library(ISLR)
## Warning: package 'ISLR' was built under R version 3.4.4
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.4
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
set.seed(08)
Auto%>%filter(origin==02)%>%
select(mpg,cylinders,displacement,horsepower,weight,acceleration)%>%
sample_n(size=50,replace=FALSE)->datos_parcial
Modelo de Regresion Multiple
library(stargazer)
## Warning: package 'stargazer' was built under R version 3.4.4
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
options(scipen = 9999)
modelo_lineal<-lm(formula = mpg~cylinders+displacement+horsepower+weight+acceleration,data = datos_parcial)
summary(modelo_lineal)
##
## Call:
## lm(formula = mpg ~ cylinders + displacement + horsepower + weight +
## acceleration, data = datos_parcial)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6230 -2.3799 -0.6386 1.7206 9.5078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.248241 9.055414 4.776 0.0000201 ***
## cylinders 1.837901 1.762651 1.043 0.3028
## displacement -0.018272 0.072907 -0.251 0.8033
## horsepower -0.177105 0.072270 -2.451 0.0183 *
## weight -0.003394 0.003621 -0.937 0.3537
## acceleration 0.085544 0.374206 0.229 0.8202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.776 on 44 degrees of freedom
## Multiple R-squared: 0.5208, Adjusted R-squared: 0.4663
## F-statistic: 9.562 on 5 and 44 DF, p-value: 0.000003155
stargazer(modelo_lineal,title="regresion multiple", type="text",digits=8)
##
## regresion multiple
## ===============================================
## Dependent variable:
## ---------------------------
## mpg
## -----------------------------------------------
## cylinders 1.83790100
## (1.76265100)
##
## displacement -0.01827223
## (0.07290731)
##
## horsepower -0.17710500**
## (0.07227045)
##
## weight -0.00339445
## (0.00362134)
##
## acceleration 0.08554387
## (0.37420570)
##
## Constant 43.24824000***
## (9.05541400)
##
## -----------------------------------------------
## Observations 50
## R2 0.52075350
## Adjusted R2 0.46629370
## Residual Std. Error 4.77618200 (df = 44)
## F Statistic 9.56215900*** (df = 5; 44)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
nuevo modelo de regresion lineal
library(stargazer)
options(scipen = 9999)
modelo_lineal2<-lm(formula = mpg~cylinders+displacement+horsepower+weight,data = datos_parcial)
summary(modelo_lineal)
##
## Call:
## lm(formula = mpg ~ cylinders + displacement + horsepower + weight +
## acceleration, data = datos_parcial)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6230 -2.3799 -0.6386 1.7206 9.5078
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 43.248241 9.055414 4.776 0.0000201 ***
## cylinders 1.837901 1.762651 1.043 0.3028
## displacement -0.018272 0.072907 -0.251 0.8033
## horsepower -0.177105 0.072270 -2.451 0.0183 *
## weight -0.003394 0.003621 -0.937 0.3537
## acceleration 0.085544 0.374206 0.229 0.8202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.776 on 44 degrees of freedom
## Multiple R-squared: 0.5208, Adjusted R-squared: 0.4663
## F-statistic: 9.562 on 5 and 44 DF, p-value: 0.000003155
stargazer(modelo_lineal,title="regresion multiple", type="text",digits=8)
##
## regresion multiple
## ===============================================
## Dependent variable:
## ---------------------------
## mpg
## -----------------------------------------------
## cylinders 1.83790100
## (1.76265100)
##
## displacement -0.01827223
## (0.07290731)
##
## horsepower -0.17710500**
## (0.07227045)
##
## weight -0.00339445
## (0.00362134)
##
## acceleration 0.08554387
## (0.37420570)
##
## Constant 43.24824000***
## (9.05541400)
##
## -----------------------------------------------
## Observations 50
## R2 0.52075350
## Adjusted R2 0.46629370
## Residual Std. Error 4.77618200 (df = 44)
## F Statistic 9.56215900*** (df = 5; 44)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
presentacion de resultados
stargazer(modelo_lineal,modelo_lineal2 , title = "tabla de regresion para data frame 1 2 y 3", type = "text", digits = 8)
##
## tabla de regresion para data frame 1 2 y 3
## ==========================================================================
## Dependent variable:
## ------------------------------------------------------
## mpg
## (1) (2)
## --------------------------------------------------------------------------
## cylinders 1.83790100 1.81696900
## (1.76265100) (1.74163600)
##
## displacement -0.01827223 -0.01992219
## (0.07290731) (0.07178113)
##
## horsepower -0.17710500** -0.19033030***
## (0.07227045) (0.04285440)
##
## weight -0.00339445 -0.00290977
## (0.00362134) (0.00290469)
##
## acceleration 0.08554387
## (0.37420570)
##
## Constant 43.24824000*** 44.85228000***
## (9.05541400) (5.66354900)
##
## --------------------------------------------------------------------------
## Observations 50 50
## R2 0.52075350 0.52018430
## Adjusted R2 0.46629370 0.47753400
## Residual Std. Error 4.77618200 (df = 44) 4.72561900 (df = 45)
## F Statistic 9.56215900*** (df = 5; 44) 12.19650000*** (df = 4; 45)
## ==========================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
intervalos de confianza para los modelos 1, 2
confint(object = modelo_lineal,level = .95)
## 2.5 % 97.5 %
## (Intercept) 24.99825243 61.498229214
## cylinders -1.71448876 5.390289980
## displacement -0.16520726 0.128662808
## horsepower -0.32275648 -0.031453420
## weight -0.01069278 0.003903872
## acceleration -0.66861820 0.839705943
confint(object = modelo_lineal2,level = .95)
## 2.5 % 97.5 %
## (Intercept) 33.445303556 56.259248988
## cylinders -1.690865612 5.324804332
## displacement -0.164496802 0.124652424
## horsepower -0.276643488 -0.104017087
## weight -0.008760115 0.002940578
vector de coeficientes estimados
options(scipen=999)
modelo_lineal$coefficients
## (Intercept) cylinders displacement horsepower weight
## 43.248240823 1.837900608 -0.018272227 -0.177104948 -0.003394454
## acceleration
## 0.085543869
modelo_lineal2$coefficients
## (Intercept) cylinders displacement horsepower weight
## 44.852276272 1.816969360 -0.019922189 -0.190330288 -0.002909769