Tarea_2
Sergio Rocha
5/9/2020
Datos de temperatura y humedad relativa
Graficos estandarizados y sin estandarizar
Datos_temp <- read.delim("~/Nacional/7.computacion/Tareas/Tarea2/Acacias_Meta_2011_2019_JOINT.txt")
Temp<-Datos_temp$Tmed
Hume<-Datos_temp$RHUM
estandarizar <- function(x){
media = mean(x)
desv = sd(x)
z = (x - media)/desv
return(z)
}
Temp_z <- estandarizar(Temp)
Hume_z <- estandarizar(Hume)
par(mfrow = c(1,2))
plot(Temp, Hume, main = 'Sin estandarizar')
points(x=mean(Temp), y=mean(Hume), col = 'red', pch=20)
plot(Temp_z, Hume_z, main = 'Estandarizado')
points(x=mean(Temp_z), y=mean(Hume_z), col = 'blue', pch=20)
linea <- lm(Hume~Temp)
linea2 <- lm(Hume_z~Temp_z)
par(mfrow = c(1,2))
plot(Temp, Hume, main = 'Sin estandarizar', xlim = c(0,100), ylim = c(0,100))
abline(linea)
points(x=mean(Temp), y=mean(Hume), col = 'red', pch=20)
plot(Temp_z, Hume_z, main = 'Estandarizado', xlim = c(-4,3), ylim = c(-4,3))
abline(linea2)
points(x=mean(Temp_z), y=mean(Hume_z), col = 'blue', pch=20)
linea <- lm(Hume~Temp)
linea2 <- lm(Hume_z~Temp_z)
par(mfrow = c(2,2))
plot(Temp, Hume, main = 'Sin estandarizar')
points(x=mean(Temp), y=mean(Hume), col = 'red', pch=20)
plot(Temp_z, Hume_z, main = 'Estandarizado')
points(x=mean(Temp_z), y=mean(Hume_z), col = 'blue', pch=20)
plot(Temp, Hume, main = 'Sin estandarizar', xlim = c(0,100), ylim = c(0,100))
abline(linea)
points(x=mean(Temp), y=mean(Hume), col = 'red', pch=20)
plot(Temp_z, Hume_z, main = 'Estandarizado', xlim = c(-4,3), ylim = c(-4,3))
abline(linea2)
points(x=mean(Temp_z), y=mean(Hume_z), col = 'blue', pch=20)
pearson <- cor(Temp, Hume, method = 'pearson')
spearman <-cor(Temp, Hume, method = 'spearman')
pearson## [1] -0.6034923spearman## [1] -0.6447906pearson_z <- cor(Temp_z, Hume_z, method = 'pearson')
spearman_z <-cor(Temp_z, Hume_z, method = 'spearman')
pearson_z## [1] -0.6034923spearman_z## [1] -0.6447906Modelos de crecimiento
Brody
library(growthmodels)
growth <- brody(0:100, 10, 5, 0.3)
par(bg = 'lightblue', fg = 'darkblue', lwd=2)
plot(growth, main = 'Brody', col.main= 'darkblue', type = 'l', col = 'red', lwd = 2)
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
### chapmanRichards
growth2 <- chapmanRichards(0:100, 10, 0.5, 0.3, 0.5)
par(bg = rgb(0.8, 0.6, 0.8), fg = rgb(0.8,0,0), lwd = 2)
plot(growth2, main = 'chapmanRichards', pch=15, col = 'purple')
grid(10,10, 5)
GeneralisedRichards
growth3 <- generalisedRichard(0:100, 5, 10, 0.3, 0.5, 1, 3)
plot(growth3, main = 'GeneralisedRichards', pch=18, col = 'grey')
grid(10, 10, col = rgb(0.1, 0.1, 0.5))
gompertz
growth4 <- gompertz(0:100, 10, 0.5, 0.3)
par(bg = rgb(0.2,0.4,0.4))
plot(growth4, main = 'gompertz', col.main= 'aquamarine', pch=16, col = 'aquamarine')
grid(10, 10, col=rgb(0.8,0.8,0.7))
logistic
growth5 <- logistic(0:100, 10, 0.5, 0.3)
plot(growth5)
Loglogistic
growth6 <- logistic(0:100, 10, 0.5, 0.3)
plot(growth6)
mononuclear
growth_m <- monomolecular(0:100, 10, 0.5, 0.3)
plot(growth_m)
Ejercicios
Ejercicio1
Datos
L1 = c(62,50,46,44,43,41,43,45,41,36,41,42,48,51,60,68,71,61,56,55,66,72,65,70,69,59,56,54,59,67,61)
