tests: Comparison tests/methods available
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables
tests R Documentation
Comparison tests/methods available
Description
Below is a listing of tests available internally within gtsummary.
These methods are available to be called in add_p(), add_difference(), and add_difference_row()
Tests listed with ... may have additional arguments
passed to them using add_p(test.args=). For example, to
calculate a p-value from t.test() assuming equal variance, use
tbl_summary(trial, by = trt) %>% add_p(age ~ "t.test", test.args = age ~ list(var.equal = TRUE))
tbl_summary() %>% add_p()
alias description pseudo-code details
't.test' t-test t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...)
'mood.test' Mood two-sample test of scale mood.test(variable ~ as.factor(by), data = data, ...) Not to be confused with the Brown-Mood test of medians
'oneway.test' One-way ANOVA oneway.test(variable ~ as.factor(by), data = data, ...)
'kruskal.test' Kruskal-Wallis test kruskal.test(data[[variable]], as.factor(data[[by]]))
'wilcox.test' Wilcoxon rank-sum test wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...)
'chisq.test' chi-square test of independence chisq.test(x = data[[variable]], y = as.factor(data[[by]]), ...)
'chisq.test.no.correct' chi-square test of independence chisq.test(x = data[[variable]], y = as.factor(data[[by]]), correct = FALSE)
'fisher.test' Fisher's exact test fisher.test(data[[variable]], as.factor(data[[by]]), conf.level = 0.95, ...)
'mcnemar.test' McNemar's test tidyr::pivot_wider(id_cols = group, ...); mcnemar.test(by_1, by_2, conf.level = 0.95, ...)
'mcnemar.test.wide' McNemar's test mcnemar.test(data[[variable]], data[[by]], conf.level = 0.95, ...)
'lme4' random intercept logistic regression lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(by ~ variable + (1 \UFF5C group), data, family = binomial))
'paired.t.test' Paired t-test tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...)
'paired.wilcox.test' Paired Wilcoxon rank-sum test tidyr::pivot_wider(id_cols = group, ...); wilcox.test(by_1, by_2, paired = TRUE, conf.int = TRUE, conf.level = 0.95, ...)
'prop.test' Test for equality of proportions prop.test(x, n, conf.level = 0.95, ...) For dichotomous comparisons, the 'variable' is first converted to a logical.
'ancova' ANCOVA lm(variable ~ by + adj.vars)
'emmeans' Estimated Marginal Means or LS-means lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept.
tbl_svysummary() %>% add_p()
alias description pseudo-code details
'svy.t.test' t-test adapted to complex survey samples survey::svyttest(~variable + by, data)
'svy.wilcox.test' Wilcoxon rank-sum test for complex survey samples survey::svyranktest(~variable + by, data, test = 'wilcoxon')
'svy.kruskal.test' Kruskal-Wallis rank-sum test for complex survey samples survey::svyranktest(~variable + by, data, test = 'KruskalWallis')
'svy.vanderwaerden.test' van der Waerden's normal-scores test for complex survey samples survey::svyranktest(~variable + by, data, test = 'vanderWaerden')
'svy.median.test' Mood's test for the median for complex survey samples survey::svyranktest(~variable + by, data, test = 'median')
'svy.chisq.test' chi-squared test with Rao & Scott's second-order correction survey::svychisq(~variable + by, data, statistic = 'F')
'svy.adj.chisq.test' chi-squared test adjusted by a design effect estimate survey::svychisq(~variable + by, data, statistic = 'Chisq')
'svy.wald.test' Wald test of independence for complex survey samples survey::svychisq(~variable + by, data, statistic = 'Wald')
'svy.adj.wald.test' adjusted Wald test of independence for complex survey samples survey::svychisq(~variable + by, data, statistic = 'adjWald')
'svy.lincom.test' test of independence using the exact asymptotic distribution for complex survey samples survey::svychisq(~variable + by, data, statistic = 'lincom')
'svy.saddlepoint.test' test of independence using a saddlepoint approximation for complex survey samples survey::svychisq(~variable + by, data, statistic = 'saddlepoint')
'emmeans' Estimated Marginal Means or LS-means survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used.
tbl_survfit() %>% add_p()
alias description pseudo-code
'logrank' Log-rank test survival::survdiff(Surv(.) ~ variable, data, rho = 0)
'tarone' Tarone-Ware test survival::survdiff(Surv(.) ~ variable, data, rho = 1.5)
'petopeto_gehanwilcoxon' Peto & Peto modification of Gehan-Wilcoxon test survival::survdiff(Surv(.) ~ variable, data, rho = 1)
'survdiff' G-rho family test survival::survdiff(Surv(.) ~ variable, data, ...)
