Estimado Javier Villacampa Conzález

Copie parte del código que ested envió para ver que pasa en mi 
computadora, solo modifique el if(try ... al principio y lógicamene al 
final, llame las librerías y lo corrí, copio o pego tal cuál la salida 
con algunos errores, no preste atención al porqué, entiendo que el 
código debería estar bien y el problema es con la versión de R.

En mi computadora paso lo siguiente:


R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

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Escriba 'q()' para salir de R.

 > version
                _
platform       x86_64-w64-mingw32
arch           x86_64
os             mingw32
system         x86_64, mingw32
status
major          3
minor          0.2
year           2013
month          09
day            25
svn rev        63987
language       R
version.string R version 3.0.2 (2013-09-25)
nickname       Frisbee Sailing
 > library(lme4)
Loading required package: lattice
Loading required package: Matrix
 > require(LCFdata)
Loading required package: LCFdata
 > library(LMERConvenienceFunctions)
 >
 >   data(eeg)
 >   # restrict to electrode Fz and 80--180 ms window
 >   eeg <- eeg[eeg$Time >= 80 & eeg$Time <= 180, ]
 >   eeg <- eeg[, c("Subject", "Item", "Time", "Fz",
+                  "FreqB", "LengthB", "WMC")]
 >   # mean center FreqB
 >   eeg$FreqBc <- eeg$FreqB - mean(eeg$FreqB)
 >   # split FreqBc into 3 categories. Doesn't make sense,
 >   # but it's merely for example
 >   eeg$FreqBdc <- "high"
 >   eeg$FreqBdc[eeg$FreqBc<=quantile(eeg$FreqBc)[3]] <- "mid"
 >   eeg$FreqBdc[eeg$FreqBc<=quantile(eeg$FreqBc)[2]] <- "low"
 >   eeg$FreqBdc <- as.factor(eeg$FreqBdc)
 >   eeg$FreqBdc <- relevel(eeg$FreqBdc, "low")
 >   # mean center LengthB
 >   eeg$LengthBc <- eeg$LengthB - mean(eeg$LengthB)
 >   # mean center WMC
 >   eeg$WMCc <- eeg$WMC - mean(eeg$WMC)
 >   ### demonstrate plotDensity3d,fnc
 >   plotDensity3d.fnc(x = sort(unique(eeg$WMCc)),
+                     y = sort(unique(eeg$LengthBc)))
 >   ### demonstrate plotDensity3d,fnc
 >   plotRaw3d.fnc(data = eeg, response = "Fz", pred = "WMCc",
+                 intr = "LengthBc", plot.type = "persp", theta = 150)
 >   ### Analyze data
 >   # fit initial model
 >   m0 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2 + (1 | Subject),
+              data = eeg)
Aviso en rankMatrix(X) :
  rankMatrix(<large sparse Matrix>, method = 'tolNorm2') coerces to 
dense matrix.
   Probably should rather use  method = 'qrLINPACK' !?
 >   m1 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2 + (1 | Subject) +
+                (1 | Item), data = eeg)
Aviso en rankMatrix(X) :
  rankMatrix(<large sparse Matrix>, method = 'tolNorm2') coerces to 
dense matrix.
   Probably should rather use  method = 'qrLINPACK' !?
 >   # which model to choose?
 >   relLik(m0, m1)
       AIC(x)       AIC(y)         diff       relLik
3.820644e+05 3.816235e+05 4.408375e+02 5.329033e+95
 >   # choose m1
 >   # check model assumptions
 >   mcp.fnc(m1)
Error en match.arg(type) :
   'arg' should be one of "working", "response", "deviance", "pearson", 
"partial"
 >   # remove outliers
 >   eeg <- romr.fnc(m1, eeg, trim = 2.5)
n.removed = 972
percent.removed = 1.755146
 >   eeg$n.removed
[1] 972
 >   eeg$percent.removed
[1] 1.755146
 >   eeg<-eeg$data
 >   # update model
 >   m1 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2 + (1 | Subject) +
+                (1 | Item), data = eeg)
Aviso en rankMatrix(X) :
  rankMatrix(<large sparse Matrix>, method = 'tolNorm2') coerces to 
dense matrix.
   Probably should rather use  method = 'qrLINPACK' !?
 >   # re-check model assumptions
 >   mcp.fnc(m1)
Error en match.arg(type) :
   'arg' should be one of "working", "response", "deviance", "pearson", 
"partial"
 >   # forward-fit random effect structure (simple for the purposes
 >   # of the example).
 >   m2 <- ffRanefLMER.fnc(model = m1, data = eeg, ran.effects =
+                           c("(0 + LengthBc | Subject)", "(0 + WMCc | 
Item)"),
+                         log.file = FALSE)
evaluating addition of (0+LengthBc|Subject) to model
         log-likelihood ratio test p-value = 0.7470898
         not adding (0+LengthBc|Subject) to model
evaluating addition of (0+WMCc|Item) to model
         log-likelihood ratio test p-value = 5.827713e-78
         adding (0+WMCc|Item) to model
 >   # backfit model m2. In this case, could use bfFixefLMER_t.fnc instead.
 >   m3 <- bfFixefLMER_F.fnc(m2, log.file = FALSE)
Error en qr(model@X) :
   error in evaluating the argument 'x' in selecting a method for 
function 'qr': Error: no hay un slot de nombre "X" para ese objeto de 
clase "lmerMod"
 >   # The calls to ffRanefLMER.fnc and bfFixefLMER_F.fnc could
 >   # be replaced by a call to fitLMER.fnc. In this latter case, however,
 >   # bfFixefLMER_F.fnc would be called first, then the random effect
 >   # structure would be forward fitted, and finally teh fixed effects
 >   # would be backfitted again.
 >   m3b <- fitLMER.fnc(model = m1, ran.effects = c("(0 + LengthBc | 
Subject)",
+                                                  "(0 + WMCc | Item)"),
+                      backfit.on = "F", log.file = FALSE)
======================================================
===              backfitting fixed effects         ===
======================================================
Error en qr(model@X) :
   error in evaluating the argument 'x' in selecting a method for 
function 'qr': Error: no hay un slot de nombre "X" para ese objeto de 
clase "lmerMod"
 >   pamer.fnc(m3b)
Error en anova(model) : objeto 'm3b' no encontrado
 >   # The results are the same. This may not necessarily be the case
 >   # elsewhere. First forward fitting the random effect structure and
 >   # then backfitting the fixed effects, potentially pruning irrelevant
 >   # random effects, is probably the best approach. Nonetheless, there is
 >   # no hard evidence to this effect.
 >   # check model assumptions
 >   mcp.fnc(m3)
Error en mcp.fnc(m3) : objeto 'm3' no encontrado
 >   # check significance of model terms
 >   pamer.fnc(m3)
Error en anova(model) : objeto 'm3' no encontrado
 >   # Only the intercept is significant. For purposes of the
 >   # example, let's perform a posthoc analysis on FreqBdc on
 >   # model m2.
 >   m2.ph <-
+     mcposthoc.fnc(model = m2, var = list(ph1 = "FreqBdc"))
processing job "FreqBdchigh" (1 of 3) ...
Aviso en rankMatrix(X) :
  rankMatrix(<large sparse Matrix>, method = 'tolNorm2') coerces to 
dense matrix.
   Probably should rather use  method = 'qrLINPACK' !?
Error en my.update(model = model, ph.list.element = x, verbosity = 
verbosity) :
   tentativa de obtener un slot "coefs" de un objeto cuya clase 
("summary.merMod") que no es un objecto clase S4
 >   if(try(require(languageR,quietly=TRUE))){
+     # Generate plot for Length X Freq with function plotLMER.fnc
+     require(languageR)
+     plotLMER.fnc(m2, pred = "LengthBc", intr = list("FreqBdc",
+
+ levels(eeg$FreqBdc), "beg", list(1 : 3, 1 : 3)))
+   }
Error en plotLMER.fnc(m2, pred = "LengthBc", intr = list("FreqBdc", 
levels(eeg$FreqBdc),  :
   argument should be a mer model object
 >   # effect of Length at each FreqB bin
 >   summary(object = m2.ph, term = "LengthBc")
Error en summary(object = m2.ph, term = "LengthBc") :
   error in evaluating the argument 'object' in selecting a method for 
function 'summary': Error: objeto 'm2.ph' no encontrado
 >   # Length effect different Freq bins? Start with low
 >   # versus mid and high
 >   smry <- summary(object = m2.ph, term = "FreqBdclow:LengthBc")
Error en summary(object = m2.ph, term = "FreqBdclow:LengthBc") :
   error in evaluating the argument 'object' in selecting a method for 
function 'summary': Error: objeto 'm2.ph' no encontrado
 >   # mid versus low and high
 >   smry <- summary(object = m2.ph, term = "FreqBdcmid:LengthBc")
Error en summary(object = m2.ph, term = "FreqBdcmid:LengthBc") :
   error in evaluating the argument 'object' in selecting a method for 
function 'summary': Error: objeto 'm2.ph' no encontrado
 >

