我正在尝试使用R中的glmer函数来模拟嵌套生存。为了将曝光天数合并到模型中，我使用了Ben Bolker优秀的用户定义链接函数（here）。但是，当我尝试为模型中的每个结果协变量生成预测时（以便我可以在ggplot2中绘制预测），我收到一条错误消息：
Error in predictSE.merMod(i, se.fit = TRUE, newdata = newdata[obs, ], :
Only canonical link is supported with current version of function
错误似乎很明显，但我想知道是否有人找到了使用自定义链接功能进行图表预测的方法。
这是我的代码;道歉它很乱（null模型会创建一条错误消息，所以我需要将它从模型平均中排除）：
NestSuccessExposure<-read.csv("NestSuccessExposureAUG.csv")
NestSuccessExposure<-na.omit(NestSuccessExposure)
NestSuccessExposureScaled<-scale(NestSuccessExposure[,c(4:8)])
NestSuccessExposureScaled<-cbind(NestSuccessExposureScaled,NestSuccessExposure[,c(1,2,3,9,10)])
library(MASS)
library(lme4)
logexp <- function(exposure = 1)
{
linkfun <- function(mu) qlogis(mu^(1/exposure))
linkinv <- function(eta) plogis(eta)^exposure
logit_mu_eta <- function(eta) {
ifelse(abs(eta)>30,.Machine$double.eps,
exp(eta)/(1+exp(eta))^2)
}
mu.eta <- function(eta) {
exposure * plogis(eta)^(exposure-1) *
logit_mu_eta(eta)
}
valideta <- function(eta) TRUE
link <- paste("logexp(", deparse(substitute(exposure)), ")",
sep="")
structure(list(linkfun = linkfun, linkinv = linkinv,
mu.eta = mu.eta, valideta = valideta,
name = link),
class = "link-glm")
}
#Scale
NestSuccessExposure <- na.omit(NestSuccessExposure)
noscaleVars <- c("SUCCESS","YEAR","EXPOSURE","QUAL","MGMT")
noscaleCols <- which(names(NestSuccessExposure) %in% noscaleVars)
NestSuccessExposureScaled<- NestSuccessExposure
NestSuccessExposureScaled[-noscaleCols] <-
scale(NestSuccessExposure[-noscaleCols])
NestSuccessExposureScaled <- subset(NestSuccessExposureScaled,
EXPOSURE>0)
#Run GLMM (linear mixed-effects model)
NSExposure<-glmer(SUCCESS~TALL+SHRUB+CANOPY+DISTROAD+NTDBH+QUAL+MGMT+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
summary(NSExposure)
library(MuMIn)
library(AICcmodavg)
options(na.action="na.fail")
dredgeNestSuccess<-dredge(NSExposure)
avgmodnest<-model.avg(dredgeNestSuccess, subset = delta < 2, fit=T)
summary(avgmodnest)
#Check errors by running one model at a time
NSExposure<-glmer(SUCCESS~DISTROAD+MGMT+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
summary(NSExposure)
#Errors: NULL/intercept only
#Conduct model averaging without intercept-only model
mod1<-glmer(SUCCESS~DISTROAD+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod2<-glmer(SUCCESS~DISTROAD+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod3<-glmer(SUCCESS~TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod4<-glmer(SUCCESS~DISTROAD+MGMT+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod5<-glmer(SUCCESS~MGMT+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod6<-glmer(SUCCESS~MGMT+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod7<-glmer(SUCCESS~MGMT+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod8<-glmer(SUCCESS~DISTROAD+NTDBH+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod9<-glmer(SUCCESS~QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod10<-glmer(SUCCESS~MGMT+QUAL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod11<-glmer(SUCCESS~DISTROAD+NTDBH+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod12<-glmer(SUCCESS~NTDBH+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
mod13<-glmer(SUCCESS~DISTROAD+MGMT+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
selectmodavg<-model.avg(mod1, mod2, mod3, mod4, mod5, mod6, mod7, mod8, mod9, mod10, mod11, mod12, mod13)
summary(selectmodavg)
#to get confidence intervals of coefficients use:
cbind( est = coef(selectmodavg), confint(selectmodavg))
#to get odds ratios of coefficients use:
exp(cbind(OR=coef(selectmodavg),confint(selectmodavg)))
######################## Plot
#MGMT - ALL
nseq<-function(x, len = length(x)) seq(min(x, na.rm = TRUE), max(x, na.rm=TRUE), length = len)
newdata1<-as.data.frame(lapply(lapply(NestSuccessExposureScaled[,c(4,8,9,13)], mean), rep, 3))
newdata1$MGMT<-c("E","U","C")
cand<-list()
cand[[1]]<-glmer(SUCCESS~DISTROAD+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[2]]<-glmer(SUCCESS~DISTROAD+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[3]]<-glmer(SUCCESS~TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[4]]<-glmer(SUCCESS~DISTROAD+MGMT+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[5]]<-glmer(SUCCESS~MGMT+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[6]]<-glmer(SUCCESS~MGMT+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[7]]<-glmer(SUCCESS~MGMT+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[8]]<-glmer(SUCCESS~DISTROAD+NTDBH+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[9]]<-glmer(SUCCESS~QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[10]]<-glmer(SUCCESS~MGMT+QUAL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[11]]<-glmer(SUCCESS~DISTROAD+NTDBH+QUAL+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[12]]<-glmer(SUCCESS~NTDBH+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
cand[[13]]<-glmer(SUCCESS~DISTROAD+MGMT+TALL+(1|YEAR),
family=binomial(link=logexp(NestSuccessExposureScaled$EXPOSURE)),
data=NestSuccessExposureScaled)
Modnames<-c("DISTROAD+QUAL+TALL", "DISTROAD+TALL", "TALL", "DISTROAD+MGMT+QUAL+TALL", "MGMT+QUAL+TALL", "MGMT+TALL", "MGMT",
"DISTROAD+NTDBH+TALL", "QUAL+TALL", "QUAL", "DISTROAD+NTDBH+QUAL+TALL", "NTDBH+TALL", "DISTROAD+MGMT+TALL")
predvals<-modavgPred(cand.set = cand, modnames = Modnames, newdata = newdata1, type = "response", uncond.se = "revised")
critval <- 1.96
upr <- predvals$mod.avg.pred + (critval*predvals$uncond.se)
lwr <- predvals$mod.avg.pred - (critval*predvals$uncond.se)
fit <- predvals$mod.avg.pred
predictions<-cbind(newdata1,predvals)
library(arm)
library(ggplot2)
plotMGMT<-(ggplot(data=predictions,aes(x=MGMT,y=fit))+
theme_bw()+theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank())+
geom_errorbar(aes(ymin=lwr,ymax=upr, width=0.2))+
