R中的双因子ANOVA Errorbar图

Question

我们正在为生物学学生教授统计课程,并试图将R用作计算和数据可视化平台.尽可能地,我们希望避免使用额外的包并在R中做任何非常"花哨"的事情; 课程的重点是统计,而不是编程.然而,对于双因素ANOVA设计,我们还没有找到在R中生成误差条图的非常好的方法.我们使用ggplot2包来制作绘图,虽然它确实有一个生成95%CI错误栏的内置stat_summary方法,但这些计算方式可能并不总是正确的方法.下面,我手动完成ANOVA的代码并手动计算95%CI(从总残差方差估算标准误差,而不仅仅是组内方差ggplot的汇总方法会使用).最后,实际上是一个情节.

所以问题是......有更简单/更快/更简单的方法来完成所有这些吗？

#   LIZARD LENGTH DATA
island.1 <- c(0.2, 5.9, 6.1, 6.5)
island.2 <- c(5.6, 14.8, 15.5, 16.4)
island.3 <- c(0.8, 3.9, 4.3, 4.9)
sex.codes <- c("Male", "Female", "Male", "Female")

#   PUTTING DATA TOGETHER IN A DATA FRAME
df.1 <- data.frame(island.1, island.2, island.3, sex.codes)

#   MELTING THE DATA FRAME INTO LONG FORM
library(reshape)
df.2 <- melt(df.1)

#   MEAN BY CELL
mean.island1.male <- with(df.2, mean(value[variable == "island.1" & sex.codes == "Male"]))
mean.island1.female <- with(df.2, mean(value[variable == "island.1" & sex.codes == "Female"]))
mean.island2.male <- with(df.2, mean(value[variable == "island.2" & sex.codes == "Male"]))
mean.island2.female <- with(df.2, mean(value[variable == "island.2" & sex.codes == "Female"]))
mean.island3.male <- with(df.2, mean(value[variable == "island.3" & sex.codes == "Male"]))
mean.island3.female <- with(df.2, mean(value[variable == "island.3" & sex.codes == "Female"]))

#   ADDING CELL MEANS TO DATA FRAME
df.2$means[df.2$variable == "island.1" & df.2$sex.codes == "Male"] <- mean.island1.male
df.2$means[df.2$variable == "island.1" & df.2$sex.codes == "Female"] <- mean.island1.female
df.2$means[df.2$variable == "island.2" & df.2$sex.codes == "Male"] <- mean.island2.male
df.2$means[df.2$variable == "island.2" & df.2$sex.codes == "Female"] <- mean.island2.female
df.2$means[df.2$variable == "island.3" & df.2$sex.codes == "Male"] <- mean.island3.male
df.2$means[df.2$variable == "island.3" & df.2$sex.codes == "Female"] <- mean.island3.female

#   LINEAR MODEL
lizard.model <- lm(value ~ variable*sex.codes, data=df.2)

#   CALCULATING RESIDUALS BY HAND:
df.2$residuals.1 <- df.2$value - df.2$means

#   CONFIRMING RESIDUALS FROM LINEAR MODEL:
df.2$residuals.2 <- residuals(lizard.model)

#   TWO FACTOR MAIN EFFECT ANOVA
lizard.anova <- anova(lizard.model)        

#   INTERACTION PLOT
interaction.plot(df.2$variable, df.2$sex.codes, df.2$value)

#   SAMPLE SIZE IN EACH CELL
n <- length(df.2$value[df.2$variable == "island.1" & df.2$sex.codes == "Male"])
# > n
# [1] 2

#   NOTE: JUST FOR CLARITY, PRETEND n=10
n <- 10

#   CALCULATING STANDARD ERROR
island.se <- sqrt(lizard.anova$M[4]/n)

#   HALF CONFIDENCE INTERVAL
island.ci.half <- qt(0.95, lizard.anova$D[4]) * island.se

#   MAKING SUMMARY DATA FRAME
summary.df <- data.frame(
        Means = c(mean.island1.male,
                mean.island1.female,
                mean.island2.male,
                mean.island2.female,
                mean.island3.male,
                mean.island3.female),
        Location = c("island1",
                "island1",
                "island2",
                "island2",
                "island3",
                "island3"),
        Sex = c("male",
                "female",
                "male",
                "female",
                "male",
                "female"),      
        CI.half = rep(island.ci.half, 6)        
        )

# > summary.df
# Means Location    Sex  CI.half
# 1  3.15  island1   male 2.165215
# 2  6.20  island1 female 2.165215
# 3 10.55  island2   male 2.165215
# 4 15.60  island2 female 2.165215
# 5  2.55  island3   male 2.165215
# 6  4.40  island3 female 2.165215

#   GENERATING THE ERRORBAR PLOT
library(ggplot2)

qplot(data=summary.df,
        y=Means,
        x=Location,
        group=Sex,
        ymin=Means-CI.half,
        ymax=Means+CI.half,
        geom=c("point", "errorbar", "line"),
        color=Sex,
        shape=Sex,
        width=0.25) + theme_bw()

Answer 1

我不得不承认我对你的代码感到非常困惑.不要把这当作个人批评,但我强烈建议你学习你的学生尽可能多地使用R的力量.他们只能从中受益,而我的经验是,如果我不把线条和代码行弄得乱七八糟,他们会更快地理解正在发生的事情.

首先,您不必手动计算平均值.做就是了:

df.2$mean <- with(df.2,ave(value,sex.codes,variable,FUN=mean))

另见?ave.这比你的例子中的代码杂乱更清楚.如果你有lizard.model,你可以使用

fitted(lizard.model)

并将这些值与均值进行比较.

然后我强烈反对你.您计算的不是预测的标准误差.要正确执行此操作,请使用该predict()功能

outcome <- predict(lizard.model,se.fit=TRUE)
df.2$CI.half <- outcome$se / 2

要获得预测均值的置信区间,如果您希望学生正确理解,则必须使用正确的公式.使用来自Faraway的R来看看这个令人难以置信的伟大的实用回归和Anova的第3.5节.它包含大量代码示例,其中所有内容都以方便,简洁的方式手动计算.它将为您和您的学生服务.我从中学到了很多东西,并经常在向学生解释这些内容时将其作为指导.

现在要获取摘要数据框,您可以选择一些选项,但这个选项有效并且非常容易理解.

summary.df <- unique(df.2[,-c(3,5,6)])
names(summary.df) <- c('Sex','Location','Means','CI.half')

现在你可以运行你的情节代码了.

或者,如果您希望对值进行预测错误,可以使用以下命令:

lizard.predict <- predict(lizard.model,interval='prediction')
df.2$lower <- lizard.predict[,2]
df.2$upper <- lizard.predict[,3]

summary.df <- unique(df.2[,-3])
names(summary.df)[1:3] <- c('Sex','Location','Means')


qplot(data=summary.df,
        y=Means,
        x=Location,
        group=Sex,
        ymin=lower,
        ymax=upper,
        geom=c("point", "errorbar", "line"),
        color=Sex,
        shape=Sex,
        width=0.25) + theme_bw()

PS:如果我在这里和那里听起来很苛刻,那不是故意的.英语不是我的母语,我仍然不熟悉语言的微妙之处.

Answer 2

这是使用 sciplot 包的另一次尝试。计算置信区间的替代方法可以在参数 ci.fun 中传递。

lineplot.CI(variable,value, group =sex.codes , data = df.2, cex = 1.5,
            xlab = "Location", ylab = "means", cex.lab = 1.2, x.leg = 1,
            col = c("blue","red"), pch = c(16,16))