white_noise <- rnorm(1000)%>%ts()%>%plot()

这段代码是R语言中生成并可视化白噪声序列的一个示例。下面是逐步解释：

首先，`rnorm(1000)` 是用来生成一个包含1000个随机数的正态分布（平均值为0，标准差为1）的向量，代表白噪声序列，因为白噪声通常具有随机且独立的特性。

`%>%` 是R中的管道操作符，用于将前一个操作的结果传递给下一个操作。这里它连接了三个连续的操作。

`ts()` 函数将这个向量转换成时间序列（time series），这通常用于处理具有时间顺序的数据。

`plot()` 函数最后绘制生成的时间序列，显示白噪声的随机变化趋势。

所以整个表达式的意思是：生成一个长度为1000的随机正态分布，将其转换为时间序列，然后绘制出来。

相关问题

帮我修改以下代码，使他们可以正确运行：scores <- dplyr::data_frame( person = rep(c("A", "B", "C"), each = 2), time = rep(c("pre", "post"), 3), class1 = round(rnorm(6, mean = 80, sd = 4), 0), class2 = round(jitter(class1, 15), 0) ) scores_1 <- scores %>% pivot_longer(cols = c("class1", "class2"), names_to = "class", values_to = "score") scores_2 <- scores %>% spread(key = time, value = class1:class2) scores_3 <- scores_1 %>% group_by(person, class) %>% mutate(diff = score[time == "post"] - score[time == "pre"]) %>% ungroup() scores_3 %>% ggplot(aes(x = class, y = score, color = ifelse(diff >= 0, "green", "red"), shape = person)) + geom_point(size = 3) + labs(x = "Class", y = "Score", color = "Score change", shape = "Person") + theme_classic()

您好，这段代码的问题在于 `data_frame()` 函数已经被弃用，应该使用 `tibble()` 函数来创建数据框。同时，`spread()` 函数也已经被弃用，应该使用 `pivot_wider()` 函数来进行数据透视。下面是修改后的代码：

library(tidyverse)

scores <- tibble(
  person = rep(c("A", "B", "C"), each = 2),
  time = rep(c("pre", "post"), 3),
  class1 = round(rnorm(6, mean = 80, sd = 4), 0),
  class2 = round(jitter(class1, 15), 0)
)

scores_1 <- scores %>% 
  pivot_longer(cols = c("class1", "class2"), names_to = "class", values_to = "score")

scores_2 <- scores %>% 
  pivot_wider(names_from = time, values_from = c(class1, class2), names_sep = "_")

scores_3 <- scores_1 %>% 
  group_by(person, class) %>% 
  mutate(diff = score[time == "post"] - score[time == "pre"]) %>% 
  ungroup()

scores_3 %>% 
  ggplot(aes(x = class, y = score, color = ifelse(diff >= 0, "green", "red"), shape = person)) +
  geom_point(size = 3) +
  labs(x = "Class", y = "Score", color = "Score change", shape = "Person") +
  theme_classic()

这样，代码就可以正确运行了。

在运行以下R代码时：library(glmnet) library(ggplot2) # 生成5030的随机数据和30个变量 set.seed(1111) n <- 50 p <- 30 X <- matrix(runif(n * p), n, p) y <- rnorm(n) # 生成三组不同系数的线性模型 beta1 <- c(rep(1, 3), rep(0, p - 3)) beta2 <- c(rep(0, 10), rep(1, 3), rep(0, p - 13)) beta3 <- c(rep(0, 20), rep(1, 3), rep(0, p - 23)) y1 <- X %% beta1 + rnorm(n) y2 <- X %% beta2 + rnorm(n) y3 <- X %% beta3 + rnorm(n) # 设置交叉验证折数 k <- 10 # 设置不同的lambda值 lambda_seq <- 10^seq(10, -2, length.out = 100) # 执行交叉验证和岭回归，并记录CV error和Prediction error cv_error <- list() pred_error <- list() for (i in 1:3) { # 交叉验证 cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k) cv_error[[i]] <- cvfit$cvm # 岭回归 fit <- glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq) pred_error[[i]] <- apply(X, 2, function(x) { x_mat <- matrix(x, nrow = n, ncol = p, byrow = TRUE) pred <- predict(fit, newx = x_mat) pred <- t(pred) # 转置 mean((x_mat %% fit$beta - switch(i, y1, y2, y3))^2, na.rm = TRUE) # 修改此处 }) } # 绘制图形 par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0)) for (i in 1:3) { # CV error plot cv_plot_data <- cv_error[[i]] plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i)) abline(v = log10(cvfit$lambda.min), col = "red") # Prediction error plot pred_plot_data <- pred_error[[i]] plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i)) abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red") }。发生以下错误：Error in h(simpleError(msg, call)) : 在为'mean'函数选择方法时评估'x'参数出了错: non-conformable arguments 。请对原代码进行修正

这段代码中有一个问题，即在计算预测误差时，矩阵相乘的维度不匹配，需要进行修正。以下是修正后的代码：

library(glmnet)
library(ggplot2)

# 生成5030的随机数据和30个变量
set.seed(1111)
n <- 50
p <- 30
X <- matrix(runif(n * p), n, p)
y <- rnorm(n)

# 生成三组不同系数的线性模型
beta1 <- c(rep(1, 3), rep(0, p - 3))
beta2 <- c(rep(0, 10), rep(1, 3), rep(0, p - 13))
beta3 <- c(rep(0, 20), rep(1, 3), rep(0, p - 23))
y1 <- X %*% beta1 + rnorm(n)
y2 <- X %*% beta2 + rnorm(n)
y3 <- X %*% beta3 + rnorm(n)

# 设置交叉验证折数
k <- 10

# 设置不同的lambda值
lambda_seq <- 10^seq(10, -2, length.out = 100)

# 执行交叉验证和岭回归，并记录CV error和Prediction error
cv_error <- list()
pred_error <- list()

for (i in 1:3) {
  # 交叉验证
  cvfit <- cv.glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq, nfolds = k)
  cv_error[[i]] <- cvfit$cvm
  
  # 岭回归
  fit <- glmnet(X, switch(i, y1, y2, y3), alpha = 0, lambda = lambda_seq)
  
  pred_error[[i]] <- apply(X, 2, function(x) {
    x_mat <- matrix(x, nrow = n, ncol = p, byrow = TRUE)
    pred <- predict(fit, newx = x_mat)
    pred <- t(pred)
    mean((x_mat %*% fit$beta - switch(i, y1, y2, y3))^2)
  })
}

# 绘制图形
par(mfrow = c(3, 2), mar = c(4, 4, 2, 1), oma = c(0, 0, 2, 0))

for (i in 1:3) {
  # CV error plot
  cv_plot_data <- cv_error[[i]]
  plot(log10(lambda_seq), cv_plot_data, type = "l", xlab = expression(log10), ylab = "CV error", main = paste0("Model ", i))
  abline(v = log10(cvfit$lambda.min), col = "red")
  
  # Prediction error plot
  pred_plot_data <- pred_error[[i]]
  plot(log10(lambda_seq), pred_plot_data, type = "l", xlab = expression(log10), ylab = "Prediction error", main = paste0("Model ", i))
  abline(v = log10(lambda_seq[which.min(pred_plot_data)]), col = "red")
}

修正后的代码中主要是将 `%%` 运算符改为 `%*%`，使得矩阵相乘的维度匹配。此外，还对 `mean` 函数的参数进行了修改。