网格中的模拟时间序列图集

Question

我想在网格排列中绘制以下一组时间序列。

set.seed(289805)
sd1_AR0.8 <- arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
set.seed(671086)
sd1_AR0.9 <- arima.sim(n = 10, model = list(ar = 0.9, order = c(1, 0, 0)), sd = 1)
set.seed(799837)
sd1_AR0.95 <- arima.sim(n = 10, model = list(ar = 0.95, order = c(1, 0, 0)), sd = 1)
set.seed(289805)
sd3_AR0.8 <- arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 3)
set.seed(671086)
sd3_AR0.9 <- arima.sim(n = 10, model = list(ar = 0.9, order = c(1, 0, 0)), sd = 3)
set.seed(799837)
sd3_AR0.95 <- arima.sim(n = 10, model = list(ar = 0.95, order = c(1, 0, 0)), sd = 3)
set.seed(289805)
sd5_AR0.8 <- arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 5)
set.seed(671086)
sd5_AR0.9 <- arima.sim(n = 10, model = list(ar = 0.9, order = c(1, 0, 0)), sd = 5)
set.seed(799837)
sd5_AR0.95 <- arima.sim(n = 10, model = list(ar = 0.95, order = c(1, 0, 0)), sd = 5)
set.seed(289805)
sd10_AR0.8 <- arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 10)
set.seed(671086)
sd10_AR0.9 <- arima.sim(n = 10, model = list(ar = 0.9, order = c(1, 0, 0)), sd = 10)
set.seed(799837)
sd10_AR0.95 <- arima.sim(n = 10, model = list(ar = 0.95, order = c(1, 0, 0)), sd = 10)

上面的 R 代码模拟 AR 时间序列，在三个水平上具有不同的 $\phi$ 值（$\phi = 0.8, 0.9, 0.95$）并在四个水平上变化 standard deviation (标准差 = 1, 3, 5, 10).

我希望网格是 3 by 4 使得前三 (3) 组系列运行在一行三 (3) 列中。

我曾尝试绘制一个系列，例如 :

set.seed(289805)
sd1_AR0.8 <- arima.sim(n = 10, model = list(ar = 0.8, order = c(1, 0, 0)), sd = 1)
library(ggplot2)
p <- ggplot(NULL, aes(y = sd1_AR0.8, x = seq_along(sd1_AR0.8)))
p + geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))

我更想要 :

Answer 1

拼凑而成。阅读 vignettes 调整标题、创建 1 个标题、组合图例等

library(patchwork)
p1 <- ggplot(NULL, aes(y = sd1_AR0.8, x = seq_along(sd1_AR0.8))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p2 <- ggplot(NULL, aes(y = sd1_AR0.8, x = seq_along(sd1_AR0.9))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p3 <- ggplot(NULL, aes(y = sd1_AR0.8, x = seq_along(sd1_AR0.95))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p4 <- ggplot(NULL, aes(y = sd3_AR0.8, x = seq_along(sd3_AR0.8))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p5 <- ggplot(NULL, aes(y = sd3_AR0.9, x = seq_along(sd3_AR0.9))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p6 <- ggplot(NULL, aes(y = sd3_AR0.95, x = seq_along(sd3_AR0.95))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p7 <- ggplot(NULL, aes(y = sd5_AR0.8, x = seq_along(sd5_AR0.8))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p8 <- ggplot(NULL, aes(y = sd5_AR0.9, x = seq_along(sd5_AR0.9))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p9 <- ggplot(NULL, aes(y = sd5_AR0.95, x = seq_along(sd5_AR0.95))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p10 <- ggplot(NULL, aes(y = sd10_AR0.8, x = seq_along(sd10_AR0.8))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p11 <- ggplot(NULL, aes(y = sd10_AR0.9, x = seq_along(sd10_AR0.9))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))
p12 <- ggplot(NULL, aes(y = sd10_AR0.95, x = seq_along(sd10_AR0.95))) +
  geom_line(color = "#F2AA4CFF") + geom_point(color = "#101820FF") + xlab('Time') + ylab('Value') + scale_y_continuous(expand = c(0,0))


# plot in a 3 by 4 grid by using plot_layout
p1 + p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9 + p10 + p11 + p12 + plot_layout(ncol = 3)

网格中的模拟时间序列图集

Plot Set of Simulated Time Series in Grids

grid

plot

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