PRACTICAS DEL VIDEO DE INTRODUCCIÓN AL MODELO DE REGRESIÓN LINEAL MÚLTIPLE EN R
Johan Mendoza
2023-04-04
PRÁCTICA I
IMPORTACIÓN DE DATOS
library(dplyr)
library(readr)
ejemplo_regresion <- read_csv("C:/Users/johan/OneDrive/Escritorio/ejemplo_regresion.csv")
head(ejemplo_regresion, n = 6)## # A tibble: 6 × 3
## X1 X2 Y
## <dbl> <dbl> <dbl>
## 1 3.92 7298 0.75
## 2 3.61 6855 0.71
## 3 3.32 6636 0.66
## 4 3.07 6506 0.61
## 5 3.06 6450 0.7
## 6 3.11 6402 0.72EJEMPLO DE REGRESIÓN LINEAL MÚLTIPLE
library(stargazer)
# Indicar que se muestren todos los decimales
options(scipen = 9999)
# Creación del objeto de regresión lineal(lm) donde tenemos el argumento de formula "formuls"donde indicamos la variable endógena y las variables exógenas. No es necesario escribir parámetros ni el error
modelo_lineal<-lm(formula = Y~X1+X2,data = ejemplo_regresion)
# Para observar el resumen del objeto utilizaremos summary
summary(modelo_lineal)##
## Call:
## lm(formula = Y ~ X1 + X2, data = ejemplo_regresion)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085090 -0.039102 -0.003341 0.030236 0.105692
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.56449677 0.07939598 19.705 0.00000000000000182 ***
## X1 0.23719747 0.05555937 4.269 0.000313 ***
## X2 -0.00024908 0.00003205 -7.772 0.00000009508790794 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0533 on 22 degrees of freedom
## Multiple R-squared: 0.8653, Adjusted R-squared: 0.8531
## F-statistic: 70.66 on 2 and 22 DF, p-value: 0.000000000265stargazer(modelo_lineal, title = "Ejemplo de regresión múltiple",type="text")##
## Ejemplo de regresión múltiple
## ===============================================
## Dependent variable:
## ---------------------------
## Y
## -----------------------------------------------
## X1 0.237***
## (0.056)
##
## X2 -0.0002***
## (0.00003)
##
## Constant 1.564***
## (0.079)
##
## -----------------------------------------------
## Observations 25
## R2 0.865
## Adjusted R2 0.853
## Residual Std. Error 0.053 (df = 22)
## F Statistic 70.661*** (df = 2; 22)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01OBJETOS DENTRO DEL MODELO LINEAL
Vector de coeficientes estimados
options(scipen=999)
#con el $ nos muestra una lista del modelo lineal
modelo_lineal$coefficients## (Intercept) X1 X2
## 1.5644967711 0.2371974748 -0.0002490793Matriz de varianza - covarianza de los parámetros
var_covar<-vcov(modelo_lineal)
print(var_covar)## (Intercept) X1 X2
## (Intercept) 0.0063037218732 0.000240996434 -0.000000982806321
## X1 0.0002409964344 0.003086843196 -0.000001675537651
## X2 -0.0000009828063 -0.000001675538 0.000000001027106INTERVALOS DE CONFIANZA
confint(object = modelo_lineal,level = .95)## 2.5 % 97.5 %
## (Intercept) 1.3998395835 1.7291539588
## X1 0.1219744012 0.3524205485
## X2 -0.0003155438 -0.0001826148VALORES AJUSTADOS
plot(modelo_lineal$fitted.values,main = "VALORES AJUSTADOS",ylab= "Y",xlab = "CASOS")
modelo_lineal$fitted.values%>% as.matrix()## [,1]