Javier Marcuzzi

El 17/10/2013 04:09 p.m., Javier Villacampa González escribió:
> if(try(require(LCFdata,quietly=TRUE))){
>    ### Load and format data
>    require(LCFdata)
>    data(eeg)
>    # restrict to electrode Fz and 80--180 ms window
>    eeg <- eeg[eeg$Time >= 80 & eeg$Time <= 180, ]
>    eeg <- eeg[, c("Subject", "Item", "Time", "Fz",
>                   "FreqB", "LengthB", "WMC")]
>    # mean center FreqB
>    eeg$FreqBc <- eeg$FreqB - mean(eeg$FreqB)
>    # split FreqBc into 3 categories. Doesn't make sense,
>    # but it's merely for example
>    eeg$FreqBdc <- "high"
>    eeg$FreqBdc[eeg$FreqBc<=quantile(eeg$FreqBc)[3]] <- "mid"
>    eeg$FreqBdc[eeg$FreqBc<=quantile(eeg$FreqBc)[2]] <- "low"
>    eeg$FreqBdc <- as.factor(eeg$FreqBdc)
>    eeg$FreqBdc <- relevel(eeg$FreqBdc, "low")
>    # mean center LengthB
>    eeg$LengthBc <- eeg$LengthB - mean(eeg$LengthB)
>    # mean center WMC
>    eeg$WMCc <- eeg$WMC - mean(eeg$WMC)
>    ### demonstrate plotDensity3d,fnc
>    plotDensity3d.fnc(x = sort(unique(eeg$WMCc)),
>                      y = sort(unique(eeg$LengthBc)))
>    ### demonstrate plotDensity3d,fnc
>    plotRaw3d.fnc(data = eeg, response = "Fz", pred = "WMCc",
>                  intr = "LengthBc", plot.type = "persp", theta = 150)
>    ### Analyze data
>    # fit initial model
>    m0 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2  + (1 | Subject),
>               data = eeg)
>    m1 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2  + (1 | Subject) +
>                 (1 | Item), data = eeg)
>    # which model to choose?
>    relLik(m0, m1)
>    # choose m1
>    # check model assumptions
>    mcp.fnc(m1)
>    # remove outliers
>    eeg <- romr.fnc(m1, eeg, trim = 2.5)
>    eeg$n.removed
>    eeg$percent.removed
>    eeg<-eeg$data
>    # update model
>    m1 <- lmer(Fz ~ (FreqBdc + LengthBc + WMCc)^2  + (1 | Subject) +
>                 (1 | Item), data = eeg)
>    # re-check model assumptions
>    mcp.fnc(m1)
>    # forward-fit random effect structure (simple for the purposes
>    # of the example).
>    m2 <- ffRanefLMER.fnc(model = m1, data = eeg, ran.effects =
>                            c("(0 + LengthBc | Subject)", "(0 + WMCc | Item)"),
>                          log.file = FALSE)
>    # backfit model m2. In this case, could use bfFixefLMER_t.fnc instead.
>    m3 <- bfFixefLMER_F.fnc(m2, log.file = FALSE)
>    # The calls to ffRanefLMER.fnc and bfFixefLMER_F.fnc could
>    # be replaced by a call to fitLMER.fnc. In this latter case, however,
>    # bfFixefLMER_F.fnc would be called first, then the random effect
>    # structure would be forward fitted, and finally teh fixed effects
>    # would be backfitted again.
>    m3b <- fitLMER.fnc(model = m1, ran.effects = c("(0 + LengthBc | Subject)",
>                                                   "(0 + WMCc | Item)"),
> backfit.on = "F", log.file = FALSE)
>    pamer.fnc(m3b)
>    # The results are the same. This may not necessarily be the case
>    # elsewhere. First forward fitting the random effect structure and
>    # then backfitting the fixed effects, potentially pruning irrelevant
>    # random effects, is probably the best approach. Nonetheless, there is
>    # no hard evidence to this effect.
>    # check model assumptions
>    mcp.fnc(m3)
>    # check significance of model terms
>    pamer.fnc(m3)
>    # Only the intercept is significant. For purposes of the
>    # example, let's perform a posthoc analysis on FreqBdc on
>    # model m2.
>    m2.ph <-
>      mcposthoc.fnc(model = m2, var = list(ph1 = "FreqBdc"))
>    if(try(require(languageR,quietly=TRUE))){
>      # Generate plot for Length X Freq with function plotLMER.fnc
>      require(languageR)
>      plotLMER.fnc(m2, pred = "LengthBc", intr = list("FreqBdc",
>
> levels(eeg$FreqBdc), "beg", list(1 : 3, 1 : 3)))
>    }
>    # effect of Length at each FreqB bin
>    summary(object = m2.ph, term = "LengthBc")
>    # Length effect different Freq bins? Start with low
>    # versus mid and high
>    smry <- summary(object = m2.ph, term = "FreqBdclow:LengthBc")
>    # mid versus low and high
>    smry <- summary(object = m2.ph, term = "FreqBdcmid:LengthBc")
> }
> ## End(Not run)


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