Multicolinealidad
Marta Abigail Meza Robles
2023-05-12
#DATOS DEL MODELO
library(wooldridge)
data(hpricel)## Warning in data(hpricel): data set 'hpricel' not foundhead(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 7.829630library(stargazer)##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazermodelo_estimado<-lm(price~lotsize+sqrft+bdrms,data = hprice1)
options(scipen = 9999)
stargazer(modelo_estimado,type = "html",title = "modelo estimado")##
## <table style="text-align:center"><caption><strong>modelo estimado</strong></caption>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td><em>Dependent variable:</em></td></tr>
## <tr><td></td><td colspan="1" style="border-bottom: 1px solid black"></td></tr>
## <tr><td style="text-align:left"></td><td>price</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">lotsize</td><td>0.002<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.001)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">sqrft</td><td>0.123<sup>***</sup></td></tr>
## <tr><td style="text-align:left"></td><td>(0.013)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">bdrms</td><td>13.853</td></tr>
## <tr><td style="text-align:left"></td><td>(9.010)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td style="text-align:left">Constant</td><td>-21.770</td></tr>
## <tr><td style="text-align:left"></td><td>(29.475)</td></tr>
## <tr><td style="text-align:left"></td><td></td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>88</td></tr>
## <tr><td style="text-align:left">R<sup>2</sup></td><td>0.672</td></tr>
## <tr><td style="text-align:left">Adjusted R<sup>2</sup></td><td>0.661</td></tr>
## <tr><td style="text-align:left">Residual Std. Error</td><td>59.833 (df = 84)</td></tr>
## <tr><td style="text-align:left">F Statistic</td><td>57.460<sup>***</sup> (df = 3; 84)</td></tr>
## <tr><td colspan="2" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"><em>Note:</em></td><td style="text-align:right"><sup>*</sup>p<0.1; <sup>**</sup>p<0.05; <sup>***</sup>p<0.01</td></tr>
## </table>print(modelo_estimado)##
## Call:
## lm(formula = price ~ lotsize + sqrft + bdrms, data = hprice1)
##
## Coefficients:
## (Intercept) lotsize sqrft bdrms
## -21.770308 0.002068 0.122778 13.852522#EVIDENCIA DE LA INDEPENDENCIA DE LOS REGRESORES
##Se basa la matriz XtX
library(stargazer)
X_mat<-model.matrix(modelo_estimado)
stargazer(head(X_mat,n=6),type="html")##
## <table style="text-align:center"><tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>(Intercept)</td><td>lotsize</td><td>sqrft</td><td>bdrms</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>1</td><td>6,126</td><td>2,438</td><td>4</td></tr>
## <tr><td style="text-align:left">2</td><td>1</td><td>9,903</td><td>2,076</td><td>3</td></tr>
## <tr><td style="text-align:left">3</td><td>1</td><td>5,200</td><td>1,374</td><td>3</td></tr>
## <tr><td style="text-align:left">4</td><td>1</td><td>4,600</td><td>1,448</td><td>3</td></tr>
## <tr><td style="text-align:left">5</td><td>1</td><td>6,095</td><td>2,514</td><td>4</td></tr>
## <tr><td style="text-align:left">6</td><td>1</td><td>8,566</td><td>2,754</td><td>5</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr></table>print(X_mat)## (Intercept) lotsize sqrft bdrms
## 1 1 6126 2438 4
## 2 1 9903 2076 3
## 3 1 5200 1374 3
## 4 1 4600 1448 3
## 5 1 6095 2514 4
## 6 1 8566 2754 5
## 7 1 9000 2067 3
## 8 1 6210 1731 3
## 9 1 6000 1767 3
## 10 1 2892 1890 3
## 11 1 6000 