## 1 0.6765303
## 2 0.7133412
## 3 0.6991023
## 4 0.6721832
## 5 0.6837597
## 6 0.7075753
## 7 0.7397638
## 8 0.7585979
## 9 0.7943078
## 10 0.7935605
## 11 0.7984347
## 12 0.8272778
## 13 0.8021665
## 14 0.7992462
## 15 0.7544349
## 16 0.7339716
## 17 0.7048866
## 18 0.6930338
## 19 0.6350898
## 20 0.6127185
## 21 0.5701215
## 22 0.4796371
## 23 0.4374811
## 24 0.3953981
## 25 0.3773799Residuos del modelo
plot(modelo_lineal$residuals,main = "RESIDUOS",ylab ="RESIDUOS",xlab = "CASOS")
modelo_lineal$residuals %>% matrix()## [,1]
## [1,] 0.073469743
## [2,] -0.003341163
## [3,] -0.039102258
## [4,] -0.062183196
## [5,] 0.016240338
## [6,] 0.012424659
## [7,] 0.030236216
## [8,] -0.018597878
## [9,] 0.105692240
## [10,] 0.026439478
## [11,] -0.048434733
## [12,] -0.057277771
## [13,] -0.022166535
## [14,] 0.040753758
## [15,] 0.035565142
## [16,] -0.033971640
## [17,] -0.024886579
## [18,] 0.026966239
## [19,] -0.085089833
## [20,] 0.017281530
## [21,] -0.010121525
## [22,] -0.069637086
## [23,] 0.072518915
## [24,] 0.074601871
## [25,] -0.057379932PRÁCTICA II
Reproduce todas las salidas de la presentación con los datos del siguiente ejercicio:
IMPORTACIÓN DE DATOS
library(readxl)
practica2_video <- read_excel("C:/Users/johan/OneDrive/Escritorio/practica2_video.xlsx")
head(practica2_video,n=10)## # A tibble: 10 × 3
## Y X1 X2
## <dbl> <dbl> <dbl>
## 1 320 50 7.4
## 2 450 53 5.1
## 3 370 60 4.2
## 4 470 63 3.9
## 5 420 69 1.4
## 6 500 82 2.2
## 7 570 100 7
## 8 640 104 5.7
## 9 670 113 13.1
## 10 780 130 16.4MODELO DE REGRESIÓN LINEAL MÚLTIPLE
library(stargazer)
modelo2_lineal <-lm(formula = Y~X1+X2+X1*X2,data = practica2_video)
# Usando summary
summary(modelo2_lineal)##
## Call:
## lm(formula = Y ~ X1 + X2 + X1 * X2, data = practica2_video)
##
## Residuals:
## Min 1Q Median 3Q Max
## -108.527 -37.595 -2.745 52.292 102.808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 303.50401 71.54695 4.242 0.000621 ***
## X1 2.32927 0.47698 4.883 0.000166 ***
## X2 -25.07113 11.48487 -2.183 0.044283 *
## X1:X2 0.28617 0.07681 3.726 0.001840 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 67.68 on 16 degrees of freedom
## Multiple R-squared: 0.9634, Adjusted R-squared: 0.9566
## F-statistic: 140.4 on 3 and 16 DF, p-value: 0.00000000001054# Usando stargazer
stargazer(modelo2_lineal,title = "PRÁCTICA 2 MODELO DE REGRESIÓN LINEAL",type = "text",digits = 8)##
## PRÁCTICA 2 MODELO DE REGRESIÓN LINEAL
## ================================================
## Dependent variable:
## ----------------------------
## Y
## ------------------------------------------------
## X1 2.32927500***
## (0.47698220)
##
## X2 -25.07113000**
## (11.48487000)
##
## X1:X2 0.28616860***
## (0.07681293)
##
## Constant 303.50400000***
## (71.54695000)
##
## ------------------------------------------------
## Observations 20
## R2 0.96341370
## Adjusted R2 0.95655370
## Residual Std. Error 67.67775000 (df = 16)
## F Statistic 140.44060000*** (df = 3; 16)
## ================================================
## Note: *p<0.1; **p<0.05; ***p<0.01OBJETOS DENTRO DEL MODELO
Vector de coeficientes estimados
