在同一图中绘制观察数据并通过两个模型（lm 和 lme）预测数据答案

【问题标题】：plot observed data and predict data by two models (lm and lme) in the same plot在同一图中绘制观察数据并通过两个模型（lm 和 lme）预测数据
【发布时间】：2020-08-16 07:56:13
【问题描述】：

如何在同一个图中绘制观察数据和不同模型（lm 和 lme）的结果？我尝试了下面的代码，但它只适用于积分。我想添加不同颜色的内联模型预测的数据。

#Data
d <- runif(160,0,100)#data
y <- rnorm(16,1,0.05)*x + rnorm(16,0,0.5)#data
df = data.frame(d,y)

#Models
#linear - 1
m1 = lm(y~d, data = df)
summary(m1)
# linear - 2
m2 = lm(y~d+I(d^2), data = df)
summary(m2)

df$Class10<-with(df,ifelse(d<20,"<20",ifelse(d<30,"20-30",
 ifelse(d<40,"30-40",ifelse(d<50,"40-50",ifelse(d<60,"50-60",
 ifelse(d<70,"60-70",ifelse(d<80,"70-80",ifelse(d<90,"80-90",
 ifelse(d>=90,">90","ERROR"))))))))))
# number of classes
length(unique(df$Class10))
# classes
sort(unique(df$Class10))
# observations by class
table(df$Class10)
plot(table(df$Class10))

# b0
m10 = lme(y~d, random=~1|Class10, method="ML" ,data = df)

# b1
m10 = lme(y~d, random=~-1+d|Class10, method="ML" , data = df)

# 
m10 = lme(y~d, random=~d|Class10, method="ML" , data = df,
control = lmeControl(niterEM = 5200, msMaxIter = 5200))

#plot points - It works
plot(df$d, df$y)  
points(df$d, predict(m1), col="blue")
points(df$d, predict(m10, level=1), col="red")

#curve
plot(df$d, df$y)
curve(predict(m1,newdata=data.frame(d=x)),lwd=2, add=T)
curve(predict(m10,newdata=data.frame(d=x)),lwd=1, add=T)#error
# line
plot(df$d,df$y)  
curve(predict(m1,newdata=data.frame(d=x)),lwd=2, add=T)
lines(df$d, predict(m10, level=1),col="green")#error

例如，ggplot2 中有什么方法吗？

【问题讨论】：

标签： ggplot2 plot lm lme4 mixed-models

【解决方案1】：

这是一个方法！我喜欢使用broom 和broom.mixed 来获得一个完整的标题，其中包含每个模型的预测值。

library(tidyverse)
library(lme4)
library(broom)
library(broom.mixed)


df <- ChickWeight

lin <- lm(weight ~ Time,df)

mlm <- lmer(weight ~ Time + (1 | Chick),df)

df <- df %>% 
  mutate(linpred = broom::augment(lin)[,3] %>% pull(),
         mlmpred = broom.mixed::augment(mlm)[,4] %>% pull())

ggplot(df,aes(Time,weight,group = Chick)) + 
  geom_line(alpha = .2) + 
  geom_line(aes(y = linpred,color = 'Fixed Linear Effect')) + 
  geom_line(aes(y = mlmpred,color = 'Random Intercepts'), alpha = .4) +
  scale_color_manual(values = c('blue','red')) +
  labs(color = '') +
  theme_minimal()

【讨论】：