Question

我正在分析不同处理中的（植物群落的一部分的生物量）与（植物群落总生物量）在R中的时间（即重复测量）之间的比例。因此，使用β回归似乎很自然。混合组件（可与glmmTMB包一起使用），以便考虑重复的措施。

我的问题是使用lsmeans包中的函数lsmeans来计算我的治疗之间的事后比较。 glmsTMB对象不是由lsmeans函数处理的，所以Ben Bolker在recommended to add the following code before loading the packages {glmmTMB} and {lsmeans}上：

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)
}

这是我的代码和数据：

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，我收到以下错误消息，我从未遇到过，而且我找不到任何信息：

在model.matrix.default中（trms，m，contrasts.arg =对比）：变量＆＃39;情节＆＃39;缺席，其对比度将被忽略

有人能发光吗？谢谢！

Answer 1

这不是错误消息，而是（无害的）警告消息。之所以发生这种情况，是因为我编写的黑客攻击方法并不排除仅在随机效应中使用的因子变量。你应该更担心这个输出：

注意：由于参与交互，结果可能会产生误导

警告您正在评估包含交互的模型中的主要效果;你必须仔细考虑这一点，以确保你做得对。

错误消息lsmeans用于与glmmTMB的beta混合回归模型

1 个答案: