我如何在R中使用95％的置信区间绘制LMM的截距和系数？

发布于 2025-01-28 12:41:09 字数 3159 浏览 4 评论 0原文

为了可视化我的线性混合模型（LMM）的结果，我想绘制Spagetti图，以跟踪所有参与者随时间变化的变化。我想将其与绘制LMM的截距和系数（包括95％置信区间）结合使用。但是，到目前为止，我还没有成功将这些阴谋结合在一起，我正在寻求一些帮助。

对于我的LMM，我使用了R中LME4软件包的一部分的“ LMER”功能，

这是我代码的第一部分运行线性混合模型并获取所有测量值的Spagetti图，每个主题随着时间的推移

#Load subset of my data that I obtained with the 'dput' function
Longitudinal_measures_volume <- structure(list(id = structure(c(1L, 2L, 
3L, 9L, 10L, 11L, 13L, 18L, 19L, 1L, 2L, 3L, 9L, 10L, 11L, 13L, 18L, 3L, 9L, 11L, 13L, 18L, 19L), .Label = c("01_01", "01_02", "01_05", "01_06", "01_07", "01_08", "01_11", "01_12", "01_13", "01_14", "01_17", "01_19", "01_21", "01_22", "01_24", "01_25", "01_27", "01_28", "01_30", "01_33", "01_35", "02_01", "02_02","02_03", "02_04", "02_05", "02_06", "02_07", "02_09", "02_10","02_11", "02_12", "02_13", "02_14", "02_15", "02_18", "02_20",                        "02_21", "02_23", "02_24", "02_25", "03_01", "03_02", "03_03", "03_04", "03_05", "03_06", "03_07", "03_08", "03_09", "03_10","03_11", "03_12", "03_13", "03_14", "03_15", "03_16", "03_18","03_19", "03_20"), class = "factor"), Time = c(67, 41, 41, 46, 39, 42, 38, 41, 47, 119, 105, 111, 95, 95, 98, 103, 95, 195, 193, 196, 222, 188, 211), Volume1 = c(3.45897015360706, 0.898545274090074, -1.46885896648035, 0.48984364347097, 2.52266602788419, 0.930927639876306, -1.79340703526533, 2.58022117920645, -1.88239745399949,3.22242849165164, 0.615590786285958, -1.14628038654842, 0.996278646866944, 2.16556421777876, 1.64058648629247, -1.13969644900845, 2.45418152346369, -0.307093266274559, 1.51877037424232, 1.74322136278721, -1.72984060264199, 1.96975730534075, -1.25357412221767)), row.names = c(1L, 2L, 3L, 8L, 9L, 10L, 12L, 17L, 18L, 60L, 61L, 62L, 68L, 69L, 70L, 72L, 77L, 79L, 85L, 86L, 88L, 93L, 94L), class = "data.frame")

#load packages
library(lme4)
library(ggeffects)


#LMM model (example)
LMMmodel_example <- lmer(Volume1 ~ Time + (1 |id), data=Longitudinal_measures_volume)
summary(LMMmodel_example)

#Obtain spagetti plots for all measurements over time per subject
plot <- ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id)) + geom_line(aes(x=Time, y=Volume1, color=id))

print(plot)

，我编写了以下内容，分开的代码行，以绘制我的LMM的截距和系数（具有95％置信区间）：

Group_line <- ggpredict(LMMmodel_example, terms = c("Time"),  interval = "confidence", ci.lvl=0.95)
plot(Group_line)

现在，我想将这两个图组合到一个地块中，在其中我具有意大利面图和一个带有截距和系数的图（包括95％置信区间）我的LMM作为覆盖层。我的第一个尝试是以下内容：

plot_overlay <- ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id)) + geom_line(aes(x=Time, y=Volume1, color=id)) + ggpredict(LMMmodel_example, terms = c("Time"),  interval = "confidence", ci.lvl=0.95)

然后我收到以下错误语句：

Error in ggplot(data = Longitudinal_measures_volume, aes(x = Time, y = Volume1,  : 
  non-numeric argument to binary operator
In addition: Warning message:
Incompatible methods ("+.gg", "Ops.data.frame") for "+"*

我认为错误可能是由于“ GGPREDICT”功能现在正在寻找与“ GGPLOT”所指定的相同美学。作为一个潜在的解决方案，我尝试在GGPREDICT函数中添加'sashit.aes = false'，但不幸的是，这并不能解决我的错误。

我将感谢任何帮助结合这些阴谋的帮助，非常感谢！对其他功能的建议也非常欢迎。

原文

To visualize the results of my linear mixed model (LMM) I would like to plot spagetti plots that track the change in volume1 over time for all participants. I would like to combine this with plotting the intercept and coefficient (including 95% confidence intervals) of my LMM as an overlay. However, so far I have not succeeded to combine these plots and I am looking for some help.

For my LMM I used the 'lmer' function that is part of the lme4 package in R.

Here is the first part of my code to run the linear mixed model and obtain spagetti plots for all measurments over time per subject

#Load subset of my data that I obtained with the 'dput' function
Longitudinal_measures_volume <- structure(list(id = structure(c(1L, 2L, 
3L, 9L, 10L, 11L, 13L, 18L, 19L, 1L, 2L, 3L, 9L, 10L, 11L, 13L, 18L, 3L, 9L, 11L, 13L, 18L, 19L), .Label = c("01_01", "01_02", "01_05", "01_06", "01_07", "01_08", "01_11", "01_12", "01_13", "01_14", "01_17", "01_19", "01_21", "01_22", "01_24", "01_25", "01_27", "01_28", "01_30", "01_33", "01_35", "02_01", "02_02","02_03", "02_04", "02_05", "02_06", "02_07", "02_09", "02_10","02_11", "02_12", "02_13", "02_14", "02_15", "02_18", "02_20",                        "02_21", "02_23", "02_24", "02_25", "03_01", "03_02", "03_03", "03_04", "03_05", "03_06", "03_07", "03_08", "03_09", "03_10","03_11", "03_12", "03_13", "03_14", "03_15", "03_16", "03_18","03_19", "03_20"), class = "factor"), Time = c(67, 41, 41, 46, 39, 42, 38, 41, 47, 119, 105, 111, 95, 95, 98, 103, 95, 195, 193, 196, 222, 188, 211), Volume1 = c(3.45897015360706, 0.898545274090074, -1.46885896648035, 0.48984364347097, 2.52266602788419, 0.930927639876306, -1.79340703526533, 2.58022117920645, -1.88239745399949,3.22242849165164, 0.615590786285958, -1.14628038654842, 0.996278646866944, 2.16556421777876, 1.64058648629247, -1.13969644900845, 2.45418152346369, -0.307093266274559, 1.51877037424232, 1.74322136278721, -1.72984060264199, 1.96975730534075, -1.25357412221767)), row.names = c(1L, 2L, 3L, 8L, 9L, 10L, 12L, 17L, 18L, 60L, 61L, 62L, 68L, 69L, 70L, 72L, 77L, 79L, 85L, 86L, 88L, 93L, 94L), class = "data.frame")

#load packages
library(lme4)
library(ggeffects)


#LMM model (example)
LMMmodel_example <- lmer(Volume1 ~ Time + (1 |id), data=Longitudinal_measures_volume)
summary(LMMmodel_example)

#Obtain spagetti plots for all measurements over time per subject
plot <- ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id)) + geom_line(aes(x=Time, y=Volume1, color=id))

print(plot)

Then I wrote the following, separate, line of code to plot the intercept and coefficient(with 95% confidence interval) of my LMM:

Group_line <- ggpredict(LMMmodel_example, terms = c("Time"),  interval = "confidence", ci.lvl=0.95)
plot(Group_line)

Now, I would want to combine these two plots into one, where I have the spagetti plots and a plot with the intercept and coefficient (including 95% confidence interval) of my LMM as an overlay. My first attempt was the following:

plot_overlay <- ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id)) + geom_line(aes(x=Time, y=Volume1, color=id)) + ggpredict(LMMmodel_example, terms = c("Time"),  interval = "confidence", ci.lvl=0.95)

I then received the following error statement:

Error in ggplot(data = Longitudinal_measures_volume, aes(x = Time, y = Volume1,  : 
  non-numeric argument to binary operator
In addition: Warning message:
Incompatible methods ("+.gg", "Ops.data.frame") for "+"*

I thought that the error might be due to that the 'ggpredict' function is now looking for the same aesthetics as specfied with 'ggplot'. As a potential solution I tried to add 'inherit.aes=FALSE' to the ggpredict function, but unfortunately this didn't solve my error.

I would be grateful for any help to combine these plots, thanks a lot in advance! Recommendations for other functions are also very welcome.

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找个人就嫁了吧 2025-02-04 12:41:09

请有可再现的例子吗？您提供的模型在我的R中不起作用，因此我不会在GGPLOT上遇到问题。

也许您可以考虑GEOM_RIBBOM功能，并带有类似的内容：

ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id))+ geom_line(aes(x=Time, y=Volume1, color=id)) + 
geom_ribbon(aes(ymin = minpred, ymax = maxpred), alpha = 0.1)

Is it possible to have a reproducible example please? the model you provide does not work in my R so I don't get the problem on the ggplot.

Maybe you can think about the geom_ribbon function, with something like :

ggplot(data=Longitudinal_measures_volume, aes(x=Time, y=Volume1, group =id))+ geom_line(aes(x=Time, y=Volume1, color=id)) + 
geom_ribbon(aes(ymin = minpred, ymax = maxpred), alpha = 0.1)

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捶死心动 2025-02-04 12:41:09

ggeffects的输出可以转换为数据框架，可用于添加geom_ribbon和geom_line geom_ribbon 。

我认为颜色太多，无法在图上识别您的单个ID值，因此我创建了一个带有直接标签的版本作为选项：

library(geomtextpath)

plot <- ggplot(data=Longitudinal_measures_volume, 
               aes(x = Time, y = Volume1, group = id)) + 
  geom_textline(aes(x = Time, y = Volume1, label = id, hjust = id), 
                vjust = -0.3,
                linewidth = 1.5, alpha = 0.8,
                linecolor = 'deepskyblue4') +
  geom_point() +
  scale_hjust_manual(values = c(0, 0.2, 0, 0.4, 1, 0.75, 1, 0.9, 1)) +
  theme_light(base_size = 16)

Group_line <- ggpredict(LMMmodel_example, terms = c("Time"),  
                        interval = "confidence", ci.lvl = 0.95)

plot + 
  geom_ribbon(aes(x = x, ymax = conf.high, ymin = conf.low), 
              data = as.data.frame(Group_line), inherit.aes = FALSE,
              alpha = 0.2, fill = 'lightblue') +
  geom_textline(aes(x = x, y = predicted, label = 'Overall'), linetype = 2,
            data = as.data.frame(Group_line), inherit.aes = FALSE)

The output of ggeffects can be converted to a data frame, which can be used to add a geom_ribbon and geom_line to your existing plot.

I think there are too many colors to make your individual ID values identifiable on the plot, so I have created a version with direct labelling as an option:

library(geomtextpath)

plot <- ggplot(data=Longitudinal_measures_volume, 
               aes(x = Time, y = Volume1, group = id)) + 
  geom_textline(aes(x = Time, y = Volume1, label = id, hjust = id), 
                vjust = -0.3,
                linewidth = 1.5, alpha = 0.8,
                linecolor = 'deepskyblue4') +
  geom_point() +
  scale_hjust_manual(values = c(0, 0.2, 0, 0.4, 1, 0.75, 1, 0.9, 1)) +
  theme_light(base_size = 16)

Group_line <- ggpredict(LMMmodel_example, terms = c("Time"),  
                        interval = "confidence", ci.lvl = 0.95)

plot + 
  geom_ribbon(aes(x = x, ymax = conf.high, ymin = conf.low), 
              data = as.data.frame(Group_line), inherit.aes = FALSE,
              alpha = 0.2, fill = 'lightblue') +
  geom_textline(aes(x = x, y = predicted, label = 'Overall'), linetype = 2,
            data = as.data.frame(Group_line), inherit.aes = FALSE)