options(scipen = 999)
modelo2_lineal$coefficients## (Intercept) X1 X2 X1:X2
## 303.5040143 2.3292746 -25.0711288 0.2861686Matriz de Varianza - Covarianza de los parametros
var_covar2 <- vcov(modelo2_lineal)
print(var_covar2)## (Intercept) X1 X2 X1:X2
## (Intercept) 5118.96645 -31.10997447 -722.8989902 4.493190281
## X1 -31.10997 0.22751204 4.5755139 -0.033223456
## X2 -722.89899 4.57551391 131.9021598 -0.822206343
## X1:X2 4.49319 -0.03322346 -0.8222063 0.005900226INTERVALOS DE CONFIANZA
confint(modelo2_lineal,level = .95)## 2.5 % 97.5 %
## (Intercept) 151.8312499 455.1767786
## X1 1.3181175 3.3404318
## X2 -49.4179582 -0.7242993
## X1:X2 0.1233324 0.4490047VALORES AJUSTADOS
plot(modelo2_lineal$fitted.values,main = "VALORES AJUSTADOS",ylab =("Y"),xlab = ("CASOS"))
modelo2_lineal$fitted.values %>% as.matrix()## [,1]
## 1 340.3238
## 2 376.4442
## 3 410.0762
## 4 422.7825
## 5 456.7683
## 6 490.9729
## 7 561.2516
## 8 572.4839
## 9 661.8956
## 10 805.2546
## 11 743.9514
## 12 802.6063
## 13 921.3246
## 14 1038.5268
## 15 966.3846
## 16 967.1923
## 17 1087.4101
## 18 1280.2249
## 19 1349.9604
## 20 1214.1649RESIDUOS DEL MODELO
plot(modelo2_lineal$residuals, main = "RESIDUOS DEL MODELO", ylab = "Y", xlab = "CASOS")
modelo2_lineal$residuals %>% as.matrix()## [,1]
## 1 -20.323767
## 2 73.555820
## 3 -40.076233
## 4 47.217467
## 5 -36.768268
## 6 9.027138
## 7 8.748419
## 8 67.516125
## 9 8.104393
## 10 -25.254613
## 11 -53.951414
## 12 -102.606335
## 13 -11.324647
## 14 -108.526815
## 15 -26.384626
## 16 102.807683
## 17 72.589856
## 18 -70.224936
## 19 100.039646
## 20 5.835106MATRICES A & P & M
Matriz X
mat_x <- model.matrix(modelo2_lineal)
print(mat_x)## (Intercept) X1 X2 X1:X2
## 1 1 50 7.4 370.0
## 2 1 53 5.1 270.3
## 3 1 60 4.2 252.0
## 4 1 63 3.9 245.7
## 5 1 69 1.4 96.6
## 6 1 82 2.2 180.4
## 7 1 100 7.0 700.0
## 8 1 104 5.7 592.8
## 9 1 113 13.1 1480.3
## 10 1 130 16.4 2132.0
## 11 1 150 5.1 765.0
## 12 1 181 2.9 524.9
## 13 1 202 4.5 909.0
## 14 1 217 6.2 1345.4
## 15 1 229 3.2 732.8
## 16 1 240 2.4 576.0
## 17 1 243 4.9 1190.7
## 18 1 247 8.8 2173.6
## 19 1 249 10.1 2514.9
## 20 1 254 6.7 1701.8
## attr(,"assign")
## [1] 0 1 2 3Matriz X^TX
mat_xx <- t(mat_x) %*% mat_x
print(mat_xx)## (Intercept) X1 X2 X1:X2
## (Intercept) 20.0 3036.0 121.20 18754.2
## X1 3036.0 574618.0 18754.20 3537032.8
## X2 121.2 18754.2 999.94 152648.7
## X1:X2 18754.2 3537032.8 152648.68 27682881.9Matriz A
solve(mat_xx) %*% t(mat_x) -> mat_A
print(mat_A)## 1 2 3 4
