Lab 4 Aula 7
2022-10-03
Pacotes
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(olsrr)## Warning: package 'olsrr' was built under R version 4.1.3##
## Attaching package: 'olsrr'## The following object is masked from 'package:datasets':
##
## riverslibrary(lmtest)## Warning: package 'lmtest' was built under R version 4.1.3## Carregando pacotes exigidos: zoo## Warning: package 'zoo' was built under R version 4.1.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericModelo linear multivariado
mtcars %>%
lm(mpg ~ cyl + disp + hp + wt, data=.) %>%
summary()##
## Call:
## lm(formula = mpg ~ cyl + disp + hp + wt, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0562 -1.4636 -0.4281 1.2854 5.8269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.82854 2.75747 14.807 1.76e-14 ***
## cyl -1.29332 0.65588 -1.972 0.058947 .
## disp 0.01160 0.01173 0.989 0.331386
## hp -0.02054 0.01215 -1.691 0.102379
## wt -3.85390 1.01547 -3.795 0.000759 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.513 on 27 degrees of freedom
## Multiple R-squared: 0.8486, Adjusted R-squared: 0.8262
## F-statistic: 37.84 on 4 and 27 DF, p-value: 1.061e-10Gráfico de dispersão
ggplot(mtcars, aes(mpg, wt))+
geom_point()+
stat_smooth(method="lm")## `geom_smooth()` using formula 'y ~ x'
Breusch-Pagan teste para heterocedasticidade:
reg <- mtcars %>%
lm(mpg ~ cyl + disp + hp + wt, data=.)
bptest(reg)##
## studentized Breusch-Pagan test
##
## data: reg
## BP = 2.7623, df = 4, p-value = 0.5984VIF teste para multicolinearidade
ols_vif_tol(reg)## Variables Tolerance VIF
## 1 cyl 0.14841846 6.737707
## 2 disp 0.09640147 10.373286
## 3 hp 0.29360104 3.405983
## 4 wt 0.20626997 4.848016Transformação de variáveis
Para transformar uma variável ou criar uma nova com base em valores
de colunas existentes, podemos usar a função mutate do
pacote dplyr que faz parte do tidyverse.
No exemplo abaixo, estamos gerando uma nova coluna em nossa tabela com a divisão de dois campos já existentes nesta tabela:
mtcars <- mtcars %>%
mutate(nova_coluna = hp / cyl)
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## nova_coluna
## Mazda RX4 18.33333
## Mazda RX4 Wag 18.33333
## Datsun 710 23.25000
## Hornet 4 Drive 18.33333
## Hornet Sportabout 21.87500
## Valiant 17.50000
## Duster 360 30.62500
## Merc 240D 15.50000
## Merc 230 23.75000
## Merc 280 20.50000
## Merc 280C 20.50000
## Merc 450SE 22.50000
## Merc 450SL 22.50000
## Merc 450SLC 22.50000
## Cadillac Fleetwood 25.62500
## Lincoln Continental 26.87500
## Chrysler Imperial 28.75000
## Fiat 128 16.50000
## Honda Civic 13.00000
## Toyota Corolla 16.25000
## Toyota Corona 24.25000
## Dodge Challenger 18.75000
## AMC Javelin 18.75000
## Camaro Z28 30.62500
## Pontiac Firebird 21.87500
## Fiat X1-9 16.50000
## Porsche 914-2 22.75000
## Lotus Europa 28.25000
## Ford Pantera L 33.00000
## Ferrari Dino 29.16667
## Maserati Bora 41.87500
## Volvo 142E 27.25000Usando a mesma função, podemos calcular o logaritmo natural de cada observação de uma variável e armazená-lo em uma nova coluna:
mtcars <- mtcars %>%
mutate(log_disp = log(disp))
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## nova_coluna log_disp
## Mazda RX4 18.33333 5.075174
## Mazda RX4 Wag 18.33333 5.075174
## Datsun 710 23.25000 4.682131
## Hornet 4 Drive 18.33333 5.552960
## Hornet Sportabout 21.87500 5.886104
## Valiant 17.50000 5.416100
## Duster 360 30.62500 5.886104
## Merc 240D 15.50000 4.988390
## Merc 230 23.75000 4.947340
## Merc 280 20.50000 5.121580
## Merc 280C 20.50000 5.121580
## Merc 450SE 22.50000 5.619676
## Merc 450SL 22.50000 5.619676
## Merc 450SLC 22.50000 5.619676
## Cadillac Fleetwood 25.62500 6.156979
## Lincoln Continental 26.87500 6.131226
## Chrysler Imperial 28.75000 6.086775
## Fiat 128 16.50000 4.365643
## Honda Civic 13.00000 4.326778
## Toyota Corolla 16.25000 4.264087
## Toyota Corona 24.25000 4.788325
## Dodge Challenger 18.75000 5.762051
## AMC Javelin 18.75000 5.717028
## Camaro Z28 30.62500 5.857933
## Pontiac Firebird 21.87500 5.991465
## Fiat X1-9 16.50000 4.369448
## Porsche 914-2 22.75000 4.789989
## Lotus Europa 28.25000 4.554929
## Ford Pantera L 33.00000 5.860786
## Ferrari Dino 29.16667 4.976734
## Maserati Bora 41.87500 5.707110
## Volvo 142E 27.25000 4.795791