[R-sig-ME] Mixed-model polytomous ordered logistic regression ?

Ken Knoblauch ken.knoblauch at inserm.fr
Tue Jan 4 09:36:26 CET 2011


Emmanuel Charpentier <emm.charpentier at ...> writes:

> 
> Le lundi 03 janvier 2011 à 19:54 +0100, Rune Haubo a écrit :
> > Hi Emmanuel,
> > 
> > clmm() currently only allows for one random intercept, 
but Ken
> > Knoblauch proposed a polmer() function
> > (http://finzi.psych.upenn.edu/R-sig-mixed-models/
2010q2/003778.html)
> > that seem to be an extension of glmer() similar to your glm() 
hack,
> > but for mixed models.
> 
> That was my point, and Ken Kornblauch has been kind 
enough to send me
> his code, which seems quite good. I have not yet worked 
on his code (and
> probably won't for the next month at least), but it's 
probably a good
> start.
> 

Because the posted code is full of bugs and because I'm far from 
convinced that my code is "good", let alone "quite", I'm posting the 
latest version with an example so that others won't be mislead by 
the previously posted  version and to encourage somebody with a 
bit more time and motivation either to improve the code or 
propose something better.

I also repost the Faces data set (making this an overly long post) 
which comes from 

Maloney, L. T., and Dal Martello, M. F. (2006). Kin recognition 
and the perceived facial similarity of children. Journal of Vision, 
6(10):4, 1047–1056, http://journalofvision.org/6/10/4/.

One thing that you will notice in the example below is the odd 
way in  which I'm forced (because of my limited programming 
skills) to specify the random effects in the random terms.

All the caveats of Emmanuel apply as far as I understand it.

polmer <- function (formula, data, lnk = "logit", cor = FALSE, ...) 
{
    require(lme4)
    fxForm <- lme4:::nobars(formula)
    fxForm[[2]] <- NULL
    rTerms <- lme4:::findbars(formula)
    ranTerms <- sapply(rTerms, deparse)
    fixTerms <- labels(terms(fxForm))
    ranNames <- as.vector(sapply(ranTerms, function(x) strsplit(x, 
        "\\| ")[[1]][2]))
    LeftRanNames <- as.vector(sapply(ranTerms, function(x) 
       strsplit(x,  " | ", fix = TRUE)[[1]][1]))
    NoRanEff <- !(length(ranTerms) > 0)
    Resp <- ordered(data[[as.character(formula[[2]])]])
    Cuts <- as.numeric(levels(Resp)[-length(levels(Resp))])
    cumRat <- as.vector(sapply(Cuts, function(x) Resp <= x))
    fRat <- gl(length(Cuts), nrow(data), nrow(data) * length(Cuts))
    X <- model.matrix(~fRat - 1)
    labs <- sapply(Cuts, function(x) paste(x, "|", x + 1, sep = ""))
    colnames(X) <- labs
    fX <- -model.matrix(fxForm, data)[, -1]
    fX.names <- if (inherits(fX, "matrix")) 
        colnames(fX)
    else paste(fixTerms, levels(data[[fixTerms]])[2], sep = "")
    fX <- kronecker(matrix(rep(1, length(Cuts)), nc = 1), fX)
    X <- cbind(X, fX)
    colnames(X)[-seq(length(Cuts))] <- fX.names
    p.df <- data.frame(cumRat = cumRat, X = X)
    names(p.df) <- c("cumRat", colnames(X))
    frm <- if (!NoRanEff) {
        tmp <- sapply(seq_len(length(ranNames)), function(x) 
            rep(data[[ranNames[x]]], length(Cuts)))
        for (ix in seq_len(ncol(tmp))) assign(ranNames[ix], tmp[, 
            ix])
        rxForm <- paste(paste("(", ranTerms, ")", sep = "", 
            collapse = " + "), " - 1")
        as.formula(paste("cumRat ~ .  + ", rxForm))
    }
    else as.formula("cumRat ~ . - 1")
    for (ix in LeftRanNames) if (ix != "1") 
        assign(ix, rep(data[[ix]], each = length(Cuts)))
    CLL <- if (NoRanEff) 
        substitute(glm(FRM, data = p.df, family = binomial(LNK), 
            ...), list(FRM = frm, LNK = lnk))
    else substitute(glmer(FRM, data = p.df, family = binomial(LNK), 
        ...), list(FRM = frm, LNK = lnk))
    res <- eval(CLL)
    if (inherits(res, "mer")) 
        print(res, cor = cor)
    else print(res)
    invisible(res)
}


