library(wooldridge)
data("hprice1")
head(force(hprice1), n=5)
## price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
## 1 300 349.1 4 6126 2438 1 5.703783 5.855359 8.720297 7.798934
## 2 370 351.5 3 9903 2076 1 5.913503 5.862210 9.200593 7.638198
## 3 191 217.7 3 5200 1374 0 5.252274 5.383118 8.556414 7.225482
## 4 195 231.8 3 4600 1448 1 5.273000 5.445875 8.433811 7.277938
## 5 373 319.1 4 6095 2514 1 5.921578 5.765504 8.715224 7.829630
modelo_lineal<-lm(formula = price ~lotsize+sqrft+bdrms, data = hprice1)
summary(modelo_lineal)
##
## Call:
## lm(formula = price ~ lotsize + sqrft + bdrms, data = hprice1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -120.026 -38.530 -6.555 32.323 209.376
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.177e+01 2.948e+01 -0.739 0.46221
## lotsize 2.068e-03 6.421e-04 3.220 0.00182 **
## sqrft 1.228e-01 1.324e-02 9.275 1.66e-14 ***
## bdrms 1.385e+01 9.010e+00 1.537 0.12795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 59.83 on 84 degrees of freedom
## Multiple R-squared: 0.6724, Adjusted R-squared: 0.6607
## F-statistic: 57.46 on 3 and 84 DF, p-value: < 2.2e-16
#Prueba de White
#Literal A
library(lmtest)
prueba_white<- bptest(modelo_lineal,~ I(lotsize^2)+I(sqrft^2)+I(bdrms^2)+lotsize*sqrft+lotsize*bdrms+sqrft*bdrms, data= hprice1)
print(prueba_white)
##
## studentized Breusch-Pagan test
##
## data: modelo_lineal
## BP = 33.732, df = 9, p-value = 9.953e-05
GL<-3+3+1
VC<-qchisq(p=0.95,df=GL )
#Dado que P-value es mayor que el nivel de significancia se establece que hay evidencia estadistica para determinar que que no hay heterocedasticidad.
#Literal B
library(fastGraph)
shadeDist(xshade = prueba_white$statistic, ddist="dchisq", parm1 =9 , lower.tail = FALSE, sub=paste("VC:",VC, "BP:", prueba_white$statistic))