library(readxl)
Datos_chipaque <- read.delim("~/MANEJOS/MANEJO INTEGRADO DE ENFERMEDADES/PROYECTO INDIVIDUAL/Datos chipaque.csv", sep = ";")
Datos_chipaque
## muestra muestreo longitud latitud inc sev
## 1 1 1 -73.99945 4.446047 40.00 1.00
## 2 2 1 -73.99921 4.446315 0.00 0.00
## 3 3 1 -73.99938 4.446385 25.00 1.00
## 4 4 1 -73.99947 4.446292 37.50 1.00
## 5 5 1 -73.99961 4.446222 87.50 1.00
## 6 6 1 -73.99970 4.446238 25.00 1.00
## 7 7 1 -73.99972 4.446445 50.00 1.00
## 8 8 1 -73.99963 4.446558 0.00 0.00
## 9 9 1 -73.99998 4.447025 41.60 1.00
## 10 10 1 -74.00010 4.446742 0.00 0.00
## 11 1 2 -73.99934 4.446038 25.00 1.00
## 12 2 2 -73.99925 4.446345 25.00 1.00
## 13 3 2 -73.99940 4.446562 25.00 1.00
## 14 4 2 -73.99952 4.446370 37.50 1.00
## 15 5 2 -73.99956 4.446158 62.50 5.00
## 16 6 2 -73.99989 4.446360 12.50 1.00
## 17 7 2 -73.99977 4.446583 50.00 5.00
## 18 8 2 -73.99967 4.446725 50.00 5.00
## 19 9 2 -74.00015 4.447033 87.50 10.00
## 20 10 2 -74.00023 4.446912 100.00 5.00
## 21 1 3 -73.99941 4.446080 85.71 24.16
## 22 2 3 -73.99926 4.446353 71.42 10.00
## 23 3 3 -73.99949 4.446540 100.00 23.00
## 24 4 3 -73.99964 4.446443 85.71 11.00
## 25 5 3 -73.99971 4.446340 85.71 33.33
## 26 6 3 -73.99991 4.446542 100.00 8.71
## 27 7 3 -73.99982 4.446665 85.71 8.57
## 28 8 3 -73.99970 4.446732 100.00 24.28
## 29 9 3 -74.00004 4.446867 100.00 12.14
## 30 10 3 -74.00006 4.446702 85.71 6.83
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
dt1 <- filter(Datos_chipaque, muestreo=="1")
dt2 <- filter(Datos_chipaque, muestreo=="2")
dt3 <- filter(Datos_chipaque, muestreo=="3")
dt <- rbind(dt1$inc,dt2$inc, dt3$inc)
dt
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 40.00 0.00 25 37.50 87.50 25.0 50.00 0 41.6 0.00
## [2,] 25.00 25.00 25 37.50 62.50 12.5 50.00 50 87.5 100.00
## [3,] 85.71 71.42 100 85.71 85.71 100.0 85.71 100 100.0 85.71
dt <- colMeans(dt)
dt
## [1] 50.23667 32.14000 50.00000 53.57000 78.57000 45.83333 61.90333 50.00000
## [9] 76.36667 61.90333
# Función para calcular el coeficiente de variación acumulado
coef_variacion_acumulado <- function(dt) {
# Inicializar un vector para almacenar los coeficientes de variación
cv_acumulado <- numeric(length(dt))
# Calcular el coeficiente de variación acumulado
for (i in 1:length(dt)) {
media_acumulada <- mean(dt[1:i])
sd_acumulada <- sd(dt[1:i])
cv_acumulado[i] <- (sd_acumulada / media_acumulada) * 100 # Multiplicamos por 100 para obtener porcentaje
}
return(cv_acumulado)
}
resultado <- coef_variacion_acumulado(dt)
print(resultado)
## [1] NA 31.06772 23.52485 20.87124 31.42973 29.28849 26.99301 25.26871
## [9] 26.61676 25.07317
library(ggplot2)
dt <- c(1:10)
dt
## [1] 1 2 3 4 5 6 7 8 9 10
df <- data.frame(dt,resultado)
df
## dt resultado
## 1 1 NA
## 2 2 31.06772
## 3 3 23.52485
## 4 4 20.87124
## 5 5 31.42973
## 6 6 29.28849
## 7 7 26.99301
## 8 8 25.26871
## 9 9 26.61676
## 10 10 25.07317
ggplot(df, aes(x=dt,y=resultado))+
geom_point()+
stat_smooth(method = "lm", formula = y ~ poly(x, 2, raw = TRUE))+
geom_vline(xintercept = 7,linetype = 2,color = 2)+
labs(x="Número de muestras (n)", y= "Coeficiente de variación (%)",
title= "Optimización de muestreo en función de la incidencia")+
theme_minimal()
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
st <- rbind(dt1$sev,dt2$sev, dt3$sev)
st
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1.00 0 1 1 1.00 1.00 1.00 0.00 1.00 0.00
## [2,] 1.00 1 1 1 5.00 1.00 5.00 5.00 10.00 5.00
## [3,] 24.16 10 23 11 33.33 8.71 8.57 24.28 12.14 6.83
st <- colMeans(st)
st
## [1] 8.720000 3.666667 8.333333 4.333333 13.110000 3.570000 4.856667
## [8] 9.760000 7.713333 3.943333
# Función para calcular el coeficiente de variación acumulado
coef_variacion_acumulado <- function(st) {
# Inicializar un vector para almacenar los coeficientes de variación
cv_acumulado <- numeric(length(st))
# Calcular el coeficiente de variación acumulado
for (i in 1:length(st)) {
media_acumulada <- mean(st[1:i])
sd_acumulada <- sd(st[1:i])
cv_acumulado[i] <- (sd_acumulada / media_acumulada) * 100 # Multiplicamos por 100 para obtener porcentaje
}
return(cv_acumulado)
}
resultado <- coef_variacion_acumulado(st)
print(resultado)
## [1] NA 57.69504 40.72261 42.02783 50.01354 54.57336 53.40972 49.25338
## [9] 45.69847 47.45119
library(ggplot2)
st <- c(1:10)
st
## [1] 1 2 3 4 5 6 7 8 9 10
df <- data.frame(st,resultado)
df
## st resultado
## 1 1 NA
## 2 2 57.69504
## 3 3 40.72261
## 4 4 42.02783
## 5 5 50.01354
## 6 6 54.57336
## 7 7 53.40972
## 8 8 49.25338
## 9 9 45.69847
## 10 10 47.45119
ggplot(df, aes(x=st,y=resultado))+
geom_point()+
stat_smooth(method = "lm", formula = y ~ poly(x, 2, raw = TRUE))+
geom_vline(xintercept = 8,linetype = 2,color = 2)+
labs(x="Número de muestras (n)", y= "Coeficiente de variación (%)",
title= "Optimización de muestreo en función de la severidad")+
theme_minimal()
## Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
