Ejercicio Multicolinealidad
CHRISTIAN ALEXANDER PANIAGUA MUÑOZ PM24066
2026-04-17
EJERCICIO: MULTICOLINEALIDAD
library(wooldridge)
library(printr)
data(hprice1)
head(force(hprice1), n = 5)| price | assess | bdrms | lotsize | sqrft | colonial | lprice | lassess | llotsize | lsqrft |
|---|---|---|---|---|---|---|---|---|---|
| 300 | 349.1 | 4 | 6126 | 2438 | 1 | 5.703783 | 5.855359 | 8.720297 | 7.798934 |
| 370 | 351.5 | 3 | 9903 | 2076 | 1 | 5.913503 | 5.862210 | 9.200593 | 7.638198 |
| 191 | 217.7 | 3 | 5200 | 1374 | 0 | 5.252274 | 5.383118 | 8.556414 | 7.225481 |
| 195 | 231.8 | 3 | 4600 | 1448 | 1 | 5.273000 | 5.445875 | 8.433811 | 7.277938 |
| 373 | 319.1 | 4 | 6095 | 2514 | 1 | 5.921578 | 5.765504 | 8.715224 | 7.829630 |
1.ESTIMACIÓN DEL MODELO
price = α0 + α1(lotsize) + α2(sqrft) + α3(bdrms) + ε
modelo_precio<-lm(price ~ lotsize + sqrft + bdrms, data = hprice1)
library(stargazer)
stargazer(modelo_precio, title = "Modelo Precio", type = "html", digits = 4)| Dependent variable: | |
| price | |
| lotsize | 0.0021*** |
| (0.0006) | |
| sqrft | 0.1228*** |
| (0.0132) | |
| bdrms | 13.8525 |
| (9.0101) | |
| Constant | -21.7703 |
| (29.4750) | |
| Observations | 88 |
| R2 | 0.6724 |
| Adjusted R2 | 0.6607 |
| Residual Std. Error | 59.8335 (df = 84) |
| F Statistic | 57.4602*** (df = 3; 84) |
| Note: | p<0.1; p<0.05; p<0.01 |
2. PRUEBAS DE MULTICOLINEALIDAD
a) ÍNDICE DE CONDICIÓN
library(olsrr)
col_diag<-ols_coll_diag(modelo_precio)
col_diag## Tolerance and Variance Inflation Factor
## ---------------------------------------
## Variables Tolerance VIF
## 1 lotsize 0.9641236 1.037211
## 2 sqrft 0.7048934 1.418654
## 3 bdrms 0.7159922 1.396663
##
##
## Eigenvalue and Condition Index
## ------------------------------
## Eigenvalue Condition Index intercept lotsize sqrft bdrms
## 1 3.48158596 1.000000 0.003663034 0.0277802824 0.004156293 0.002939554
## 2 0.45518380 2.765637 0.006800735 0.9670803174 0.006067321 0.005096396
## 3 0.03851083 9.508174 0.472581427 0.0051085488 0.816079307 0.016938178
## 4 0.02471941 11.867781 0.516954804 0.0000308514 0.173697079 0.975025872Valores del índice de condición menores a 10 indican ausencia de multicolinealidad, entre 10 y 30 indican multicolinealidad moderada y mayores a 30 indican multicolinealidad severa.
b) PRUEBA DE FARRAR-GRAUBER
library(mctest)
fg <- imcdiag(modelo_precio)
fg##
## Call:
## imcdiag(mod = modelo_precio)
##
##
## All Individual Multicollinearity Diagnostics Result
##
## VIF TOL Wi Fi Leamer CVIF Klein IND1 IND2
## lotsize 1.0372 0.9641 1.5815 3.2002 0.9819 607.9171 0 0.0227 0.1750
## sqrft 1.4187 0.7049 17.7928 36.0043 0.8396 831.4835 0 0.0166 1.4396
## bdrms 1.3967 0.7160 16.8582 34.1130 0.8462 818.5944 0 0.0168 1.3854
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test
##
## bdrms , coefficient(s) are non-significant may be due to multicollinearity
##
## R-square of y on all x: 0.6724
##
## * use method argument to check which regressors may be the reason of collinearity
## ===================================Gráfico de la prueba FG
library(fastGraph)
# Usa un valor aproximado del estadístico (ejemplo)
shadeDist(10,
ddist = "dchisq",
parm1 = 3,
col = c("black", "red"),
lower.tail = FALSE)
La hipótesis nula establece que no existe multicolinealidad. Si el p-value es menor a 0.05, se rechaza la hipótesis nula, indicando evidencia de multicolinealidad entre los regresores
c) FACTORES INFLACIONARIOS DE LA VARIANZA (VIF)
library(car)
vif_valores<-vif(modelo_precio)
vif_valores## lotsize sqrft bdrms
## 1.037211 1.418654 1.396663Gráfico de los VIF
barplot(vif_valores, main = "Factores Inflacionarios de la Varianza (VIF)", ylab = "Valor VIF")
abline(h = 5, lty = 2)
abline(h = 10, lty = 2)