我正在根据lmer()
对象在混合效果模型中绘制固定效果的交互作用。为此,我根据模型预测新值。这工作正常,除了由于我如何生成它们,因此预测在整个可能的x轴范围内扩展。现在,我可以通过基于循环定义new.dat(将最大和最小值取决于分组变量“ Variety”)来将预测的回归线限制在其各自的分组变量的范围内,但是-还有一个更优雅的方法/更容易解决这个问题的方法?我是否错过了某些东西(我是R的新手)?
数据:
library(datasets)
data("Oats")
# manipulate data so it resembles more my actual data
Oats <- Oats %>%
filter((Variety == "Golden Rain" & nitro>=0.2) | (Variety == "Marvellous" & nitro <=0.4) | (Variety == "Victory" & nitro<=0.4 & nitro>=0.2)) #%>%
模型和绘图:
mod2 <- lmer(yield ~ nitro * Variety + (1| Variety), data=Oats)
new.dat <- data.frame(nitro=seq(min(Oats$nitro),max(Oats$nitro), length.out = 48), Variety= Oats$Variety)
new.dat$pred<-predict(mod2,newdata=new.dat,re.form=~0)
ggplot(data=Oats, aes(x=nitro, y=yield, col = Variety)) +
geom_point() +
geom_line(data=new.dat, aes(y=pred)) +
geom_point(data=new.dat, aes(y=pred))
非常感谢每个提示!
您可以通过计算每组的最小值/最大值,然后按组计算序列来获得。与tidyverse保持一致,因为您的代码已使用它:
library(tidyverse)
library(pairwiseCI)
#> Loading required package: MCPAN
#> Loading required package: coin
#> Loading required package: survival
library(lme4)
#> Loading required package: Matrix
#>
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#>
#> expand, pack, unpack
data("Oats")
## manipulate data so it resembles more my actual data
Oats <-
Oats %>%
filter((Variety == "Golden Rain" & nitro>=0.2) | (Variety == "Marvellous" & nitro <=0.4) | (Variety == "Victory" & nitro<=0.4 & nitro>=0.2)) #%>%
mod2 <- lmer(yield ~ nitro * Variety + (1| Variety), data=Oats)
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> unable to evaluate scaled gradient
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> Hessian is numerically singular: parameters are not uniquely determined
## Calculate min/max by group
all_vals <-
Oats %>%
group_by(Variety) %>%
summarize(min_nitro = min(nitro),
max_nitro = max(nitro))
## Calculate sequence for each group
new.dat <-
all_vals %>%
group_split(Variety) %>%
map_dfr(~ data.frame(Variety = .x$Variety, nitro = seq(.x$min_nitro, .x$max_nitro, length.out = 20)))
new.dat$pred<-predict(mod2,newdata=new.dat,re.form=~0)
ggplot(data=Oats, aes(x=nitro, y=yield, col = Variety)) +
geom_point() +
geom_line(data=new.dat, aes(y=pred)) +
geom_point(data=new.dat, aes(y=pred))