R语言tidy风格医学统计学

rstatix提供一个简单直观的管道友好的框架，与整洁的设计理念一致，用于执行基本的统计检验，包括t检验，Wilcoxon检验，方差分析，Kruskal-Wallis和相关性分析。每个分析的输出会自动转换成一个整洁的数据框架，以方便可视化。

附加功能可用于重塑，重新排序，操作和可视化相关矩阵。功能还包括析因实验的分析，包括重复测量设计、析因设计、正交设计等。

可以计算几个效应大小指标，包括方差分析eta平方，t检验的Cohen's d和分类变量之间的关联的Cramer's v。该软件包包含用于识别单变量和多变量异常值、评估正态性和方差齐性的辅助函数。

主要函数

描述性统计

• get_summary_stat():计算描述性的统计指标；
• freq_table(): 分类变量的频率表；
• get_mode(): 众数；
• identify_outliers(): 使用boxplot鉴别离群值；
• mahalanobis_distance(): 计算Mahalanobi距离和离群点；
• shapiro_test() and mshapiro_test(): 正态性检验.

比较均值

• t_test(): 单样本、配对样本、独立样本t检验；
• wilcox_test(): 单样本、配对样本、独立样本秩和检验；
• sign_test(): 符号检验；
• anova_test(): 基于car::Anova()改写，可以做：独立测量、重复测量、混合anova；
• get_anova_test_table(): 从anova_test() 提取结果，可自动执行球形检验.；
• welch_anova_test(): Welch one-Way ANOVA test. 基于stats::oneway.test()改写；
• kruskal_test(): kruskal-wallis rank sum test；
• friedman_test(): Friedman rank sum test；
• get_comparisons(): 创建需要比较的组；
• get_pvalue_position: 使用ggplot2添加p值时可自动计算添加坐标

增强R中的ANOVA

• factorial_design(): 建立因子化的设计，方便使用car::Anova()进行分析，对于重复测量Anova非常有帮助；
• anova_summary(): 提取美观的Anova检验的结果，包括从car:Anova()或者stats:aov()中，主要结果包含Anova结果表、一般效应量、和一些假设检验，比如球形检验。

事后检验（post-hoc）

• tukey_hsd(): tukey post-hoc tests;
• dunn_test(): 计算Kruskal-Wallis的成对比较；
• games_howell_test(): Games-Howell test；
• emmeans_test(): estimated marginal means

比较比例

• prop_test(), pairwise_prop_test() and row_wise_prop_test(). Performs one-sample and two-samples z-test of proportions. Wrappers around the R base function prop.test() but have the advantage of performing pairwise and row-wise z-test of two proportions, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.
• fisher_test(), pairwise_fisher_test() and row_wise_fisher_test(): Fisher's exact test for count data. Wrappers around the R base function fisher.test() but have the advantage of performing pairwise and row-wise fisher tests, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.
• chisq_test(), pairwise_chisq_gof_test(), pairwise_chisq_test_against_p(): Performs chi-squared tests, including goodness-of-fit, homogeneity and independence tests.
• binom_test(), pairwise_binom_test(), pairwise_binom_test_against_p(): Performs exact binomial test and pairwise comparisons following a significant exact multinomial test. Alternative to the chi-square test of goodness-of-fit-test when the sample.
• multinom_test(): performs an exact multinomial test. Alternative to the chi-square test of goodness-of-fit-test when the sample size is small.
• mcnemar_test(): performs McNemar chi-squared test to compare paired proportions. Provides pairwise comparisons between multiple groups.
• cochran_qtest(): extension of the McNemar Chi-squared test for comparing more than two paired proportions.
• prop_trend_test(): Performs chi-squared test for trend in proportion. This test is also known as Cochran-Armitage trend test

比较方差

• levene_test(): Pipe-friendly framework to easily compute Levene's test for homogeneity of variance across groups.
• box_m(): Box's M-test for homogeneity of covariance matrices

计算效应量

• cohens_d(): Compute cohen's d measure of effect size for t-tests.
• wilcox_effsize(): Compute Wilcoxon effect size (r).
• eta_squared() and partial_eta_squared(): Compute effect size for ANOVA.
• kruskal_effsize(): Compute the effect size for Kruskal-Wallis test as the eta squared based on the H-statistic.
• friedman_effsize(): Compute the effect size of Friedman test using the Kendall's W value.
• cramer_v(): Compute Cramer's V, which measures the strength of the association between categorical variables

相关性分析

计算相关性

• cor_test(): correlation test between two or more variables using Pearson, Spearman or Kendall methods.
• cor_mat(): compute correlation matrix with p-values. Returns a data frame containing the matrix of the correlation coefficients. The output has an attribute named "pvalue", which contains the matrix of the correlation test p-values.
• cor_get_pval(): extract a correlation matrix p-values from an object of class cor_mat().
• cor_pmat(): compute the correlation matrix, but returns only the p-values of the correlation tests.
• as_cor_mat(): convert a cor_test object into a correlation matrix format.

重塑相关矩阵

• cor_reorder(): reorder correlation matrix, according to the coefficients, using the hierarchical clustering method.
• cor_gather(): takes a correlation matrix and collapses (or melt) it into long format data frame (paired list)
• cor_spread(): spread a long correlation data frame into wide format (correlation matrix).

相关矩阵取子集

• cor_select(): subset a correlation matrix by selecting variables of interest.
• pull_triangle(), pull_upper_triangle(), pull_lower_triangle(): pull upper and lower triangular parts of a (correlation) matrix.
• replace_triangle(), replace_upper_triangle(), replace_lower_triangle(): replace upper and lower triangular parts of a (correlation) matrix.

可视化相关矩阵

• cor_as_symbols(): replaces the correlation coefficients, in a matrix, by symbols according to the value.
• cor_plot(): visualize correlation matrix using base plot.
• cor_mark_significant(): add significance levels to a correlation matrix

添加P值和显著性标记

• adjust_pvalue(): add an adjusted p-values column to a data frame containing statistical test p-values
• add_significance(): add a column containing the p-value significance level
• p_round(), p_format(), p_mark_significant(): rounding and formatting p-values

提取统计信息

• get_pwc_label(): Extract label from pairwise comparisons.
• get_test_label(): Extract label from statistical tests.
• create_test_label(): Create labels from user specified test results

数据处理辅助函数

• df_select(), df_arrange(), df_group_by(): wrappers arround dplyr functions for supporting standard and non standard evaluations.
• df_nest_by(): Nest a tibble data frame using grouping specification. Supports standard and non standard evaluations.
• df_split_by(): Split a data frame by groups into subsets or data panel. Very similar to the function df_nest_by(). The only difference is that, it adds labels to each data subset. Labels are the combination of the grouping variable levels.
• df_unite(): Unite multiple columns into one.
• df_unite_factors(): Unite factor columns. First, order factors levels then merge them into one column. The output column is a factor.
• df_label_both(), df_label_value(): functions to label data frames rows by by one or multiple grouping variables.
• df_get_var_names(): Returns user specified variable names. Supports standard and non standard evaluation

其他

• doo(): alternative to dplyr::do for doing anything. Technically it uses nest() + mutate() + map() to apply arbitrary computation to a grouped data frame.
• sample_n_by(): sample n rows by group from a table
• convert_as_factor(), set_ref_level(), reorder_levels(): Provides pipe-friendly functions to convert simultaneously multiple variables into a factor variable.
• make_clean_names(): Pipe-friendly function to make syntactically valid column names (for input data frame) or names (for input vector).
• counts_to_cases(): converts a contingency table or a data frame of counts into a data frame of individual observations

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