Plots en regresiones
Oscar Ramirez Alan
28/10/2016
Paquetes requeridos “visred” y “ggplot2”
library(visreg)
lm1 <- lm(Ozone ~ Solar.R + Wind,data=airquality)
visreg(lm1, partial=T)
#Funcion Partial permite visualizar los residuos=TRUE
library(visreg)
lm2 <- lm(Ozone ~ Solar.R, data=airquality)
visreg(lm2, partial=F)
library(ggplot2)
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point()## Warning: Removed 37 rows containing missing values (geom_point).
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point()+geom_smooth(se = FALSE)## Warning: Removed 37 rows containing non-finite values (stat_smooth).## Warning: Removed 37 rows containing missing values (geom_point).
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point()+geom_smooth(method = lm,se = FALSE)## Warning: Removed 37 rows containing non-finite values (stat_smooth).## Warning: Removed 37 rows containing missing values (geom_point).
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point(aes(colour = factor(Month)))+geom_smooth(se = FALSE)## Warning: Removed 37 rows containing non-finite values (stat_smooth).## Warning: Removed 37 rows containing missing values (geom_point).
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point(colour = "red3", size = 2)+geom_smooth(se = FALSE)## Warning: Removed 37 rows containing non-finite values (stat_smooth).## Warning: Removed 37 rows containing missing values (geom_point).
ggplot(airquality, aes(x=Temp, y=Ozone))+ geom_point(shape = 21, colour = "black", fill = "yellow", size = 2, stroke = 1)+ geom_smooth(se = FALSE)## Warning: Removed 37 rows containing non-finite values (stat_smooth).## Warning: Removed 37 rows containing missing values (geom_point).
Recta con dos pendientes
data(mtcars)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1ggplot(mtcars) +
geom_jitter(aes(x=disp,y=mpg), colour="blue") + geom_smooth(aes(disp,mpg), method=lm, se=FALSE) +
geom_jitter(aes(x=hp,y=mpg), colour="red") + geom_smooth(aes(hp,mpg), method=lm, se=FALSE) +
labs(x = "Displacement", y = "Unidades (N)")
En algunas ocasiones podriamos tener organizados nuestros datos en forma concatenados por columnas.
plantas <- data.frame(y = runif(100),
x = runif(100),
ID = sample(c("GrupoA","GrupoB"), size = 100, replace = TRUE))
plantas = within(plantas, {
y[ID == "GrupoA"] = y[ID == "GrupoA"] + 0.5
y[ID == "GruposB"] = y[ID == "GrupoB"] - 0.5
})
head(plantas)## y x ID
## 1 0.8419401 0.47235632 GrupoA
## 2 0.5084067 0.44529016 GrupoB
## 3 0.7746432 0.43098675 GrupoB
## 4 0.6685016 0.69141651 GrupoB
## 5 0.5612129 0.67445061 GrupoB
## 6 0.9377578 0.01555604 GrupoAggplot(plantas, aes(x = x, y = y, color = ID)) +
geom_point() + geom_smooth(method = "lm")
#Multiples regresiones
#Para ello hay que ajustar los datos con "reshape"
library(reshape2)
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1mtcars2 = melt(mtcars, id.vars='mpg') #Utilizamos la variable dependiente
head(mtcars2)## mpg variable value
## 1 21.0 cyl 6
## 2 21.0 cyl 6
## 3 22.8 cyl 4
## 4 21.4 cyl 6
## 5 18.7 cyl 8
## 6 18.1 cyl 6ggplot(mtcars2) +
geom_jitter(aes(value,mpg, colour=variable),) + geom_smooth(aes(value,mpg, colour=variable), method=lm, se=FALSE) +
facet_wrap(~variable, scales="free_x") +
labs(x = "Displacement", y = "Unidades (N)")