## (Intercept) -0.0269643876 0.21786063243 0.294409235 0.3152011324
## X1 0.0003999215 -0.00102548702 -0.001444105 -0.0015490795
## X2 0.0388047049 -0.00653630884 -0.022176619 -0.0266881805
## X1:X2 -0.0002334413 0.00002923967 0.000116450 0.0001404267
## 5 6 7 8
## (Intercept) 0.5227546767 0.3904003745 0.02028463523 0.09313147605
## X1 -0.0026669364 -0.0018298820 0.00009025813 -0.00023211650
## X2 -0.0659240840 -0.0449422757 0.01799521280 0.00379733432
## X1:X2 0.0003536128 0.0002236575 -0.00009921704 -0.00003296119
## 9 10 11 12
## (Intercept) -0.2653043830 -0.2622967332 0.0443152123 -0.0545535472
## X1 0.0011696748 0.0005835039 0.0002043562 0.0012880664
## X2 0.0665768143 0.0616052800 0.0015591322 0.0122720018
## X1:X2 -0.0002833581 -0.0001595452 -0.0000370955 -0.0001763267
## 13 14 15 16
## (Intercept) -0.072917817 -0.01500641231 -0.2239786763 -0.3262481783
## X1 0.001143413 0.00042125956 0.0024639940 0.0033485820
## X2 0.010376875 -0.00402060621 0.0315412720 0.0476387398
## X1:X2 -0.000121078 0.00002711295 -0.0003105392 -0.0004487073
## 17 18 19 20
## (Intercept) -0.1381839072 0.1833273040 0.2993764155 0.0043929480
## X1 0.0015362166 -0.0014987044 -0.0025763624 0.0001734280
## X2 0.0122853632 -0.0478476959 -0.0696793809 -0.0166375793
## X1:X2 -0.0001273987 0.0004096502 0.0006014359 0.0001280827Matriz P
mat_x %*% mat_A -> mat_p
print(mat_p)## 1 2 3 4 5
## 1 0.19381324 0.129036273 0.1011834639 0.092212497 0.032406355
## 2 0.12903627 0.138078127 0.1362473103 0.134947532 0.140775748
## 3 0.10118346 0.136247310 0.1439664947 0.145553529 0.174967755
## 4 0.09221250 0.134947532 0.1455535292 0.148028057 0.184516411
## 5 0.03240635 0.140775748 0.1749677551 0.184516411 0.280601341
## 6 0.04908672 0.124665653 0.1482115949 0.154795589 0.222824650
## 7 0.12125181 0.090025536 0.0762773231 0.071774601 0.042121378
## 8 0.09743028 0.091286297 0.0868470703 0.085219176 0.079247655
## 9 0.18100528 0.059638434 0.0130924780 -0.001586386 -0.118761676
## 10 0.16372580 0.039690827 -0.0087497204 -0.024475658 -0.151199641
## 11 0.05234526 0.053070750 0.0537768719 0.054155902 0.057015148
## 12 0.03542173 0.028640088 0.0298385285 0.031131985 0.034470683
## 13 0.01624282 0.007877722 0.0087581517 0.009838119 0.008809143
## 14 -0.01366413 -0.005856117 0.0002150787 0.002514228 0.011050760
## 15 0.01772693 -0.016465255 -0.0219215745 -0.022035577 -0.039803397
## 16 0.02768589 -0.027101345 -0.0383247931 -0.039743813 -0.071846910
## 17 -0.01759892 -0.028544954 -0.0265168679 -0.024791215 -0.027292170
## 18 -0.09411030 -0.029398837 -0.0043234403 0.002953959 0.052502132
## 19 -0.12253784 -0.029967514 0.0037031147 0.013088796 0.082154982
## 20 -0.06266315 -0.036646273 -0.0228023690 -0.018097732 0.005439657
## 6 7 8 9 10 11
## 1 0.049086720 0.12125181 0.097430285 0.181005277 0.163725800 0.05234526
## 2 0.124665653 0.09002554 0.091286297 0.059638434 0.039690827 0.05307075
## 3 0.148211595 0.07627732 0.086847070 0.013092478 -0.008749720 0.05377687
## 4 0.154795589 0.07177460 0.085219176 -0.001586386 -0.024475658 0.05415590
## 5 0.222824650 0.04212138 0.079247655 -0.118761676 -0.151199641 0.05701515
## 6 0.181824864 0.04937652 0.076505860 -0.074039863 -0.107699756 0.05781048
## 7 0.049376516 0.08582501 0.073428333 0.119350110 0.115608957 0.04969791
## 8 0.076505860 0.07342833 0.071096772 0.067854942 0.054959357 0.05246510
## 9 -0.074039863 0.11935011 