CS计算机代考程序代写 ## —-echo=FALSE———————————————————-

## —-echo=FALSE———————————————————-
set.seed(1938)

## —-eval=FALSE———————————————————-
## install.packages(“foreign”)

## —-results=”hide”——————————————————
library(foreign)
cdata <- read.dta("crime.dta") summary(cdata) ## ------------------------------------------------------------------------ plot(cdata[, -c(1, 2)]) ## ----eval=FALSE---------------------------------------------------------- ## plot(log(cdata[, -c(1, 2)])) ## ----eval=FALSE---------------------------------------------------------- ## cor(cdata[, -c(1, 2)]) ## ------------------------------------------------------------------------ round(100 * cor(cdata[, -c(1, 2)])) ## ------------------------------------------------------------------------ m1 <- lm(crime ~ single, data = cdata) # fit model (compute estimates) summary(m1) # show results ## ------------------------------------------------------------------------ confint(m1) ## ------------------------------------------------------------------------ newdata <- data.frame(single = 11.5) predict(m1, newdata, interval = "confidence") ## ------------------------------------------------------------------------ predict(m1, newdata, interval = "prediction") ## ------------------------------------------------------------------------ n <- nrow(cdata) # sample size RSS <- sum(m1$residuals^2) # extract, square and sum the residuals sqrt(RSS / n) # MLE ## ----fig.width=8, fig.height=4------------------------------------------- par(mfrow = c(1, 2)) # set up a 1*2 array for drawing plots plot(m1, 1:2) # this will draw 2 plots, filling up the 1*2 array ## ------------------------------------------------------------------------ cdata[c(9, 25, 51),] ## ------------------------------------------------------------------------ y <- cdata$crime sd(y) ## ------------------------------------------------------------------------ m0 <- lm(crime ~ 1, data = cdata) summary(m0) ## ------------------------------------------------------------------------ TSS <- sum((y - mean(y))^2) 1 - sum(RSS / TSS) ## ----results="hide"------------------------------------------------------ m2 <- lm(crime ~ poverty, data = cdata) summary(m2) ## ------------------------------------------------------------------------ library(MASS) m3 <- rlm(crime ~ single, data = cdata) summary(m3) ## ------------------------------------------------------------------------ hweights <- data.frame(state = cdata$state, resid = m3$resid, weight = m3$w) hweights2 <- hweights[order(m3$w), ] hweights2[1:15, ]