Practica5
16040461
4/3/2020
#Cargar librerias
library(readr)
# install.packages("corrplot") # Nuevo
library(corrplot) # Para correlación## corrplot 0.84 loadedlibrary(caret) # Para dividir conjunto de datos## Loading required package: lattice## Loading required package: ggplot2# install.packages("MASS") # NUEVO
library(MASS)
#Datos
oxidoNit <- c(0.90, 0.91, 0.96, 0.89, 1.00, 1.10, 1.15, 1.03, 0.77, 1.07, 1.07, 0.94, 1.10, 1.10, 1.10, 0.91, 0.87, 0.78, 0.82, 0.95)
humedad <- c(72.4, 41.6, 34.3, 35.1, 10.7, 12.9, 8.3, 20.1, 72.2, 24.0, 23.2, 47.4, 31.5, 10.6, 11.2, 73.3, 75.4, 96.6, 107.4, 54.9)
temperatura <- c(76.3, 70.3, 77.1, 68.0, 79.0, 67.4, 66.8, 76.9, 77.7, 67.7, 76.8, 86.6, 76.9, 86.3, 86.0, 76.3, 77.9, 78.7, 86.8, 70.9)
presion <- c(29.18, 29.35, 29.24, 29.27, 29.78, 29.39, 29.69, 29.48, 29.09, 29.60, 29.38, 29.35, 29.63, 29.56, 29.48, 29.40, 29.28, 29.29, 29.03, 29.37)
datos <- data.frame(oxidoNit, humedad, temperatura, presion)
datos## oxidoNit humedad temperatura presion
## 1 0.90 72.4 76.3 29.18
## 2 0.91 41.6 70.3 29.35
## 3 0.96 34.3 77.1 29.24
## 4 0.89 35.1 68.0 29.27
## 5 1.00 10.7 79.0 29.78
## 6 1.10 12.9 67.4 29.39
## 7 1.15 8.3 66.8 29.69
## 8 1.03 20.1 76.9 29.48
## 9 0.77 72.2 77.7 29.09
## 10 1.07 24.0 67.7 29.60
## 11 1.07 23.2 76.8 29.38
## 12 0.94 47.4 86.6 29.35
## 13 1.10 31.5 76.9 29.63
## 14 1.10 10.6 86.3 29.56
## 15 1.10 11.2 86.0 29.48
## 16 0.91 73.3 76.3 29.40
## 17 0.87 75.4 77.9 29.28
## 18 0.78 96.6 78.7 29.29
## 19 0.82 107.4 86.8 29.03
## 20 0.95 54.9 70.9 29.37#Longitudes de observaciones de cada variable
length(oxidoNit)## [1] 20length(temperatura)## [1] 20length(humedad)## [1] 20length(presion)## [1] 20#Cargar los datos a una variable data.frame
datos <- data.frame(oxidoNit, humedad, temperatura, presion)
datos## oxidoNit humedad temperatura presion
## 1 0.90 72.4 76.3 29.18
## 2 0.91 41.6 70.3 29.35
## 3 0.96 34.3 77.1 29.24
## 4 0.89 35.1 68.0 29.27
## 5 1.00 10.7 79.0 29.78
## 6 1.10 12.9 67.4 29.39
## 7 1.15 8.3 66.8 29.69
## 8 1.03 20.1 76.9 29.48
## 9 0.77 72.2 77.7 29.09
## 10 1.07 24.0 67.7 29.60
## 11 1.07 23.2 76.8 29.38
## 12 0.94 47.4 86.6 29.35
## 13 1.10 31.5 76.9 29.63
## 14 1.10 10.6 86.3 29.56
## 15 1.10 11.2 86.0 29.48
## 16 0.91 73.3 76.3 29.40
## 17 0.87 75.4 77.9 29.28
## 18 0.78 96.6 78.7 29.29
## 19 0.82 107.4 86.8 29.03
## 20 0.95 54.9 70.9 29.37#Explorando los datos
summary(datos)## oxidoNit humedad temperatura presion
## Min. :0.7700 Min. : 8.30 Min. :66.80 Min. :29.03
## 1st Qu.:0.8975 1st Qu.: 18.30 1st Qu.:70.75 1st Qu.:29.28
## Median :0.9550 Median : 34.70 Median :76.90 Median :29.38
## Mean :0.9710 Mean : 43.16 Mean :76.52 Mean :29.39
## 3rd Qu.:1.0775 3rd Qu.: 72.25 3rd Qu.:78.78 3rd Qu.:29.50
## Max. :1.1500 Max. :107.40 Max. :86.80 Max. :29.78str(datos)## 'data.frame': 20 obs. of 4 variables:
## $ oxidoNit : num 0.9 0.91 0.96 0.89 1 1.1 1.15 1.03 0.77 1.07 ...
## $ humedad : num 72.4 41.6 34.3 35.1 10.7 12.9 8.3 20.1 72.2 24 ...
## $ temperatura: num 76.3 70.3 77.1 68 79 67.4 66.8 76.9 77.7 67.7 ...
## $ presion : num 29.2 29.4 29.2 29.3 29.8 ...#Graficas
#Visualizando los datos con plot()
plot(datos)
#Correlaciones
cor(x=datos, method = "pearson")## oxidoNit humedad temperatura presion
## oxidoNit 1.0000000 -0.8773635 -0.1807651 0.7740084
## humedad -0.8773635 1.0000000 0.2535071 -0.7554882
## temperatura -0.1807651 0.2535071 1.0000000 -0.1936361
## presion 0.7740084 -0.7554882 -0.1936361 1.0000000#Mostrando correlaciones con pairs()
pairs(x=datos, lower.panel = NULL)
corrplot(corr = cor(x=datos, method = "pearson"), method = "number")
modelo <- lm(oxidoNit ~ ., data = datos)
modelo##
## Call:
## lm(formula = oxidoNit ~ ., data = datos)
##
## Coefficients:
## (Intercept) humedad temperatura presion
## -3.5077781 -0.0026250 0.0007989 0.1541550#Probando el modelo con un nuevo dato
#Para una humedad de 50%,
#temperatura de 76 ◦F y
#presión barométrica de 29.30,
#¿Cuánto es la cantidad estimada de óxido nitroso
h = 50
t = 76
p = 29.30
nuevodato <- data.frame(humedad = h, temperatura = t, presion = p)
nuevodato## humedad temperatura presion
## 1 50 76 29.3y.predict <- predict(modelo, nuevodato)
y.predict## 1
## 0.9384342# Es lo mismo si usamos los coeficientes
y.predict.2 <- modelo$coefficients[1] + modelo$coefficients[2] * h + modelo$coefficients[3] * t + modelo$coefficients[4] * p
y.predict.2## (Intercept)
## 0.9384342#¿El modelo predice bien o mal?
#Ver ificar las las correlaciones
#El coefieicnete de determinanción
#Las significaciones
summary(modelo)##
## Call:
## lm(formula = oxidoNit ~ ., data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.11799 -0.02526 0.01345 0.04103 0.06523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.5077781 3.0048641 -1.167 0.26017
## humedad -0.0026250 0.0006549 -4.008 0.00101 **
## temperatura 0.0007989 0.0020451 0.391 0.70121
## presion 0.1541550 0.1013675 1.521 0.14784
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05617 on 16 degrees of freedom
## Multiple R-squared: 0.8005, Adjusted R-squared: 0.763
## F-statistic: 21.4 on 3 and 16 DF, p-value: 7.609e-06#Interpretación
#Se realizaron 20 obsevaciones de tipo numerico
#hay una importante correlacion entre la humedad con la variable independiente de tipo negativa y es inversamente proporcional -hay un 80% entre las variables con las dependientes
#Con respeto a la predicción, el intervalo de predicción es mayor que el de confianza.
#Se espera que en promedio el óxido nitroso tenga un valor de 0.9384342 ppm.R Markdown
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