Faces <- structure(list(SimRating = c(10, 10, 6, 1, 10, 
9, 9, 8, 9, 0, 
5, 3, 9, 10, 0, 1, 2, 6, 0, 7, 4, 1, 5, 0, 1, 0, 7, 0, 1, 0, 
7, 5, 8, 3, 10, 9, 5, 9, 9, 8, 8, 10, 10, 9, 8, 2, 7, 8, 0, 10, 
0, 0, 4, 0, 7, 0, 10, 7, 3, 0, 9, 8, 2, 3, 10, 4, 2, 1, 4, 0, 
1, 5, 6, 10, 9, 5, 2, 8, 1, 7, 2, 2, 6, 0, 6, 1, 4, 2, 5, 1, 
9, 3, 4, 0, 9, 5, 8, 3, 7, 4, 6, 2, 3, 9, 4, 0, 2, 4, 0, 4, 3, 
4, 3, 1, 4, 0, 2, 0, 2, 0, 5, 5, 7, 6, 7, 7, 8, 3, 8, 4, 4, 3, 
2, 7, 0, 6, 5, 7, 5, 2, 1, 3, 7, 0, 5, 1, 2, 1, 3, 1, 10, 6, 
3, 10, 9, 6, 3, 8, 10, 9, 4, 8, 9, 10, 8, 3, 6, 9, 3, 10, 3, 
4, 9, 3, 8, 9, 6, 3, 4, 3, 6, 6, 3, 4, 8, 9, 7, 7, 8, 2, 5, 6, 
4, 6, 6, 3, 4, 5, 2, 7, 3, 2, 5, 3, 4, 2, 5, 2, 4, 2, 9, 7, 8, 
0, 10, 10, 9, 7, 9, 7, 10, 8, 8, 9, 7, 0, 5, 3, 0, 7, 3, 0, 6, 
0, 7, 2, 8, 0, 6, 0, 9, 10, 4, 0, 10, 10, 10, 6, 8, 1, 9, 5, 
0, 6, 2, 0, 8, 10, 0, 5, 4, 3, 2, 1, 5, 0, 9, 0, 4, 0, 10, 10, 
8, 4, 10, 10, 9, 9, 9, 2, 8, 7, 9, 10, 3, 2, 5, 7, 3, 6, 4, 2, 
7, 3, 3, 4, 8, 4, 7, 0, 10, 7, 7, 0, 10, 8, 9, 3, 9, 1, 8, 8, 
0, 9, 6, 0, 2, 8, 0, 7, 5, 0, 2, 0, 2, 0, 7, 0, 1, 0, 4, 3, 0, 
2, 6, 8, 3, 7, 3, 1, 2, 5, 2, 5, 2, 2, 0, 0, 0, 2, 0, 0, 2, 0, 
0, 0, 1, 0, 1, 0, 9, 4, 6, 8, 8, 10, 9, 10, 3, 4, 6, 1, 6, 3, 
0, 0, 5, 6, 0, 1, 6, 2, 5, 0, 3, 0, 5, 0, 0, 0, 6, 8, 7, 3, 9, 
9, 9, 3, 3, 0, 8, 7, 9, 8, 7, 0, 5, 2, 0, 0, 0, 0, 6, 0, 0, 0, 
1, 0, 0, 0, 8, 6, 6, 1, 9, 8, 10, 7, 9, 2, 0, 7, 8, 10, 8, 2, 
6, 5, 0, 5, 0, 0, 0, 0, 1, 4, 7, 0, 0, 0, 7, 2, 7, 1, 5, 0, 7, 
3, 9, 6, 3, 4, 9, 10, 2, 0, 2, 4, 1, 3, 5, 0, 7, 3, 4, 3, 8, 
3, 0, 0, 10, 6, 2, 4, 8, 6, 8, 8, 6, 6, 7, 4, 8, 10, 4, 1, 6, 
3, 3, 5, 0, 0, 9, 3, 1, 0, 0, 0, 0, 0, 10, 3, 10, 9, 7, 7, 9, 
9, 4, 8, 10, 8, 5, 10, 5, 9, 4, 1, 1, 1, 0, 10, 2, 0, 9, 10, 
2, 6, 8, 4, 10, 10, 8, 7, 10, 10, 10, 10, 8, 7, 10, 4, 10, 10, 
10, 5, 8, 8, 3, 9, 8, 8, 6, 9, 10, 10, 10, 2, 10, 5, 8, 7, 5, 
4, 8, 9, 7, 6, 8, 2, 6, 5, 5, 6, 1, 2, 4, 4, 1, 4, 2, 3, 4, 0, 
4, 1, 4, 2, 6, 0, 10, 8, 0, 8, 10, 9, 10, 9, 7, 2, 7, 8, 5, 9, 
3, 4, 7, 9, 2, 10, 3, 1, 1, 5, 2, 1, 7, 0, 3, 3, 4, 2, 4, 8, 
7, 6, 4, 3, 3, 2, 3, 2, 1, 8, 6, 0, 1, 4, 2, 6, 2, 1, 5, 0, 3, 
0, 6, 1, 1, 1, 6, 5, 10, 3, 10, 10, 10, 10, 10, 4, 10, 4, 10, 
7, 10, 5, 6, 9, 0, 5, 0, 0, 4, 0, 1, 0, 10, 0, 1, 5, 9, 4, 9, 
8, 10, 7, 9, 9, 8, 0, 7, 4, 5, 9, 1, 1, 2, 7, 0, 6, 3, 6, 2, 
8, 7, 1, 5, 3, 4, 2, 9, 9, 9, 8, 10, 10, 1, 2, 7, 3, 10, 1, 5, 
8, 0, 1, 7, 8, 0, 7, 7, 4, 9, 2, 6, 2, 8, 0, 2, 0, 8, 7, 5, 6, 
10, 8, 8, 8, 8, 5, 7, 6, 8, 7, 4, 4, 5, 6, 1, 5, 2, 5, 2, 3, 
4, 5, 5, 4, 3, 0, 9, 6, 8, 8, 9, 4, 8, 6, 9, 7, 3, 7, 3, 9, 4, 
6, 3, 8, 3, 7, 3, 3, 3, 2, 5, 1, 8, 2, 4, 3, 10, 6, 8, 6, 10, 
10, 8, 8, 8, 5, 7, 7, 9, 9, 6, 0, 5, 3, 0, 7, 2, 0, 7, 1, 5, 
1, 10, 1, 8, 0, 10, 6, 10, 9, 8, 8, 8, 8, 10, 5, 9, 8, 10, 10, 
0, 0, 8, 7, 0, 9, 0, 3, 4, 5, 4, 0, 7, 0, 1, 0, 8, 7, 10, 6, 
8, 8, 10, 8, 4, 0, 9, 5, 0, 9, 8, 3, 4, 10, 0, 5, 6, 3, 8, 4, 
1, 0, 7, 0, 1, 0, 5, 2, 3, 3, 9, 5, 3, 4, 7, 0, 6, 3, 2, 7, 2, 
4, 2, 6, 1, 5, 1, 0, 5, 1, 1, 0, 4, 1, 2, 1, 8, 9, 5, 5, 8, 9, 
7, 7, 7, 1, 2, 7, 8, 7, 2, 2, 5, 5, 3, 5, 5, 7, 3, 3, 4, 2, 7, 
1, 3, 2), sibs = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = c("0", 
"1")), agediff = c(29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 
45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 
60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 
15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 
45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 
40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 
37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 
56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 
44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 
70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 
0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 
15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 
