Test#

Basic#

numeric(data, variable)

Compute descriptive statistics of numeric variables.

numeric_grouped(data, variable, group)

Compute descriptive statistics of a numeric variable in different groups.

categorical(data, variable)

Compute descriptive statistics of a categorical variable.

contingency(data, variable_1, variable_2[, kind])

Compute the contingency table of two categorical variables.

t-Test#

one_sample_t_test(data, variable, expect[, kind])

Test whether the mean value of a variable is different from the expected value.

two_sample_t_test(data, variable, between, group)

Test whether the mean values of a variable are different in two groups.

paired_t_test(data, variable, between, ...)

Test whether the mean values of a variable are different in two paired groups.

pairwise_t_test(data, variable, between)

Test whether the mean values of a variable are different between every two groups.

ANOVA#

one_way_anova(data, variable, between)

Test whether the mean values of a variable are different between several groups.

two_way_anova(data, variable, between_1, ...)

Test whether the mean values of a variable are different between several groups, when the groups are classified in two ways.

one_way_ancova(data, variable, between, ...)

Test whether the mean values of a variable are different between several groups, when another variable is controlled.

two_way_ancova(data, variable, between_1, ...)

Test whether the mean values of a variable are different between several groups classified in two ways, when another variable is controlled.

multivariate_anova(data, variable, between)

Test whether the mean values of several variables are different between several groups.

repeated_measures_anova(data, variable, ...)

Test whether the mean values of a variable are different between several groups on repeated measured data.

Exact Test#

binomial_test(data, variable, expect)

Test whether the proportion of a categorical variable is different from the expected proportion.

fisher_exact_test(data, variable_1, variable_2)

Test whether there is an association between two categorical variables.

mcnemar_exact_test(data, variable_1, ...)

Test whether the proportions of a categorical variable are different in two paired groups.

Chi-Square Test#

chi_square_test(data, variable_1, variable_2)

Test whether there is an association between two categorical variables.

chi_square_test_fit(data, variable, expect)

Test whether the proportion of a categorical variable is different from the expected proportion.

mcnemar_test(data, variable_1, variable_2, pair)

Test whether the proportions of a categorical variable are different in two paired groups.

mantel_haenszel_test(data, variable_1, ...)

Test whether there is an association between two categorical variables in stratified data.

Linear Regression#

correlation(data, x, y)

Test whether there is a correlation between two numeric variables.

correlation_matrix(data, variable)

Compute the correlation coefficients between every two variables.

simple_linear_regression(data, x, y)

Fit an equation that predicts a numeric variable from another numeric variable.

multiple_linear_regression(data, x_numeric, ...)

Fit an equation that predicts a numeric variable from other variables.

Logistic Regression#

simple_logistic_regression(data, x, y, target)

Fit an equation that predicts a dichotomous categorical variable from a numeric variable.

multiple_logistic_regression(data, ...)

Fit an equation that predicts a dichotomous categorical variable from other variables.

ordered_logistic_regression(data, x_numeric, ...)

Fit an equation that predicts an ordered categorical variable from other variables.

multinomial_logistic_regression(data, ...)

Fit an equation that predicts a multinomial categorical variable from other variables.

Nonparametric#

median_test(data, variable, expect)

Test whether the mean value of a variable is different from the expected value with nonparametric methods.

sign_test(data, variable, between, group, pair)

Test whether the mean values of a variable are different in two paired groups with nonparametric methods.

wilcoxon_signed_rank_test(data, variable, ...)

Test whether the mean values of a variable are different in two paired groups with nonparametric methods.

wilcoxon_rank_sum_test(data, variable, ...)

Test whether the mean values of a variable are different in two groups with nonparametric methods.

kruskal_wallis_test(data, variable, between)

Test whether the mean values of a variable are different between several groups with nonparametric methods.

friedman_test(data, variable, between, subject)

Test whether the mean values of a variable are different between several groups on repeated measured data with nonparametric methods.

spearman_rank_correlation(data, x, y)

Test whether there is a correlation between two numeric variables with nonparametric methods.

Others#

screening_test(data, disease, ...)

Compute some common statistics of a screening test.

epidemiologic_study(data, disease, ...)

Compute some common statistics of an epidemiologic study.

factor_analysis(data, x, factors[, analyze])

Find the underlying factors of a set of variables.

principal_component_analysis(data, x[, ...])

Find the linear combination of a set of variables to manifest the variation of data.

linear_discriminant_analysis(data, x, y[, ...])

Find the linear combination of a set of variables to distinguish between groups.