La función crea.bloque permite crear una matriz en bloque a partir de dos matrices:
crea.bloque<-function(mat1,mat2){
nf1<-nrow(mat1);nc1<-ncol(mat1)
nf2<-nrow(mat2);nc2<-ncol(mat2)
mat1<-cbind(mat1,matrix(0,nrow=nf1,ncol=nc2))
mat2<-cbind(matrix(0,nrow=nf2,ncol=nc1),mat2)
matdef<-rbind(mat1,mat2)
return(matdef)
}La funcion ceros.a.5 corrige los ceros en 0.5
ceros.a.5<-function(Tabla){
Nro.col<-ncol(Tabla)
if(any(Tabla==0))Tabla<-matrix(ifelse
(as.vector(Tabla)==0,0.5,Tabla),ncol=Nro.col)
return(Tabla)
}La funcion identidad permite construir una matriz diagonal con k elementos iguales a 1 en su diagonal principal
identidad<-function(k)diag(rep(1,k))La funcion bloque permite construir una matriz donde su primera fila esta compuesta de ceros y debajo se adiciona una matriz diagonal con k elementos iguales a 1 en su diagonal principal.
bloque<-function(k)rbind(0,identidad(k))La funcion repita toma dos matrices apartir de las cuales se genera una nueva matriz de mayor dimension. La primera matriz aparece en las primeras columnas y la segunda se adiciona ocupando las ultimas columnas de la nueva matriz generada. Cada fila aparece repetida k veces una debajo de la otra.
repita<-function(X,X2,k){
temp<-NULL
for(i in 1:nrow(X))temp<-rbind(temp,cbind
(matrix(rep(X[i,],k),ncol=ncol(X),byrow=T),X2))
return(temp)
}El ejemplo consiste en hombres y mujeres clasificados por joven, adulto y adulto mayor
nro.vars<-2 # Hombre, mujer
Nro.Estratos<-12 # Combinacion joven, adulto y adulto mayor con P+ y P-
niveles<-c(2,3) # (Hombre, mujer), (joven, adulto, adulto mayor)Generación de datos
set.seed(21)
datos <- replicate(6,sample(15:35,4, replace = TRUE))
N <- matrix(datos, 12, 2, byrow = T)
N## [,1] [,2]
## [1,] 31 20
## [2,] 29 18
## [3,] 35 34
## [4,] 17 18
## [5,] 35 32
## [6,] 29 34
## [7,] 16 27
## [8,] 18 15
## [9,] 26 20
## [10,] 25 28
## [11,] 15 27
## [12,] 31 34| Sexo | Edad | Condicion positiva | Condicion negativa |
|---|---|---|---|
| Mujer | Joven P+ | 31 | 20 |
| Joven P- | 29 | 18 | |
| Adulto P+ | 35 | 34 | |
| Adulto P- | 17 | 18 | |
| Adulto mayor P+ | 35 | 32 | |
| Adulto mayor P- | 29 | 34 | |
| Hombre | Joven P+ | 16 | 27 |
| Joven P- | 18 | 15 | |
| Adulto P+ | 26 | 20 | |
| Adulto P- | 25 | 28 | |
| Adulto mayor P+ | 15 | 27 | |
| Adulto mayor P- | 31 | 34 |
matriz.diseño<-function(nro.vars,niveles){
niveles<-niveles-1
temp1<-bloque(niveles[nro.vars])
for(i in (nro.vars-1):1){
temp2<-bloque(niveles[i])
temp1<-repita(temp2,temp1,nrow(temp1))
}
temp<-cbind(1,temp1)
temp<-temp[rep(1:nrow(temp),each=2),]
indi<-matrix(rep(c(0,1),nrow(temp)/2),ncol=1)
temp2<-NULL
for(ii in 1:ncol(temp)) temp2<-cbind(temp2,indi)
return(cbind(temp,temp*temp2))
}
X <- matriz.diseño(2,c(2,3))
set.seed(21)
datos <- replicate(6,sample(15:35,4, replace = TRUE))
N <- matrix(datos, 12, 2, byrow = T)
nro.vars<-2
A <- matrix(c(1,0,0,0,
0,0,0,1),ncol=4,byrow=T)
Nro.Estratos<-12
niveles<-c(2,3)logistica.bivariable<-function(N,Nro.Estratos,A,X,nro.vars,niveles, imprimir.inter=T){ #Le quite alpha
A<-kronecker(identidad(Nro.Estratos/2),A)
print('Matriz A')
print(A)
#Convertir la matriz N en un vector
N<-matrix(t(N),ncol=1,byrow=F)
print('Tabla de contingencia como vector')
print(N)
# Convertir los ceros que puedan haber en la matriz N a 0.5
# usando la funcion auxiliar ceros.a.5
N<-ceros.a.5(N)
print('ceros a 0.5')
print('N')
#calcular el vector pi-gorro
donde.voy<-0
for(i in 1:Nro.Estratos){
# i=1
identifica<-c('Estrato ',i)
print(identifica)
temp<-N[(donde.voy+1):(donde.voy+2)]
print('Frecuencias de la subpoblación')
print(temp)
donde.voy<-donde.voy+2
n.temp<-sum(temp)
probab<-matrix(temp/n.temp,ncol=1)
print('p estimado del estrato')
print(t(probab))
temp1<- t(probab)
temp2<-rev(probab)
varcov.p<-(matrix(c(temp1, temp2), 2, byrow = T))/n.temp
print('Matriz de varianzas y covarianzas estimada del estrato')
print(varcov.p)
#Matriz de varianza y covarianzas pi-gorro
if(i==1){
varcov.grande<-varcov.p
prob.grande<-probab
}
else{
