如何将Y值的比数比置于X对at图(或其他R图)的均值之上或以下的标准差处
如何将Y值的比数比置于X对at图(或其他R图)的均值之上或以下的标准差处
提问于 2021-03-03 23:23:41
我有这样的数据。
Probability <- c(1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0)
Score2 <- c(2,3,4,2,3,4,2,3,4,4,3,2,3,2,3,2,3,2,3,4,2,3,4,2,3,3,4,3,2,3,2,3,2,3)
Data2 <- data.frame(Probability, Score2)
Data
Probability Score2
1 1 2
2 1 3
3 1 4
4 1 2
5 1 3
6 1 4
7 1 2
8 1 3
9 1 4
10 0 4
11 0 3
12 0 2
13 0 3
14 0 2
15 0 3
16 0 2
17 0 3
18 1 2
19 1 3
20 1 4
21 1 2
22 1 3
23 1 4
24 1 2
25 1 3
26 1 3
27 0 4
28 0 3
29 0 2
30 0 3
31 0 2
32 0 3
33 0 2
34 0 3我不仅需要绘制一个相关图，还需要添加比较Y在X平均值之上和以下的1标准差的赔率比(与相应的p值)。
下面给我所有的东西，除了+/- 1SD和它的p-值的赔率比。
ggplot(Data2, aes(Score2, Probability))+
geom_smooth(method='lm', alpha = .3, color = "black")+
stat_cor(method = "pearson", label.x.npc = "left", label.y.npc= "top", label.sep = "
")+
scale_colour_grey(start = .6, end = .2)+
scale_fill_grey(start = 0.6, end = 1)+
theme_classic()+
scale_y_continuous(breaks = c(0,0.25,0.5,0.75,1), limits = c(0, 1))
问题
我如何添加优势比(比较概率在1 SD以上和以下的平均值X)？
回答 2
Stack Overflow用户
发布于 2021-03-04 00:57:07
你也可以用线性概率模型的对比包来估计CI。
library(ggpubr)
mean_x = mean(Score2)
sd_x = sd(Score2)线性概率模型
lm_prob <- lm(Probability ~ Score2,data=Data2)
pred_probs = predict(lm_prob,newdata = data.frame(Score2 = c(mean_x - sd_x, mean_x+sd_x)))
or_pred = (pred_probs[2]/(1-pred_probs[2]))/(pred_probs[1]/(1-pred_probs[1]))Logistic回归
glm_prob <- glm(Probability ~ Score2,data=Data2,family=binomial())
glm_pred_probs = predict(glm_prob,newdata = data.frame(Score2 = c(mean_x - sd_x, mean_x+sd_x)),type = "response",se.fit = TRUE)
glm_or_pred = (glm_pred_probs$fit[2]/(1-glm_pred_probs$fit[2]))/(glm_pred_probs$fit[1]/(1-glm_pred_probs$fit[1]))对比语句可以用mean_x + sd_x和mean_x - sd_x更新，不会改变结果.
library(contrast)
glm_contrast <-
contrast(glm_prob,
list(Score2 = sd_x),
list(Score2 = -sd_x)
)
print(glm_contrast, X = TRUE)
or_ci = paste0(round(exp(glm_contrast$Contrast),2),
", 95% CI:",
round(exp(glm_contrast$Lower),2),
", ",
round(exp(glm_contrast$Upper),2),
", p = ",
round(glm_contrast$Pvalue,3)
)绘图
ggplot(Data2, aes(Score2, Probability))+
geom_smooth(method='lm', alpha = .3, color = "black")+
stat_cor(method = "pearson", label.x.npc = "left", label.y.npc= "top", label.sep = "
")+
annotate("text", x=3.0, y=0.9, label= paste0("OR = ",or_ci)) +
scale_colour_grey(start = .6, end = .2)+
scale_fill_grey(start = 0.6, end = 1)+
theme_classic()+
scale_y_continuous(breaks = c(0,0.25,0.5,0.75,1), limits = c(0, 1))Stack Overflow用户
发布于 2021-03-04 19:02:11
@jvargh7帮我解决了大部分问题(减去标准偏差括号) here
图书馆(Ggpubr)
### @jVargh7's code ###
library(ggplot2)
library(ggpubr)
mean_x = mean(Score2)
sd_x = sd(Score2)
lm_prob <- lm(Probability ~ Score2,data=Data2)
pred_probs = predict(lm_prob,newdata = data.frame(Score2 = c(mean_x - sd_x, mean_x+sd_x)))
or_pred = (pred_probs[2]/(1-pred_probs[2]))/(pred_probs[1]/(1-pred_probs[1]))
glm_prob <- glm(Probability ~ Score2,data=Data2,family=binomial())
glm_pred_probs = predict(glm_prob,newdata = data.frame(Score2 = c(mean_x - sd_x, mean_x+sd_x)),type = "response",se.fit = TRUE)
glm_or_pred = (glm_pred_probs$fit[2]/(1-glm_pred_probs$fit[2]))/(glm_pred_probs$fit[1]/(1-glm_pred_probs$fit[1]))
install.packages("contrast")
library(contrast)
glm_contrast <- contrast(glm_prob, list(Score2 = sd_x), list(Score2 = -sd_x))
print(glm_contrast, X = TRUE)
or_ci = paste0(round(exp(glm_contrast$Contrast),2),
", 95% CI: ",
round(exp(glm_contrast$Lower),2),
", ",
round(exp(glm_contrast$Upper),2),
", p = ",
round(glm_contrast$Pvalue,3)
)
ggplot(Data2, aes(Score2, Probability))+
geom_smooth(method='lm', alpha = .3, color = "black")+
stat_cor(method = "pearson", label.x = 2.75, label.y= .6, label.sep = ",")+
scale_colour_grey(start = .6, end = .2)+
scale_fill_grey(start = 0.6, end = 1)+
theme_classic()+
scale_y_continuous(breaks = c(0,0.25,0.5,0.75,1), limits = c(0, 1))+
### new code ###
geom_bracket(xmin = mean(Score2)-sd(Score2),
xmax = mean(Score2)+sd(Score2),
y.position = .9,
label = paste0("OR = ", or_ci),
tip.length = c(0.42, 0.29),
vjust = -1)下面的示例输出
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/66466426
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