Naming a random effect in lmer
On Sun, May 24, 2009 at 10:41 PM, Leigh Ann Starcevich <lah at peak.org> wrote:
Thanks for your thoughts on this.? I tried the approach for the grouping variable using the "within" function.? I looked at a subset of my data for which I do not get the deparse error in lmer and compared the results.? The approach using the "within" function to form the grouping variable underestimates that variance component.
But what you tried is not what I wrote. The paste function creates a
character string - that's all. You could create a numeric variable or
a factor by parsing and evaluating the resulting character string but
that is rarely a good way of doing things. See
library(fortunes)
fortune("rethink")
What I wrote was to evaluate the expression
Z29 <- Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z9 + Z9
within the data frame and that does work. See the attached.
I still don't really understand the approach. It is going about
things in an awkward way and I suspect it is the result of someone
with experience in SAS or SPSS trying to emulate their approach in R.
It is (or was, the last time I used either of those systems) common to
name variables like Z2, Z3, Z4, ... so you can access groups of
variables in those languages. In R that is unnecessary because data
can be packaged into arbitrary, self-describing structures, including
matrices. If the natural thing to do with the variables Z2, Z3, ...,
Z13 is to accumulate them in sums then it would be much simpler if you
create a matrix from them and use that.
Strangely, the Zt matrix for the
random effects in the proposed approach does not appear to contain rows for
the Zgrouped variable, although it does report a variance estimate for the
random effect.? The results for the standard approach appear correct for
this simulated data set.? Any other ideas on how to make the grouping work?
Thanks -- Leigh Ann
# Assume that only mb=10 years of data are available to compare to results
# that avoid deparse error
# Group the grouping variable using within
testsamp1<-testsamp
Zs2<- paste("Z",2:9,sep="")
Zsum2 <- paste(Zs2,collapse="+")
testsamp1 <- within(testsamp1, Zgrouped<-Zsum2)
fittest29.1<-lmer(LogY ~ WYear + (1+WYear|Site) +? (1|Zgrouped), data =
testsamp1[testsamp1$WYear<=10,])
Linear mixed model fit by REML
Formula: LogY ~ WYear + (1 + WYear | Site) + (1 | Zgrouped)
?? Data: testsamp1[testsamp1$WYear <= 10, ]
??? AIC??? BIC logLik deviance REMLdev
?-42.96 -26.55? 28.48?? -63.37? -56.96
Random effects:
?Groups?? Name??????? Variance?? Std.Dev. Corr
?Site???? (Intercept) 0.07351401 0.271135
????????? WYear?????? 0.02620422 0.161877 0.057
?Zgrouped (Intercept) 0.00036891 0.019207
?Residual???????????? 0.01065189 0.103208
Number of obs: 77, groups: Site, 7; Zgrouped, 1
Fixed effects:
????????????? Estimate Std. Error t value
(Intercept)? 0.9857992? 0.1065573?? 9.251
WYear?????? -0.0002676? 0.0612968? -0.004
Correlation of Fixed Effects:
????? (Intr)
WYear 0.044
# Use the standard formulation for the grouping variable with reduced number
of
# terms to avoid deparse error
Trendformula2 <-as.formula(paste("LogY ~ WYear + (1+WYear|Site) +? (1|",
randommodel=paste(paste(Zs2,collapse="+"), ")")))
fittest29.2<-lmer(Trendformula2, data = testsamp[testsamp$WYear<=10,])
summary(fittest29.2)
Linear mixed model fit by REML
Formula: Trendformula2
?? Data: testsamp[testsamp$WYear <= 10, ]
??? AIC??? BIC logLik deviance REMLdev
?-147.5 -131.1? 80.77?? -167.7? -161.5
Random effects:
?Groups??????????????????????????????? Name??????? Variance? Std.Dev. Corr
?Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z8 + Z9 (Intercept) 0.0095237 0.097589
?Site????????????????????????????????? (Intercept) 0.0765445 0.276667
?????????????????????????????????????? WYear?????? 0.0262909 0.162145 0.046
?Residual????????????????????????????????????????? 0.0011282 0.033589
Number of obs: 77, groups: Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z8 + Z9, 11; Site,
7
Fixed effects:
????????????? Estimate Std. Error t value
(Intercept)? 0.9857992? 0.1183912?? 8.327
WYear?????? -0.0002676? 0.0619989? -0.004
Correlation of Fixed Effects:
????? (Intr)
WYear -0.020
fittest29.1 at Zt
15 x 77 sparse Matrix of class "dgCMatrix"
fittest29.2 at Zt
25 x 77 sparse Matrix of class "dgCMatrix"
At 09:37 AM 5/24/2009 -0500, Douglas Bates wrote:
Hi Bill,
I'm about to take a look at this.? If I understand the issue, very
long expressions for what I call the "grouping factor" of a random
effects term (the expressions on the right hand side of the vertical
bar) are encountering problems with deparse.? I should have realized
that, any time one uses deparse, disaster looms.
