Displaying 7 results from an estimated 7 matches for "lmerfactorlist".
2010 Jul 13
1
A problem about the package "lme4" in R-2.11.1
...se it to run the
simulation on the Linux system, it appears the following problem.
Attaching package: 'lme4'
The following object(s) are masked from 'package:stats':
AIC
Error in names(argNew)[1] <- names(formals(new))[[1]] :
replacement has length zero
Calls: initial ... lmerFactorList -> lapply -> FUN -> as -> asMethod ->
fac2sparse
Execution halted
However, when I use this package in R-2.11.0, it can work to run the simulation
as I do in R-2.11.1.
How could we fix this problem? Thank you so much.
Sincerely,
Joe
[[alternative HTML version delet...
2011 Feb 25
1
linear model lme4
...f I treated it as a
fixed effect.
For the first case, my formula is:
lmer.result <- lmer(expression ~ cancerClass + (1|beadchip))
For the second case, I want to do:
lmer.result2 <- lmer(expression ~ cancerClass + beadchip)
However, I get an error in the second case:
> Error in lmerFactorList(formula, fr, 0L, 0L):
No random effects terms specified in formula
Is there any way that I can get lmer() to accept a formula without a random
effect?
many thanks
[[alternative HTML version deleted]]
2007 Aug 07
1
lmer() : crossed-random-effects specification
...del? I reviewed Baltes (2007)
"Linear mixed model implementation" and learned how to detect if my
design is nested or crossed, but not how to specify my model once I
know it is crossed as in my case.
If the previous specification is correct, (2) why do I get this error message?
Error in lmerFactorList(formula, fr$mf, !is.null(family)): number of
levels in grouping factor(s) 'idl:schlid' is too large
In addition: Warning messages:
1: numerical expression has 30113 elements: only the first used in: idl:schlid
2: numerical expression has 30113 elements: only the first used in: idl:schlid
M...
2007 Feb 20
1
Simplification of Generalised Linear mixed effects models using glmmPQL
...ork
logLik(m1)##this does not work?
##################this does not work
class(m1) <- "lme"
class(m2) <- "lme"
anova.lme(m1,m2)
#################################
m3<-lmer(dev~env*har*treat+dens + (1|pop/rep), family = Gamma)
## this generates an error
Error in lmerFactorList(formula, mf, fltype) :
number of levels in grouping factor(s) 'rep:pop', 'pop' is too
large
In addition: Warning messages:
1: numerical expression has 1851 elements: only the first used in:
rep:pop
2: numerical expression has 1851 elements: only the first used in:
rep:pop...
2007 Aug 07
0
lmer() - crossed random effects specification
...del? I reviewed Baltes (2007)
"Linear mixed model implementation" and learned how to detect if my
design is nested or crossed, but not how to specify my model once I
know it is crossed as in my case.
If the previous specification is correct, (2) why do I get this error message?
Error in lmerFactorList(formula, fr$mf, !is.null(family)): number of
levels in grouping factor(s) 'idl:schlid' is too large
In addition: Warning messages:
1: numerical expression has 30113 elements: only the first used in: idl:schlid
2: numerical expression has 30113 elements: only the first used in: idl:schlid
M...
2007 Jan 26
0
R crash with modified lmer code
...y <- get(family, mode = "function", envir =
parent.frame())
if (is.function(family))
family <- family()
if (is.null(family$family)) {
print(family)
stop("'family' not recognized")
}
fltype <- mkFltype(family)
FL <- lmerFactorList(formula, mf, fltype)
nFacs <- length(FL) #My
insert
cnames <- with(FL, c(lapply(Ztl, rownames), list(.fixed =
colnames(X))))
nc <- with(FL, sapply(Ztl, nrow))
Ztl <- with(FL, .Call(Ztl_sparse, fl, Ztl))
Ztl[[nFacs]] <- t(ra...
2011 Jun 30
1
Analysing insecticide biossays using lmer
...or 0 to exit
1: lmer(y ~ logdose * geno + (1 | geno/logdose) - 1, family =
"quasibinomial", data = dataf)
2: eval.parent(mc)
3: eval(expr, p)
4: eval(expr, envir, enclos)
5: glmer(formula = y ~ logdose * geno + (1 | geno/logdose) - 1, data =
dataf, family = "quasibinomi
6: lmerFactorList(formula, fr, 0, 0)
7: sapply(seq_along(fl)[-1], function(i) isNested(fl[[i - 1]], fl[[i]]))
8: lapply(X, FUN, ...)
9: FUN(2[[1]], ...)
10: isNested(fl[[i - 1]], fl[[i]])
11: stopifnot(length(f1) == length(f2))
S?lection : summary (model1)
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