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2要因とも対応がない場合（CRF_pqデザイン）�U　[データ数が異なるケース]

ソースコード　　datはデータ行列,pは要因Aの水準数,qは要因Bの水準数

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anova.CRFpq2 <- function(dat,p,q){
        num <- p * q
        ABjk <- c()
        njk <- c()
        my.mean <- c()
        for(i in 1:num){
                ABjk[i] <- sum(dat[,i][!is.na(dat[,i])])
                njk[i] <- length(dat[,i][!is.na(dat[,i])])
                my.mean[i] <- mean(dat[,i][!is.na(dat[,i])])
        }

        ABS <- sum(dat[!is.na(dat)]^2)
        a <- ABjk
        b <- njk
        AB <- sum(ABjk^2 / njk)

        AB.table <- matrix(my.mean,ncol=q,byrow=TRUE)
        AB.row.mean <- apply(AB.table,1,mean)
        AB.col.mean <- apply(AB.table,2,mean)

        G = sum(AB.table)
        X = G ^ 2 / (p * q)
        A = q * sum(AB.row.mean^2)
        B = p * sum(AB.col.mean^2)
        AB2 = sum(AB.table^2)

        nn <- p*q / sum(1/njk)

        SSa <- nn * (A - X)
        SSb <- nn * (B - X)
        SSab <- nn * (AB2 - A - B + X)
        SSwc <- ABS - AB

        dfA <- p - 1
        dfB <- q - 1
        dfAB <- (p - 1) * (q - 1)
        dfWC <- sum(njk) - p * q

        MSa <- SSa / dfA
        MSb <- SSb / dfB
        MSab <- SSab / dfAB
        MSwc <- SSwc / dfWC

        Fa <- MSa / MSwc
        Fb <- MSb / MSwc
        Fab <- MSab / MSwc

        pA <- 1 - pf(Fa,dfA,dfWC)
        pB <- 1 - pf(Fb,dfB,dfWC)
        pAB <- 1 - pf(Fab,dfAB,dfWC)

        result <- matrix(c(
                SSa, dfA, MSa, Fa, pA,
                SSb, dfB, MSb, Fb, pB,
                SSab, dfAB, MSab, Fab, pAB,
                SSwc, dfWC, MSwc, NA, NA), ncol =5, byrow = TRUE)

        dimnames(result) <- list(c(
                "行効果:A", "列効果:B", "交互作用:A*B", "誤差:WC"),c(
                "SS", "df", "MS", "F-value", "p-value"))

        result
}

〜使用例〜

> dat
     [,1] [,2] [,3] [,4]
[1,]    6    3    5    5
[2,]    6    1    4    2
[3,]    4    2    5    4
[4,]    8    2    4    6
[5,]    7   NA   NA    3
[6,]    5   NA   NA    4
> anova.CRFpq2(dat,2,2)
                SS df      MS    F-value     p-value
行効果:A       0.3  1  0.3000  0.2086957 0.653932594
列効果:B      24.3  1 24.3000 16.9043478 0.000816642
交互作用:A*B  14.7  1 14.7000 10.2260870 0.005603254
誤差:WC       23.0 16  1.4375         NA          NA