groupwise comparison of continuous data
DESCRIPTION
TRANSCRIPT
Groupwise comparison of continuous variables in
2012-11-26 @HSPHKazuki Yoshida, M.D. MPH-CLE student
FREEDOMTO KNOW
Group Website is at:
http://rpubs.com/kaz_yos/useR_at_HSPH
n Introduction
n Reading Data into R (1)
n Reading Data into R (2)
n Descriptive, continuous
n Descriptive, categorical
n Deducer
n Graphics
Previously in this group
Group Website: http://rpubs.com/kaz_yos/useR_at_HSPH
Menu
n Groupwise comparison of continuous variables
Ingredients
n one group vs null hypothesis
n two group comparison
n multi-group comparison
n Distribution-free alternative for each
n Creating a new variable
n t.test()
n wilcox.test()
n anova(lm())
n kruskal.test()
n BSDA::SIGN.test()
Statistics Programming
Open R Studio
BSDAInstall and Load
http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20bI&product_isbn_issn=9780538733496
Download comma-separated and Excel
BONEDEN.DAT.txtBETACAR.DAT.txt
Put them in folder
Read in BONEDEN.DAT.txt
Name it bone
Bone density in twins with discordant smoking exposure
bone[1:15 , 1:12]
Extract 1st to 15th rows Extract 1st to 12th columns
Indexing: extraction of data from data frame
Don’t forget commaColon in between
age vector within bone data frame
bone$age
Extracted as a vector
bone$fn.diff <- bone$fn1 - bone$fn2
Creating a new variablenew variable subtraction
alternatively:bone <- within(bone, {
fn.diff <-fn1 - fn2})
t.testt.test(bone$fn.diff, mu = 0)
One-sample t-test
t.testt.test(bone$fn1, bone$fn2, paired = TRUE)
Paired t-test
t.testt.test(age ~ zyg, data = bone, var.equal = TRUE)
Independent two group
comparison
outcome ~ predictor
formula
age ~ zyg
In the case of t-test
continuous variable to be compared
grouping variable to separate groups
var.testvar.test(age ~ zyg, data = bone)
Variance comparison
(F-test)
tsum.testtsum.test(mean.x = 51.38, s.x = 10.74, n.x = 21, mean.y = 46.20, s.y = 12.48, n.y = 20, var.equal = TRUE)
t-test with summary data BSDA package
Distribution-free (non-parametric)
methods
wilcox.testwilcox.test(bone$fn.diff, mu = 0, correct = FALSE)
One-sample
SIGN.testSIGN.test(bone$fn.diff, md = 0)
One-sample BSDA package
wilcox.testwilcox.test(bone$fn1, bone$fn2, paired = TRUE,
correct = FALSE)
Paired
wilcox.testwilcox.test(age ~ zyg, data = bone)
Independent two group
comparison
3+ group comparison
Read in BETACAR.DAT.txt
Name it vitA
Plasma level of carotene by different formula of beta-carotene
anovaanova(lm(Base1lvl ~ factor(Prepar), data = vitA))
Independent 3+ group
comparison
kruskal.testkruskal.test(Base1lvl ~ factor(Prepar), data = vitA)
Distribution-free