The document discusses various statistical tests that can be used to compare continuous variables between groups, including the independent and paired t-test, Wilcoxon signed-rank test, ANOVA, and Kruskal-Wallis test. It provides examples of how to conduct each test in R, such as using t.test() to perform an independent two group comparison and kruskal.test() for a distribution-free multi-group comparison. The document also introduces ways to extract data from data frames and create new variables for analysis.

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 group
n Introduction
n Reading Data into R (1)
n Reading Data into R (2)
n Descriptive, continuous
n Descriptive, categorical
Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH

4. Menu
n Groupwise comparison of continuous
variables

5. Ingredients
Statistics Programming
n one group vs null hypothesis n Creating a new variable
n 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. Open
R Studio

7. Install and Load
BSDA

8. Download comma-separated and Excel
Put them in folder
BONEDEN.DAT.txt
BETACAR.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 with
discordant smoking exposure

10. Indexing: extraction of data from
data frame
Extract 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$age
Extracted 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. Variance
comparison
(F-test)
var.test
var.test(age ~ zyg, data = bone)

20. t-test with
BSDA package
summary 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.test
wilcox.test(bone$fn.diff, mu = 0, correct = FALSE)

23. One-sample BSDA package
SIGN.test
SIGN.test(bone$fn.diff, md = 0)

24. Paired
wilcox.test
wilcox.test(bone$fn1, bone$fn2, paired = TRUE,
correct = FALSE)

25. Independent
two group
comparison
wilcox.test
wilcox.test(age ~ zyg, data = bone)

26. 3+ group
comparison

27. Read in BETACAR.DAT.txt
Name it vitA
Plasma 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)