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Groupwise comparison of continuous data
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Groupwise comparison of continuous data

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  • 1. Groupwise comparison of continuous variables in 2012-10-29 @HSPH Kazuki Yoshida, M.D. MPH-CLE student FREEDOM TO  KNOW
  • 2. Group Website is at:http://rpubs.com/kaz_yos/useR_at_HSPH
  • 3. Previously in this groupn Introductionn Reading Data into R (1)n Reading Data into R (2)n Descriptive, continuousn Descriptive, categorical Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH
  • 4. Menun Groupwise comparison of continuous variables
  • 5. Ingredients Statistics Programmingn one group vs null hypothesis n Creating a new variablen two group comparison n t.test()n multi-group comparison n wilcox.test()n Distribution-free alternative n anova(lm()) for each n kruskal.test() n BSDA::SIGN.test()
  • 6. OpenR Studio
  • 7. Install and Load BSDA
  • 8. Download comma-separated and Excel Put them in folderBONEDEN.DAT.txtBETACAR.DAT.txt http://www.cengage.com/cgi-wadsworth/course_products_wp.pl? fid=M20bI&product_isbn_issn=9780538733496
  • 9. Read in BONEDEN.DAT.txt Name it bone Bone density in twins withdiscordant smoking exposure
  • 10. Indexing: extraction of data from data frameExtract 1st to 15th rows Extract 1st to 12th columns bone[1:15 , 1:12] Colon in between Don’t forget comma
  • 11. age vector within bone data frame
  • 12. bone$ageExtracted as a vector
  • 13. Creating a new variable new variable subtraction bone$fn.diff <- bone$fn1 - bone$fn2 alternatively: bone <- within(bone, { fn.diff <-fn1 - fn2 })
  • 14. One-sample t-test t.test t.test(bone$fn.diff, mu = 0)
  • 15. Paired t-test t.test t.test(bone$fn1, bone$fn2, paired = TRUE)
  • 16. Independent two group comparison t.test t.test(age ~ zyg, data = bone, var.equal = TRUE)
  • 17. formula outcome ~ predictor
  • 18. In the case of t-test continuous variable grouping variable to to be compared separate groups age ~ zyg
  • 19. Variancecomparison (F-test) var.test var.test(age ~ zyg, data = bone)
  • 20. t-test with BSDA packagesummary data tsum.test tsum.test(mean.x = 51.38, s.x = 10.74, n.x = 21, mean.y = 46.20, s.y = 12.48, n.y = 20, var.equal = TRUE)
  • 21. Distribution-free(non-parametric) methods
  • 22. One-sample wilcox.testwilcox.test(bone$fn.diff, mu = 0, correct = FALSE)
  • 23. One-sample BSDA packageSIGN.test SIGN.test(bone$fn.diff, md = 0)
  • 24. Pairedwilcox.testwilcox.test(bone$fn1, bone$fn2, paired = TRUE, correct = FALSE)
  • 25. Independent two group comparison wilcox.test wilcox.test(age ~ zyg, data = bone)
  • 26. 3+ groupcomparison
  • 27. Read in BETACAR.DAT.txt Name it vitAPlasma level of carotene by different formula of beta-carotene
  • 28. Independent 3+ group comparison anova anova(lm(Base1lvl ~ factor(Prepar), data = vitA))
  • 29. Distribution- free kruskal.test kruskal.test(Base1lvl ~ factor(Prepar), data = vitA)