Central Limit Theorem

Seccion2.R

setwd("/Users/kepler/Dropbox/Classroom/UOC - Master Bioinformatica/Software para el Análisis de Datos/PACS/PAC2")


#A. Generación de valores aleatorios
valores <- runif(100000, min = 1, max = 5)

#B. Agrupación en 20 columnas
matriz <- matrix(valores, ncol = 20)

# histogramas 
jpeg("histogramas.jpg")
par(mfrow = c(3,2))
hist(matriz[,1], main =  "Histograma columna 1", xlab = "x", ylab = "N")
hist(matriz[,2], main =  "Histograma columna 2", xlab = "x", ylab = "N")
hist(matriz[,3], main =  "Histograma columna 3", xlab = "x", ylab = "N")
hist(matriz[,4], main =  "Histograma columna 4", xlab = "x", ylab = "N")
hist(matriz[,5], main =  "Histograma columna 5", xlab = "x", ylab = "N")
dev.off()

#C.Suma de valores por fila y generación de histograma
vectorSuma <- NULL
for (x in 1:nrow(matriz)) {
  vectorSuma[x] <- sum(matriz[x,])
}


jpeg("histograma_suma.jpg")
hist(vectorSuma, main =  "Histograma suma", xlab = "suma", ylab = "N")

dev.off()

jpeg("qq.jpg")
qqnorm(vectorSuma)
qqline(vectorSuma)
dev.off()

# Ajuste a curva normal
hist(vectorSuma, main =  "Ajuste a curva normal", xlab = "suma", ylab = "N", probability = T)
curve(dnorm(x, 60, 5.164), from = 40, col="red", to = 80,   add=T)