varcov.grande<-crea.bloque(varcov.grande,varcov.p)
prob.grande<-rbind(prob.grande,probab)
print('vector que acumula las probabilidades de todos los estratos anteriores')
print(prob.grande)
}
}
#Calcular el vector f = A*pi-gorro
f<-A%*%prob.grande
print('vector f= A*pi-gorro')
print(f)
# Calculo de la matriz de varianzas y covarianzas Sigma f gorro (A*sigma pi-gorro*A')
var.cov.f<-A%*%varcov.grande%*%t(A)
print('var.cov.f gorro')
print(var.cov.f)
# Calcular los betas utilizando inversa generalizada y
library(MASS)
temp<-solve(t(X)%*%ginv(var.cov.f)%*%X)
beta<-temp%*%t(X)%*%ginv(var.cov.f)%*%f
print('Betas estimados')
print(beta)
# matriz de varianzas y covarianzas del modelo
#o matriz de varianzas y covarianzas de beta
Sigma.beta<-temp
print('Matriz de varianzas y covarianzas de Beta')
print(Sigma.beta)
# Calcular estimacion de f-gorro, osea f*
f.gor<-X%*%beta
print('f.gorro*')
print(f.gor)
# error, Z score, p-valor
Z<-nrow(beta)/2
LM<-function(f,X,Sf){
require(MASS)
temp<-t(X)%*%ginv(Sf)
varcov.b<-solve(temp%*%X)
beta<-varcov.b%*%temp%*%f
valores.z<-beta/sqrt(diag(varcov.b))
estadisticos<-cbind(beta,sqrt(diag(varcov.b)),valores.z,2*pnorm(abs(valores.z),lower.tail=F))
colnames(estadisticos)<-c('beta','error','z-valor','p-valor')
estadisticos_sensibilidad <- estadisticos[1:Z,]
print('********** Estimacion del modelo para la sensibilidad *****************')
print(estadisticos_sensibilidad)
print('*********************************************')
estadisticos_especificidad <- estadisticos[(Z+1):nrow(beta),]
print('********** Estimacion del modelo para la especificidad*****************')
print(estadisticos_especificidad)
print('*********************************************')
}
LM(f,X,var.cov.f)
}logistica.bivariable(N=N,Nro.Estratos=12,A=A,X=X,nro.vars=2,niveles=c(2,3))## [1] "Matriz A"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 1 0 0 0 0 0 0 0 0 0 0 0 0
## [2,] 0 0 0 1 0 0 0 0 0 0 0 0 0
## [3,] 0 0 0 0 1 0 0 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0 0 1 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 1 0 0 0 0
## [6,] 0 0 0 0 0 0 0 0 0 0 0 1 0
## [7,] 0 0 0 0 0 0 0 0 0 0 0 0 1
## [8,] 0 0 0 0 0 0 0 0 0 0 0 0 0
## [9,] 0 0 0 0 0 0 0 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0 0 0 0 0 0 0 0
## [11,] 0 0 0 0 0 0 0 0 0 0 0 0 0
## [12,] 0 0 0 0 0 0 0 0 0 0 0 0 0
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## [1,] 0 0 0 0 0 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0 0 0
## [6,] 0 0 0 0 0 0 0 0 0 0 0
## [7,] 0 0 0 0 0 0 0 0 0 0 0
## [8,] 0 0 1 0 0 0 0 0 0 0 0
## [9,] 0 0 0 1 0 0 0 0 0 0 0
## [10,] 0 0 0 0 0 0 1 0 0 0 0
## [11,] 0 0 0 0 0 0 0 1 0 0 0
## [12,] 0 0 0 0 0 0 0 0 0 0 1
## [1] "Tabla de contingencia como vector"
## [,1]
## [1,] 31
## [2,] 20
## [3,] 29
## [4,] 18
## [5,] 35
## [6,] 34
## [7,] 17
## [8,] 18
## [9,] 35
## [10,] 32
## [11,] 29
## [12,] 34
## [13,] 16
## [14,] 27
## [15,] 18
## [16,] 15
## [17,] 26
## [18,] 20
## [19,] 25
## [20,] 28
## [21,] 15
## [22,] 27
## [23,] 31
## [24,] 34
## [1] "ceros a 0.5"
## [1] "N"
## [1] "Estrato " "1"
## [1] "Frecuencias de la subpoblación"
## [1] 31 20
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.6078431 0.3921569
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.01191849 0.00768935
## [2,] 0.00768935 0.01191849
## [1] "Estrato " "2"
## [1] "Frecuencias de la subpoblación"
## [1] 29 18
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.6170213 0.3829787
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.013128112 0.008148483
## [2,] 0.008148483 0.013128112
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [1] "Estrato " "3"
## [1] "Frecuencias de la subpoblación"
## [1] 35 34
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.5072464 0.4927536
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.007351397 0.007141357
## [2,] 0.007141357 0.007351397
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [1] "Estrato " "4"
## [1] "Frecuencias de la subpoblación"
## [1] 17 18
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.4857143 0.5142857