2336 4
## 12 1 7047 2634 5
## 13 1 12237 3375 3
## 14 1 6460 1899 3
## 15 1 6519 2312 3
## 16 1 3597 1760 4
## 17 1 5922 2000 4
## 18 1 7123 1774 3
## 19 1 5642 1376 3
## 20 1 8602 1835 4
## 21 1 5494 2048 3
## 22 1 7800 2124 3
## 23 1 6003 1768 3
## 24 1 5218 1732 4
## 25 1 9425 1440 3
## 26 1 6114 1932 3
## 27 1 6710 1932 3
## 28 1 8577 2106 3
## 29 1 8400 3529 7
## 30 1 9773 2051 4
## 31 1 4806 1573 4
## 32 1 15086 2829 4
## 33 1 5763 1630 3
## 34 1 6383 1840 4
## 35 1 9000 2066 4
## 36 1 3500 1702 4
## 37 1 10892 2750 4
## 38 1 15634 3880 5
## 39 1 6400 1854 4
## 40 1 8880 1421 2
## 41 1 6314 1662 3
## 42 1 28231 3331 5
## 43 1 7050 1656 4
## 44 1 5305 1171 3
## 45 1 6637 2293 5
## 46 1 7834 1764 3
## 47 1 1000 2768 3
## 48 1 8112 3733 4
## 49 1 5850 1536 3
## 50 1 6660 1638 4
## 51 1 6637 1972 3
## 52 1 15267 1478 2
## 53 1 5146 1408 3
## 54 1 6017 1812 3
## 55 1 8410 1722 3
## 56 1 5625 1780 4
## 57 1 5600 1674 4
## 58 1 6525 1850 4
## 59 1 6060 1925 3
## 60 1 5539 2343 4
## 61 1 7566 1567 3
## 62 1 5484 1664 4
## 63 1 5348 1386 6
## 64 1 15834 2617 5
## 65 1 8022 2321 4
## 66 1 11966 2638 4
## 67 1 8460 1915 4
## 68 1 15105 2589 4
## 69 1 10859 2709 4
## 70 1 6300 1587 3
## 71 1 11554 1694 3
## 72 1 6000 1536 3
## 73 1 31000 3662 5
## 74 1 4054 1736 3
## 75 1 20700 2205 2
## 76 1 5525 1502 3
## 77 1 92681 1696 4
## 78 1 8178 2186 3
## 79 1 5944 1928 4
## 80 1 18838 1294 3
## 81 1 4315 1535 4
## 82 1 5167 1980 3
## 83 1 7893 2090 4
## 84 1 6056 1837 3
## 85 1 5828 1715 3
## 86 1 6341 1574 3
## 87 1 6362 1185 2
## 88 1 4950 1774 4
## attr(,"assign")
## [1] 0 1 2 3XX_matrix<-t(X_mat)%*%X_mat
stargazer(XX_matrix,type = "html")##
## <table style="text-align:center"><tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>(Intercept)</td><td>lotsize</td><td>sqrft</td><td>bdrms</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">(Intercept)</td><td>88</td><td>793,748</td><td>177,205</td><td>314</td></tr>
## <tr><td style="text-align:left">lotsize</td><td>793,748</td><td>16,165,159,010</td><td>1,692,290,257</td><td>2,933,767</td></tr>
## <tr><td style="text-align:left">sqrft</td><td>177,205</td><td>1,692,290,257</td><td>385,820,561</td><td>654,755</td></tr>
## <tr><td style="text-align:left">bdrms</td><td>314</td><td>2,933,767</td><td>654,755</td><td>1,182</td></tr>
## <tr><td colspan="5" style="border-bottom: 1px solid black"></td></tr></table>print(XX_matrix)## (Intercept) lotsize sqrft bdrms
## (Intercept) 88 793748 177205 314
## lotsize 793748 16165159010 1692290257 2933767
## sqrft 177205 1692290257 385820561 654755
## bdrms 314 2933767 654755 1182##Cálculo de la matriz de normalización:
library(stargazer)
options(scipen = 999)
Sn<-solve(diag(sqrt(diag(XX_matrix))))
stargazer(Sn,type = "html")##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">0.107</td><td>0</td><td>0</td><td>0</td></tr>
## <tr><td style="text-align:left">0</td><td>0.00001</td><td>0</td><td>0</td></tr>
## <tr><td style="text-align:left">0</td><td>0</td><td>0.0001</td><td>0</td></tr>
## <tr><td style="text-align:left">0</td><td>0</td><td>0</td><td>0.029</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>print(Sn)## [,1] [,2] [,3] [,4]
## [1,] 0.1066004 0.000000000000 0.00000000000 0.00000000
## [2,] 0.0000000 0.000007865204 0.00000000000 0.00000000
## [3,] 0.0000000 0.000000000000 0.00005091049 0.00000000
## [4,] 0.0000000 0.000000000000 0.00000000000 0.02908649##XtX normalizada:
library(stargazer)