15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 
5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 
68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 
36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 
45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 
60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 
15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 
45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 
40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 
37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 
56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 
44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 
70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 
0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 
15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 
15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 
5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 
68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 
36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 
45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 
60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 
15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 
45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 
40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 
37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 
56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 
44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 
70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 
0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 
15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 
15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 
5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 
68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 
36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 
45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 
60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 
15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 
45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 
40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 
37, 47, 44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 
56, 27, 70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 
44, 25, 0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 
70, 57, 15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 
0, 26, 15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 
15, 33, 5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 
15, 58, 68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 
5, 44, 36, 15, 60, 14, 45, 29, 37, 47, 44, 25, 0, 26, 15, 58, 
68, 60, 45, 71, 15, 67, 40, 63, 56, 27, 70, 57, 15, 33, 5, 44, 
36, 15, 60, 14, 45), gendiff = structure(c(1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L), class = "factor", 
.Label = c("diff", "same")), Obs = structure(c(1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 
28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 
31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 32L, 32L), .Label = c("S1", "S2", "S3", "S4", 
"S5", "S6", "S7", "S8", "S9", "S10", "S11", "S12", "S13", "S14", 
"S15", "S16", "S17", "S18", "S19", "S20", "S21", "S22", "S23", 
"S24", "S25", "S26", "S27", "S28", "S29", "S30", "S31", "S32"
), class = "factor"), Image = structure(c(1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
30L), class = "factor", .Label = c("Im1", "Im2", "Im3", "Im4", 
"Im5", "Im6", "Im7", "Im8", "Im9", "Im10", "Im11", "Im12", "Im13", 
"Im14", "Im15", "Im16", "Im17", "Im18", "Im19", "Im20", "Im21", 
"Im22", "Im23", "Im24", "Im25", "Im26", "Im27", "Im28", "Im29", 
"Im30"))), .Names = c("SimRating", "sibs", "agediff", "gendiff", 
"Obs", "Image"), row.names = c(NA, -960L), class = "data.frame")

polmer(SimRating ~ sibs + (sibs1 + 0 | Obs) + (sibs1 + 0 | Image), 
     Faces)
Generalized linear mixed model fit by the Laplace approximation 
Formula: cumRat ~ . + (sibs1 + 0 | Obs) + (sibs1 + 0 | Image) - 1 
   Data: p.df 
  AIC  BIC logLik deviance
 8432 8525  -4203     8406
Random effects:
 Groups Name  Variance Std.Dev.
 Obs    sibs1 0.94592  0.97258 
 Image  sibs1 1.38249  1.17579 
Number of obs: 9600, groups: Obs, 32; Image, 30

Fixed effects:
       Estimate Std. Error z value Pr(>|z|)    
`0|1`  -1.23966    0.09679 -12.808  < 2e-16 ***
`1|2`  -0.68896    0.08504  -8.101 5.44e-16 ***
`2|3`  -0.16518    0.07929  -2.083   0.0372 *  
`3|4`   0.34346    0.07801   4.403 1.07e-05 ***
`4|5`   0.78978    0.07984   9.891  < 2e-16 ***
`5|6`   1.24687    0.08403  14.838  < 2e-16 ***
`6|7`   1.70021    0.08993  18.907  < 2e-16 ***
`7|8`   2.34703    0.10044  23.367  < 2e-16 ***
`8|9`   3.28946    0.11950  27.527  < 2e-16 ***
`9|10`  4.37926    0.15016  29.164  < 2e-16 ***
sibs1   2.20756    0.35457   6.226 4.78e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Thanks.

best,

Ken

-- 
Ken Knoblauch
Inserm U846
Stem-cell and Brain Research Institute
Department of Integrative Neurosciences
18 avenue du Doyen Lépine
69500 Bron
France
tel: +33 (0)4 72 91 34 77
fax: +33 (0)4 72 91 34 61
portable: +33 (0)6 84 10 64 10
http://www.sbri.fr/members/kenneth-knoblauch.html




More information about the R-sig-mixed-models mailing list