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# Goal: Simulate a dataset from the OLS model and obtain # obtain OLS estimates for it. x <- runif(100, 0, 10) # 100 draws from U(0,10) y <- 2 + 3*x + rnorm(100) # beta = [2, 3] and sigma = 1 # You want to just look at OLS results? summary(lm(y ~ x)) # Suppose x and y were packed together in a data frame -- D <- data.frame(x,y) summary(lm(y ~ x, D)) # Full and elaborate steps -- d <- lm(y ~ x) # Learn about this object by saying ?lm and str(d) # Compact model results -- print(d) # Pretty graphics for regression diagnostics -- par(mfrow=c(2,2)) plot(d) d <- summary(d) # Detailed model results -- print(d) # Learn about this object by saying ?summary.lm and by saying str(d) cat("OLS gave slope of ", d$coefficients[2,1], "and a error sigma of ", d$sigma, "\n") ## I need to drop down to a smaller dataset now -- x <- runif(10) y <- 2 + 3*x + rnorm(10) m <- lm(y ~ x) # Now R supplies a wide range of generic functions which extract # useful things out of the result of estimation of many kinds of models. residuals(m) fitted(m) AIC(m) AIC(m, k=log(10)) # SBC vcov(m) logLik(m)