L2 = c(39,40,37,37,37,40,42,47,43,45,44,45,55,46,41,44,48,49,45,40,38,41,41,39,42,42,47,46,43,43,41)1. Función de Tmed
Media <- function(x){
suma <- sum(x)
n <- length(x)
M <- suma/n
return(M)
}
Media(L1)## [1] 54.90323Media(L2)## [1] 42.806452. Función número de T Menores que la media
Menores <- function(x){
a <- x<mean(x)
b <- x[a]
c <- length(b)
return(c)
}
Menores(L1)## [1] 14Menores(L2)## [1] 163. Función de cuántos días la T de L1 fue mayor que la T de L2
x_mayores_y <- function(x,y){
may <- x>y
cual <- x[may]
cuan <- length(cual)
dias <- which(may)
print('Cuantos'); print(cuan); print('Cuales'); print(dias)
}
x_mayores_y(L1,L2)## [1] "Cuantos"
## [1] 25
## [1] "Cuales"
## [1] 1 2 3 4 5 6 7 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 314. Función que muestra el qué día ocurrio la máxima diferencia de T
mx_dif <- function(x,y){
dif <- x-y
mx <- max(dif)
dia <- dif == mx
qdia <- which(dia)
return(qdia)
}
mx_dif(L1, L2)## [1] 22 24Función cuántos días la diferencia de T fue de al menos 3°F
mn_3 <- function(x, y){
difer <- x-y
almenos_3 <- difer >= 3
cual <- difer[almenos_3]
num <- length(cual)
return(num)
}
mn_3(L1, L2)## [1] 23Conversión de °F a °C
Fh_C <- function(x){
C <- (x-32)*5/9
return(C)
}
L1_2 <- Fh_C(L1)
L2_2 <- Fh_C(L2)
L1_2## [1] 16.666667 10.000000 7.777778 6.666667 6.111111 5.000000 6.111111
## [8] 7.222222 5.000000 2.222222 5.000000 5.555556 8.888889 10.555556
## [15] 15.555556 20.000000 21.666667 16.111111 13.333333 12.777778 18.888889
## [22] 22.222222 18.333333 21.111111 20.555556 15.000000 13.333333 12.222222
## [29] 15.000000 19.444444 16.111111L2_2## [1] 3.888889 4.444444 2.777778 2.777778 2.777778 4.444444 5.555556
## [8] 8.333333 6.111111 7.222222 6.666667 7.222222 12.777778 7.777778
## [15] 5.000000 6.666667 8.888889 9.444444 7.222222 4.444444 3.333333
## [22] 5.000000 5.000000 3.888889 5.555556 5.555556 8.333333 7.777778
## [29] 6.111111 6.111111 5.0000007 Graficas de L1 y L2
library(lattice)
dias <- c(1:31)
xyplot(L1_2~dias, type = 'b', pch=6, main = 'L1')
xyplot(L2_2~dias, type = 'b')
punto 2
set.seed(1514)
Datos_1 <- rnorm(50, 5.5, 0.5)
options(digits = 2)
cv.n <- function(x){
md <- mean(x)
dev <- sd(x)
n <- dev/md
md_min <- mean(x[-min(x)])
dev_min <- sd(x[-min(x)])
n_min <- dev_min/md_min
md_max <- mean(x[-max(x)])
dev_max <- sd(x[-max(x)])
n_max <- dev_max/md_max
md_min_max <- mean(x[-c(min(x), max(x))])
dev_min_max <- sd(x[-c(min(x), max(x))])
n_min_max <- dev_min_max/md_min_max
qn_1 <- quantile(x, 0.025)
qn_2 <- quantile(x, 0.975)
md_truncado5<-mean(x[x>qn_1 & x<qn_2])
dev_truncado5<-sd(x[x>qn_1 & x<qn_2])
n_truncado5<- dev_truncado5/md_truncado5
Tabla <- data.frame(
"Para" = c('n', 'n-min', 'n-max', 'n-min-max', 'n-truncado5'),
"c.vn" = c(n, n_min, n_max, n_min_max, n_truncado5),
"Media simulada" = c(md, md_min, md_max, md_min_max, md_truncado5),
"Desviación simulada" = c(dev, dev_min, dev_max, dev_min_max, dev_truncado5))
print(Tabla)
options(digits = 2)
}
cv.n(Datos_1)## Para c.vn Media.simulada Desviación.simulada
## 1 n 0.096 5.5 0.53
## 2 n-min 0.097 5.5 0.53
## 3 n-max 0.097 5.5 0.54
## 4 n-min-max 0.098 5.5 0.54
## 5 n-truncado5 0.083 5.5 0.46