I can tell you the reason that the collection of random-effects terms
is being named is partly for the printed form and partly so that terms
with the same grouping factor can be associated.
I guess my simplistic solution to the problem would be to precompute
these sums and give them names, if it is the sum like
Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z8 + Z9
that is important, why not evaluate the sum
testGroupSamp <- within(testGroupSamp, Z29 <- Z2 + Z3 + Z4 + Z5 + Z6 +
Z7 + Z8 + Z9)
and use Z29 as the grouping factor.
Even the use of variables with names like Z1, Z2, ... and the use of
expressions like paste("Z", 2:9, sep = "") is not idiomatic R/S code.
It's an SPSS/SASism.? (You know I never realized before how close the
word "SASism", meaning a construction that is natural in SAS, is to
"Sadism".)? Why not create a matrix Z and evaluate these sums as
matrix/vector products?
Zs2<- paste("Z",2:9,sep="")
On Fri, May 22, 2009 at 5:30 PM, William Dunlap <wdunlap at tibco.com> wrote:
-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of spencerg Sent: Friday, May 22, 2009 3:01 PM To: Leigh Ann Starcevich Cc: r-help at r-project.org Subject: Re: [R] Naming a random effect in lmer [ ... elided statistical advice ... ] If you and your advisor still feel that what you are doing makes sense, I suggest you first get the source code via "svn checkout svn://svn.r-forge.r-project.org/svnroot/lme4" (or by downloading "lme4_0.999375-30.tar.gz" from "http://cran.fhcrc.org/web/packages/lme4/index.html"), then walk through the code line by line using the "debug" function (or "browser" or the "debug" package). From this, you will likely see either (a) how you can do what you want differently to achieve the same result or (b) how to modify the code so it does what you want.
The coding error is right in the error message: ? Error in names(bars) <- unlist(lapply(bars, function(x) deparse(x[[3]]))) and I suspect that traceback() would tell you that came from a call to lmerFactorList. That code implicitly assumes that deparse() will produce a scalar character vector, but it doesn't if the input expression is complicated enough. Changing the ??? deparse(x[[3]]) to ??? deparse(x[[3]])[1] or ??? paste(collapse=" ", deparse(x[[3]])[1]) would fix it.? The first truncates the name and the second my make a very long name. There is at least one other use of that idiom in the lme4 code and your dataset and analysis may require that all of them be fixed.
Hope this helps. Spencer Leigh Ann Starcevich wrote:
Here is a test data set and code. I am including the data set after the code and discussion to make reading easier. Apologies
for the size
of the data set, but my problem occurs when there are a lot of Z
variables. Thanks for your time.
