#Make design #Make one specific dataset set.seed(55) N=10; #Set true parameters beta0 = 20; beta1 = 0.4; beta2 = 0.4; beta3 = 4; beta4 = 0; #Sample some covariates/measurments x1=rnorm(N,10,2) x2=rnorm(N,50,2.5) x3=rnorm(N,3,1) x4 = x3+rnorm(N,0,0.1) #Calculate mu mu = beta0 + beta1*x1 + beta2*x2 + beta3*x3 + beta4*x4; mu #Simulate data y=mu + rnorm(N,0,0.5) #Fit full modell result = lm(y~x1+x2+x3+x4) summary(result) #Fit true modell result = lm(y~x1+x2+x3) summary(result) #Fit modell with x3 without x4 result = lm(y~x1+x2+x4) summary(result) Sparrows=data.frame(y,x1,x2,x3,x4) pairs(Sparrows) #A change in one data y2=y y2[7]=52 #Fit full modell result = lm(y2~x1+x2+x3+x4) summary(result) #Fit true modell result = lm(y2~x1+x2+x3) summary(result) #Fit modell with x3 without x4 result = lm(y2~x1+x2+x4) summary(result) #Fit model to result = lm(x1~x2+x3+x4) summary(result) result = lm(x2~x1+x3+x4) summary(result) result = lm(x3~x1+x2+x4) summary(result) result = lm(x4~x1+x2+x3) summary(result)