带有glmmTMB的beta混合回归模型的错误消息lsmeans

Question

我正在分析R中不同处理(即重复测量)中的(植物群落一部分的生物量)与(植物群落总生物量)的比率。因此，为了考虑重复测量，似乎很自然地使用带有混合成分(可从glmmTMB软件包获得)的beta回归。

我的问题是关于使用lsmeans包中的函数lsmeans在我的处理中计算后期比较。glmmTMB对象不是由lsmeans函数处理的，因此recommended to add the following code before loading the packages {glmmTMB} and {lsmeans}上的Ben Bolker

recover.data.glmmTMB <- function(object, ...) {
   fcall <- getCall(object)
   recover.data(fcall,delete.response(terms(object)),
             attr(model.frame(object),"na.action"), ...)}
lsm.basis.glmmTMB <- function (object, trms, xlev, grid, vcov.,
                           mode = "asymptotic", component="cond", ...) {
    if (mode != "asymptotic") stop("only asymptotic mode is available")
    if (component != "cond") stop("only tested for conditional component")
    if (missing(vcov.)) 
        V <- as.matrix(vcov(object)[[component]])
    else V <- as.matrix(.my.vcov(object, vcov.))
    dfargs = misc = list()
    if (mode == "asymptotic") {
       dffun = function(k, dfargs) NA
    }
    ## use this? misc = .std.link.labels(family(object), misc)
    contrasts = attr(model.matrix(object), "contrasts")
    m = model.frame(trms, grid, na.action = na.pass, xlev = xlev)
    X = model.matrix(trms, m, contrasts.arg = contrasts)
    bhat = fixef(object)[[component]]
    if (length(bhat) < ncol(X)) {
        kept = match(names(bhat), dimnames(X)[[2]])
        bhat = NA * X[1, ]
        bhat[kept] = fixef(object)[[component]]
        modmat = model.matrix(trms, model.frame(object), contrasts.arg = contrasts)
        nbasis = estimability::nonest.basis(modmat)
     }
    else nbasis = estimability::all.estble
    list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun, 
        dfargs = dfargs, misc = misc)
}

以下是我的代码和数据：

trt=c(rep("T5",13),rep("T4",13),
  rep("T3",13),rep("T1",13),rep("T2",13),rep("T1",13),
  rep("T2",13),rep("T3",13),rep("T5",13),rep("T4",13))
 year=rep(2005:2017,10)
 plot=rep(LETTERS[1:10],each=13)
  ratio=c(0.0046856237844411,0.00100861922394448,0.032516291436091,0.0136507743972955,0.0940240065096705,0.0141337428305094,0.00746709315018945,0.437009092691189,0.0708021091805216,0.0327952505849285,0.0192685194751524,0.0914696394299481,0.00281889216102303,0.0111928453399615,0.00188119596836005,NA,0.000874623692966351,0.0181192859074754,0.0176635391424644,0.00922358069727823,0.0525280029990213,0.0975006760149882,0.124726170684951,0.0187132600944396,0.00672592365451266,0.106399234215126,0.0401776844073239,0.00015382736648373,0.000293356424756535,0.000923659501144292,0.000897412901472504,0.00315930225856196,0.0636501228611642,0.0129422445492391,0.0143526630252398,0.0136775931834926,0.00159292971508751,0.0000322313783211749,0.00125352390811532,0.0000288862579879126,0.00590690336494395,0.000417043974238875,0.0000695808216192379,0.001301299696752,0.000209355138230326,0.000153151660178623,0.0000646279598274632,0.000596704590065324,9.52943306579156E-06,0.000113476446629278,0.00825405312309618,0.0001025984082064,0.000887617767039489,0.00273668802742924,0.00469409165130462,0.00312377000134233,0.0015579322817235,0.0582615988387306,0.00146933878743163,0.0405139497779372,0.259097955479886,0.00783997376383192,0.110638003652979,0.00454029511918275,0.00728290246595241,0.00104674197030363,0.00550563937846687,0.000121380392484705,0.000831904606687671,0.00475778829159394,0.000402799910756391,0.00259524300745195,0.000210249875492504,0.00550104485802363,0.000272849546913495,0.0025389089622392,0.00129370075116459,0.00132810234020792,0.00523285954007915,0.00506230599388357,0.00774104695265855,0.00098348404576587,0.174079173227248,0.0153486840317039,0.351820365452281,0.00347674458928481,0.147309225196026,0.0418825705903947,0.00591271021100856,0.0207139520537443,0.0563647804012055,0.000560012457272534,0.00191564842393647,0.01493480083524,0.00353400674061077,0.00771828473058641,0.000202009136938048,0.112695841130448,0.00761492172670762,0.038797330459115,0.217367765362878,0.0680958660605668,0.0100870294641921,0.00493875324236991,0.00136539944656238,0.00264262100866192,0.0847732305020654,0.00460985241335143,0.235802638543116,0.16336020383325,0.225776236687456,0.0204568107372349,0.0455390585228863,0.130969863489582,0.00679523322812889,0.0172325334280024,0.00299970176999806,0.00179347656925317,0.00721658257996989,0.00822443690003783,0.00913096724026346,0.0105920192618379,0.0158013204589482,0.00388803567197835,0.00366268607026078,0.0545418725650633,0.00761485067129418,0.00867583194858734,0.0188232707241144,0.018652666214789)
dat=data.frame(trt,year,plot,ratio)
require(glmmTMB)
require(lsmeans)
mod=glmmTMB(ratio~trt*scale(year)+(1|plot),family=list(family="beta",link="logit"),data=dat)
summary(mod)
ls=lsmeans(mod,pairwise~trt)`

最后，我得到了以下错误消息，这是我从未遇到过的，也找不到任何信息：

在图中(trms，m，contrasts.arg =model.matrix.default)：缺少变量'plot‘，它的对比度将被忽略

有谁能照亮他们的光芒？谢谢!

Answer 1

这不是一个错误消息，而是一个(无害的)警告消息。这是因为我写的黑客方法没有排除只在随机效应中使用的因子变量。您应该更多地关注以下输出：

注释:由于涉及交互，结果可能具有误导性

它警告您，您正在评估包含交互的模型中的主要效果；您必须仔细考虑这一点，以确保您的操作是正确的。