'coxph_lrt' Cox regression (LRT) survival::coxph(Surv(.) ~ variable, data, ...)
'coxph_wald' Cox regression (Wald) survival::coxph(Surv(.) ~ variable, data, ...)
'coxph_score' Cox regression (Score) survival::coxph(Surv(.) ~ variable, data, ...)
tbl_continuous() %>% add_p()
alias description pseudo-code
'anova_2way' Two-way ANOVA lm(continuous_variable ~ by + variable) %>% broom::glance()
't.test' t-test t.test(continuous_variable ~ variable, data = data, conf.level = 0.95, ...)
'oneway.test' One-way ANOVA oneway.test(continuous_variable ~ variable, data = data)
'kruskal.test' Kruskal-Wallis test kruskal.test(x = data[[continuous_variable]], g = data[[variable]])
'wilcox.test' Wilcoxon rank-sum test wilcox.test(continuous_variable ~ variable, data = data, ...)
'lme4' random intercept logistic regression lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(variable ~ continuous_variable + (1 \UFF5C group), data, family = binomial))
'ancova' ANCOVA lm(continuous_variable ~ variable + adj.vars)
tbl_summary() %>% add_difference()/add_difference_row()
alias description difference statistic pseudo-code details
't.test' t-test mean difference t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...)
'wilcox.test' Wilcoxon rank-sum test wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...)
'paired.t.test' Paired t-test mean difference tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...)
'prop.test' Test for equality of proportions rate difference prop.test(x, n, conf.level = 0.95, ...) For dichotomous comparisons, the 'variable' is first converted to a logical.
'ancova' ANCOVA mean difference lm(variable ~ by + adj.vars)
'ancova_lme4' ANCOVA with random intercept mean difference lme4::lmer(variable ~ by + adj.vars + (1 \UFF5C group), data)
'cohens_d' Cohen's D standardized mean difference effectsize::cohens_d(variable ~ by, data, ci = conf.level, verbose = FALSE, ...)
'hedges_g' Hedge's G standardized mean difference effectsize::hedges_g(variable ~ by, data, ci = conf.level, verbose = FALSE, ...)
'paired_cohens_d' Paired Cohen's D standardized mean difference tidyr::pivot_wider(id_cols = group, ...); effectsize::cohens_d(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...)
'paired_hedges_g' Paired Hedge's G standardized mean difference tidyr::pivot_wider(id_cols = group, ...); effectsize::hedges_g(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...)
'smd' Standardized Mean Difference standardized mean difference smd::smd(x = data[[variable]], g = data[[by]], std.error = TRUE)
'emmeans' Estimated Marginal Means or LS-means adjusted mean difference lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept.
tbl_svysummary() %>% add_difference()
alias description difference statistic pseudo-code details
'smd' Standardized Mean Difference standardized mean difference smd::smd(x = variable, g = by, w = weights(data), std.error = TRUE)
'svy.t.test' t-test adapted to complex survey samples survey::svyttest(~variable + by, data)
'emmeans' Estimated Marginal Means or LS-means adjusted mean difference survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used.
Custom Functions
To report a p-value (or difference) for a test not available in gtsummary, you can create a
custom function. The output is a data frame that is one line long. The
structure is similar to the output of broom::tidy() of a typical
statistical test. The add_p() and add_difference() functions will look for columns called
"p.value", "estimate", "statistic", "std.error", "parameter",
"conf.low", "conf.high", and "method".
You can also pass an Analysis Results Dataset (ARD) object with the results
for your custom result. These objects follow the structures outlined
by the {cards} and {cardx} packages.
