불균형 데이터를 사용하는 단방향 반복 측정 ANOVA

Question

저는 R을 처음 접했습니다. 잠시 동안 (R 도움말과 함께)이 포럼을 읽었지만 처음 게시됩니다. 여기에 각 오류를봤을 때, 나는 아직도 내 실수를 찾아 내고 고칠 수 없다.

불평등 한 샘플 크기의 단방향 반복 측정 ANOVA를 실행하려고합니다. 다음은 사용중인 데이터 및 코드의 장난감 버전입니다. 복잡성에 대한

Error in qr.qty(qr.e, resp) : 
    invalid to change the storage mode of a factor 
In addition: Warning messages: 
1: In model.response(mf, "numeric") : 
    using type = "numeric" with a factor response will be ignored 
2: In Ops.factor(y, z$residuals) : - not meaningful for factors 
3: In aov(probability ~ bin + Error(subject/bin), data = dataFrame) : 
    Error() model is singular 

죄송합니다 (

가정 : (.이 중요한 경우, 내 실제 데이터는 각각의 빈에서 최대 14 ~ 20 값으로 12 개 쓰레기통이)

## the data: average probability for a subject, given reaction time bin 
bin1=c(0.37,0.00,0.00,0.16,0.00,0.00,0.08,0.06) 
bin2=c(0.33,0.21,0.000,1.00,0.00,0.00,0.00,0.00,0.09,0.10,0.04) 
bin3=c(0.07,0.41,0.07,0.00,0.10,0.00,0.30,0.25,0.08,0.15,0.32,0.18) 

## creating the data frame 

# dependent variable column 
probability=c(bin1,bin2,bin3) 

# condition column 
bin=c(rep("bin1",8),rep("bin2",11),rep("bin3",12)) 

# subject column (in the order that will match them up with their respective 
# values in the dependent variable column) 
subject=c("S2","S3","S5","S7","S8","S9","S11","S12","S1","S2","S3","S4","S7", 
    "S9","S10","S11","S12","S13","S14","S1","S2","S3","S5","S7","S8","S9","S10", 
    "S11","S12","S13","S14") 

# putting together the data frame 
dataFrame=data.frame(cbind(probability,bin,subject)) 

## one-way repeated measures anova 
test=aov(probability~bin+Error(subject/bin),data=dataFrame) 

이 내가 얻을 오류는 그것은 복잡하다; 그것은 나에게있다.) 시간 내 주셔서 감사합니다. 불균형 반복-조치 디자인에 대한

Answer 1

, 그것은합니다 (nlme 패키지) 사용 lme에 쉬운 수 있습니다 :

## this should be the same as the data you constructed above, just 
## a slightly more compact way to do it. 
datList <- list(
    bin1=c(0.37,0.00,0.00,0.16,0.00,0.00,0.08,0.06), 
    bin2=c(0.33,0.21,0.000,1.00,0.00,0.00,0.00,0.00,0.09,0.10,0.04), 
    bin3=c(0.07,0.41,0.07,0.00,0.10,0.00,0.30,0.25,0.08,0.15,0.32,0.18)) 
subject=c("S2","S3","S5","S7","S8","S9","S11","S12", 
      "S1","S2","S3","S4","S7","S9","S10","S11","S12","S13","S14", 
      "S1","S2","S3","S5","S7","S8","S9","S10","S11","S12","S13","S14") 
d <- data.frame(probability=do.call(c,datList), 
       bin=paste0("bin",rep(1:3,sapply(datList,length))), 
       subject) 

library(nlme) 
m1 <- lme(probability~bin,random=~1|subject/bin,data=d) 
summary(m1) 

유일한 진짜 문제 해석 등 의 일부 측면이 꽤 있다는 것입니다 고전적인 2 차 제곱합 분해 접근법 (예 : 분산 구성 요소의 유의미 테스트를 수행하는 것이 매우 까다로운 경우)과는 거리가 멀다. 이 방향으로 가고 있다면 Pinheiro and Bates (Springer, 2000)가 적극 추천합니다.

그것은 수도 좋은 생각 확인 당신이 대응은 무슨 일이 일어나고 있는지/어디에 볼 수 있습니다/시뮬레이션 일부 균형 데이터를 구성하는 두 aov() 및 lme()와 분석을 수행, 출력에서보고, 만들 수 .