0.067854942 0.319570105 0.374493574 0.03291963
## 10 -0.107699756 0.11560896 0.054959357 0.374493574 0.483734939 0.01736368
## 11 0.057810481 0.04969791 0.052465096 0.032919625 0.017363681 0.05454216
## 12 0.046256974 0.03672845 0.044829330 -0.009255161 -0.061772505 0.06635373
## 13 0.021828668 0.02930695 0.033370226 0.012993057 -0.012231797 0.05889144
## 14 0.015582714 0.01795437 0.021959684 0.020061277 0.031624199 0.04841884
## 15 -0.008561643 0.02583218 0.027974307 0.007952119 -0.048452187 0.06891842
## 16 -0.027806025 0.02798608 0.027551473 0.011987645 -0.066301176 0.07573560
## 17 -0.008169078 0.01225618 0.016087216 0.007758466 -0.008609916 0.05744390
## 18 0.029069502 -0.01472189 -0.002429200 -0.006425960 0.077167686 0.02788077
## 19 0.043319093 -0.02501037 -0.009206552 -0.014246880 0.103968771 0.01765567
## 20 0.005117486 -0.00506943 0.003522672 -0.004361183 0.027154564 0.04353875
## 12 13 14 15 16
## 1 0.035421725 0.016242817 -0.0136641286 0.017726930 0.02768589
## 2 0.028640088 0.007877722 -0.0058561168 -0.016465255 -0.02710135
## 3 0.029838529 0.008758152 0.0002150787 -0.021921574 -0.03832479
## 4 0.031131985 0.009838119 0.0025142276 -0.022035577 -0.03974381
## 5 0.034470683 0.008809143 0.0110507595 -0.039803397 -0.07184691
## 6 0.046256974 0.021828668 0.0155827141 -0.008561643 -0.02780602
## 7 0.036728450 0.029306947 0.0179543655 0.025832182 0.02798608
## 8 0.044829330 0.033370226 0.0219596835 0.027974307 0.02755147
## 9 -0.009255161 0.012993057 0.0200612774 0.007952119 0.01198764
## 10 -0.061772505 -0.012231797 0.0316241994 -0.048452187 -0.06630118
## 11 0.066353735 0.058891445 0.0484188370 0.068918416 0.07573560
## 12 0.121621418 0.100578949 0.0638133975 0.150471895 0.18247104
## 13 0.100578949 0.094687541 0.0766409627 0.133403695 0.15666477
## 14 0.063813397 0.076640963 0.0879569172 0.088464457 0.09206349
## 15 0.150471895 0.133403695 0.0884644569 0.213644887 0.26420835
## 16 0.182471043 0.156664770 0.0920634863 0.264208351 0.33328907
## 17 0.108627255 0.111610526 0.0999420803 0.159565062 0.18661128
## 18 -0.011671138 0.037646387 0.1125960736 -0.012796988 -0.05523773
## 19 -0.053321689 0.012099216 0.1174654547 -0.072852378 -0.13975401
## 20 0.054765037 0.080983456 0.1111962734 0.084726697 0.07986110
## 17 18 19 20
## 1 -0.017598924 -0.094110303 -0.122537845 -0.062663147
## 2 -0.028544954 -0.029398837 -0.029967514 -0.036646273
## 3 -0.026516868 -0.004323440 0.003703115 -0.022802369
## 4 -0.024791215 0.002953959 0.013088796 -0.018097732
## 5 -0.027292170 0.052502132 0.082154982 0.005439657
## 6 -0.008169078 0.029069502 0.043319093 0.005117486
## 7 0.012256176 -0.014721888 -0.025010370 -0.005069430
## 8 0.016087216 -0.002429200 -0.009206552 0.003522672
## 9 0.007758466 -0.006425960 -0.014246880 -0.004361183
## 10 -0.008609916 0.077167686 0.103968771 0.027154564
## 11 0.057443899 0.027880775 0.017655665 0.043538746
## 12 0.108627255 -0.011671138 -0.053321689 0.054765037
## 13 0.111610526 0.037646387 0.012099216 0.080983456
## 14 0.099942080 0.112596074 0.117465455 0.111196273
## 15 0.159565062 -0.012796988 -0.072852378 0.084726697
## 16 0.186611278 -0.055237727 -0.139754007 0.079861104
## 17 0.143621327 0.072458882 0.048021095 0.117519864
## 18 0.072458882 0.282503205 0.357117396 0.179219484
## 19 0.048021095 0.357117396 0.466651545 0.201652106
## 20 0.117519864 0.179219484 0.201652106 0.154942988Matriz M
diag(20) - mat_p -> mat_m
