[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
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