# Enter data below
# Sample code
library(lme4)
mb<- length(unique(testsamp$WYear))
# Create the formula for the set of identically distributed random
effects
Zs<- paste("Z",2:(mb-1)),sep="")
Trendformula <-as.formula(paste("LogY ~ WYear +
(1+WYear|Site) + (1|",
randommodel=paste(paste(Zs,collapse="+"), ")"))) fittest<-lmer(Trendformula, data = testsamp) summary(fittest) # Here I get an error because the name of the random effect is too long to print # in the random effects output (I think). # The error message is: Error in names(bars) <- unlist(lapply(bars, function(x) # deparse(x[[3]]))) : 'names' attribute [3] must be the
same length as
the vector [2]
# However, when fewer Z variables are used in the random portion of
the model,
# there is no error.
# Using only Z2 + ... + Z9 for the random intercept
Zs2<- paste("Z",2:9,sep="")
Trendformula2 <-as.formula(paste("LogY ~ WYear + (1+WYear|Site) +
(1|", randommodel=paste(paste(Zs2,collapse="+"), ")")))
fittest2<-lmer(Trendformula2, data = testsamp)
summary(fittest2)
# Is there a way to either name the set of iid random effects
something else or
# to define a random variable that could be used in the
model to create a
# random intercept? # I have had some success in lme, but it would be helpful for my simulation if I # could conduct this analysis with lmer. My model in lme is
not correctly
# estimating one of the variance components (random Site intercept). # I am using: detach(package:lme4) library(nlme) random.model.lme
<-as.formula(paste("~-1+",paste(paste("Z",2:(mb-1),sep=""),col
lapse="+")))
n<-dim(testsamp)[1] testsampgroup <- rep(1,n) testsamp.lme <-cbind(testsamp,testsampgroup) testgroupSamp<- groupedData(LogY ~ WYearCen|testsampgroup,
inner= ~Site,
data= data.frame(testsamp.lme)) fittest3<-lme(LogY ~ WYearCen, random= pdBlocked(list(pdIdent(~-1+WYearCen:as.factor(Site)), pdIdent(~-1+as.factor(Site)), pdIdent(random.model.lme))),data= testgroupSamp) summary(fittest3) VarCorr(fittest3) # Data testsamp <- structure(list(Year = c(2008, 2008, 2008, 2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022, 2022, 2022, 2022, 2022), WYear = c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14), Site = c(4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94, 4, 18, 26, 40, 67, 75, 94), LogY = c(0.848648866298552, 0.809143925760456, 0.734173952725014, 1.46749967704437, 0.716106254860468, 0.843871512951468, 1.09120092433378, 0.800893809796851, 0.996674977596997, 0.917613604481207, 1.71928772722884, 0.797853604855215, 0.922298691760041, 0.964654422529188, 0.782903180421921, 1.13457969553106, 1.21628917384868,
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0.0207056819701757, 0.0207056819701757, 0.0207056819701757, 0.0207056819701757, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, 0.371109530696229, 0.371109530696229, 0.371109530696229, 0.371109530696229, 0.371109530696229, 0.371109530696229, 0.371109530696229, -0.392270282599825, -0.392270282599825, -0.392270282599825, -0.392270282599825, -0.392270282599825, -0.392270282599825, -0.392270282599825, -0.0361780597061064, -0.0361780597061064, -0.0361780597061064, -0.0361780597061064, -0.0361780597061064, -0.0361780597061064, -0.0361780597061064, 0.356774827793781, 0.356774827793781, 