XX_norm<-(Sn%*%XX_matrix)%*%Sn
stargazer(XX_norm,type = "html",digits = 4)##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>0.6655</td><td>0.9617</td><td>0.9736</td></tr>
## <tr><td style="text-align:left">0.6655</td><td>1</td><td>0.6776</td><td>0.6712</td></tr>
## <tr><td style="text-align:left">0.9617</td><td>0.6776</td><td>1</td><td>0.9696</td></tr>
## <tr><td style="text-align:left">0.9736</td><td>0.6712</td><td>0.9696</td><td>1</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>print(XX_norm)## [,1] [,2] [,3] [,4]
## [1,] 1.0000000 0.6655050 0.9617052 0.9735978
## [2,] 0.6655050 1.0000000 0.6776293 0.6711613
## [3,] 0.9617052 0.6776293 1.0000000 0.9695661
## [4,] 0.9735978 0.6711613 0.9695661 1.0000000##Autovalores de XtX Normalizada:
library(stargazer)
#autovalores
lambdas<-eigen(XX_norm,symmetric = TRUE)
stargazer(lambdas$values,type = "html")##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">3.482</td><td>0.455</td><td>0.039</td><td>0.025</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>print(lambdas)## eigen() decomposition
## $values
## [1] 3.48158596 0.45518380 0.03851083 0.02471941
##
## $vectors
## [,1] [,2] [,3] [,4]
## [1,] -0.5218678 -0.2571123 0.62342077 0.522392359
## [2,] -0.4243486 0.9052959 0.01913842 -0.001191578
## [3,] -0.5227488 -0.2283722 -0.77038705 0.284751906
## [4,] -0.5237518 -0.2493568 0.13222728 -0.803754412##Cálculo de κ(x)
K<-sqrt(max(lambdas$values)/min(lambdas$values))
print(K)## [1] 11.86778CONCLUSION: Como K(x) < 20 se considera que la multicolinealidad es leve y no se considera un problema
#Cálculo del Indice de Condición usando librería “mctest”
library(mctest)
X_mat<-model.matrix(modelo_estimado)
mctest(mod = modelo_estimado)##
## Call:
## omcdiag(mod = mod, Inter = TRUE, detr = detr, red = red, conf = conf,
## theil = theil, cn = cn)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6918 0
## Farrar Chi-Square: 31.3812 1
## Red Indicator: 0.3341 0
## Sum of Lambda Inverse: 3.8525 0
## Theil's Method: -0.7297 0
## Condition Number: 11.8678 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test#Cálculo del Indice de Condición usando librería “olsrr”
library(olsrr)##
## Attaching package: 'olsrr'## The following object is masked from 'package:wooldridge':
##
## cement## The following object is masked from 'package:datasets':
##
## riversols_eigen_cindex(model = modelo_estimado)## 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 0.975025872#Prueba de Farrar-Glaubar
##Calculo de |R|
library(stargazer)
Zn<-scale(X_mat[,-1])
stargazer(head(Zn,n=6),type = "html")##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lotsize</td><td>sqrft</td><td>bdrms</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">1</td><td>-0.284</td><td>0.735</td><td>0.513</td></tr>
## <tr><td style="text-align:left">2</td><td>0.087</td><td>0.108</td><td>-0.675</td></tr>
## <tr><td style="text-align:left">3</td><td>-0.375</td><td>-1.108</td><td>-0.675</td></tr>
## <tr><td style="text-align:left">4</td><td>-0.434</td><td>-0.980</td><td>-0.675</td></tr>
## <tr><td style="text-align:left">5</td><td>-0.287</td><td>0.867</td><td>0.513</td></tr>
## <tr><td style="text-align:left">6</td><td>-0.045</td><td>1.283</td><td>1.702</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>print(Zn)## lotsize sqrft bdrms
## 1 -0.284432952 0.73512302 0.5132184
## 2 0.086801976 0.10794824 -0.6752874
## 3 -0.375447923 -1.10828571 -0.6752874
## 