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我正在尝试sem在分段SEM 中运行具有随机效果的 a。我的模型运行时没有错误，并且sem.fit()运行时也没有错误或警告。但是，当我运行时，sem.coefs()我收到以下警告：

1: In if (grepl("cbind", deparse(formula(x)))) all.vars(formula(x))[-c(1:2)] else all.vars(formula(x)) :
  the condition has length > 1 and only the first element will be used

任何想法这个警告是关于什么或它意味着什么？鉴于这是一个警告而不是错误，代码仍然运行并给我估计，但我可以相信估计吗？

谢谢！

编辑

        #code: 
    library(piecewiseSEM)
    library(nlme)
    avg.forb<-list(        lme(nitrogen_variation~nat+impervious+precip.variation,random=~1|site/species,control = lmeControl(opt = "optim"),forb),   lme(po4_variation~nat+impervious+precip.variaton,random=~1|site/species,control = lmeControl(opt = "optim"),forb),
lme(nitrogen~nat +impervious+precip.variation,random=~1|site/species,control = lmeControl(opt = "optim"), forb),
lme(po4 ~nat +impervious+precip.variation,random=~1|site/species,control = lmeControl(opt = "optim"),forb),  lme(avg.height~nat+impervious+po4+po4_variation+nitrogen+nitrogen_variation+precip.variation+n_i, random=~1|site/species,control =lmeControl(opt="optim"),forb),  lme(avg.culms~nat+impervious+po4+po4_variation+nitrogen+nitrogen_variation+precip.variation+n_i,random=~1|site/species,control = lmeControl(opt = "optim"), forb),    lme(avg.chloro~nat+impervious+po4+po4_variation+nitrogen+nitrogen_variation+precip.variation+n_i,random=~1|site/species, control =lmeControl(opt="optim"),forb),  lme(avg.sla~nat+impervious+po4+po4_variation+nitrogen+nitrogen_variation+precip.variation+n_i,random=~1|site/species, control = lmeControl(opt = "optim"),forb))

sem.fit(avg.forb, conditional=T, forb) #this code gives the above error message
    #data subset: 
structure(list(site = structure(c(1L, 1L, 1L, 2L, 2L, 3L), .Label = c("Baker", "Cronkelton", "Delaware"), class = "factor"), species = structure(c(1L, 4L, 6L, 2L, 3L, 5L), .Label = c("apocynum cannabinum", "aster ericoides", "aster lanceolatus var. interior", "cirsium arvense", "impatiens capensis", "typha angustifolia"), class = "factor"), n_i = structure(c(2L, 
    1L, 1L, 2L, 2L, 2L), .Label = c("i", "n"), class = "factor"),nat=structure(c(1L, 1L, 1L, 1L, 1L, 2L), .Label = c("1", "2"), class = "factor"), impervious = structure(c(2L, 2L, 2L, 1L, 1L, 1L), .Label = c("1", "2"), class = "factor"), precip_variation = c(70.24882178, 70.24882178, 70.24882178, 21.92460821, 21.92460821, 18.90115299), po4 = c(-2.203425667, 
    -2.204119983, -2.20481541, -1.845271793, -1.844967771, -2.417936637), po4_variation = c(0.8011, 0.801, 0.8009, 0.4839, 0.484, 0.5229), nitrogen = c(0.00627, 0.00626, 0.00625, 0.00432, 0.00433, 0.01018), nitrogen_variation = c(0.7739, 0.7738, 0.7737, 0.5435, 0.5436, -0.1251), avg.height = c(99.1, 113.5559506, 191.4111012, 73.72222025, 35.42222025, 59.52222025), avg.culms = c(0.492915384, 0.78612011, 0.884606749, 0.96483549, 0.819543936, 0.831087338), avg.sla = c(179.3510333, 149.0332471, 68.77888941, 334.2177912, 798.7581389, 443.2005556), avg.chloro = c(0.900670513, 0.790832282, 0.965532685, 0.565585484, 1.106203493, 0.970209082)), .Names = c("site", "species", "n_i", "nat", "impervious", "precip_variation", "po4", "po4_variation", "nitrogen", "nitrogen_variation", "avg.height", "avg.culms", "avg.sla", "avg.chloro"), row.names = c(NA, 6L), class = "data.frame")
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