[SOLVED] CS代考计算机代写 ## —- fig.align=”center”————————————————————————————————

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n <- 25 # sample size## simulate dataset.seed(3)X <- runif(n, min = 1, max = 10)error <- rnorm(n, mean = 0, sd = 5)beta0 <- 1beta1 <- 2Y <- beta0 + beta1 * X + error## save simulated data as data framedata_sim <- data.frame(“Y” = Y, “X” = X)## OLS fitlm_obj <- lm(Y~X, data = data_sim)#### Plotpar(family = “serif”)plot(x = data_sim$X, y = data_sim$Y, main=””, axes=FALSE,pch = 16, cex = 0.8, xlab = “X”, ylab = “Y”)axis(1, tick = FALSE)axis(2, tick = FALSE, las = 2)abline(lm_obj, lty=2, lwd = 1.3, col=”darkorange”)abline(a = beta0, b = beta1, lwd=1.3, col=”darkblue”)legend(“topleft”, col=c(“darkorange”, “darkblue”),legend = c(“Sample Regression Line”, “Population Regression Line”),lwd=1.3, lty=c(2,1), bty=”n”)## Estimates coef(lm_obj)## ———————————————————————————————————————–## Sample sizesn_small<-10 # small sample sizen_large<- 100 # large sample size## True parameter valuesbeta0 <- 1beta1 <- 1## Generate explanatory variables (random design)X_n_small<- runif(n_small, min = 1, max = 10)X_n_large<- runif(n_large, min = 1, max = 10)## Monte-Carlo (MC) Simulation ## 1. Generate data## 2. Compute and store estimates## Repeat steps 1. and 2. many timesset.seed(3)## Number of Monte Carlo repetitions## How many samples to draw from the modelsrep<- 1000## Containers to store the lm-resultsn_small_list <- vector(mode = “list”, length = rep)n_large_list <- vector(mode = “list”, length = rep)for(r in 1:rep){## Sampling from the model conditionally on X_n_smallerror_n_small <- rnorm(n_small, mean = 0, sd = 5)Y_n_small <- beta0 + beta1 * X_n_small + error_n_smalln_small_list[[r]] <- lm(Y_n_small ~ X_n_small)## Sampling from the model conditionally on X_n_largeerror_n_large <- rnorm(n_large, mean = 0, sd = 5)Y_n_large <- beta0 + beta1 * X_n_large + error_n_largen_large_list[[r]] <- lm(Y_n_large ~ X_n_large)}## Reading out the parameter estimatesbeta0_estimates_n_small <- rep(NA, rep)beta1_estimates_n_small <- rep(NA, rep)beta0_estimates_n_large <- rep(NA, rep)beta1_estimates_n_large <- rep(NA, rep)for(r in 1:rep){beta0_estimates_n_small[r] <- n_small_list[[r]]$coefficients[1]beta1_estimates_n_small[r] <- n_small_list[[r]]$coefficients[2]beta0_estimates_n_large[r] <- n_large_list[[r]]$coefficients[1]beta1_estimates_n_large[r] <- n_large_list[[r]]$coefficients[2]}## —- fig.width=6, fig.height=4.5, out.width=’\textwidth’, fig.align=’center’——————————————## Plotting the resultslibrary(“scales”) # alpha() produces transparent colors## Define a common y-axis rangey_range <- range(beta0_estimates_n_small, beta1_estimates_n_small)*1.1## Generate the plotpar(family = “serif”) # Serif fonts## Layout of plotting arealayout(matrix(c(1:6), 2, 3, byrow = TRUE), widths = c(3,1,1))## Plot 1plot(x=0, y=0, axes=FALSE, xlab=”X”, ylab=”Y”, type=”n”, xlim=c(1,10), ylim=c(-5,35), main=”Small Sample (n=10)”)axis(1, tick = FALSE); axis(2, tick = FALSE, las = 2)for(r in 1:rep){abline(n_small_list[[r]], lty=2, lwd = 1.3, col=”darkorange”)}abline(a = beta0, b = beta1, lwd=1.3, col=”darkblue”)legend(“topleft”, col=c(“darkorange”, “darkblue”), legend=c(“Sample regression lines from
repeated samples (cond. on X)”, “Population regression line”),lwd=1.3, lty=c(2,1), bty=”n”)## Plot 2plot(x=rep(0,rep), y=beta0_estimates_n_small, axes=FALSE,xlab=””, ylab=””, pch=19, cex=1.2, ylim=y_range,main=expression(hat(beta)[0]~’|’~X), col=alpha(“red”,0.2))points(x = 0, y=beta0, pch=”-“, cex = 1.2, col=”black”)text(x=0, y=beta0, labels = expression(beta[0]), pos = 4)## Plot 3plot(x=rep(0,rep), y=beta1_estimates_n_small, axes=FALSE,xlab=””, ylab=””, pch=19, cex=1.2, ylim=y_range,main=expression(hat(beta)[1]~’|’~X), col=alpha(“red”,0.2))points(x = 0, y=beta1, pch=”-“, cex = 1.2, col=”black”)text(x=0, y=beta1, labels = expression(beta[1]), pos = 4)## Plot 4plot(x=0, y=0, axes=FALSE, xlab=”X”, ylab=”Y”, type=”n”, xlim=c(1,10), ylim=c(-5,35), main=”Large Sample (n=100)”)axis(1, tick = FALSE); axis(2, tick = FALSE, las = 2)for(r in 1:rep){abline(n_large_list[[r]], lty=2, lwd = 1.3, col=”darkorange”)}abline(a = beta0, b = beta1, lwd=1.3, col=”darkblue”)## Plot 5plot(x=rep(0,rep), y=beta0_estimates_n_large, axes=FALSE,xlab=””, ylab=””, pch=19, cex=1.2, ylim=y_range,main=expression(hat(beta)[0]~’|’~X), col=alpha(“red”,0.2))points(x = 0, y=beta0, pch=”-“, cex = 1.2, col=”black”)text(x=0, y=beta0, labels = expression(beta[0]), pos = 4)## Plot 6plot(x=rep(0,rep), y=beta1_estimates_n_large, axes=FALSE,xlab=””, ylab=””, pch=19, cex=1.2, ylim=y_range,main=expression(hat(beta)[1]~’|’~X), col=alpha(“red”,0.2))points(x=0, y=beta1, pch=”-“, cex = 1.2, col=”black”)text(x=0, y=beta1, labels = expression(beta[1]), pos = 4)