A <- matrix(,2,3) A[1,] <- rep(0,3); A A[1,] <- c(2,3) A[,c(1,3)] <- 2 A
时间: 2024-04-07 12:30:51 浏览: 7
这段代码介绍了 R 语言中的矩阵索引和赋值操作。
在代码中,首先定义了一个空的 2 行 3 列的矩阵 A,其中元素的值为 NA。
接下来,使用 A[1,] <- rep(0,3) 将矩阵 A 的第 1 行全部赋值为 0,返回结果为一个 2 行 3 列的矩阵,其中第 1 行的元素都为 0,第 2 行的元素都为 NA。
然后,使用 A[1,] <- c(2,3) 将矩阵 A 的第 1 行赋值为向量 c(2, 3),返回结果为一个 2 行 3 列的矩阵,其中第 1 行的元素为 2 和 3,第 2 行的元素都为 NA。
最后,使用 A[,c(1,3)] <- 2 将矩阵 A 的第 1 列和第 3 列全部赋值为 2,返回结果为一个 2 行 3 列的矩阵,其中第 1 列和第 3 列的元素都为 2,第 2 列的元素为 NA。
相关问题
用r语言写程序:Simulate a dataset containing continuous traits with given h^2. Split itinto training and testing datasets with proportion 4 ∶ 1.set.seed(20132014)n <- 5000p <- 1000h <- c(0.2, 0.8)[1]# simulate genotype (not exactly)x_r <- matrix(rnorm(n * p), ncol = p)xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow = TRUE)xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow = TRUE)x <- (x_r - xmean)/xsdPlease perform the marginal model.## [1] 526 776 50 557 801 345 298 559 178 214 • Show the locations of significant SNPs.• Replicate above figure, and highlight the significant coefficents in red.
Sure, here's some R code to perform the marginal model and plot the locations of significant SNPs:
```
# set seed for reproducibility
set.seed(20132014)
# simulate data
n <- 5000
p <- 1000
h <- c(0.2, 0.8)[1]
# simulate genotype (not exactly)
x_r <- matrix(rnorm(n * p), ncol = p)
xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow=TRUE)
xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow=TRUE)
x <- (x_r - xmean)/xsd
# simulate continuous trait
y <- sqrt(h) * x[,1] + sqrt(1 - h) * rnorm(n)
# split data into training and testing sets
train_idx <- sample(1:n, size=round(0.8*n), replace=FALSE)
x_train <- x[train_idx,]
y_train <- y[train_idx]
x_test <- x[-train_idx,]
y_test <- y[-train_idx]
# perform marginal model
library(glmnet)
fit <- glmnet(x_train, y_train, family="gaussian")
cv_fit <- cv.glmnet(x_train, y_train, family="gaussian")
lambda_min <- cv_fit$lambda.min
coef <- coef(fit, s=lambda_min)
# plot locations of significant SNPs
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggrepel)
# get SNP names
snp_names <- paste0("SNP", 1:p)
# create data frame of SNP locations and coefficients
snp_df <- data.frame(snp_names, coef[-1])
snp_df <- snp_df %>% gather(key="snp", value="coef", -snp_names)
snp_df$significant <- snp_df$coef != 0
# plot SNP locations
ggplot(snp_df, aes(x=snp, y=1, color=significant)) +
geom_point(size=3) +
scale_color_manual(values=c("gray","red")) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
labs(x="SNP", y="")
```
This will produce a plot of SNP locations with significant coefficients highlighted in red. You can adjust the `lambda_min` value to control the level of sparsity in the model and the number of significant SNPs identified.
1 Simulate a dataset containing continuous traits with given ℎ 2 . Split it into training and testing datasets with proportion 4 ∶ 1. set.seed(20132014) n <- 5000 p <- 1000 h <- c(0.2, 0.8)[1] # simulate genotype (not exactly) x_r <- matrix(rnorm(n * p), ncol = p) xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow = TRUE) xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow = TRUE) x <- (x_r - xmean)/xsd # ... 2 Please perform the marginal model. ## [1] 526 776 50 557 801 345 298 559 178 214 1 2 −0.025 0.000 0.025 0.050 0 250 500 750 1000 snp u • Show the locations of significant SNPs. • Replicate above figure, and highlight the significant coefficents in red
For the first question, I understand that you want me to simulate a dataset with continuous traits and split it into training and testing datasets with a given heritability (h2) and some specific code. Here is the modified code to simulate the dataset and split it into training and testing datasets:
```R
set.seed(20132014)
n <- 5000
p <- 1000
h2 <- 0.2
# simulate genotype (not exactly)
x_r <- matrix(rnorm(n * p), ncol = p)
xmean <- matrix(rep(colMeans(x_r), n), ncol=p, byrow = TRUE)
xsd <- matrix(rep(apply(x_r, 2, sd), n), ncol=p, byrow = TRUE)
x <- (x_r - xmean)/xsd
# simulate phenotype
g <- colSums(x) * sqrt(2 * h2 / p) # genetic effect
e <- rnorm(n) * sqrt(1 - h2) # environmental effect
y <- g + e # phenotype
# split into training and testing datasets
train_idx <- sample(1:n, size = n * 0.8, replace = FALSE)
x_train <- x[train_idx, ]
y_train <- y[train_idx]
x_test <- x[-train_idx, ]
y_test <- y[-train_idx]
```
This code simulates a dataset with n = 5000 individuals and p = 1000 SNPs. The heritability is set to 0.2. The code first simulates the genotype matrix x, and then uses it to simulate the phenotype y. The genetic effect is proportional to the sum of each SNP's effect, and the environmental effect is simulated from a normal distribution. Finally, the code splits the dataset into training (80%) and testing (20%) datasets.
For the second question, I understand that you want me to perform a marginal model and then show the locations of significant SNPs and replicate a figure highlighting significant coefficients in red. Here is the code to perform the marginal model and visualize the results:
```R
library(glmnet)
set.seed(1234)
# fit marginal model
cvfit <- cv.glmnet(x_train, y_train, alpha = 0)
lam <- cvfit$lambda.min
fit <- glmnet(x_train, y_train, alpha = 0, lambda = lam)
# identify significant SNPs
coef <- coef(fit)
nz_idx <- which(coef != 0)
nz_snps <- nz_idx - 1
# visualize results
par(mar = c(5, 4, 4, 8) + 0.1)
plot(coef, xvar = "lambda", label = TRUE, main = "Marginal Model")
abline(v = lam, lty = 2)
significant_snps <- which(abs(coef) > 0.1)
points(coef[significant_snps], col = "red", pch = 19, cex = 1.2)
```
This code fits a marginal model using the glmnet package and identifies significant SNPs based on a threshold of 0.1. The code then visualizes the results using the plot function and highlights the significant SNPs in red. The abline function adds a vertical line at the optimal lambda value selected by cross-validation.