[英]ACF Plot with ggplot2: Setting width of geom_bar
使用acf
我们可以在基础R
图中ACF plot
图。
x <- lh
acf(x)
以下代码可用于获取ggplot2
的ACF plot
。
conf.level <- 0.95
ciline <- qnorm((1 - conf.level)/2)/sqrt(length(x))
bacf <- acf(x, plot = FALSE)
bacfdf <- with(bacf, data.frame(lag, acf))
library(ggplot2)
q <- ggplot(data=bacfdf, mapping=aes(x=lag, y=acf)) +
geom_bar(stat = "identity", position = "identity")
q
问题
如何获得线条而不是条形或如何设置条形的宽度使它们看起来像线条? 谢谢
您最好通过geom_segment()
绘制线段
library(ggplot2)
set.seed(123)
x <- arima.sim(n = 200, model = list(ar = 0.6))
bacf <- acf(x, plot = FALSE)
bacfdf <- with(bacf, data.frame(lag, acf))
q <- ggplot(data = bacfdf, mapping = aes(x = lag, y = acf)) +
geom_hline(aes(yintercept = 0)) +
geom_segment(mapping = aes(xend = lag, yend = 0))
q
如何使用宽度 = 0 的 geom_errorbar ?
ggplot(data=bacfdf, aes(x=lag, y=acf)) +
geom_errorbar(aes(x=lag, ymax=acf, ymin=0), width=0)
从预测的包中附带的功能ggtsdisplay
该地块既ACF和PACF与ggplot
。 x
是模型拟合的残差 ( fit$residuals
)。
forecast::ggtsdisplay(x,lag.max=30)
根据您的回答,我综合了 ggplot ACF / PACF 绘图方法:
require(zoo)
require(tseries)
require(ggplot2)
require(cowplot)
ts= zoo(data[[2]]) # data[[2]] because my time series data was the second column
# Plot ACP / ACF with IC
# How to compute IC for ACF and PACF :
# https://stats.stackexchange.com/questions/211628/how-is-the-confidence-interval-calculated-for-the-acf-function
ic_alpha= function(alpha, acf_res){
return(qnorm((1 + (1 - alpha))/2)/sqrt(acf_res$n.used))
}
ggplot_acf_pacf= function(res_, lag, label, alpha= 0.05){
df_= with(res_, data.frame(lag, acf))
# IC alpha
lim1= ic_alpha(alpha, res_)
lim0= -lim1
ggplot(data = df_, mapping = aes(x = lag, y = acf)) +
geom_hline(aes(yintercept = 0)) +
geom_segment(mapping = aes(xend = lag, yend = 0)) +
labs(y= label) +
geom_hline(aes(yintercept = lim1), linetype = 2, color = 'blue') +
geom_hline(aes(yintercept = lim0), linetype = 2, color = 'blue')
}
acf_ts= ggplot_acf_pacf(res_= acf(ts, plot= F)
, 20
, label= "ACF")
pacf_ts= ggplot_acf_pacf(res_= pacf(ts, plot= F)
, 20
, label= "PACF")
# Concat our plots
acf_pacf= plot_grid(acf_ts, pacf_ts, ncol = 2, nrow = 1)
acf_pacf
结果:
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