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.01387755 0.01469388
## [2,] 0.01469388 0.01387755
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [1] "Estrato " "5"
## [1] "Frecuencias de la subpoblación"
## [1] 35 32
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.5223881 0.4776119
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.007796837 0.007128536
## [2,] 0.007128536 0.007796837
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [1] "Estrato " "6"
## [1] "Frecuencias de la subpoblación"
## [1] 29 34
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.4603175 0.5396825
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.007306626 0.008566390
## [2,] 0.008566390 0.007306626
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [1] "Estrato " "7"
## [1] "Frecuencias de la subpoblación"
## [1] 16 27
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.372093 0.627907
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.008653326 0.014602488
## [2,] 0.014602488 0.008653326
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [1] "Estrato " "8"
## [1] "Frecuencias de la subpoblación"
## [1] 18 15
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.5454545 0.4545455
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.01652893 0.01377410
## [2,] 0.01377410 0.01652893
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [15,] 0.5454545
## [16,] 0.4545455
## [1] "Estrato " "9"
## [1] "Frecuencias de la subpoblación"
## [1] 26 20
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.5652174 0.4347826
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.012287335 0.009451796
## [2,] 0.009451796 0.012287335
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [15,] 0.5454545
## [16,] 0.4545455
## [17,] 0.5652174
## [18,] 0.4347826
## [1] "Estrato " "10"
## [1] "Frecuencias de la subpoblación"
## [1] 25 28
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.4716981 0.5283019
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.008899964 0.009967960
## [2,] 0.009967960 0.008899964
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [15,] 0.5454545
## [16,] 0.4545455
## [17,] 0.5652174
## [18,] 0.4347826
## [19,] 0.4716981
## [20,] 0.5283019
## [1] "Estrato " "11"
## [1] "Frecuencias de la subpoblación"
## [1] 15 27
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.3571429 0.6428571
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.008503401 0.015306122
## [2,] 0.015306122 0.008503401
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [15,] 0.5454545
## [16,] 0.4545455
## [17,] 0.5652174
## [18,] 0.4347826
## [19,] 0.4716981
## [20,] 0.5283019
## [21,] 0.3571429
## [22,] 0.6428571
## [1] "Estrato " "12"
## [1] "Frecuencias de la subpoblación"
## [1] 31 34
## [1] "p estimado del estrato"
## [,1] [,2]
## [1,] 0.4769231 0.5230769
## [1] "Matriz de varianzas y covarianzas estimada del estrato"
## [,1] [,2]
## [1,] 0.007337278 0.008047337
## [2,] 0.008047337 0.007337278
## [1] "vector que acumula las probabilidades de todos los estratos anteriores"
## [,1]
## [1,] 0.6078431
## [2,] 0.3921569
## [3,] 0.6170213
## [4,] 0.3829787
## [5,] 0.5072464
## [6,] 0.4927536
## [7,] 0.4857143
## [8,] 0.5142857
## [9,] 0.5223881
## [10,] 0.4776119
## [11,] 0.4603175
## [12,] 0.5396825
## [13,] 0.3720930
## [14,] 0.6279070
## [15,] 0.5454545
## [16,] 0.4545455
## [17,] 0.5652174
## [18,] 0.4347826
## [19,] 0.4716981
## [20,] 0.5283019
## [21,] 0.3571429
## [22,] 0.6428571
## [23,] 0.4769231
## [24,] 0.5230769
## [1] "vector f= A*pi-gorro"
## [,1]
## [1,] 0.6078431
## [2,] 0.3829787
## [3,] 0.5072464
## [4,] 0.5142857
## [5,] 0.5223881
## [6,] 0.5396825
## [7,] 0.3720930
## [8,] 0.4545455
## [9,] 0.5652174
## [10,] 0.5283019
## [11,] 0.3571429
## [12,] 0.5230769
## [1] "var.cov.f gorro"
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.01191849 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [2,] 0.00000000 0.01312811 0.000000000 0.00000000 0.000000000 0.000000000
