidyr (>= 1.3.0)
Suggests:
cardx (>= 0.1.0.9020),
cardx (>= 0.1.0.9022),
knitr,
testthat (>= 3.2.0)
VignetteBuilder:
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335 changes: 329 additions & 6 deletions R/utils-add_p_tests.R
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,20 @@ add_p_test_wilcox.test <- function(data, variable, by, test.args, ...) {
)
}

add_p_test_mcnemar.test <- function(data, variable, by, test.args, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_mcnemar_test(
data = data,
variable = all_of(variable),
by = all_of(by),
conf.level = conf.level,
!!!test.args
)
)
}

add_p_test_chisq.test <- function(data, variable, by, test.args, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

Expand Down Expand Up @@ -61,6 +75,7 @@ add_p_test_chisq.test.no.correct <- function(data, variable, by, test.args, ...)

add_p_test_mood.test <- function(data, variable, by, test.args, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_mood_test(
data = data,
Expand All @@ -71,8 +86,12 @@ add_p_test_mood.test <- function(data, variable, by, test.args, ...) {
)
}

add_p_test_mcnemar.test_wide <- function(data, variable, by, test.args, ...) {
# TODO: Add this function
}
add_p_test_kruskal.test <- function(data, variable, by, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_kruskal_test(
data = data,
Expand Down Expand Up @@ -103,17 +122,321 @@ add_p_test_fisher.test <- function(data, variable, by, test.args, conf.level = 0
)
}

add_p_test_lme4 <- function(data, variable, by, group, type, ...) {
add_p_test_aov <- function(data, variable, by, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_aov(
# TODO: Update this to cardx::reformulate2()
formula = stats::reformulate(termlabels = by, response = variable),
data = data,
!!!test.args
)
)
}

add_p_test_oneway.test <- function(data, variable, by, test.args, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_oneway_test(
# TODO: Update this to cardx::reformulate2()
formula = stats::reformulate(termlabels = by, response = variable),
data = data,
!!!test.args
)
)
}

add_p_test_mood.test <- function(data, variable, by, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_mood_test(
data = data,
variable = all_of(variable),
by = all_of(by)
)
)
}

add_p_test_lme4 <- function(data, variable, by, group, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")
check_pkg_installed("lme4", reference_pkg = "cardx")
data <-
dplyr::select(data, all_of(c(variable, by, group))) %>%
dplyr::filter(stats::complete.cases(.)) |>
dplyr::mutate("{by}" := as.factor(.data[[by]]))

# formula0 <- reformulate()
cardx::ard_stats_anova(
x = data,
formulas = list(
glue::glue("as.factor({cardx::bt(by)}) ~ 1 + (1 | {cardx::bt(group)})") |> stats::as.formula(),
glue::glue("as.factor({cardx::bt(by)}) ~ {cardx::bt(variable)} + (1 | {cardx::bt(group)})") |> stats::as.formula()
),
method = "glmer",
method.args = list(family = stats::binomial),
package = "lme4",
method_text = "random intercept logistic regression"
) |>
# keep results associated with 2nd model
dplyr::filter(.data$variable %in% dplyr::last(.data$variable)) |>
dplyr::mutate(
variable = .env$variable,
warning = list(NULL)
)
}


add_p_tbl_summary_paired.t.test <- function(data, variable, by, group, test.args, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_paired_t_test(
data = data,
variable = all_of(variable),
by = all_of(by),
id = all_of(id),
conf.level = conf.level,
!!!test.args
)
)
}

add_p_tbl_summary_paired.wilcox.test <- function(data, variable, by, group, test.args, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_paired_wilcox_test(
data = data,
variable = all_of(variable),
by = all_of(by),
id = all_of(id),
conf.level = conf.level,
!!!test.args
)
)
}

add_p_test_prop.test <- function(data, variable, by, group, test.args, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_stats_prop_test(
data = data,
variable = all_of(variable),
by = all_of(by),
id = all_of(id),
conf.level = conf.level,
!!!test.args
)
)
}

add_p_test_ancova <- function(data, variable, by, adj.vars, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

cardx::ard_regression_basic(
x =
cardx::construct_model(
data = data,
formula = cardx::reformulate2(c(by, adj.vars), response = variable),
method = "lm"
)
) |>
dplyr::filter(
.data$variable %in% .env$by,
.data$stat_name %in% c("estimate", "p.value", "statistic", "std.error", "conf.low", "conf.high")
) |>
dplyr::bind_rows(
.,
dplyr::filter(., dplyr::row_number() == 1L) |>
dplyr::mutate(
stat = "method",
stat_name = "method",
stat =
dplyr::case_when(
is.null(adj.vars) ~ "One-way ANOVA",
TRUE ~ "ANCOVA"
),
fmt_fun = list(NULL)
)
)
}


add_p_test_cohens_d <- function(data, variable, by, test.args, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_effectsize_cohens_d(
data = data,
variable = all_of(variable),
by = all_of(by),
ci = conf.level,
!!!test.args
)
)
}

