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