Caso de Textiles
TextilesHolanda <- read.csv("C:/Users/MariaLourdes/Desktop/QUINTO SEMESTRE/ECONOMETRIA I/doblelog/TextilesHolanda.csv")
textil<-TextilesHolanda
- Aplicar logaritmos a las variables.
#aplicarle log a todas las variables
textil$consumolog<-log(textil$consumo)
textil$ingresolog<-log(textil$ingreso)
textil$preciolog<-log(textil$precio)
Regresion doble-log
- Calculo de la regresion doble-log.
#regresion logaritmica
reg1<- lm(formula=consumolog~ingresolog+preciolog, data=textil)
summary(reg1)
##
## Call:
## lm(formula = consumolog ~ ingresolog + preciolog, data = textil)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.055572 -0.026804 0.006832 0.019726 0.049666
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.16355 0.70480 4.489 0.00051 ***
## ingresolog 1.14316 0.15600 7.328 3.74e-06 ***
## preciolog -0.82884 0.03611 -22.952 1.65e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03118 on 14 degrees of freedom
## Multiple R-squared: 0.9744, Adjusted R-squared: 0.9707
## F-statistic: 266 on 2 and 14 DF, p-value: 7.284e-12
#R2=0.9711
#Regresion
#ln(consumo)=3.16355+1.1436*textil$ingresolog-0.82884*textil$preciolog
- ln(consumo) calculada y se le aplica antilogaritmos.
textil$lnconsumo1 <- 3.16355+1.1436*textil$ingresolog-0.82884*textil$preciolog
textil$econsumo1<-exp(textil$lnconsumo1)
#Se utiliza la formula convencional de R2
#R2 de la observada
num<- sum(textil$consumo-textil$econsumo1)^2
yprom<-textil$consumo/17
den<-sum(textil$consumo-yprom)^2
R2<-1-num/den
R2
## [1] 0.9999974
Caso mtcars
Regresion semi-log
- Aplicar logaritmos a las variables.
#aplicarle log a mpg
mtcars$mpglog<-log(mtcars$mpg)
- Calculo de la regresion semi-log.
#regresion logaritmica
reg2<- lm(mpglog~wt+hp+am, data=mtcars)
summary(reg2)
##
## Call:
## lm(formula = mpglog ~ wt + hp + am, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17137 -0.06955 -0.03865 0.07218 0.26567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7491397 0.1165798 32.159 < 2e-16 ***
## wt -0.1757558 0.0399224 -4.402 0.000142 ***
## hp -0.0016850 0.0004237 -3.976 0.000448 ***
## am 0.0516749 0.0607202 0.851 0.401970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1119 on 28 degrees of freedom
## Multiple R-squared: 0.8724, Adjusted R-squared: 0.8587
## F-statistic: 63.79 on 3 and 28 DF, p-value: 1.24e-12
#R2=0.8724
#Regresion
#ln(consumo)=3.7491397-0.1757558*wt-0.0016850*hp+0.0516749*am
- ln(consumo) calculada y se le aplica antilogaritmos.
mtcars$lnmpg1 <- 3.7491397-0.1757558*mtcars$wt-0.0016850*mtcars$hp+0.0516749*mtcars$am
mtcars$empg<-exp(mtcars$lnmpg1)
#Se utiliza la formula convencional de R2
#R2 de la observada
num<- sum(mtcars$mpg-mtcars$empg)^2
yprom<-mtcars$mpg/17
den<-sum(mtcars$mpg-yprom)^2
R2<-1-num/den
R2
## [1] 0.9999594