# TODO: Add this function to the test xlsx file
add_p_test_paired_cohens_d <- function(data, variable, by, test.args, group, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_effectsize_paired_cohens_d(
data = data,
variable = all_of(variable),
by = all_of(by),
id = all_of(group),
ci = conf.level,
!!!test.args
)
)
}

# TODO: Add this function to the test xlsx file
add_p_test_hedges_g <- function(data, variable, by, test.args, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_effectsize_hedges_g(
data = data,
variable = all_of(variable),
by = all_of(by),
ci = conf.level,
!!!test.args
)
)
}

# TODO: Add this function to the test xlsx file
add_p_test_paired_hedges_g <- function(data, variable, by, test.args, group, conf.level = 0.95, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

rlang::inject(
cardx::ard_effectsize_paired_hedges_g(
data = data,
variable = all_of(variable),
by = all_of(by),
id = all_of(group),
ci = conf.level,
!!!test.args
)
)
}

add_p_test_smd <- function(data, variable, by, ...) {
check_pkg_installed("cardx", reference_pkg = "gtsummary")

cardx::ard_smd_smd(
data = data,
variable = all_of(variable),
by = all_of(by)
)
}


# TODO: Do we need an ARD solution for this? This is just copied from the previous version
add_p_test_emmeans <- function(data, variable, by, adj.vars, conf.level = 0.95,
type, group = NULL, ...) {
if (!is.null(group)) check_pkg_installed("lme4", reference_pkg = "cardx")
if (inherits(data, "survey.design")) check_pkg_installed("survey", reference_pkg = "cardx")

data_frame <-
.extract_data_frame(data)

# checking inputs
if (!type %in% c("continuous", "dichotomous")) {
cli::cli_abort("Variable must be summary type 'continuous' or 'dichotomous'", call = get_cli_abort_call())
}
if (dplyr::n_distinct(data_frame[[by]], na.rm = TRUE) != 2) {
cli::cli_abort("`by=` must have exactly 2 levels", call = get_cli_abort_call())
}
if (type %in% "dichotomous" && dplyr::n_distinct(data_frame[[by]], na.rm = TRUE) != 2) {
cli::cli_abort("`variable=` must have exactly 2 levels", call = get_cli_abort_call())
}
if (is_survey(data) && !is.null(group)) {
cli::cli_abort("Cannot use `group=` argument with 'emmeans' and survey data.", call = get_cli_abort_call())
}

# assembling formula
# styler: off
termlabels <-
if (is.null(group)) c(by, adj.vars)
else c(by, adj.vars, glue::glue("(1 | {cardx::bt(group)})"))
response <-
if (type == "dichotomous") glue::glue("as.factor({cardx::bt(variable)})")
else cardx::bt(variable)
f <- stats::reformulate(termlabels, response)
f_by <- cardx::reformulate2(by)

rlang::inject(~ !!rlang::sym(chr_w_backtick(by)))
# styler: on

type2 <-
dplyr::case_when(
is.data.frame(data) && is.null(group) ~ type,
inherits(data, "survey.design") && is.null(group) && type == "dichotomous" ~ "dichotomous_svy",
inherits(data, "survey.design") && is.null(group) && type == "continuous" ~ "continuous_svy",
is.data.frame(data) && type == "dichotomous" ~ "dichotomous_mixed",
is.data.frame(data) && type == "continuous" ~ "continuous_mixed",
)

model_fun <-
switch(type2,
"dichotomous" =
cardx::construct_model(x = data)
purrr::partial(stats::glm, formula = f, data = data, family = stats::binomial),
"continuous" =
purrr::partial(stats::lm, formula = f, data = data),
"dichotomous_svy" =
purrr::partial(survey::svyglm, formula = f, design = data, family = stats::binomial),
"continuous_svy" =
purrr::partial(survey::svyglm, formula = f, design = data),
"dichotomous_mixed" =
purrr::partial(lme4::glmer, formula = f, data = data, family = stats::binomial),
"continuous_mixed" =
purrr::partial(lme4::lmer, formula = f, data = data)
)

emmeans_fun <-
switch(type,
"dichotomous" =
purrr::partial(emmeans::emmeans, specs = f_by, regrid = "response"),
"continuous" =
purrr::partial(emmeans::emmeans, specs = f_by)
)

# building model
model_fun() %>%
emmeans_fun() %>%
emmeans::contrast(method = "pairwise") %>%
summary(infer = TRUE, level = conf.level) %>%
tibble::as_tibble() %>%
dplyr::rename(
conf.low = any_of("asymp.LCL"),
conf.high = any_of("asymp.UCL"),
conf.low = any_of("lower.CL"),
conf.high = any_of("upper.CL")
) %>%
dplyr::select(
"estimate",
std.error = "SE",
"conf.low", "conf.high",
"p.value"
) %>%
dplyr::mutate(
method =
ifelse(
is.null(.env$adj.vars),
"Regression least-squares mean difference",
"Regression least-squares adjusted mean difference"
)
)

}

# TODO: we should be able to delete this is the emmeans functions gets deleted.
.extract_data_frame <- function(x) {
if (is.data.frame(x)) {
return(x)
}
x$variables # return survey object data frame
}

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