我发现predict
函数目前尚未在使用clmm
R包中的ordinal
函数拟合的累积链接混合模型中实现。虽然predict
在同一个套餐中为clmm2
实施,但我选择应用clmm
,因为后者允许多个随机效果。此外,我还安装了几个clmm
模型,并使用model.avg
包中的MuMIn
函数执行模型平均。理想情况下,我想使用平均模型预测概率。但是,虽然MuMIn
支持clmm
模型,但predict
也不适用于普通模型。
有没有办法破解predict
函数,这样函数不仅可以预测clmm
模型的概率,还可以预测使用clmm
的模型平均系数(即对象)类“平均”)?例如:
require(ordinal)
require(MuMIn)
mm1 <- clmm(SURENESS ~ PROD + (1|RESP) + (1|RESP:PROD), data = soup,
link = "probit", threshold = "equidistant")
## test random effect:
mm2 <- clmm(SURENESS ~ PROD + (1|RESP) + (1|RESP:PROD), data = soup,
link = "logistic", threshold = "equidistant")
#create a model selection object
mm.sel<-model.sel(mm1,mm2)
##perform a model average
mm.avg<-model.avg(mm.sel)
#create new data and predict
new.data<-soup
##predict with indivindual model
predict(mm1, new.data)
我收到以下错误消息:
在UseMethod(“预测”)中:
predict
适用于“clmm”类对象的适用方法
##predict with model average
predict(mm.avg, new.data)
返回另一个错误:
predict.averaging错误(mm.avg,new.data):
模型'mm1'和'mm2'的predict
导致错误
答案 0 :(得分:0)
我找到了一个潜在的解决方案(粘贴在下面),但无法处理我的数据。
这里的解决方案:https://gist.github.com/mainambui/c803aaf857e54a5c9089ea05f91473bc
我认为问题在于我使用的系数数量,但经验不足,无法弄清楚。希望这可以帮助某人。
这是我使用的模型和新数据,虽然它实际上是模型平均版本。但相同的预测变量。
ma10 <- clmm(Location3 ~ Sex * Grass3 + Sex * Forb3 + (1|Tag_ID), data =
IP_all_dunes)
ma_1 <- model.avg(ma10, ma8, ma5)##top 3 models
new_ma<- data.frame(Sex = c("m","f","m","f","m","f","m","f"),
Grass3 = c("1","1","1","1","0","0","0","0"),
Forb3 = c("0","0","1","1","0","0","1","1"))
# Arguments:
# - model = a clmm model
# - modelAvg = a clmm model average (object of class averaging)
# - newdata = a dataframe of new data to apply the model to
# Returns a dataframe of predicted probabilities for each row and response level
fake.predict.clmm <- function(modelAvg, newdata) {
# Actual prediction function
pred <- function(eta, theta, cat = 1:(length(theta) + 1), inv.link = plogis) {
Theta <- c(-1000, theta, 1000)
sapply(cat, function(j) inv.link(Theta[j + 1] - eta) - inv.link(Theta[j] -
eta))
}
# Multiply each row by the coefficients
#coefs <- c(model$beta, unlist(model$ST))##turn off if a model average is used
beta <- modelAvg$coefficients[2,3:12]
coefs <- c(beta, unlist(modelAvg$ST))
xbetas <- sweep(newdata, MARGIN=2, coefs, `*`)
# Make predictions
Theta<-modelAvg$coefficients[2,1:2]
#pred.mat <- data.frame(pred(eta=rowSums(xbetas), theta=model$Theta))
pred.mat <- data.frame(pred(eta=rowSums(xbetas), theta=Theta))
#colnames(pred.mat) <- levels(model$model[,1])
a<-attr(modelAvg, "modelList")
colnames(pred.mat) <- levels(a[[1]]$model[,1])
pred.mat
}