Tarea N°2 Computación Estadística

Simular datos de de temperatura y humedad relativa

Se evalúan 1776 datos de variables climáticas en Acacias, Meta. Estas variables fueron la humedad relativa y la temperatura media. Se van a estandarizar, encontrar la correlación y graficar.

library(readxl)
Acacias <- read_excel("Acacias.xlsx", 
    col_types = c("numeric", "text", "text", 
        "text", "numeric", "numeric", "text", 
        "text", "numeric", "text"))
View(Acacias)

Tmed=Acacias$Tmed
Hum=Acacias$RHUM


# funcion para estandarizar
estand = function(x){
  media = mean(x)
  desv = sd(x)
  z = (x - media)/desv
  return(z)
}

# estandarizando la temperatura
Tmed_z = estand(Tmed)

# estandarizando la humedad relativa
Hum_z = estand(Hum)

par(mfrow = c(2, 2))

hist(Tmed, main = 'Temperatura no estandarizada', xlab = 'Temperatura media (°C)', col='red')
hist(Tmed_z, main = 'Temperatura estandarizada', xlab = 'Temperatura media', col='red')

hist(Hum, main = 'Humedad no estandarizada', xlab = 'Humedad Relativa', col='lightblue')
hist(Hum_z, main = 'Humedad estandarizada', xlab = 'Humedad Relativa', col='lightblue')

Media de la temperatura sin estandarizar:

(med_Tmed = mean(Tmed))

[1] 26.20355

Desviasión de la temperatura sin estandarizar:

(desv_Tmed = sd(Tmed))

[1] 1.174865

Media de la temperatura media estandarizada = 0

(med_Tmed_z = mean(Tmed_z))

[1] -8.148022e-16

Desviación de la temperatura estandarizada = 1

(desv_acTmedc_z = sd(Tmed_z))

[1] 1

Media de la humedad sin estandarizar

(med_Hum = mean(Hum)) 

[1] 87.97851

Desviación de la humedad sin estandarizar

(desv_Hum = sd(Hum))

[1] 7.024278

Media de la humedad estandarizada = 0

(med_Hum_z = mean(Hum_z)) # 

[1] 2.866366e-16

Desviación de la humedad estandarizada = 1

(desv_Hum_z = sd(Hum_z)) 

[1] 1

par(mfrow = c(1, 2))
plot(Tmed_z, Hum_z, pch = 19, cex = 0.8, main = 'Estandarizado', xlab = 'Temperatura media', ylab = 'Humedad')
points(x = mean(Tmed_z), y = mean(Hum_z), col = 'pink', pch = 19)

plot(Tmed, Hum, pch = 19, cex = 0.8, main = 'No Estandarizado', xlab = 'Temperatura media', ylab = 'Humedad')
points(x = mean(Tmed), y = mean(Hum), col = 'green', pch = 19)

Correlaciones

• Sin estandarizar la temperatura y humedad

cr = cor(Tmed, Hum, method = 'pearson')
cr

[1] -0.6034923

• Estandarizando la temperatura y humedad

cr_z = cor(Tmed_z, Hum_z, method = 'pearson')
cr_z

[1] -0.6034923

*Spearman sin estandarizar la temperatura y humedad

cr = cor(Tmed, Hum, method = 'spearman')
cr

[1] -0.6447906

Spearman estandarizando la temperatura y humedad

cr_z = cor(Tmed_z, Hum_z, method = 'spearman')
cr_z

[1] -0.6447906

Modoelos de crecimiento usando growthmodels

growth_gr <- generalisedRichard(0:10, 5, 10, 0.3, 0.5, 1, 3)
plot(growth_gr, main= 'Generalised Richard', col = 'blue', pch=15)

growth_gz <- gompertz(0:10, 10, 0.5, 0.3)
plot(growth_gz, main= 'Gompertz', col = 'darkgreen', pch=15)

growth_bl <- blumberg(0:10, 10, 2, 0.5)
plot(growth_bl, col = 'black', pch=15, main = "Blumberg")

growth_br <- brody(0:10, 10, 5, 0.3)
plot(growth_br, col = 'red', pch=15, main = 'Brody')

growth_w <- weibull(0:10, 10, 0.5, 0.3, 0.5)
plot (growth_w, main = 'Weibull', col = 'purple', pch = 15)

growth_Bt <- vonBertalanffy(0:10, 10, 0.5, 0.3, 0.5)
plot (growth_Bt, main = 'Von Bertalanffy', col = 'yellow', pch = 15)

growth_sd <- stannard(0:10, 1, .2, .1, .5)
plot (growth_sd, main='Stannard', col = 'darkblue', pch = 15)

growth_ss <- schnute(0:10, 10, 5, .5, .5)
plot(growth_ss, main= 'Schnute', col = 'blue', pch = 15)

growth_nE <- negativeExponential(0:10, 1, 0.3)
plot(growth_nE, main= 'Negative Exponential', col = 'red', pch = 15)

growth_mon <- monomolecular(0:10, 10, 0.5, 0.3)
plot(growth_mon, main= 'Monomolecular', col = 'gray', pch = 15)

growth_m <- mmf(0:10, 10, 0.5, 4, 1)
plot(growth_m, main= 'Morgan-Mercer-Flodin', col = 'violet', pch = 15)

growth_m <- mitcherlich(0:10, 10, 0.5, 0.3)
plot(growth_m, main= 'Mitcherlich', col = 'red', pch = 19, ylim = c(0,10), xlim = c(0,10))

growth_lgg <- loglogistic(0:10, 10, 0.5, 0.3)
plot(growth_lgg, main= 'Log-logistic', col = 'orange', pch = 19, ylim = c(0,10), xlim = c(0,10))

growth_lc <-logistic(0:10, 10, 0.5, 0.3)
plot(growth_lc, main= 'Logistic', col = 'darkgreen', pch = 15)

growth_cR <- chapmanRichards(0:10, 10, 0.5, 0.3, 0.5)
plot(growth_cR, col = 'blue', pch=, main = 'ChapmanRichards')

growth_lg <- generalisedLogistic(0:10, 5, 10, 0.3, 0.5, 3)
plot (growth_lg, main = 'Generalised Logistic', col = 'green', pch = 15)

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