- T.test in r studio how to#
- T.test in r studio software#
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T.test in r studio software#
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Specialization: Python for Everybody by University of Michigan. Specialization: Data Science by Johns Hopkins University. Course: Machine Learning: Master the Fundamentals by Stanford. Stat_pvalue_manual(pwc, hide.ns = TRUE, label = "p.adj.signif") +Ĭoursera - Online Courses and Specialization Data science Subtitle = get_test_label(res.aov, detailed = TRUE), Stat_pvalue_manual(pwc, label = "p.adj", tip.length = 0, step.increase = 0.1) + Ggboxplot(PlantGrowth, x = "group", y = "weight") + # 3 weight trt1 trt2 10 10 0.00446 ** 0.0134 * Visualization: box plots with p-values # Show adjusted p-values group1 group2 n1 n2 p p.signif p.adj p.adj.signif Pairwise_t_test(weight ~ group, p.thod = "bonferroni") Pairwise T-tests for multiple groups # Pairwise comparisons From the above ANOVA table, it can be seen that there are significant differences between groups (p = 0.016), which are highlighted with “*“, F(2, 27) = 4.85, p = 0.016, eta2 = 0.26.
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