geom_point(size=5)+
ylab("Nest success\n")+coord_cartesian(ylim=c(0,1))+
xlab("\nForest Management Type")+
theme(axis.text.x=element_text(size=20),
axis.text.y=element_text(size=20),
axis.title.x=element_text(size=20),
axis.title.y=element_text(size=20,angle=90),
panel.border = element_blank(),
axis.line = element_line(colour = "black"),
legend.position="none"))
plotMGMT
############################################
这是我的数据集：
dput(NestSuccessExposure)
structure(list(SUCCESS = c(0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 0L, 1L, 0L), YEAR = c(2011L, 2011L, 2011L, 2011L, 2011L,
2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2013L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L), EXPOSURE = c(17, 17, 9, 9, 10, 9, 9, 7, 1, 9, 21, 15,
7, 15, 3, 17, 17, 8, 3, 1.5, 24, 6, 23, 2, 24, 22, 5.5, 2.5,
17, 1.5, 0.5, 8, 7.5, 21, 1.5, 17, 3.5, 1, 0.5, 10.5, 4, 1.5,
1, 17, 5, 28, 22, 20, 12, 5, 6, 2, 14, 21, 22, 3.5, 9, 17, 15,
1, 11, 15, 19, 14, 9, 14, 6, 4, 5.5, 14, 23, 3, 18, 12, 24, 4.5,
2, 4.5), TALL = c(23.85, 28.9, 22.925, 25, 14.7, 17.3, 24.05,
18.675, 25.6, 20.15, 23.55, 21.125, 32.75, 28.5, 20.075, 20.95,
21.35, 23.425, 17.667, 25.025, 21.9, 20.85, 22.75, 23.35, 19.85,
29.05, 21.125, 29.65, 27.575, 27.95, 29.35, 17.45, 23.225, 26.35,
27.6, 23.2, 22.05, 22.45, 32, 24.4, 26.267, 25.4, 25.925, 21.6,
23.55, 23.9, 26.93, 29.5, 14.98, 23.55, 24.8, 17.6, 31.38, 28.05,
22.95, 19.9, 24.8, 24.5, 27.2, 31.83, 30.48, 21.63, 18.3, 26.33,
23, 27.25, 24.95, 14.75, 20.95, 21.35, 25.55, 23.2, 19.08, 11.75,
14.8, 26, 26.05, 20.45), SLOPE = c(12, 35.5, 48.75, 18, 31, 27.5,
31, 31.25, 24.75, 43.75, 0, 0, 36, 18.75, 17, 0, 19.75, 45, 0,
42, 17.5, 29.5, 39, 26, 0, 27, 39.5, 47.5, 44.5, 51, 48.5, 0,
6, 45.25, 33.75, 36, 19, 22.75, 57.25, 0, 30.5, 0, 0, 30.75,
34, 38, 20, 0, 27, 48, 10.25, 5.25, 38.5, 39, 23, 0, 12.75, 0,
48, 51, 35.5, 30.5, 0, 53.5, 67.5, 0, 4, 68.5, 0, 73, 54.5, 18.5,
0, 0, 0, 0, 41.5, 7), SHRUB = c(21L, 25L, 65L, 31L, 36L, 31L,
44L, 45L, 21L, 77L, 44L, 47L, 18L, 41L, 26L, 95L, 41L, 18L, 57L,
25L, 35L, 57L, 44L, 24L, 11L, 28L, 82L, 13L, 3L, 6L, 6L, 55L,
46L, 48L, 37L, 33L, 19L, 58L, 24L, 23L, 34L, 60L, 61L, 33L, 101L,
13L, 45L, 88L, 93L, 11L, 16L, 13L, 43L, 33L, 65L, 9L, 64L, 14L,
38L, 12L, 17L, 29L, 20L, 11L, 31L, 14L, 0L, 15L, 0L, 6L, 8L,
14L, 12L, 12L, 34L, 27L, 19L, 48L), CANOPY = c(0.9, 0.85, 0.9,
0.95, 0.95, 1, 0.85, 0.7, 0.95, 0.95, 0.95, 0.95, 0.8, 0.9, 0.9,
0.75, 1, 1, 0.8, 0.9, 0.85, 0.8, 0.6, 0.85, 1, 0.85, 1, 1, 0.95,
0.75, 0.9, 0.85, 0.75, 0.95, 0.9, 0.85, 0.95, 0.9, 0.85, 0.85,
0.9, 0.9, 0.85, 0.85, 0.85, 1, 0.85, 0.85, 0.9, 0.95, 0.4, 0.75,
0.8, 0.95, 0.85, 0.7, 0.9, 0.79, 0.9, 0.8, 0.75, 0.85, 0.8, 0.95,
0.75, 0.95, 0.9, 0.85, 0.85, 0.65, 0.8, 0.85, 0.9, 0.5, 0.6,
0.85, 0.85, 0.85), DISTROAD = c(6.81, 19.02, 158.83, 7.56, 70.87,
31.28, 39.32, 181.36, 246.67, 48.58, 3.45, 218.62, 38.63, 75.47,
4.51, 7.51, 80.56, 362.92, 1.93, 197.36, 361.38, 82.29, 59.05,
31.32, 71.92, 81.46, 179.79, 211.74, 238.64, 318.72, 329.96,
216.96, 14.53, 158.3, 104.38, 94.61, 293.89, 99.84, 178.64, 6.43,
56.28, 385.88, 116.18, 425.32, 4.02, 119.75, 8.31, 4.31, 1.17,
63.24, 4.62, 119.46, 65.45, 121.6, 4.38, 6.21, 17.36, 181.41,
205.12, 243.77, 349.98, 3.98, 57.57, 209.79, 247.05, 466.59,
114.51, 100.86, 320.05, 331.62, 306.9, 0.95, 101.84, 14.01, 12.61,
117.33, 27.26, 34.58), NTDBH = c(24.5, 56.2, 31.5, 26.2, 37.2,
47.5, 57, 80.1, 58.8, 27.8, 62.3, 27.8, 18.9, 19, 36.4, 41.6,
32.1, 46.1, 31.5, 41.9, 41, 47.5, 62.9, 46.8, 63.1, 27.6, 71.1,
73.4, 47, 34.7, 36.8, 39.1, 54.9, 48, 38.1, 28.5, 42.1, 65, 32.3,
69.4, 60, 64.2, 39.4, 57.6, 39.7, 53.2, 42.35, 37.25, 65, 91.5,
54.2, 18.25, 40, 27.4, 21.3, 56, 25.3, 42.5, 64.75, 34.9, 21.7,
56.1, 29.8, 34.1, 38.4, 55.1, 51, 52.9, 41.8, 48.1, 53.9, 28.2,
37.1, 28.9, 55.5, 43, 34.6, 37.5), NESTHT = c(12.6, 26.6, 18,
10, 14, 15.5, 23, 17, 21.5, 26, 18.5, 15.5, 14.5, 18, 29.5, 18,
12.5, 16, 19, 24, 20, 14.2, 16.8, 20.2, 26.8, 13.4, 20.4, 25.8,
19.6, 18.4, 15.2, 23.2, 24, 19.8, 16.8, 20.4, 20, 15, 18, 26.2,
24, 21.8, 22.4, 13.4, 23, 16.8, 9, 18.2, 20, 26.8, 22.2, 13,
26.4, 14.6, 11.4, 23.6, 15, 16.8, 20.6, 20, 14, 24.5, 21.8, 18.8,
11.2, 23.5, 21.5, 12.4, 19.4, 19.4, 17.2, 15.6, 22, 9.8, 20.2,
24.4, 13, 21), TREEHT = c(20.5, 29, 26.5, 18.5, 22, 40, 28, 33.5,
25.5, 29, 29, 19.5, 15.5, 19, 29.5, 28, 21, 25.5, 31.5, 30, 24.5,
27, 27.8, 26.4, 32.4, 18.6, 26.6, 34.1, 24.4, 24.8, 30.2, 28.2,
28.6, 26.6, 31.8, 25.2, 27.2, 29.8, 22.2, 32.8, 29, 30.6, 27.8,
26.4, 28, 25.2, 23.8, 21.6, 30.4, 29.8, 27, 17, 28.6, 23.2, 18.4,
24.8, 23, 27.8, 29, 24, 20.4, 26.5, 26.8, 27.4, 22.4, 30.5, 26,
20.8, 24.6, 24.6, 24, 18.6, 24.6, 12.6, 23.2, 28.4, 34.6, 25),
GRAPE = c(1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L), QUAL = c(0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L), MGMT = structure(c(2L,
2L, 3L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 2L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 1L, 3L, 1L,
2L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 2L, 2L,
3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L,
2L, 3L), .Label = c("C", "E", "U"), class = "factor")), .Names = c("SUCCESS",
"YEAR", "EXPOSURE", "TALL", "SLOPE", "SHRUB", "CANOPY", "DISTROAD",
"NTDBH", "NESTHT", "TREEHT", "GRAPE", "QUAL", "MGMT"), na.action = structure(c(6L,
14L, 21L, 25L, 32L, 35L, 48L, 51L, 74L, 83L, 88L), .Names = c("6",
"14", "21", "25", "32", "35", "48", "51", "74", "83", "88"), class = "omit"), row.names = c(1L,
2L, 3L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 15L, 16L, 17L,
18L, 19L, 20L, 22L, 23L, 24L, 26L, 27L, 28L, 29L, 30L, 31L, 33L,
34L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
49L, 50L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 84L, 85L, 86L, 87L, 89L), class = "data.frame")
提前致谢！