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我安装了以下形式的混合模型：

global.mod <- lmer(log(y) ~ 
                       x1 + x2 + x3 + x4 + x5 + (1 + x1|a/b),
                       REML = FALSE, data = lmerDat,
                       na.action = 'na.fail', control = lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)))

然后我用它predge来创建下面的模型组合，效果很好。

require(parallel) || require(snow)
clusterType <- if(length(find.package("snow", quiet = TRUE))) "SOCK" else "PSOCK"
clust <- try(makeCluster(getOption("cl.cores", 4), type = clusterType))
clusterEvalQ(clust, library(lme4))
clusterExport(clust, "lmerDat")

 model.set <- pdredge(global.mod, clust, 
                      m.lim = c(2, NA), rank = AIC, extra = "adjR^2", trace = 2)

然后我尝试以稍微不同的格式指定我的模型，如下所示：

 PredictorVariables <- names(lmerDat)[c(5:9)] # this is x1 till x5
 fixed.part <- paste("log(y) ~", paste(PredictorVariables, collapse=" + "))
 random.part <- paste('(1 + x1|a/b)')
 Formula <- formula(paste(fixed.part, random.part, sep = " + "))

 global.mod <- lmer(Formula, data = lmerDat, na.action = 'na.fail', control = 
 lmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)), REML = FALSE)    

 require(parallel) || require(snow)

 clusterType <- if(length(find.package("snow", quiet = TRUE))) "SOCK" else "PSOCK"
 clust <- try(makeCluster(getOption("cl.cores", 4), type = clusterType))
 clusterEvalQ(clust, library(lme4))
 clusterExport(clust, "lmerDat")

 model.set <- pdredge(global.mod, clust, 
                      m.lim = c(2, NA), rank = AIC, extra = "adjR^2", trace = 2)

但是，这给了我以下错误

 Error in sprintf(gettext(fmt, domain = domain), ...) : 
 invalid format '%d'; use format %f, %e, %g or %a for numeric objects

不幸的是，后者是我想要做模型规范的方式，因为先验我不知道预测变量的名称是什么names(lmerDat)[c(5:9)]。谁能帮我理解错误以及如何解决它？

编辑

我的回溯输出如下（请注意，这是我的原始数据而不是上面的虚拟数据）。

 7: sprintf(gettext(fmt, domain = domain), ...)     
 6: gettextf(Message, ..., domain = domain)    
 5: structure(list(message = as.character(message), call = call), 
   class = class)      
 4: simpleError(gettextf(Message, ..., domain = domain), Call)
 3: stop(simpleError(gettextf(Message, ..., domain = domain), Call))
 2: cry(, "number of non-fixed predictors [%d] exceeds the allowed maximum of %d (with %d variants)", 
   nov, novMax, nVariants)
 1: pdredge(global.mod, clust)
4

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