问题描述
我正在运行一个模型，该模型的响应变量是计数数据和带有大量零的非正态（严重右尾）。
我正在使用 glmmTMB 包并使用泊松分布运行初始 glmm，其中 id（代码）和月份（montyear）作为随机因素。
m0 <- glmmTMB(deg~ SE_score + species + season + TL +
(1|code) + (1|monthyear),family=poisson(),data=node_dat)
我使用了 check_overdispersion() 并且毫不奇怪地发现模型中存在过度分散。我使用负二项分布 (nbinom2) 运行了第二个模型，并使用 DHARMa 包绘制了 QQplots 和残差，但我仍然有很多离散。
m1 <- glmmTMB(deg~ SE_score + species + season + TL +
(1|code) + (1|monthyear),family=nbinom2(),data=node_dat)
res1 <- simulateResiduals(m1)
plot(res1)
我检查了零通胀，但没有零通胀，并使用了 nbinom1 系列，但它在所有测试（KS、分散和异常值）中产生了更高的 AIC 和显着偏差。
我不确定下一个处理这个问题的方法是什么，也不确定为什么残差看起来很奇怪。
我的数据子集如下
structure(list(station = structure(c(25L,22L,53L,45L,24L,65L,12L,64L,27L,28L,2L,15L,46L,10L,16L,11L,59L,19L,8L,25L,36L,39L,67L,66L,52L,3L,41L,12L),.Label = c("AR01","BE01","BE02","BEUWM01","BL01","BL02","GCB01","GCB02","GCB03","GCB04","NI01","NI01b","NI02","NI03","PB01","PB02","PB03","PB04","PB05","PB06","PB07","PB08","PB09","PB10","PB11","PB12","PB13","PB14","PB15","PB16","PB17","PB18","PB19","PB20","PB21","PB22","PB23","PB24","PB25","PB26","PB27","PB28","PB29","PB30","PB4G01","PB4G02","PBUWM01","PBUWM02","SA01","SA02","SA02b","SA03","SA04","SA05","SA06","SA07","SA11","SA4G01","SAUWM01","SB01","SB02/AR02","SB03/AR05","SB04/AR06","VB01","VB02","VB03","VB04"),class = "factor"),monthyear = structure(c(56L,57L,40L,7L,51L,4L,43L,34L,37L,33L,42L,47L,21L,56L,30L,54L,18L,55L,26L,25L),.Label = c("2014/01","2014/02","2014/03","2014/04","2014/05","2014/06","2014/07","2014/08","2014/09","2014/10","2014/11","2014/12","2015/01","2015/02","2015/03","2015/04","2015/05","2015/06","2015/07","2015/08","2015/09","2015/10","2015/11","2015/12","2016/01","2016/02","2016/03","2016/04","2016/05","2016/06","2016/07","2016/08","2016/09","2016/10","2016/11","2016/12","2017/01","2017/02","2017/03","2017/04","2017/05","2017/06","2017/07","2017/08","2017/09","2017/10","2017/11","2017/12","2018/01","2018/02","2018/03","2018/04","2018/05","2018/06","2018/07","2018/08","2018/09","2018/10","2018/11","2018/12"),deg = c(0,2,0),gs = c(0,btw = c(0,code = structure(c(169L,120L,87L,197L,121L,173L,183L,191L,141L,5L,85L,130L,90L,188L,82L,99L,202L,193L,69L,142L,195L,117L,118L,89L,132L,172L,15L),.Label = c("2390","13573","13574","13575","13576","19318","19319","19321","19322","19506","19514","19519","19520","19524","25537","25540","25541","25543","25546","25549","25552","25553","27583","27585","27586","27591","27592","27593","27594","27595","27596","27597","27600","27601","27605","27607","27608","27613","27614","27617","27619","27620","27621","27626","27627","27629","27630","27631","27632","28608","28611","28612","28618","28625","28628","28629","28631","28632","28633","28638","28641","28644","28662","28672","28674","52978","54815","54846","54852","54860","54863","54865","54866","54868","54877","54882","54883","54884","54886","54890","54892","54895","54896","54901","54904","54914","54919","54920","54922","54925","54931","54932","54938","54952","54954","54955","54958","54959","54962","59950","59953","59954","59955","59957","59958","59959","59960","59961","59962","59964","59966","59969","59970","59971","59972","59973","59975","59976","59979","59981","59988","2388","12950","12952","12956","12958","12960","12962","12964","12966","12968","13577","14203","19320","19523","25534","25535","25536","25539","25542","25544","25545","25547","25548","25550","27584","27588","27589","27590","27598","27599","27602","27603","27604","27606","27609","27610","27611","27615","27616","27618","27622","27624","27625","28624","28627","28637","28639","28642","28660","28670","34176","34177","34178","34179","52975","52977","54817","54821","54822","54825","54845","54849","54880","54887","54889","54893","54898","54899","54905","54911","54912","54915","54933","54947","54957","54961","59951","59963","59968","59978","59991","59992","59993","59994","59995"),species = structure(c(2L,1L,1L),.Label = c("Grey Reef Shark","Silvertip Shark"
),SE_score = c(0.13,0.29,0.08,0.1,0.14,0.07,0.18,0.01,0.12,0.09,0.06,0.21,0.03,0.16,0.11,0.15,0.13,0.22,0.31,0.31),region = structure(c(6L,6L,9L,5L),.Label = c("Acoustic Release","Benares","Blenheim","Grand Chagos Bank","Nelsons Island","Peros Banhos","Saloman","Speakers Bank","Victory Bank"
),date = structure(c(1533078000,1456790400,1522537200,1535756400,1491001200,1404169200,1504220400,1519862400,1506812400,1393632000,1396306800,1414800000,1498863600,1475276400,1483228800,1472684400,1496271600,1509494400,1441062000,1533078000,1464735600,1527807600,1433113200,1530399600,1417392000,1454284800,1391212800,1451606400),tzone = "",class = c("POSIXct","POSIXt")),month = c(8,3,4,9,7,10,11,1,6,8,12,1),season = structure(c(1L,2L),.Label = c("dry.season","wet.season"),sex = structure(c(3L,.Label = c("","F","M","U"),TL = c(103,131,118,86,112,123,115,137,70,104,134,100,122,117,135,151,106,132,139,125,114,108,144,119,94,140,88)),row.names = c(NA,-36L),na.action = structure(c(`107971` = 107971L),class = "omit"),class = "data.frame")
解决方法
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