0.356774827793781, 0.356774827793781, 0.356774827793781, 0.356774827793781, 0.356774827793781, -0.0061434441010547, -0.0061434441010547, -0.0061434441010547, -0.0061434441010547, -0.0061434441010547, -0.0061434441010547, -0.0061434441010547, -0.344032869658285, -0.344032869658285, -0.344032869658285, -0.344032869658285, -0.344032869658285, -0.344032869658285, -0.344032869658285, -0.00614344410104096, -0.00614344410104096, -0.00614344410104096, -0.00614344410104096, -0.00614344410104096, -0.00614344410104096, -0.00614344410104096, 0.356774827793798, 0.356774827793798, 0.356774827793798, 0.356774827793798, 0.356774827793798, 0.356774827793798, 0.356774827793798, -0.0361780597061478, -0.0361780597061478, -0.0361780597061478, -0.0361780597061478, -0.0361780597061478, -0.0361780597061478, -0.0361780597061478, -0.392270282599797, -0.392270282599797, -0.392270282599797, -0.392270282599797, -0.392270282599797, -0.392270282599797, -0.392270282599797, 0.371109530696221, 0.371109530696221, 0.371109530696221, 0.371109530696221, 0.371109530696221, 0.371109530696221, 0.371109530696221, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, -0.141981819224062, 0.0207056819701758, 0.0207056819701758, 0.0207056819701758, 0.0207056819701758, 0.0207056819701758, 0.0207056819701758, 0.0207056819701758 ), Z11 = c(-0.00849937774690609, -0.00849937774690609, -0.00849937774690609, -0.00849937774690609, -0.00849937774690609, -0.00849937774690609, -0.00849937774690609, 0.0716376124382085, 0.0716376124382085, 0.0716376124382085, 0.0716376124382085, 0.0716376124382085, 0.0716376124382085, 0.0716376124382085, -0.248910348302249, -0.248910348302249, -0.248910348302249, -0.248910348302249, -0.248910348302249, -0.248910348302249, -0.248910348302249, 0.43225406827121, 0.43225406827121, 0.43225406827121,
0.43225406827121,
0.43225406827121, 0.43225406827121, 0.43225406827121,
-0.307191795709549,
-0.307191795709549, -0.307191795709549, -0.307191795709549, -0.307191795709549, -0.307191795709549, -0.307191795709549, -0.146917815339487, -0.146917815339487, -0.146917815339487, -0.146917815339487, -0.146917815339487, -0.146917815339487, -0.146917815339487, 0.360616455833108, 0.360616455833108, 0.360616455833108, 0.360616455833108, 0.360616455833108, 0.360616455833108, 0.360616455833108, 2.37442501121216e-14, 2.37442501121216e-14, 2.37442501121216e-14, 2.37442501121216e-14, 2.37442501121216e-14, 2.37442501121216e-14, 2.37442501121216e-14, -0.360616455833123, -0.360616455833123, -0.360616455833123, -0.360616455833123, -0.360616455833123, -0.360616455833123, -0.360616455833123, 0.146917815339424, 0.146917815339424, 0.146917815339424, 0.146917815339424, 0.146917815339424, 0.146917815339424, 0.146917815339424, 0.307191795709669, 0.307191795709669, 0.307191795709669, 0.307191795709669, 0.307191795709669, 0.307191795709669, 0.307191795709669, -0.432254068271316, -0.432254068271316, -0.432254068271316, -0.432254068271316, -0.432254068271316, -0.432254068271316, -0.432254068271316, 0.248910348302301, 0.248910348302301, 0.248910348302301, 0.248910348302301, 0.248910348302301, 0.248910348302301, 0.248910348302301, -0.0716376124382226, -0.0716376124382226, -0.0716376124382226, -0.0716376124382226, -0.0716376124382226, -0.0716376124382226, -0.0716376124382226, 0.00849937774690764, 0.00849937774690764, 0.00849937774690764, 0.00849937774690764, 0.00849937774690764, 0.00849937774690764, 0.00849937774690764), Z12 = c(0.00295373795124409,