Example calculating a p-value from a t-test assuming a common variance
between groups.
ttest_common_variance <- function(data, variable, by, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), var.equal = TRUE) %>%
broom::tidy()
}
trial[c("age", "trt")] %>%
tbl_summary(by = trt) %>%
add_p(test = age ~ "ttest_common_variance")
A custom add_difference() is similar, and accepts arguments conf.level=
and adj.vars= as well.
ttest_common_variance <- function(data, variable, by, conf.level, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), conf.level = conf.level, var.equal = TRUE) %>%
broom::tidy()
}
Function Arguments
For tbl_summary() objects, the custom function will be passed the
following arguments: custom_pvalue_fun(data=, variable=, by=, group=, type=, conf.level=, adj.vars=).
While your function may not utilize each of these arguments, these arguments
are passed and the function must accept them. We recommend including ...
to future-proof against updates where additional arguments are added.
The following table describes the argument inputs for each gtsummary table type.
argument tbl_summary tbl_svysummary tbl_survfit tbl_continuous
data= A data frame A survey object A survfit() object A data frame
variable= String variable name String variable name NA String variable name
by= String variable name String variable name NA String variable name
group= String variable name NA NA String variable name
type= Summary type Summary type NA NA
conf.level= Confidence interval level NA NA NA
adj.vars= Character vector of adjustment variable names (e.g. used in ANCOVA) NA NA Character vector of adjustment variable names (e.g. used in ANCOVA)
continuous_variable= NA NA NA String of the continuous variable name
gtsummary documentation built on Aug. 9, 2025, 1:08 a.m.
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| tests | R Documentation |
Comparison tests/methods available
Description
Below is a listing of tests available internally within gtsummary.
These methods are available to be called in add_p(), add_difference(), and add_difference_row()
Tests listed with ... may have additional arguments
passed to them using add_p(test.args=). For example, to
calculate a p-value from t.test() assuming equal variance, use
tbl_summary(trial, by = trt) %>% add_p(age ~ "t.test", test.args = age ~ list(var.equal = TRUE))
tbl_summary() %>% add_p()
| alias | description | pseudo-code | details |
't.test' | t-test | t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...) | |
'mood.test' | Mood two-sample test of scale | mood.test(variable ~ as.factor(by), data = data, ...) | Not to be confused with the Brown-Mood test of medians |
'oneway.test' | One-way ANOVA | oneway.test(variable ~ as.factor(by), data = data, ...) | |
'kruskal.test' | Kruskal-Wallis test | kruskal.test(data[[variable]], as.factor(data[[by]])) | |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...) | |
'chisq.test' | chi-square test of independence | chisq.test(x = data[[variable]], y = as.factor(data[[by]]), ...) | |
'chisq.test.no.correct' | chi-square test of independence | chisq.test(x = data[[variable]], y = as.factor(data[[by]]), correct = FALSE) | |
'fisher.test' | Fisher's exact test | fisher.test(data[[variable]], as.factor(data[[by]]), conf.level = 0.95, ...) | |
'mcnemar.test' | McNemar's test | tidyr::pivot_wider(id_cols = group, ...); mcnemar.test(by_1, by_2, conf.level = 0.95, ...) | |
'mcnemar.test.wide' | McNemar's test | mcnemar.test(data[[variable]], data[[by]], conf.level = 0.95, ...) | |
'lme4' | random intercept logistic regression | lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(by ~ variable + (1 \UFF5C group), data, family = binomial)) | |
'paired.t.test' | Paired t-test | tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...) | |
'paired.wilcox.test' | Paired Wilcoxon rank-sum test | tidyr::pivot_wider(id_cols = group, ...); wilcox.test(by_1, by_2, paired = TRUE, conf.int = TRUE, conf.level = 0.95, ...) | |
'prop.test' | Test for equality of proportions | prop.test(x, n, conf.level = 0.95, ...) | For dichotomous comparisons, the 'variable' is first converted to a logical. |
'ancova' | ANCOVA | lm(variable ~ by + adj.vars) | |
'emmeans' | Estimated Marginal Means or LS-means | lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept. |
