# Exercício 1 eutad = read.table("palmadulto.txt", header=TRUE) dist = matrix(NA, nrow=102, ncol=102) for (i in 1:101) { for (j in (i+1):102) { difx2 = (eutad$gx[i] - eutad$gx[j]) ^ 2 dify2 = (eutad$gy[i] - eutad$gy[j]) ^ 2 dist[i,j] = sqrt(difx2 + dify2) dist[j,i] = sqrt(difx2 + dify2) } } nn = apply(dist, 1, min, na.rm=TRUE) mnn = mean(nn) vt = rep(NA, 1000) vt[1] = mnn for (k in 2:1000) { xsim = round(runif(nrow(eutad), 0, 320), 0) ysim = round(runif(nrow(eutad), 0, 320), 0) mxy = matrix(NA, ncol=nrow(eutad), nrow=nrow(eutad)) for (l in 1:(nrow(eutad) - 1)) { for (m in (l+1):nrow(eutad)) { mxy[l,m] = sqrt( ((xsim[l] - xsim[m]) ^ 2) + ((ysim[l] - ysim[m]) ^ 2) ) mxy[m,l] = sqrt( ((xsim[l] - xsim[m]) ^ 2) + ((ysim[l] - ysim[m]) ^ 2) ) } } vt[k] = mean(apply(mxy, 1, min, na.rm=TRUE)) } hist(vt, main="", xlab="Distâncias médias geradas as acaso", ylab="Frequência") abline(v=vt[1], col="red", lty=2) pvalue = (sum(abs(vt) >= abs(vt[1])))/length(vt) #======================================================================================= # Exercício 2 animais = read.table("animais.txt", sep=";", header=TRUE, dec=",") animais = animais[complete.cases(animais),] x = log(animais$body) - mean(log(animais$body)) y = log(animais$brain) - mean(log(animais$brain)) beta = sum(x * y) / sum(x ^ 2) vt = rep(NA, 1000) vt[1] = beta for (i in 2:999) { sim_brain = sample(animais$brain) x = log(animais$body) - mean(log(animais$body)) y = log(sim_brain) - mean(log(sim_brain)) vt[i] = sum(x * y) / sum(x ^ 2) } hist(vt, main="", xlab="Inclinações médias geradas as acaso", ylab="Frequência") abline(v=vt[1], col="red", lty=2) pvalue = (sum(abs(vt) >= abs(vt[1]))) / length(vt) #=======================================================================================