print(mat_m)## 1 2 3 4 5
## 1 0.80618676 -0.129036273 -0.1011834639 -0.092212497 -0.032406355
## 2 -0.12903627 0.861921873 -0.1362473103 -0.134947532 -0.140775748
## 3 -0.10118346 -0.136247310 0.8560335053 -0.145553529 -0.174967755
## 4 -0.09221250 -0.134947532 -0.1455535292 0.851971943 -0.184516411
## 5 -0.03240635 -0.140775748 -0.1749677551 -0.184516411 0.719398659
## 6 -0.04908672 -0.124665653 -0.1482115949 -0.154795589 -0.222824650
## 7 -0.12125181 -0.090025536 -0.0762773231 -0.071774601 -0.042121378
## 8 -0.09743028 -0.091286297 -0.0868470703 -0.085219176 -0.079247655
## 9 -0.18100528 -0.059638434 -0.0130924780 0.001586386 0.118761676
## 10 -0.16372580 -0.039690827 0.0087497204 0.024475658 0.151199641
## 11 -0.05234526 -0.053070750 -0.0537768719 -0.054155902 -0.057015148
## 12 -0.03542173 -0.028640088 -0.0298385285 -0.031131985 -0.034470683
## 13 -0.01624282 -0.007877722 -0.0087581517 -0.009838119 -0.008809143
## 14 0.01366413 0.005856117 -0.0002150787 -0.002514228 -0.011050760
## 15 -0.01772693 0.016465255 0.0219215745 0.022035577 0.039803397
## 16 -0.02768589 0.027101345 0.0383247931 0.039743813 0.071846910
## 17 0.01759892 0.028544954 0.0265168679 0.024791215 0.027292170
## 18 0.09411030 0.029398837 0.0043234403 -0.002953959 -0.052502132
## 19 0.12253784 0.029967514 -0.0037031147 -0.013088796 -0.082154982
## 20 0.06266315 0.036646273 0.0228023690 0.018097732 -0.005439657
## 6 7 8 9 10 11
## 1 -0.049086720 -0.12125181 -0.097430285 -0.181005277 -0.163725800 -0.05234526
## 2 -0.124665653 -0.09002554 -0.091286297 -0.059638434 -0.039690827 -0.05307075
## 3 -0.148211595 -0.07627732 -0.086847070 -0.013092478 0.008749720 -0.05377687
## 4 -0.154795589 -0.07177460 -0.085219176 0.001586386 0.024475658 -0.05415590
## 5 -0.222824650 -0.04212138 -0.079247655 0.118761676 0.151199641 -0.05701515
## 6 0.818175136 -0.04937652 -0.076505860 0.074039863 0.107699756 -0.05781048
## 7 -0.049376516 0.91417499 -0.073428333 -0.119350110 -0.115608957 -0.04969791
## 8 -0.076505860 -0.07342833 0.928903228 -0.067854942 -0.054959357 -0.05246510
## 9 0.074039863 -0.11935011 -0.067854942 0.680429895 -0.374493574 -0.03291963
## 10 0.107699756 -0.11560896 -0.054959357 -0.374493574 0.516265061 -0.01736368
## 11 -0.057810481 -0.04969791 -0.052465096 -0.032919625 -0.017363681 0.94545784
## 12 -0.046256974 -0.03672845 -0.044829330 0.009255161 0.061772505 -0.06635373
## 13 -0.021828668 -0.02930695 -0.033370226 -0.012993057 0.012231797 -0.05889144
## 14 -0.015582714 -0.01795437 -0.021959684 -0.020061277 -0.031624199 -0.04841884
## 15 0.008561643 -0.02583218 -0.027974307 -0.007952119 0.048452187 -0.06891842
## 16 0.027806025 -0.02798608 -0.027551473 -0.011987645 0.066301176 -0.07573560
## 17 0.008169078 -0.01225618 -0.016087216 -0.007758466 0.008609916 -0.05744390
## 18 -0.029069502 0.01472189 0.002429200 0.006425960 -0.077167686 -0.02788077
## 19 -0.043319093 0.02501037 0.009206552 0.014246880 -0.103968771 -0.01765567
## 20 -0.005117486 0.00506943 -0.003522672 0.004361183 -0.027154564 -0.04353875
## 12 13 14 15 16
## 1 -0.035421725 -0.016242817 0.0136641286 -0.017726930 -0.02768589
## 2 -0.028640088 -0.007877722 0.0058561168 0.016465255 0.02710135
## 3 -0.029838529 -0.008758152 -0.0002150787 0.021921574 0.03832479
## 4 -0.031131985 -0.009838119 -0.0025142276 0.022035577 0.03974381
## 5 -0.034470683 -0.008809143 -0.0110507595 0.039803397 0.07184691
## 6 -0.046256974 -0.021828668 -0.0155827141 0.008561643 0.02780602
## 7 -0.036728450 -0.029306947 -0.0179543655 -0.025832182 -0.02798608
## 8 -0.044829330 -0.033370226 -0.0219596835 -0.027974307 -0.02755147
## 9 0.009255161 -0.012993057 -0.0200612774 -0.007952119 -0.01198764
## 10 0.061772505 0.012231797 -0.0316241994 0.048452187 0.06630118
## 11 -0.066353735 -0.058891445 -0.0484188370 -0.068918416 -0.07573560
## 12 0.878378582 -0.100578949 -0.0638133975 -0.150471895 -0.18247104
## 13 -0.100578949 0.905312459 -0.0766409627 -0.133403695 -0.15666477
## 14 -0.063813397 -0.076640963 0.9120430828 -0.088464457 -0.09206349
## 15 -0.150471895 -0.133403695 -0.0884644569 0.786355113 -0.26420835
## 16 -0.182471043 -0.156664770 -0.0920634863 -0.264208351 0.66671093
## 17 -0.108627255 -0.111610526 -0.0999420803 -0.159565062 -0.18661128
## 18 0.011671138 -0.037646387 -0.1125960736 0.012796988 0.05523773
## 19 0.053321689 -0.012099216 -0.1174654547 0.072852378 0.13975401
## 20 -0.054765037 -0.080983456 -0.1111962734 -0.084726697 -0.07986110
## 17 18 19 20
## 1 0.017598924 0.094110303 0.122537845 0.062663147
## 2 0.028544954 0.029398837 0.029967514 0.036646273
## 3 0.026516868 0.004323440 -0.003703115 0.022802369
## 4 0.024791215 -0.002953959 -0.013088796 0.018097732
## 5 0.027292170 -0.052502132 -0.082154982 -0.005439657
## 6 0.008169078 -0.029069502 -0.043319093 -0.005117486
## 7 -0.012256176 0.014721888 0.025010370 0.005069430
## 8 -0.016087216 0.002429200 0.009206552 -0.003522672
## 9 -0.007758466 0.006425960 0.014246880 0.004361183
## 10 0.008609916 -0.077167686 -0.103968771 -0.027154564
## 11 -0.057443899 -0.027880775 -0.017655665 -0.043538746
## 12 -0.108627255 0.011671138 0.053321689 -0.054765037
## 13 -0.111610526 -0.037646387 -0.012099216 -0.080983456
## 14 -0.099942080 -0.112596074 -0.117465455 -0.111196273
## 15 -0.159565062 0.012796988 0.072852378 -0.084726697
## 16 -0.186611278 0.055237727 0.139754007 -0.079861104
## 17 0.856378673 -0.072458882 -0.048021095 -0.117519864
## 18 -0.072458882 0.717496795 -0.357117396 -0.179219484
## 19 -0.048021095 -0.357117396 0.533348455 -0.201652106
## 20 -0.117519864 -0.179219484 -0.201652106 0.845057012