i具有如下的数据(dtf.long):
nutrition fertilizer season seedlingdensity plandensity fitted
nitrogen none wet 5 19 6
nitrogen none dry 5 19 8
nitrogen phos wet 4 23 16
nitrogen phos dry 5 19 10
iron none wet 5 29 21
iron none dry 5 19 14
iron phos wet 4 23 12
iron phos dry 5 20 14
....
...
总共有16个重复。我想在X轴上绘制Y轴上的对数(幼苗密度)的回归,并以食物为单位。两种类型的肥料可以以一种颜色和不同的PCH进行季节。
我试图编写一个代码,但我仍然不知道如何为季节编码PCH
回归中的模型拟合存储在列拟合中
summary_dat = dtf.long %>%
group_by(nutrition, fertilizer, season) %>%
summarise(mean_predict=mean(fitted),
sd_predict = sd(fitted),
n_predict = n()) %>%
mutate(se_predict = sd_predict / sqrt(n_predict),
lower_ci = mean_predict - qt(1 - (0.05 / 2), n_predict - 1) * se_predict,
upper_ci = mean_predict + qt(1 - (0.05 / 2), n_predict - 1) * se_predict)
ggplot() +
geom_point(data=dtf.long, aes(x=log(plantdensity), y=log(seedlingdensity), group=fertilizer, color = fertilizer), position=position_dodge(width=0.5)) +
geom_errorbar(data=summary_dat, aes(x=log(plantdensity), ymax=upper_ci, ymin=lower_ci, group=fertilizer, color=fertilizer), position=position_dodge(width=0.5), width=0.2) +
geom_point(data=summary_dat, aes(log(plantdensity), y=mean_predict, group=fertilizer, color=fertilizer), size=3, position=position_dodge(width=0.5)) +
facet_grid(nutrition ~ .) + xlab("log(Plant Density") + ylab("log(Seedling Density)")
您可以将参数shape
添加到geom_point
的调用中,并将其映射到data.frame中的变量season
的值。
ggplot() +
geom_point(data=dtf.long,
aes(x=log(plantdensity),
y=log(seedlingdensity),
group=fertilizer,
color = fertilizer,
shape = season),
position=position_dodge(width=0.5)) +
geom_errorbar(data=summary_dat,
aes(x=log(plantdensity),
ymax=upper_ci,
ymin=lower_ci),
position=position_dodge(width=0.5), width=0.2) +
facet_grid(nutrition ~ .) +
xlab("log(Plant Density") +
ylab("log(Seedling Density)")
您可以在GGPLOT网站的此页面上找到更多示例