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carData::Adler

데이터 > 패키지에 있는 데이터 > 첨부된 패키지에서 데이터셋 읽기... 기능을 선택하면, 위와 같은 메뉴 창을 보게된다.

carData를 선택하여 두번 클릭하면, 오른쪽에 carData 패키지에 내장된 데이터셋 목록이 등장한다. Adler 데이터셋을 선택한다.

data(Adler, package="carData")  # Adler 데이터셋 활성화시키기
help("Adler", package="carData")# 도움말파일 열기

Experimenter Expectations

Description

The Adler data frame has 108 rows and 3 columns.

The “experimenters” were the actual subjects of the study. They collected ratings of the apparent success of people in pictures who were pre-selected for their average appearance of success. The experimenters were told prior to collecting data that particular subjects were either high or low in their tendency to rate appearance of success, and were instructed to get good data, scientific data, or were given no such instruction. Each experimenter collected ratings from 18 randomly assigned subjects. This version of the Adler data is taken from Erickson and Nosanchuk (1977). The data described in the original source, Adler (1973), have a more complex structure.

Usage

Adler

Format

This data frame contains the following columns:

instruction

a factor with levels: good, good data; none, no stress; scientific, scientific data.

expectation

a factor with levels: high, expect high ratings; low, expect low ratings.

rating

The average rating obtained.

Source

Erickson, B. H., and Nosanchuk, T. A. (1977) Understanding Data. McGraw-Hill Ryerson.

References

Adler, N. E. (1973) Impact of prior sets given experimenters and subjects on the experimenter expectancy effect. Sociometry 36, 113–126.

통계 > 평균 > 다원 분산 분석...
Statistics > Means > Multi-way ANOVA...

다중 분산 분석 (Multi-way ANOVA)은 두개 이상의 요인형 변수들의 작용으로 하나의 수치형 변수에 영향을 주었는가를 점검하는 통계기법이다.

data(Adler, package="carData")
help("Adler", package="carData")

AnovaModel.1 <- lm(rating ~ expectation*instruction, data=Adler, contrasts=list(expectation 
  ="contr.Sum", instruction ="contr.Sum"))
Anova(AnovaModel.1)
Tapply(rating ~ expectation + instruction, mean, na.action=na.omit, data=Adler) # means
Tapply(rating ~ expectation + instruction, sd, na.action=na.omit, data=Adler) # std. deviations
xtabs(~ expectation + instruction, data=Adler) # counts

?Anova  # car패키지의 Anova 도움말 보기

## Two-Way Anova

mod <- lm(conformity ~ fcategory*partner.status, data=Moore,
  contrasts=list(fcategory=contr.sum, partner.status=contr.sum))
Anova(mod)
Anova(mod, type=3)  # note use of contr.sum in call to lm()

## One-Way MANOVA
## See ?Pottery for a description of the data set used in this example.

summary(Anova(lm(cbind(Al, Fe, Mg, Ca, Na) ~ Site, data=Pottery)))

## MANOVA for a randomized block design (example courtesy of Michael Friendly:
##  See ?Soils for description of the data set)

soils.mod <- lm(cbind(pH,N,Dens,P,Ca,Mg,K,Na,Conduc) ~ Block + Contour*Depth,
    data=Soils)
Manova(soils.mod)
summary(Anova(soils.mod), univariate=TRUE, multivariate=FALSE,
    p.adjust.method=TRUE)

## a multivariate linear model for repeated-measures data
## See ?OBrienKaiser for a description of the data set used in this example.

phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
    levels=c("pretest", "posttest", "followup"))
hour <- ordered(rep(1:5, 3))
idata <- data.frame(phase, hour)
idata

mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
                     post.1, post.2, post.3, post.4, post.5,
                     fup.1, fup.2, fup.3, fup.4, fup.5) ~  treatment*gender,
                data=OBrienKaiser)
(av.ok <- Anova(mod.ok, idata=idata, idesign=~phase*hour))

summary(av.ok, multivariate=FALSE)

## A "doubly multivariate" design with two  distinct repeated-measures variables
## (example courtesy of Michael Friendly)
## See ?WeightLoss for a description of the dataset.

imatrix <- matrix(c(
	1,0,-1, 1, 0, 0,
	1,0, 0,-2, 0, 0,
	1,0, 1, 1, 0, 0,
	0,1, 0, 0,-1, 1,
	0,1, 0, 0, 0,-2,
	0,1, 0, 0, 1, 1), 6, 6, byrow=TRUE)
colnames(imatrix) <- c("WL", "SE", "WL.L", "WL.Q", "SE.L", "SE.Q")
rownames(imatrix) <- colnames(WeightLoss)[-1]
(imatrix <- list(measure=imatrix[,1:2], month=imatrix[,3:6]))
contrasts(WeightLoss$group) <- matrix(c(-2,1,1, 0,-1,1), ncol=2)
(wl.mod<-lm(cbind(wl1, wl2, wl3, se1, se2, se3)~group, data=WeightLoss))
Anova(wl.mod, imatrix=imatrix, test="Roy")

## mixed-effects models examples:

## Not run: 
	library(nlme)
	example(lme)
	Anova(fm2)

## End(Not run)

## Not run: 
	library(lme4)
	example(glmer)
	Anova(gm1)

## End(Not run)