4 -0.434420906 -0.98007871 -0.6752874
## 5 -0.287479889 0.86679507 0.5132184
## 6 -0.044609488 1.28260155 1.7017243
## 7 -0.001952363 0.09235550 -0.6752874
## 8 -0.276176734 -0.48977357 -0.6752874
## 9 -0.296817278 -0.42740260 -0.6752874
## 10 -0.602297331 -0.21430178 -0.6752874
## 11 -0.296817278 0.55840526 0.5132184
## 12 -0.193909423 1.07469831 1.7017243
## 13 0.316206880 2.35850081 -0.6752874
## 14 -0.251604658 -0.19870903 -0.6752874
## 15 -0.245805648 0.51682462 -0.6752874
## 16 -0.533004076 -0.43953029 0.5132184
## 17 -0.304483766 -0.02372381 0.5132184
## 18 -0.186439512 -0.41527491 -0.6752874
## 19 -0.332004492 -1.10482065 -0.6752874
## 20 -0.041071109 -0.30959076 0.5132184
## 21 -0.346551161 0.05943749 -0.6752874
## 22 -0.119898329 0.19110954 -0.6752874
## 23 -0.296522414 -0.42567007 -0.6752874
## 24 -0.373678733 -0.48804104 0.5132184
## 25 0.039820167 -0.99393893 -0.6752874
## 26 -0.285612412 -0.14153564 -0.6752874
## 27 -0.227032582 -0.14153564 -0.6752874
## 28 -0.043528316 0.15992405 -0.6752874
## 29 -0.060925346 2.62530997 4.0787359
## 30 0.074024497 0.06463507 0.5132184
## 31 -0.414173515 -0.76351284 0.5132184
## 32 0.596230262 1.41254107 0.5132184
## 33 -0.320111607 -0.66475880 -0.6752874
## 34 -0.259172858 -0.30092813 0.5132184
## 35 -0.001952363 0.09062297 0.5132184
## 36 -0.542538041 -0.54001685 0.5132184
## 37 0.184009110 1.27567144 0.5132184
## 38 0.650092253 3.23342695 1.7017243
## 39 -0.257501956 -0.27667275 0.5132184
## 40 -0.013746960 -1.02685694 -1.8637932
## 41 -0.265954751 -0.60931793 -0.6752874
## 42 1.888230032 2.28226963 1.7017243
## 43 -0.193614558 -0.61971309 0.5132184
## 44 -0.365127650 -1.45998869 -0.6752874
## 45 -0.234207628 0.48390660 1.7017243
## 46 -0.116556527 -0.43260018 -0.6752874
## 47 -0.788258804 1.30685693 -0.6752874
## 48 -0.089232378 2.97874548 0.5132184
## 49 -0.311560524 -0.82761633 -0.6752874
## 50 -0.231946997 -0.65089858 0.5132184
## 51 -0.234207628 -0.07223456 -0.6752874
## 52 0.614020445 -0.92810290 -1.8637932
## 53 -0.380755491 -1.04937979 -0.6752874
## 54 -0.295146377 -0.34943888 -0.6752874
## 55 -0.059942463 -0.50536631 -0.6752874
## 56 -0.333675393 -0.40487975 0.5132184
## 57 -0.336132600 -0.58852761 0.5132184
## 58 -0.245215918 -0.28360286 0.5132184
## 59 -0.290919980 -0.15366333 -0.6752874
## 60 -0.342128187 0.57053295 0.5132184
## 61 -0.142897793 -0.77390800 -0.6752874
## 62 -0.347534044 -0.60585288 0.5132184
## 63 -0.360901253 -1.08749538 2.8902301
## 64 0.669749914 1.04524535 1.7017243
## 65 -0.098078326 0.53241736 0.5132184
## 66 0.289570750 1.08162842 0.5132184
## 67 -0.055028048 -0.17098860 0.5132184
## 68 0.598097739 0.99673459 0.5132184
## 69 0.180765596 1.20463783 0.5132184
## 70 -0.267330787 -0.73925746 -0.6752874
## 71 0.249075968 -0.55387707 -0.6752874
## 72 -0.296817278 -0.82761633 -0.6752874
## 73 2.160390349 2.85573606 1.7017243
## 74 -0.488086320 -0.48111093 -0.6752874
## 75 1.148020806 0.33144423 -1.8637932
## 76 -0.343504223 -0.88652225 -0.6752874
## 77 8.222911296 -0.55041201 0.5132184
## 78 -0.082745350 0.29852621 -0.6752874
## 79 -0.302321424 -0.14846575 0.5132184
## 80 0.965007982 -1.24688787 -0.6752874
## 81 -0.462433073 -0.82934886 0.5132184
## 82 -0.378691437 -0.05837435 -0.6752874
## 83 -0.110757517 0.13220362 0.5132184
## 84 -0.291313133 -0.30612571 -0.6752874
## 85 -0.313722867 -0.51749400 -0.6752874
## 86 -0.263300966 -0.76178031 -0.6752874
## 87 -0.261236912 -1.43573331 -1.8637932
## 88 -0.400019999 -0.41527491 0.5132184
## attr(,"scaled:center")
## lotsize sqrft bdrms
## 9019.863636 2013.693182 3.568182
## attr(,"scaled:scale")
## lotsize sqrft bdrms
## 10174.1504141 577.1915827 0.8413926##Calcular la matriz R
library(stargazer)
