R，ggplot2，jtools：如何修改三向Johnson-Neyman交互图
问题描述 投票：1回答：1
我正在使用jtools程序包中的Johnson-Neyman分析进行三向交互。我已经生成了一个绘图（使用ggplot2），并试图修改该绘图的某些部分。我已经成功修改了某些部分，但没有其他部分。
这是我想修改的内容：
1）我试图为每个图表分别指定一条垂直线（目前，我的代码将两条图表的线都放置在xintercept=0.403处）；和
2）我正在尝试删除每个图表的标题周围的框/边框（当前显示为“低M”和“高M”）。为了清楚起见，我想在保留标签的同时取下盒子/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
1个回答
0
投票
投票
[我们从情节的某些部分开始，即lms2.plot。我从包交互中使用了interact_plot，因为interact_plot为deprecated in jtools。应该会给您相同的结果。
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))
要控制放置xintercept的位置，您需要创建一个单独的数据框，其中包含xintercept和用于拆分构面的变量，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，删除边框。一旦使用theme_classic（），使用theme（）修改面板的调用将不起作用。似乎a work around是要定义一个新经典：
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()
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