tbl_svysummary() %>% add_p()
| alias | description | pseudo-code | details |
'svy.t.test' | t-test adapted to complex survey samples | survey::svyttest(~variable + by, data) | |
'svy.wilcox.test' | Wilcoxon rank-sum test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'wilcoxon') | |
'svy.kruskal.test' | Kruskal-Wallis rank-sum test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'KruskalWallis') | |
'svy.vanderwaerden.test' | van der Waerden's normal-scores test for complex survey samples | survey::svyranktest(~variable + by, data, test = 'vanderWaerden') | |
'svy.median.test' | Mood's test for the median for complex survey samples | survey::svyranktest(~variable + by, data, test = 'median') | |
'svy.chisq.test' | chi-squared test with Rao & Scott's second-order correction | survey::svychisq(~variable + by, data, statistic = 'F') | |
'svy.adj.chisq.test' | chi-squared test adjusted by a design effect estimate | survey::svychisq(~variable + by, data, statistic = 'Chisq') | |
'svy.wald.test' | Wald test of independence for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'Wald') | |
'svy.adj.wald.test' | adjusted Wald test of independence for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'adjWald') | |
'svy.lincom.test' | test of independence using the exact asymptotic distribution for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'lincom') | |
'svy.saddlepoint.test' | test of independence using a saddlepoint approximation for complex survey samples | survey::svychisq(~variable + by, data, statistic = 'saddlepoint') | |
'emmeans' | Estimated Marginal Means or LS-means | survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used. |
tbl_survfit() %>% add_p()
| alias | description | pseudo-code |
'logrank' | Log-rank test | survival::survdiff(Surv(.) ~ variable, data, rho = 0) |
'tarone' | Tarone-Ware test | survival::survdiff(Surv(.) ~ variable, data, rho = 1.5) |
'petopeto_gehanwilcoxon' | Peto & Peto modification of Gehan-Wilcoxon test | survival::survdiff(Surv(.) ~ variable, data, rho = 1) |
'survdiff' | G-rho family test | survival::survdiff(Surv(.) ~ variable, data, ...) |
'coxph_lrt' | Cox regression (LRT) | survival::coxph(Surv(.) ~ variable, data, ...) |
'coxph_wald' | Cox regression (Wald) | survival::coxph(Surv(.) ~ variable, data, ...) |
'coxph_score' | Cox regression (Score) | survival::coxph(Surv(.) ~ variable, data, ...) |
tbl_continuous() %>% add_p()
| alias | description | pseudo-code |
'anova_2way' | Two-way ANOVA | lm(continuous_variable ~ by + variable) %>% broom::glance() |
't.test' | t-test | t.test(continuous_variable ~ variable, data = data, conf.level = 0.95, ...) |
'oneway.test' | One-way ANOVA | oneway.test(continuous_variable ~ variable, data = data) |
'kruskal.test' | Kruskal-Wallis test | kruskal.test(x = data[[continuous_variable]], g = data[[variable]]) |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(continuous_variable ~ variable, data = data, ...) |
'lme4' | random intercept logistic regression | lme4::glmer(by ~ (1 \UFF5C group), data, family = binomial) %>% anova(lme4::glmer(variable ~ continuous_variable + (1 \UFF5C group), data, family = binomial)) |
'ancova' | ANCOVA | lm(continuous_variable ~ variable + adj.vars) |
tbl_summary() %>% add_difference()/add_difference_row()
| alias | description | difference statistic | pseudo-code | details |
't.test' | t-test | mean difference | t.test(variable ~ as.factor(by), data = data, conf.level = 0.95, ...) | |
'wilcox.test' | Wilcoxon rank-sum test | wilcox.test(as.numeric(variable) ~ as.factor(by), data = data, conf.int = TRUE, conf.level = conf.level, ...) | ||
'paired.t.test' | Paired t-test | mean difference | tidyr::pivot_wider(id_cols = group, ...); t.test(by_1, by_2, paired = TRUE, conf.level = 0.95, ...) | |
'prop.test' | Test for equality of proportions | rate difference | prop.test(x, n, conf.level = 0.95, ...) | For dichotomous comparisons, the 'variable' is first converted to a logical. |
'ancova' | ANCOVA | mean difference | lm(variable ~ by + adj.vars) | |
'ancova_lme4' | ANCOVA with random intercept | mean difference | lme4::lmer(variable ~ by + adj.vars + (1 \UFF5C group), data) | |
'cohens_d' | Cohen's D | standardized mean difference | effectsize::cohens_d(variable ~ by, data, ci = conf.level, verbose = FALSE, ...) | |
'hedges_g' | Hedge's G | standardized mean difference | effectsize::hedges_g(variable ~ by, data, ci = conf.level, verbose = FALSE, ...) | |
'paired_cohens_d' | Paired Cohen's D | standardized mean difference | tidyr::pivot_wider(id_cols = group, ...); effectsize::cohens_d(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...) | |
'paired_hedges_g' | Paired Hedge's G | standardized mean difference | tidyr::pivot_wider(id_cols = group, ...); effectsize::hedges_g(by_1, by_2, paired = TRUE, conf.level = 0.95, verbose = FALSE, ...) | |
'smd' | Standardized Mean Difference | standardized mean difference | smd::smd(x = data[[variable]], g = data[[by]], std.error = TRUE) | |
'emmeans' | Estimated Marginal Means or LS-means | adjusted mean difference | lm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, glm(family = binomial) and emmeans(regrid = "response") arguments are used. When group is specified, lme4::lmer() and lme4::glmer() are used with the group as a random intercept. |
tbl_svysummary() %>% add_difference()
| alias | description | difference statistic | pseudo-code | details |
'smd' | Standardized Mean Difference | standardized mean difference | smd::smd(x = variable, g = by, w = weights(data), std.error = TRUE) | |
'svy.t.test' | t-test adapted to complex survey samples | survey::svyttest(~variable + by, data) | ||
'emmeans' | Estimated Marginal Means or LS-means | adjusted mean difference | survey::svyglm(variable ~ by + adj.vars, data) %>% emmeans::emmeans(specs =~by) %>% emmeans::contrast(method = "pairwise") %>% summary(infer = TRUE, level = conf.level) | When variable is binary, survey::svyglm(family = binomial) and emmeans(regrid = "response") arguments are used. |
Custom Functions
To report a p-value (or difference) for a test not available in gtsummary, you can create a
custom function. The output is a data frame that is one line long. The
structure is similar to the output of broom::tidy() of a typical
statistical test. The add_p() and add_difference() functions will look for columns called
"p.value", "estimate", "statistic", "std.error", "parameter",
"conf.low", "conf.high", and "method".
You can also pass an Analysis Results Dataset (ARD) object with the results for your custom result. These objects follow the structures outlined by the {cards} and {cardx} packages.
Example calculating a p-value from a t-test assuming a common variance between groups.
ttest_common_variance <- function(data, variable, by, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), var.equal = TRUE) %>%
broom::tidy()
}
trial[c("age", "trt")] %>%
tbl_summary(by = trt) %>%
add_p(test = age ~ "ttest_common_variance")
A custom add_difference() is similar, and accepts arguments conf.level=
and adj.vars= as well.
ttest_common_variance <- function(data, variable, by, conf.level, ...) {
data <- data[c(variable, by)] %>% dplyr::filter(complete.cases(.))
t.test(data[[variable]] ~ factor(data[[by]]), conf.level = conf.level, var.equal = TRUE) %>%
broom::tidy()
}
Function Arguments
For tbl_summary() objects, the custom function will be passed the
following arguments: custom_pvalue_fun(data=, variable=, by=, group=, type=, conf.level=, adj.vars=).
While your function may not utilize each of these arguments, these arguments
are passed and the function must accept them. We recommend including ...
to future-proof against updates where additional arguments are added.
The following table describes the argument inputs for each gtsummary table type.
| argument | tbl_summary | tbl_svysummary | tbl_survfit | tbl_continuous |
data= | A data frame | A survey object | A survfit() object | A data frame |
variable= | String variable name | String variable name | NA | String variable name |
by= | String variable name | String variable name | NA | String variable name |
group= | String variable name | NA | NA | String variable name |
type= | Summary type | Summary type | NA | NA |
conf.level= | Confidence interval level | NA | NA | NA |
adj.vars= | Character vector of adjustment variable names (e.g. used in ANCOVA) | NA | NA | Character vector of adjustment variable names (e.g. used in ANCOVA) |
continuous_variable= | NA | NA | NA | String of the continuous variable name |
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