使用 ggplot2 function 的混合效应 model 残差图

Question

我正在尝试使用 ggplot2 function 绘制混合效应 model 的残余效应。 但是，在执行搜索后，我发现了一些可用的功能，但在我看来，对于 function nlme 它们不起作用。

我打算制作的图表是以下示例的图表：

数据在这里。

数据： https://drive.google.com/file/d/19mykz4B7jkTilbtwPQb3NUI09YZwohhs/view?usp=sharing

我最初尝试的计算例程如下，请参阅执行 ggplot2 中的 function 时出现的错误。

library(splines)
library(ggplot2)
library(nlme)
library(gridExtra)

setwd("C:\\Users\\Desktop")
datanew1 = read.table("dadosnew.csv", header = T, sep=";", dec = ",")
datanew1$DummyVariable = as.factor(datanew1$DummyVariable)
datanew1$Variable2 = as.factor(datanew1$Variable2)
datanew1$Variable3 = as.factor(datanew1$Variable3)
#############################################################################
############################## Model ########################################
#############################################################################
model <-  lme(Response~(bs(Variable1, df=3)) + DummyVariable,
                         random=~1|Variable2/Variable3, datanew1, method="REML")
completemodel <- update(model, weights = varIdent(form=~1|DummyVariable))

p1 <- qplot(.fitted, .resid, data = completemodel) +
  geom_hline(yintercept = 0) +
  geom_smooth(se = FALSE)

Erro: `data` must be a data frame, or other object coercible by `fortify()`, not an S3 object with class lme
Run `rlang::last_error()` to see where the error occurred.

p2 <- qplot(sample =.stdresid, data = completemodel, stat = "qq") + geom_abline()
grid.arrange(p1,p2)

Erro: `data` must be a data frame, or other object coercible by `fortify()`, not an S3 object with class lme
Run `rlang::last_error()` to see where the error occurred.
Além disso: Warning message:
`stat` is deprecated 

我尝试制作图表的另一种方法是使用下面的 function，但我没有成功。

ggplot(completemodel, aes(.fitted, .resid)) + geom_point()

Erro: `data` must be a data frame, or other object coercible by `fortify()`, not an S3 object with class lme
Run `rlang::last_error()` to see where the error occurred.

Answer 1

[解决方案]

library(splines)
library(ggplot2)
library(nlme)
library(gridExtra)

datanew1 = read.table("E:/Downloads/dadosnew.csv", header = T, sep=";", dec = ",")

datanew1$DummyVariable = as.factor(datanew1$DummyVariable)
datanew1$Variable2 = as.factor(datanew1$Variable2)
datanew1$Variable3 = as.factor(datanew1$Variable3)


model <-  lme(Response~(bs(Variable1, df=3)) + DummyVariable,
              random=~1|Variable2/Variable3, datanew1, method="REML")
completemodel <- update(model, weights = varIdent(form=~1|DummyVariable))

df_model <- broom.mixed::augment(completemodel)
#> Registered S3 method overwritten by 'broom.mixed':
#>   method      from 
#>   tidy.gamlss broom
df_model[".stdresid"] <- resid(completemodel, type = "pearson")

p1 <- ggplot(df_model, aes(.fitted, .resid)) + 
  geom_point() +
  geom_hline(yintercept = 0) +
  geom_smooth(se=FALSE)

p2 <- ggplot(df_model, aes(sample = .stdresid)) +
  geom_qq() +
  geom_qq_line()

grid.arrange(p1,p2)
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'