0.00295373795124409,
0.00295373795124409, 0.00295373795124409, 0.00295373795124409,
0.00295373795124409, 0.00295373795124409, -0.0299593420769036,
-0.0299593420769036, -0.0299593420769036, -0.0299593420769036,
-0.0299593420769036, -0.0299593420769036, -0.0299593420769036,
0.132074282677049, 0.132074282677049, 0.132074282677049,
0.132074282677049, 0.132074282677049, 0.132074282677049,
0.132074282677049, -0.323223324378965, -0.323223324378965,
-0.323223324378965, -0.323223324378965, -0.323223324378965,
-0.323223324378965, -0.323223324378965, 0.450234056282428,
0.450234056282428, 0.450234056282428, 0.450234056282428,
0.450234056282428, 0.450234056282428, 0.450234056282428,
-0.27385370433672, -0.27385370433672, -0.27385370433672,
-0.27385370433672, -0.27385370433672, -0.27385370433672,
-0.27385370433672, -0.15317241090017, -0.15317241090017,
-0.15317241090017, -0.15317241090017, -0.15317241090017,
-0.15317241090017, -0.15317241090017, 0.389893409563986,
0.389893409563986, 0.389893409563986, 0.389893409563986,
0.389893409563986, 0.389893409563986, 0.389893409563986,
-0.153172410899934, -0.153172410899934, -0.153172410899934,
-0.153172410899934, -0.153172410899934, -0.153172410899934,
-0.153172410899934, -0.273853704336934, -0.273853704336934,
-0.273853704336934, -0.273853704336934, -0.273853704336934,
-0.273853704336934, -0.273853704336934, 0.450234056282481,
0.450234056282481, 0.450234056282481, 0.450234056282481,
0.450234056282481, 0.450234056282481, 0.450234056282481,
-0.323223324378928, -0.323223324378928, -0.323223324378928,
-0.323223324378928, -0.323223324378928, -0.323223324378928,
-0.323223324378928, 0.132074282677017, 0.132074282677017,
0.132074282677017, 0.132074282677017, 0.132074282677017,
0.132074282677017, 0.132074282677017, -0.0299593420768935,
-0.0299593420768935, -0.0299593420768935, -0.0299593420768935,
-0.0299593420768935, -0.0299593420768935, -0.0299593420768935,
0.00295373795124293, 0.00295373795124293, 0.00295373795124293,
0.00295373795124293, 0.00295373795124293, 0.00295373795124293,
0.00295373795124293), Z13 = c(-0.000820388989417742,
-0.000820388989417742,
-0.000820388989417742, -0.000820388989417742, -0.000820388989417742,
-0.000820388989417742, -0.000820388989417742, 0.00984466787301128,
0.00984466787301128, 0.00984466787301128, 0.00984466787301128,
0.00984466787301128, 0.00984466787301128, 0.00984466787301128,
-0.0533252843121323, -0.0533252843121323, -0.0533252843121323,
-0.0533252843121323, -0.0533252843121323, -0.0533252843121323,
-0.0533252843121323, 0.170640909798784, 0.170640909798784,
0.170640909798784, 0.170640909798784, 0.170640909798784,
0.170640909798784, 0.170640909798784, -0.351946876459913,
-0.351946876459913, -0.351946876459913, -0.351946876459913,
-0.351946876459913, -0.351946876459913, -0.351946876459913,
0.469262501946444, 0.469262501946444, 0.469262501946444,
0.469262501946444, 0.469262501946444, 0.469262501946444,
0.469262501946444, -0.351946876459763, -0.351946876459763,
-0.351946876459763, -0.351946876459763, -0.351946876459763,
-0.351946876459763, -0.351946876459763, 1.30897668096353e-14,
1.30897668096353e-14, 1.30897668096353e-14, 1.30897668096353e-14,
1.30897668096353e-14, 1.30897668096353e-14, 1.30897668096353e-14,
0.351946876459646, 0.351946876459646, 0.351946876459646,
0.351946876459646, 0.351946876459646, 0.351946876459646,
0.351946876459646, -0.469262501946203, -0.469262501946203,
-0.469262501946203, -0.469262501946203, -0.469262501946203,
-0.469262501946203, -0.469262501946203, 0.351946876459683,
0.351946876459683, 0.351946876459683, 0.351946876459683,
0.351946876459683, 0.351946876459683, 0.351946876459683,
-0.170640909798657, -0.170640909798657, -0.170640909798657,
-0.170640909798657, -0.170640909798657, -0.170640909798657,
-0.170640909798657, 0.0533252843120879, 0.0533252843120879,
0.0533252843120879, 0.0533252843120879, 0.0533252843120879,
0.0533252843120879, 0.0533252843120879, -0.009844667873002,
-0.009844667873002, -0.009844667873002, -0.009844667873002,
-0.009844667873002, -0.009844667873002, -0.009844667873002,
0.000820388989416903, 0.000820388989416903, 0.000820388989416903,
0.000820388989416903, 0.000820388989416903, 0.000820388989416903,
0.000820388989416903), Z14 = c(0.000157883934625923,
0.000157883934625923,
0.000157883934625923, 0.000157883934625923, 0.000157883934625923,
0.000157883934625923, 0.000157883934625923, -0.00221037508476475,
-0.00221037508476475, -0.00221037508476475, -0.00221037508476475,
-0.00221037508476475, -0.00221037508476475, -0.00221037508476475,
0.0143674380509829, 0.0143674380509829, 0.0143674380509829,
0.0143674380509829, 0.0143674380509829, 0.0143674380509829,
0.0143674380509829, -0.0574697522039801, -0.0574697522039801,
-0.0574697522039801, -0.0574697522039801, -0.0574697522039801,
-0.0574697522039801, -0.0574697522039801, 0.158041818561072,
0.158041818561072, 0.158041818561072, 0.158041818561072,
0.158041818561072, 0.158041818561072, 0.158041818561072,
-0.316083637122359, -0.316083637122359, -0.316083637122359,
-0.316083637122359, -0.316083637122359, -0.316083637122359,
-0.316083637122359, 0.474125455683776, 0.474125455683776,
0.474125455683776, 0.474125455683776, 0.474125455683776,
0.474125455683776, 0.474125455683776, -0.541857663638757,
-0.541857663638757, -0.541857663638757, -0.541857663638757,
-0.541857663638757, -0.541857663638757, -0.541857663638757,
0.474125455683927, 0.474125455683927, 0.474125455683927,
0.474125455683927, 0.474125455683927, 0.474125455683927,
0.474125455683927, -0.316083637122536, -0.316083637122536,
-0.316083637122536, -0.316083637122536, -0.316083637122536,
-0.316083637122536, -0.316083637122536, 0.158041818561182,
0.158041818561182, 0.158041818561182, 0.158041818561182,
0.158041818561182, 0.158041818561182, 0.158041818561182,
-0.0574697522040225, -0.0574697522040225, -0.0574697522040225,
-0.0574697522040225, -0.0574697522040225, -0.0574697522040225,
-0.0574697522040225, 0.0143674380509932, 0.0143674380509932,
0.0143674380509932, 0.0143674380509932, 0.0143674380509932,
0.0143674380509932, 0.0143674380509932, -0.00221037508476627,
-0.00221037508476627, -0.00221037508476627, -0.00221037508476627,
-0.00221037508476627, -0.00221037508476627, -0.00221037508476627,
0.00015788393462604, 0.00015788393462604, 0.00015788393462604,
0.00015788393462604, 0.00015788393462604, 0.00015788393462604,
0.00015788393462604), WYearCen = c(-7, -7, -7, -7, -7, -7,
-7, -6, -6, -6, -6, -6, -6, -6, -5, -5, -5, -5, -5, -5, -5,
-4, -4, -4, -4, -4, -4, -4, -3, -3, -3, -3, -3, -3, -3, -2,
-2, -2, -2, -2, -2, -2, -1, -1, -1, -1, -1, -1, -1, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5,
5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7)), .Names = c("Year",
"WYear", "Site", "LogY", "Y", "Z2", "Z3", "Z4", "Z5", "Z6", "Z7",
"Z8", "Z9", "Z10", "Z11", "Z12", "Z13", "Z14", "WYearCen"),
row.names
= c(4L, 18L, 26L, 40L, 67L, 75L, 94L, 104L, 118L, 126L, 140L, 167L, 175L, 194L, 204L, 218L, 226L, 240L, 267L, 275L, 294L, 304L, 318L, 326L, 340L, 367L, 375L, 394L, 404L, 418L, 426L, 440L, 467L, 475L, 494L, 504L, 518L, 526L, 540L, 567L, 575L, 594L, 604L, 618L, 626L, 640L, 667L, 675L, 694L, 704L, 718L, 726L, 740L, 767L, 775L, 794L, 804L, 818L, 826L, 840L, 867L, 875L, 894L, 904L, 918L, 926L, 940L, 967L, 975L, 994L, 1004L, 1018L, 1026L, 1040L, 1067L, 1075L, 1094L, 1104L, 1118L, 1126L, 1140L, 1167L, 1175L, 1194L, 1204L, 1218L, 1226L, 1240L, 1267L, 1275L, 1294L, 1304L, 1318L, 1326L, 1340L, 1367L, 1375L, 1394L, 1404L, 1418L, 1426L, 1440L, 1467L, 1475L, 1494L), class = "data.frame") At 11:17 AM 5/22/2009 -0700, spencerg wrote:
The first exaample on the "lmer" help page uses a formula
"Reaction ~
Days + (Days|Subject)". Here, "Subject" is the name of a column in the data.frame "sleepstudy", with levels "308", "309", ... . Does this answer your question? If no, please provide commented, minimal, self-contained, reproducible code, as requested in the posting guide "http://www.R-project.org/posting-guide.html". Your example is not "self-contained, reproducible". Hope this helps. Spencer Leigh Ann Starcevich wrote:
Dear guRus: I am using lmer for a mixed model that includes a random
intercept
for a set of effects that have the same distribution, Normal(0, sig2b). This set of effects is of variable size, so I am using an as.formula statement to create the formula for lmer. For
example, if
the set of random effects has dimension 8, then the lmer call is:
Zs<- paste("Z",1:mb,sep="")
Trendformula <-as.formula(paste("LogY ~ WYear + (1+WYear|Site) +
(1|", paste(paste(Zs,collapse="+"), ")")))
fit2.4a<-lmer(Trendformula, data = testsamp)
which, for mb=8, expands to:
fit1<-lmer(LogY ~ WYear + (1 | Site) + (1 | Year) + (1 |
Z1+ Z2 + Z3
+ Z4 + Z5 + Z6 + Z7 + Z8), data = testsamp) I have no problems with this. However, if the set of
random effects
has a dimension of 30, then the lmer call is: fit2<-lmer(LogY ~ WYear + (1 | Site) + (1 | Year) + (1 |
Z1+Z2 + Z3
+ Z4 + Z5 + Z6 + Z7 + Z8 + Z9 + Z10 + Z11 + Z12 + Z13 +
Z14 + Z15 +
Z16 + Z17 + Z18 + Z19 + Z20 + Z21 + Z22 + Z23 + Z24 + Z25 + Z26 + Z27 + Z28 + Z29+ Z30), data = testsamp) In this case, I get an error because the name "Z1+Z2 + Z3
+ Z4 + Z5
+ Z6 + Z7 + Z8 + Z9 + Z10 + Z11 + Z12 + Z13 + Z14 + Z15 +
Z16 + Z17
+ Z18 + Z19 + Z20 + Z21 + Z22 + Z23 + Z24 + Z25 + Z26 +
Z27 + Z28 +
Z29+ Z30" is too long to print in the output. Is there any way to name the random effect in lmer so that the shorter (and more descriptive) name may be used and the error avoided? Or
is there a
way to combine these into a single variable prior to the lmer function call? In SAS, I am able to parameterize these as
a Toeplitz
structure with bandwidth 1. Thanks for any help. Leigh Ann Starcevich Doctoral student Oregon State University Corvallis, Oregon [[alternative HTML version deleted]]
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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library(lme4)
Loading required package: Matrix Loading required package: lattice Attaching package: 'Matrix' The following object(s) are masked from package:stats : xtabs The following object(s) are masked from package:base : rcond
sessionInfo()
R version 2.9.0 (2009-04-17) i486-pc-linux-gnu locale: LC_CTYPE=en_US.UTF-8;LC_NUMERIC=C;LC_TIME=en_US.UTF-8;LC_COLLATE=en_US.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_US.UTF-8;LC_PAPER=en_US.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_US.UTF-8;LC_IDENTIFICATION=C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] lme4_0.999375-31 Matrix_0.999375-26 lattice_0.17-25 loaded via a namespace (and not attached): [1] grid_2.9.0
load("testsamp.rda")
testsamp <- within(testsamp, testsamp, Z29 <- Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z8 + Z9)
str(testsamp)
'data.frame': 105 obs. of 20 variables: $ Year : num 2008 2008 2008 2008 2008 ... $ WYear : num 0 0 0 0 0 0 0 1 1 1 ... $ Site : num 4 18 26 40 67 75 94 4 18 26 ... $ LogY : num 0.849 0.809 0.734 1.467 0.716 ... $ Y : num 2.34 2.25 2.08 4.34 2.05 ... $ Z2 : num 0.472 0.472 0.472 0.472 0.472 ... $ Z3 : num -0.456 -0.456 -0.456 -0.456 -0.456 ... $ Z4 : num 0.394 0.394 0.394 0.394 0.394 ... $ Z5 : num -0.308 -0.308 -0.308 -0.308 -0.308 ... $ Z6 : num 0.219 0.219 0.219 0.219 0.219 ... $ Z7 : num -0.142 -0.142 -0.142 -0.142 -0.142 ... $ Z8 : num 0.0833 0.0833 0.0833 0.0833 0.0833 ... $ Z9 : num -0.044 -0.044 -0.044 -0.044 -0.044 ... $ Z10 : num 0.0207 0.0207 0.0207 0.0207 0.0207 ... $ Z11 : num -0.0085 -0.0085 -0.0085 -0.0085 -0.0085 ... $ Z12 : num 0.00295 0.00295 0.00295 0.00295 0.00295 ... $ Z13 : num -0.00082 -0.00082 -0.00082 -0.00082 -0.00082 ... $ Z14 : num 0.000158 0.000158 0.000158 0.000158 0.000158 ... $ WYearCen: num -7 -7 -7 -7 -7 -7 -7 -6 -6 -6 ... $ Z29 : num 0.218 0.218 0.218 0.218 0.218 ...
xtabs(~ Z29, testsamp)
Z29
-1.63314896653237 -0.592612960445885 -0.394922672042026 -0.233838363771464
7 7 7 7
-0.214767257996307 -0.0925109179084174 -0.0376120692964267 0.0181504978689960
7 7 7 7
0.0208109028608321 0.129704969588931 0.197693713890356 0.21834978504526
7 7 7 7
0.227443614453709 0.269153880224658 2.11810584406015
7 7 7
(fittest29.1 <- lmer(LogY ~ WYear + (1+WYear|Site) + (1|Z29), testsamp, subset = WYear<=10))
Linear mixed model fit by REML
Formula: LogY ~ WYear + (1 + WYear | Site) + (1 | Z29)
Data: testsamp
Subset: WYear <= 10
AIC BIC logLik deviance REMLdev
-147.5 -131.1 80.77 -167.7 -161.5
Random effects:
Groups Name Variance Std.Dev. Corr
Z29 (Intercept) 0.0095237 0.097589
Site (Intercept) 0.0765445 0.276667
WYear 0.0262909 0.162145 0.046
Residual 0.0011282 0.033589
Number of obs: 77, groups: Z29, 11; Site, 7
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.9857992 0.1183912 8.327
WYear -0.0002676 0.0619989 -0.004
Correlation of Fixed Effects:
(Intr)
WYear -0.020
proc.time()
user system elapsed 26.245 0.188 26.512