Question

当您安装了可能包含随机效果的模型时，您如何预测mgcv::gam？

此网站上的其他主题是＆＃34;排除＆＃34;技巧对我不起作用（https://stats.stackexchange.com/questions/131106/predicting-with-random-effects-in-mgcv-gam）

ya <- rnorm(100, 0, 1)
yb <- rnorm(100,0,1.5)
yc <- rnorm(100, 0, 2)
yd <- rnorm(100, 0, 2.5)

yy <- c(ya,yb,yc,yd) #so, now we've got data from 4 different groups. 
xx <- c(rep("a", 100), rep("b",100), rep("c",100),rep("d",100)) #groups
zz <- rnorm(400,0,1) #some other covariate

model <- gam(yy ~ zz + s(xx, bs = "re")) #the model

predictdata <- data.frame( zz = 5 )   #new data
predict(model, newdata = predictdata, exclude = "s(xx)")   #prediction

，这会产生错误

Error in model.frame.default(ff, data = newdata, na.action = na.act) : 
  variable lengths differ (found for 'xx')
In addition: Warning messages:
1: In predict.gam(model, newdata = predictdata, exclude = "s(xx)") :
  not all required variables have been supplied in  newdata!

2: 'newdata' had 1 row but variables found have 400 rows

我的mgcv包是最新的。

编辑：

如果您将predictdata更改为

predictdata <- data.frame(zz = 5, xx = "f")

然后它说

Error in predict.gam(model, newdata = predictdata, exclude = "s(xx)") : 
  f not in original fit

Answer 1

我试验了你的例子，似乎＆＃39;排除＆＃39;声明确实有效，即使您必须在新数据值中指定用于拟合模型的原始数据集中包含的随机效果。然而，这让我有点不安。另一个警告是，排除＆＃39;似乎没有在具有由组分别估计的方差结构的模型上工作（我尝试使用另一个数据集），即类似s（xx，s =＆＃34; re＆＃34;，by = group）。您可能希望发布或将问题移至交叉验证，以便其他统计人员/分析师可以看到它可能提供更好的答案。

以下是我的代码。请注意，我更改了组a和d的均值，但总体平均值应该大约为零。

ya <- rnorm(100, 1, 1)
yb <- rnorm(100, 0,1.5)
yc <- rnorm(100, 0, 2)
yd <- rnorm(100, -1, 2.5)

yy <- c(ya,yb,yc,yd) #so, now we've got data from 4 different groups. 
xx <- c(rep("a", 100), rep("b",100), rep("c",100),rep("d",100)) #groups
zz <- rnorm(400,0,1) #some other covariate

some.data= data.frame(yy,xx,zz)
model <- gam(yy ~ zz + s(xx, bs = "re"),data=some.data) #the model


# the intercept is the overall mean when zz is zero
summary(model)

 predictdata <- data.frame(zz = c(0,0,0,0), xx =c("a","b","c","d"))  #new data

#excluding random effects. Estimate should be the same for all and should be the intercept  
predict(model, newdata = predictdata, exclude = "s(xx)") 

#including random effects. Estimates should differ by group with 'a' larger and 'd' smaller
predict(model, newdata = predictdata)

你怎么预测gam？可重复的示例

1 个答案: