R、ggplot2、jtools：如何修正三路约翰逊-内曼交互图答案

【问题标题】：R, ggplot2, jtools: How to amend three-way Johnson-Neyman interaction chartR、ggplot2、jtools：如何修正三路约翰逊-内曼交互图
【发布时间】：2019-11-12 02:22:15
【问题描述】：

我正在使用 jtools 包中的 Johnson-Neyman 分析来探索三向交互。我已经生成了一个情节（使用ggplot2）并试图修改情节的一部分。我已经成功地修改了一些部分，但没有修改其他部分。

以下是我要修改的内容：

1) 我正在尝试为每个图表分别指定一条垂直线（目前我的代码在两个图表上将这条线放在xintercept=0.403）；和

2) 我正在尝试删除每个图表标题周围的框/边框（当前显示为“Low M”和“High M”）。需要明确的是，我想在保留标签的同时移除框/边框。

#Import data.
df <- structure(list(Y_Variable = c(1, 2, 1, 1.8, 1, 1, NA, 2, 1, 1, 
                                    3.2, 1, 3, 4.2, 2, 1.8, 1, 3.6, 1, 5.4, 1, 2, 1, 1.4, 1, 1, 1.4, 
                                    1, 1, 1, 1, 1, 1, 1, 3.8, 2.2, 1, 3.2, 1, 2.8, 4, 3.6, 1, 1, 
                                    3.4, 1, 2, 4, 1, 2.4, NA, 1, 2.6, 1, 1, 1, 1, 3.4, 1.4, 1, 4, 
                                    2.6, 2, NA, 4, 1, 3, 3.2, 1, 4.6, 1, 1.2, 3, 1, 1.2, NA, 1, 2.6, 
                                    2.6, 1, 1, 3.8, 1, 1, 4.8, 1.8, 1, 2, 4.4, 1, 1.8, 4.2, 2.4, 
                                    3.4, 1.6, 1, 2.8, 1, 4.6, 1.2, 3.4, 2.2, 3.4, 1, 2.2, 5.2, 2.6, 
                                    1, 2.8, 1, 3, 3.2, 3, 2.2, 3, 1, 3, 2, 1, 1, 1.6, 1, 1.2, 1, 
                                    3.2, 1.8, 1.4, 1, 1, 1.8, 2.8, 1, 2, NA, 1, 2.6, 2.6, 1, 4.6, 
                                    3.8, 3.2, 1, 2.5, 1, 1, 7, 2, 2.2, 2.2, 2),
                     X_Variable = c(-0.229752333333333,0.186914333333334, -0.729752333333333, 0.936914333333334, -0.479752333333333, 
                                    -1.646419, 0.770247666666667, -0.729752333333333, -0.729752333333333, 
                                    -0.646419, 0.103581, 0.853581, -0.313085666666666, 0.936914333333334, 
                                    0.603581, 0.103581, 1.27024766666667, 0.436914333333334, -1.22975233333333, 
                                    1.27024766666667, -1.06308566666667, -1.56308566666667, -0.229752333333333, 
                                    0.270247666666667, -0.979752333333333, -1.146419, -0.563085666666666, 
                                    1.103581, -0.229752333333333, 0.353581, -1.81308566666667, 0.520247666666667, 
                                    -1.81308566666667, 0.270247666666667, 1.52024766666667, 0.770247666666667, 
                                    -0.813085666666666, 0.103581, -0.646419, 0.103581, 1.28539918181818, 
                                    1.18691433333333, -1.31308566666667, 0.353581, 0.353581, 1.18691433333333, 
                                    -0.896419, 1.603581, 0.103581, 0.186914333333334, -0.313085666666666, 
                                    -1.72975233333333, 0.603581, -0.563085666666666, -0.0630856666666664, 
                                    0.376308272727273, 0.103581, 0.936914333333334, -1.06308566666667, 
                                    -1.31308566666667, 1.103581, 1.93691433333333, 1.603581, 1.27024766666667, 
                                    0.603581, -0.563085666666666, 2.27024766666667, 2.68691433333333, 
                                    -0.729752333333333, 1.46721736363636, -0.396419, 0.353581, 1.103581, 
                                    -0.896419, -0.729752333333333, 0.686914333333334, -1.896419, 
                                    0.436914333333334, 1.18691433333333, -1.396419, -0.396419, 0.686914333333334, 
                                    -0.146419, -0.979752333333333, 0.270247666666667, -1.146419, 
                                    -0.0630856666666664, 3.18691433333333, 0.186914333333334, -0.896419, 
                                    -0.979752333333333, 1.68691433333333, 2.02024766666667, 0.270247666666667, 
                                    0.520247666666667, -1.47975233333333, 1.103581, 1.103581, 1.43691433333333, 
                                    -0.396419, 0.770247666666667, 0.103581, -0.729752333333333, 0.270247666666667, 
                                    0.520247666666667, 0.186914333333334, 0.853581, -0.813085666666666, 
                                    -0.313085666666666, -0.350964454545454, 0.270247666666667, -0.396419, 
                                    0.853581, 0.353581, 1.52024766666667, -0.813085666666666, -0.146419, 
                                    -1.06308566666667, -1.81308566666667, -1.31308566666667, 0.0202476666666667, 
                                    1.02024766666667, -1.22975233333333, -1.22975233333333, -0.146419, 
                                    -0.896419, -1.56308566666667, -1.396419, 0.853581, -0.313085666666666, 
                                    -0.563085666666666, -0.563085666666666, 0.270247666666667, -0.896419, 
                                    -0.813085666666666, 0.603581, 0.353581, 0.853581, 2.103581, 1.103581, 
                                    0.103581, -1.22975233333333, 0.830853727272727, 0.270247666666667, 
                                    0.603581, 2.27024766666667, -0.313085666666666, -0.479752333333333, 
                                    0.353581, 1.46721736363636),
                     Z_Variable = c(0.206736, -0.593264,1.006736, -3.793264, 2.006736, 1.006736, 0.00673600000000008, 
                                    1.006736, -0.393264, 0.406736, 0.206736, -0.993264, 0.00673600000000008, 
                                    -1.993264, 0.406736, 1.006736, -3.993264, -0.393264, 1.406736, 
                                    -2.793264, 1.006736, 2.006736, 0.00673600000000008, -0.993264, 
                                    2.006736, 1.206736, 1.006736, -0.193264, 2.006736, 1.006736, 
                                    1.206736, -1.193264, 1.006736, 0.406736, -0.993264, 0.606736, 
                                    1.006736, 0.00673600000000008, 0.606736, 0.806736, 0.406736, 
                                    -0.393264, 2.006736, 1.006736, 0.406736, 0.00673600000000008, 
                                    0.00673600000000008, -2.193264, 0.406736, 0.206736, 1.406736, 
                                    1.006736, 0.00673600000000008, -3.393264, 0.00673600000000008, 
                                    -2.993264, -0.193264, 0.00673600000000008, 1.006736, 1.006736, 
                                    -2.393264, 0.606736, 1.006736, 0.406736, -2.193264, 1.006736, 
                                    -0.793264, -3.393264, 1.006736, 0.506736, -0.793264, 0.406736, 
                                    -1.993264, 0.00673600000000008, -0.993264, -0.593264, 1.006736, 
                                    -0.793264, -3.193264, 1.006736, 0.406736, -1.393264, -1.793264, 
                                    2.006736, 1.206736, 1.006736, -1.393264, -3.993264, -1.793264, 
                                    0.206736, 0.806736, -1.993264, 2.006736, -1.993264, 0.00673600000000008, 
                                    -0.593264, -2.993264, 1.006736, -0.393264, -2.193264, -3.793264, 
                                    -0.593264, 1.006736, 0.406736, -1.193264, -0.993264, -0.193264, 
                                    1.006736, 0.00673600000000008, 1.006736, 0.806736, -3.993264, 
                                    0.206736, -0.393264, -3.393264, 1.206736, 0.606736, -1.393264, 
                                    0.606736, -0.993264, 0.806736, -2.193264, 0.406736, 1.006736, 
                                    1.006736, 1.206736, 1.006736, 1.006736, -0.793264, 1.406736, 
                                    -2.193264, -0.393264, 1.006736, 1.806736, 1.206736, 0.00673600000000008, 
                                    1.006736, 0.406736, -0.593264, 0.00673600000000008, 1.006736, 
                                    0.806736, -0.593264, 2.006736, 1.006736, 0.673402666666667, 1.006736, 
                                    -2.193264, 0.206736, 1.006736),
                     M_Variable = c(-0.102748, -0.602748, 
                                    -0.102748, -1.352748, 1.897252, -0.102748, -1.352748, 1.647252, 
                                    -0.852748, 1.147252, 0.147252, -0.102748, -0.102748, -2.102748, 
                                    -1.352748, 1.647252, -2.602748, -0.102748, -0.102748, 0.147252, 
                                    0.897252, 1.397252, -0.352748, -0.102748, -0.102748, 0.147252, 
                                    0.397252, -0.102748, 0.647252, 0.647252, 0.897252, -2.102748, 
                                    1.147252, 0.647252, -1.602748, -0.352748, -1.602748, -0.102748, 
                                    -0.852748, 0.397252, 1.397252, -3.102748, 2.147252, 0.897252, 
                                    0.897252, 0.397252, -0.102748, -2.102748, -0.352748, -0.102748, 
                                    1.397252, -0.102748, 0.397252, 0.147252, -1.852748, -2.102748, 
                                    -1.602748, 0.147252, 0.397252, 0.647252, -1.102748, 1.397252, 
                                    1.397252, 1.147252, -0.102748, 1.897252, -1.102748, -2.602748, 
                                    -0.602748, 0.397252, -0.102748, -0.602748, -1.102748, -0.102748, 
                                    -1.102748, -0.102748, 0.897252, -0.352748, -1.102748, 0.897252, 
                                    0.647252, -2.352748, -1.352748, 0.897252, 0.397252, -0.102748, 
                                    -0.102748, -3.102748, -2.102748, -1.102748, 1.397252, -1.102748, 
                                    1.897252, -2.352748, -0.102748, -0.102748, -2.102748, -0.102748, 
                                    -1.102748, 0.147252, -2.602748, 0.647252, 1.647252, -0.102748, 
                                    -0.852748, -0.102748, -0.602748, 0.397252, -2.102748, 1.897252, 
                                    1.147252, -0.102748, -0.352748, 0.147252, -0.102748, 0.397252, 
                                    0.147252, -2.102748, -2.102748, -0.102748, 0.897252, -1.602748, 
                                    0.397252, 1.897252, -2.102748, 0.897252, -0.102748, 1.897252, 
                                    -0.602748, 0.397252, -2.102748, 0.397252, -0.102748, 1.897252, 
                                    -1.352748, 0.397252, 0.647252, 0.897252, 0.397252, 0.147252, 
                                    0.397252, 1.897252, 0.397252, 1.397252, 0.897252, -0.352748, 
                                    1.897252, -1.102748, -2.102748, 0.647252)),
                row.names = c(NA,-150L),
                class = "data.frame")

#Set basic inputs.
decimals <- 3
stddev <- 1

#Run model.
lms2 <- lm(Y_Variable~X_Variable*Z_Variable*M_Variable, data=df, na.action=na.exclude)

#Generate Johnson-Neyman plot.
lms2.plot <- interact_plot(lms2, pred="X_Variable", modx="Z_Variable", mod2="M_Variable",
                           x.label="X Label", y.label="Y Label",
                           modxvals=c((mean(df$Z_Variable, na.rm=TRUE)-(sd(df$Z_Variable, na.rm=TRUE)*stddev)),
                                      (mean(df$Z_Variable, na.rm=TRUE)+(sd(df$Z_Variable, na.rm=TRUE)*stddev))),
                           modx.labels=c("Low Z", "High Z"),
                           mod2.values=c((mean(df$M_Variable, na.rm=TRUE)-(sd(df$M_Variable, na.rm=TRUE)*stddev)),
                                         (mean(df$M_Variable, na.rm=TRUE)+(sd(df$M_Variable, na.rm=TRUE)*stddev))),
                           mod2.labels=c("Low M", "High M"),
                           colors=c("#333333", "#999999"),
                           int.width = 0.682, vary.lty=FALSE, interval=TRUE)
lms2.plot <- lms2.plot+
             coord_cartesian(xlim=c(-2.00, 4.00), ylim=c(0.00, 6.00))+
             scale_x_continuous(breaks=seq(-2.00, 4.00, 1.00))+
             scale_y_continuous(breaks=seq(0.00, 6.00, 1.00))+
             theme_classic()+
             theme(legend.title=element_blank(), legend.position="top",
                   panel.grid.major=element_line(colour="grey", size=0.5, 3))
lms2.plot <- lms2.plot+
             geom_vline(xintercept=0.403)
lms2.plot

【问题讨论】：

标签： r ggplot2 interaction

【解决方案1】：

我们从你的情节的某些部分开始，lms2.plot。我从包交互中使用了interact_plot，因为interact_plot 是deprecated in jtools。应该会给你同样的结果。

lms2.plot <- interact_plot(lms2, pred="X_Variable", modx="Z_Variable", mod2="M_Variable",
                           x.label="X Label", y.label="Y Label",
                           modxvals=c((mean(df$Z_Variable, na.rm=TRUE)-(sd(df$Z_Variable, na.rm=TRUE)*stddev)),
                                      (mean(df$Z_Variable, na.rm=TRUE)+(sd(df$Z_Variable, na.rm=TRUE)*stddev))),
                           modx.labels=c("Low Z", "High Z"),
                           mod2.values=c((mean(df$M_Variable, na.rm=TRUE)-(sd(df$M_Variable, na.rm=TRUE)*stddev)),
                                         (mean(df$M_Variable, na.rm=TRUE)+(sd(df$M_Variable, na.rm=TRUE)*stddev))),
                           mod2.labels=c("Low M", "High M"),
                           colors=c("#333333", "#999999"),
                           int.width = 0.682, vary.lty=FALSE, interval=TRUE)
lms2.plot <- lms2.plot+
             coord_cartesian(xlim=c(-2.00, 4.00), ylim=c(0.00, 6.00))+
             scale_x_continuous(breaks=seq(-2.00, 4.00, 1.00))+
             scale_y_continuous(breaks=seq(0.00, 6.00, 1.00))

要控制 xintercept 的放置位置，您需要创建一个单独的数据框，其中包含 xintercept 和用于分割 facet 的变量，称为 mod2_group。

> head(lms2.plot$data)
# A tibble: 6 x 8
  Y_Variable Z_Variable M_Variable X_Variable  ymax  ymin modx_group mod2_group
       <dbl>      <dbl>      <dbl>      <dbl> <dbl> <dbl> <fct>      <fct>     
1      0.793      -1.55      -1.34      -1.90  1.17 0.416 Low Z      Low M     
2      0.832      -1.55      -1.34      -1.85  1.20 0.462 Low Z      Low M

所以我们为 xintercept 创建一个数据框并添加它们

test <- data.frame(mod2_group=c("Low M","High M"),xintercept=c(1,3))
lms2.plot + geom_vline(data=test,aes(xintercept=xintercept))

最后一部分，去除边框。使用 theme_classic() 后，使用 theme() 修改面板的调用将不起作用。看来a work around是要定义一个新的经典：

theme_classic2 <- function(base_size = 12, base_family = ""){
  theme_bw(base_size = base_size, base_family = base_family) %+replace%
    theme(
      legend.title= element_blank(),
      panel.border     = element_blank(),
      axis.line        = element_line(colour = "black"),
      panel.grid.major=element_line(colour="grey", size=0.5, 3),
      panel.grid.major.x = element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank(),
      panel.grid.minor.x = element_blank(),
      panel.grid.minor.y = element_blank(),
      strip.background = element_blank(),
      legend.key       = element_blank()
    )
}

现在我们将它们放在一起并绘制：

lms2.plot + 
geom_vline(data=test,aes(xintercept=xintercept))+
theme_classic2()

【讨论】：

出色的工作，谢谢。你还教我如何制作自己的主题！
不客气！我也从类似的问题中了解到这一点，哈哈。付钱我猜=p