我正在尝试在ggplot2中绘制三向交互,但看起来生成的绘图中的值(尤其是中间列)中的值与我的数据集中的值不对应,即原始数据显示非单词总是慢于真实的单词,但中间的列显示相反的模式。我尝试使用类似的代码和数据绘制另一个数字,并再次绘制不正确的值。
如果有人能告诉我代码中的错误在哪里,我将不胜感激。
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生成的情节:https://i.stack.imgur.com/IytLr.png
要绘制的值:https://i.stack.imgur.com/kPMRR.png
谢谢!
答案 0 :(得分:1)
关键是在调用ggplot()之前调整数据,而不是在ggplot中调用“df $”。否则,更可能发生某些错误(例如,因子订单可能混合)。要解决这种情况,您可以尝试这样做:
require(ggplot2)
df$exp_fit <- exp(df$fit)
df$exp_lower <- exp(df$lower)
df$exp_upper <- exp(df$upper)
df$Type <- as.factor(df$Type)
df$ZQVT <- as.factor(df$ZQVT)
ggplot(df, aes(x=ZMemScore, y=exp_fit,
color=Type)) +
geom_ribbon(aes(ymin=exp_lower, ymax=exp_upper,
color=NA, fill=Type),
linetype=1, alpha=0.3) +
geom_line(aes(linetype=Type), size=1.2) +
xlab("ZMemScore") + ylab("Predicted RT (ms)") +
labs(color="Type", subtitle="ZQVT") +
facet_grid(. ~ ZQVT) +
scale_linetype_discrete(name='Type', labels=c('Nonword','Real Word')) +
theme_classic() +
scale_color_manual(values=c("black","firebrick2")) +
theme(plot.subtitle = element_text(hjust=0.5)) +
theme(text = element_text(size=14))+
guides(fill=FALSE, color=FALSE)
输出:
示例数据:
require(data.table)
df <- fread("ZMemScore ZQVT Type fit se lower upper
-1 -2 LDTNW 7.029661 0.04961080 6.932423 7.126900
0 -2 LDTNW 7.130045 0.03618878 7.059115 7.200976
1 -2 LDTNW 7.230430 0.05005473 7.132321 7.328538
2 -2 LDTNW 7.330814 0.07777387 7.178375 7.483253
-1 -1 LDTNW 6.953625 0.03015198 6.894526 7.012723
0 -1 LDTNW 7.021205 0.02288979 6.976340 7.066069
1 -1 LDTNW 7.088784 0.03319670 7.023718 7.153851
2 -1 LDTNW 7.156364 0.05141379 7.055592 7.257137
-1 0 LDTNW 6.877588 0.02308945 6.832332 6.922844
0 0 LDTNW 6.912364 0.01719115 6.878669 6.946059
1 0 LDTNW 6.947139 0.02335335 6.901366 6.992912
2 0 LDTNW 6.981915 0.03581413 6.911718 7.052111
-1 1 LDTNW 6.801552 0.03651265 6.729986 6.873117
0 1 LDTNW 6.803523 0.02498816 6.754545 6.852500
1 1 LDTNW 6.805494 0.02890588 6.748837 6.862150
2 1 LDTNW 6.807465 0.04434635 6.720545 6.894385
-1 2 LDTNW 6.725515 0.05752647 6.612762 6.838269
0 2 LDTNW 6.694682 0.03886592 6.618504 6.770860
1 2 LDTNW 6.663848 0.04441321 6.576797 6.750899
2 2 LDTNW 6.633015 0.06852165 6.498711 6.767319
-1 -2 LDTRW 6.903851 0.04518178 6.815294 6.992409
0 -2 LDTRW 6.972785 0.03334094 6.907436 7.038134
1 -2 LDTRW 7.041719 0.04585878 6.951835 7.131603
2 -2 LDTRW 7.110653 0.07082106 6.971842 7.249464
-1 -1 LDTRW 6.806363 0.02755119 6.752362 6.860364
0 -1 LDTRW 6.855938 0.02116456 6.814455 6.897421
1 -1 LDTRW 6.905514 0.03046645 6.845799 6.965229
2 -1 LDTRW 6.955089 0.04690274 6.863158 7.047020
-1 0 LDTRW 6.708874 0.02124658 6.667230 6.750518
0 0 LDTRW 6.739091 0.01594271 6.707843 6.770339
1 0 LDTRW 6.769308 0.02146327 6.727240 6.811377
2 0 LDTRW 6.799525 0.03272497 6.735384 6.863667
-1 1 LDTRW 6.611386 0.03344309 6.545836 6.676935
0 1 LDTRW 6.622244 0.02302851 6.577108 6.667381
1 1 LDTRW 6.633103 0.02646563 6.581230 6.684976
2 1 LDTRW 6.643962 0.04035829 6.564858 6.723065
-1 2 LDTRW 6.513897 0.05253701 6.410923 6.616871
0 2 LDTRW 6.505397 0.03572626 6.435373 6.575422
1 2 LDTRW 6.496898 0.04058913 6.417342 6.576453
2 2 LDTRW 6.488398 0.06223723 6.366411 6.610384")