n<-nrow(Zn)
R<-cor(X_mat[,-1])
#También se puede calcular R a través de cor(X_mat[,-1])
stargazer(R,type = "html",digits = 4)##
## <table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>lotsize</td><td>sqrft</td><td>bdrms</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">lotsize</td><td>1</td><td>0.1838</td><td>0.1363</td></tr>
## <tr><td style="text-align:left">sqrft</td><td>0.1838</td><td>1</td><td>0.5315</td></tr>
## <tr><td style="text-align:left">bdrms</td><td>0.1363</td><td>0.5315</td><td>1</td></tr>
## <tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr></table>print(R)## lotsize sqrft bdrms
## lotsize 1.0000000 0.1838422 0.1363256
## sqrft 0.1838422 1.0000000 0.5314736
## bdrms 0.1363256 0.5314736 1.0000000##Calcular |R|
determinante_R<-det(R)
print(determinante_R)## [1] 0.6917931#Aplicando la prueba de Farrer Glaubar (Bartlett)
##Estadistico χ2FG
m<-ncol(X_mat[,-1])
n<-nrow(X_mat[,-1])
chi_FG<--(n-1-(2*m+5)/6)*log(determinante_R)
print(chi_FG)## [1] 31.38122Valor Critico
gl<-m*(m-1)/2
VC<-qchisq(p = 0.95,df = gl)
print(VC)## [1] 7.814728##Regla de Desicion:
Como χ2FG ≥ V.C. se rechaza H0, por lo tanto hay evidencia de colinealidad en los regresores.
##Cálculo de FG usando “mctest”
library(mctest)
mctest::omcdiag(mod = modelo_estimado)##
## Call:
## mctest::omcdiag(mod = modelo_estimado)
##
##
## Overall Multicollinearity Diagnostics
##
## MC Results detection
## Determinant |X'X|: 0.6918 0
## Farrar Chi-Square: 31.3812 1
## Red Indicator: 0.3341 0
## Sum of Lambda Inverse: 3.8525 0
## Theil's Method: -0.7297 0
## Condition Number: 11.8678 0
##
## 1 --> COLLINEARITY is detected by the test
## 0 --> COLLINEARITY is not detected by the test##Cálculo de FG usando la “psych”
library(psych)
FG_test<-cortest.bartlett(X_mat[,-1])## R was not square, finding R from dataprint(FG_test)## $chisq
## [1] 31.38122
##
## $p.value
## [1] 0.0000007065806
##
## $df
## [1] 3#Factores Inflacionarios de la Varianza (FIV)
##Matriz de Correlación de los regresores del modelo
print(R)## lotsize sqrft bdrms
## lotsize 1.0000000 0.1838422 0.1363256
## sqrft 0.1838422 1.0000000 0.5314736
## bdrms 0.1363256 0.5314736 1.0000000##Inversa de la matriz de correlación R−1
inversa_R<-solve(R)
print(inversa_R)## lotsize sqrft bdrms
## lotsize 1.03721145 -0.1610145 -0.05582352
## sqrft -0.16101454 1.4186543 -0.73202696
## bdrms -0.05582352 -0.7320270 1.39666321##VIF’s para el modelo estimado:
VIFs<-diag(inversa_R)
print(VIFs)## lotsize sqrft bdrms
## 1.037211 1.418654 1.396663##Cálculo de los VIF’s usando “performance”
library(performance)
VIFs<-multicollinearity(x = modelo_estimado,verbose = FALSE)
VIFs## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## lotsize 1.04 [1.00, 11.02] 1.02 0.96 [0.09, 1.00]
## sqrft 1.42 [1.18, 1.98] 1.19 0.70 [0.51, 0.85]
## bdrms 1.40 [1.17, 1.95] 1.18 0.72 [0.51, 0.86]plot(VIFs)## Variable `Component` is not in your data frame :/
##Cálculo de los VIF’s usando “car”
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitVIFs_car<-vif(modelo_estimado)
print(VIFs_car)## lotsize sqrft bdrms
## 1.037211 1.418654 1.396663##Cálculo de los VIF’s usando “mctest”
library(mctest)
mc.plot(mod = modelo_estimado,vif = 2)