英文:
How to plot 95% confidence intervals for multiple factor data in ggplot2 R?
问题
以下是翻译好的代码部分：
df <- data.frame(yaxis = rnorm(120, 5, 1),xaxis = rep(c("A", "B", "C", "D", "E", "F"), times = 20),factor = rep(c("1", "2", "3", "4", "5", "6"), each = 20))df$xaxis <- as.factor(df$xaxis)df$factor <- as.factor(df$factor)library(lmerTest)mod <- lmer(yaxis ~ xaxis + (1|factor), df)mod.fit <- predictInterval(mod, df)df.fit <- cbind(df, mod.fit)ggplot(df.fit, aes(x = xaxis, y = yaxis, color = xaxis, group = xaxis)) +geom_point() +theme_classic() +geom_errorbar(aes(min = lwr, max = upr), color = "black", width = 0.3) +geom_point(aes(y = mean(fit), x = xaxis), position = position_dodge(width = 0.25), color = "black", size = 5)
请告诉我，您还有其他需要吗？
英文:
df <- data.frame (yaxis = rnorm(120,5,1),xaxis = rep(c("A","B","C","D","E", "F"), times = 20),factor = rep(c("1","2","3","4","5", "6"), each = 20))df$xaxis <- as.factor(df$xaxis)df$factor <- as.factor(df$factor )library(lmerTest)mod <- lmer(yaxis ~ xaxis + (1|factor), df)mod.fit <- predictInterval(mod, df)df.fit <- cbind(df, mod.fit)ggplot(df.fit, aes(x = xaxis, y = yaxis, color = xaxis, group = xaxis)) +geom_point() +theme_classic() +geom_errorbar(aes(min = lwr, max = upr), color = "black", width = 0.3) +geom_point(aes(y = mean(fit), x = xaxis), position = position_dodge(width = 0.25), color = "black",size = 5)
I want to plot the raw data with the predicted 95% CI and means. I tried the above but I get multiple confidence intervals for the fitted data. How can I correct it?
答案1
得分: 2
You can extract the fixed effect from your model using summary(mod)$coefficients. This allows you to get the prediction for each x-axis value and calculate the 95% confidence interval of that prediction using simple arithmetic.
英文:
You can extract the fixed effect from your model using summary(mod)$coefficients. This allows you to get the prediction for each x axis value and calculate the 95% confidence interval of that prediction using simple arithmetic.
library(tidyverse)library(lme4)mod <- lmer(yaxis ~ xaxis + (1|factor) + 0, df)summary_df <- summary(mod)$coefficients %>%as_tibble() %>%mutate(xaxis = LETTERS[1:6]) %>%rename(yaxis = Estimate) %>%mutate(ymin = yaxis - 1.96 * `Std. Error`,ymax = yaxis + 1.96 * `Std. Error`)ggplot(df, aes(x = xaxis, y = yaxis, color = xaxis, group = xaxis)) +geom_jitter(width = 0.1) +geom_errorbar(aes(ymin = ymin, ymax = ymax), data = summary_df,width = 0.4, linewidth = 1, alpha = 0.4) +geom_point(color = 'black', size = 2, data = summary_df) +theme_classic() +scale_color_brewer(palette = 'Dark2')
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