## [3,] 0.00000000 0.00000000 0.007351397 0.00000000 0.000000000 0.000000000
## [4,] 0.00000000 0.00000000 0.000000000 0.01387755 0.000000000 0.000000000
## [5,] 0.00000000 0.00000000 0.000000000 0.00000000 0.007796837 0.000000000
## [6,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.007306626
## [7,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [8,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [9,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [10,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [11,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [12,] 0.00000000 0.00000000 0.000000000 0.00000000 0.000000000 0.000000000
## [,7] [,8] [,9] [,10] [,11]
## [1,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [2,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [3,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [4,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [5,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [6,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [7,] 0.008653326 0.00000000 0.00000000 0.000000000 0.000000000
## [8,] 0.000000000 0.01652893 0.00000000 0.000000000 0.000000000
## [9,] 0.000000000 0.00000000 0.01228733 0.000000000 0.000000000
## [10,] 0.000000000 0.00000000 0.00000000 0.008899964 0.000000000
## [11,] 0.000000000 0.00000000 0.00000000 0.000000000 0.008503401
## [12,] 0.000000000 0.00000000 0.00000000 0.000000000 0.000000000
## [,12]
## [1,] 0.000000000
## [2,] 0.000000000
## [3,] 0.000000000
## [4,] 0.000000000
## [5,] 0.000000000
## [6,] 0.000000000
## [7,] 0.000000000
## [8,] 0.000000000
## [9,] 0.000000000
## [10,] 0.000000000
## [11,] 0.000000000
## [12,] 0.007337278## Warning: package 'MASS' was built under R version 3.5.3## [1] "Betas estimados"
## [,1]
## [1,] 0.53840578
## [2,] -0.11589833
## [3,] 0.03392537
## [4,] -0.03962164
## [5,] -0.12949471
## [6,] 0.12888264
## [7,] 0.07207795
## [8,] 0.15562912
## [1] "Matriz de varianzas y covarianzas de Beta"
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.007099797 -0.003601222 -0.005751746 -0.005377237 -0.007099797
## [2,] -0.003601222 0.006215860 0.001274429 0.000628011 0.003601222
## [3,] -0.005751746 0.001274429 0.009874224 0.005142153 0.005751746
## [4,] -0.005377237 0.000628011 0.005142153 0.009144246 0.005377237
## [5,] -0.007099797 0.003601222 0.005751746 0.005377237 0.015759530
## [6,] 0.003601222 -0.006215860 -0.001274429 -0.000628011 -0.006635049
## [7,] 0.005751746 -0.001274429 -0.009874224 -0.005142153 -0.012563073
## [8,] 0.005377237 -0.000628011 -0.005142153 -0.009144246 -0.012523231
## [,6] [,7] [,8]
## [1,] 0.0036012221 0.005751746 0.0053772372
## [2,] -0.0062158605 -0.001274429 -0.0006280110
## [3,] -0.0012744294 -0.009874224 -0.0051421529
## [4,] -0.0006280110 -0.005142153 -0.0091442455
## [5,] -0.0066350489 -0.012563073 -0.0125232312
## [6,] 0.0130694214 0.000132619 0.0002422301
## [7,] 0.0001326190 0.022803656 0.0125231898
## [8,] 0.0002422301 0.012523190 0.0201436864
## [1] "f.gorro*"
## [,1]
## [1,] 0.5384058
## [2,] 0.4089111
## [3,] 0.5723312
## [4,] 0.5149144
## [5,] 0.4987841
## [6,] 0.5249185
## [7,] 0.4225075
## [8,] 0.4218954
## [9,] 0.4564328
## [10,] 0.5278987
## [11,] 0.3828858
## [12,] 0.5379029
## [1] "********** Estimacion del modelo para la sensibilidad *****************"
## beta error z-valor p-valor
## [1,] 0.53840578 0.08426030 6.3897923 1.661112e-10
## [2,] -0.11589833 0.07884073 -1.4700311 1.415533e-01
## [3,] 0.03392537 0.09936913 0.3414076 7.327968e-01
## [4,] -0.03962164 0.09562555 -0.4143416 6.786239e-01
## [1] "*********************************************"
## [1] "********** Estimacion del modelo para la especificidad*****************"
## beta error z-valor p-valor
## [1,] -0.12949471 0.1255370 -1.0315266 0.3022940
## [2,] 0.12888264 0.1143216 1.1273694 0.2595863
## [3,] 0.07207795 0.1510088 0.4773096 0.6331417
## [4,] 0.15562912 0.1419285 1.0965322 0.2728459
## [1] "*********************************************"