###########################Analisis de insecticidas#################
#######################dependidendo el numero de fractiles###########
library(readxl) #libreria para cargar datos de excel
library(profileR) #libreria para analsisi de paralelismo, planitud
## Loading required package: ggplot2
## Loading required package: RColorBrewer
## Loading required package: reshape
## Loading required package: lavaan
## This is lavaan 0.6-3
## lavaan is BETA software! Please report any bugs.
datos <- read_excel("C:/Users/Julian Gómez/Desktop/ggg.xlsx") #cargar datos
tratos <- c(rep("Cor50",5),rep("Cor100",5),rep("Cor500",5),rep("Krte300",5),rep("Agua",5)) #crear vector e tratamientos debe tener la misma longitud a la longitud de la base de datos por tratamiento
fpi <- function(BD,nfractiles){#Crear función donde BD es la base de datos y nfractiles es el numero de fractiles que se desea obtener
inter <- sum(datos[[1]])/(nfractiles+1) #se crea el tamaño de cada fratil
x <- matrix(nrow=length(BD), ncol=nfractiles) # se crea matriz vacia con tamaño longtud de datos por tratamiento y numero de fractiles
fractiles <- seq(inter,10,inter) #se crea el vector con los fractiles
for(j in 1:nfractiles){ #Se crea ciclo para que pase por todos los fractiles
for(i in 1:length(BD)){ #Ciclo para realizar la lectura de la base de datos original
x[i,j] = min(which(cumsum(BD[i])>=fractiles[j])) #Función que arroja el valor de tiempo de vida por fractil
}}
return(x)
}
F25 <-fpi(datos,3)
F20 <-fpi(datos,4)
F15 <-fpi(datos,6)
F10 <-fpi(datos,9)
F05 <-fpi(datos,19)
######################## Anlisis de paralelismo ###############
mod25 <- pbg(data=F25,group=tratos,original.names=T, profile.plot=T)
## Warning in FUN(data[x, , drop = FALSE], ...): the standard deviation is
## zero
mod20 <- pbg(data=F20,group=tratos,original.names=T, profile.plot=T)
## Warning in FUN(data[x, , drop = FALSE], ...): the standard deviation is
## zero
mod15 <- pbg(data=F15,group=tratos,original.names=T, profile.plot=T)
## Warning in FUN(data[x, , drop = FALSE], ...): the standard deviation is
## zero
mod10 <- pbg(data=F10,group=tratos,original.names=T, profile.plot=T)
## Warning in FUN(data[x, , drop = FALSE], ...): the standard deviation is
## zero
#mod05 <- pbg(data=F05,group=tratos,original.names=T, profile.plot=T)
print(mod25) ;summary(mod25)
##
## Data Summary:
## Agua Cor100 Cor50 Cor500 Krte300
## V1 6.4 6.8 7.6 5.4 1
## V2 10.0 9.0 9.4 7.0 1
## V3 14.2 14.4 12.6 10.6 1
## Call:
## pbg(data = F25, group = tratos, original.names = T, profile.plot = T)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.2071937 5.685310 8 38 9.059362e-05
## 2 Pillai 0.9901810 4.902765 8 40 2.890179e-04
## 3 Hotelling-Lawley 2.8737921 6.466032 8 36 3.318254e-05
## 4 Roy 2.4914389 12.457194 4 20 3.022446e-05
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 4 307.09 76.77 30.63 2.84e-08 ***
## Residuals 20 50.13 2.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 76.60389 2 19 8.047447e-10
print(mod20) ;summary(mod20)
##
## Data Summary:
## Agua Cor100 Cor50 Cor500 Krte300
## V1 5.2 6.6 6.2 4.4 1
## V2 9.0 7.8 8.4 6.4 1
## V3 11.6 10.0 10.2 7.6 1
## V4 14.2 14.4 12.6 10.6 1
## Call:
## pbg(data = F20, group = tratos, original.names = T, profile.plot = T)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.08002369 6.378422 12 47.91503 1.381826e-06
## 2 Pillai 1.45876299 4.732442 12 60.00000 2.216154e-05
## 3 Hotelling-Lawley 5.14590279 7.147087 12 50.00000 2.296960e-07
## 4 Roy 3.44650154 17.232508 4 20.00000 2.896511e-06
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 4 284.09 71.02 29.36 4.06e-08 ***
## Residuals 20 48.38 2.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 69.67962 3 18 4.202441e-10
print(mod15) ;summary(mod15)
##
## Data Summary:
## Agua Cor100 Cor50 Cor500 Krte300
## V1 5.2 6.6 6.2 4.4 1
## V2 6.4 6.8 7.6 5.4 1
## V3 10.0 9.0 9.4 7.0 1
## V4 11.6 10.0 10.2 7.6 1
## V5 14.2 14.4 12.6 10.6 1
## V6 16.0 16.2 14.2 11.2 1
## Call:
## pbg(data = F15, group = tratos, original.names = T, profile.plot = T)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.05777734 3.679425 20 54.01587 7.106274e-05
## 2 Pillai 1.69198851 2.785756 20 76.00000 7.080769e-04
## 3 Hotelling-Lawley 5.84635154 4.238605 20 58.00000 8.424797e-06
## 4 Roy 3.93419838 14.949954 5 19.00000 5.078570e-06
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 4 330.3 82.57 37.14 5.43e-09 ***
## Residuals 20 44.5 2.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 42.81665 5 16 1.050757e-08
print(mod10) ;summary(mod10)
##
## Data Summary:
## Agua Cor100 Cor50 Cor500 Krte300
## V1 3.6 5.0 4.0 3.6 1
## V2 5.2 6.6 6.2 4.4 1
## V3 6.4 6.8 7.6 5.4 1
## V4 9.0 7.8 8.4 6.4 1
## V5 10.0 9.0 9.4 7.0 1
## V6 11.6 10.0 10.2 7.6 1
## V7 12.8 11.6 11.2 8.8 1
## V8 14.2 14.4 12.6 10.6 1
## V9 16.0 16.2 14.2 11.2 1
## Call:
## pbg(data = F10, group = tratos, original.names = T, profile.plot = T)
##
## Hypothesis Tests:
## $`Ho: Profiles are parallel`
## Multivariate.Test Statistic Approx.F num.df den.df p.value
## 1 Wilks 0.01068062 3.752878 32 49.53681 1.523307e-05
## 2 Pillai 2.06406945 2.132380 32 64.00000 5.057840e-03
## 3 Hotelling-Lawley 19.41542244 6.977417 32 46.00000 2.382062e-09
## 4 Roy 16.58868467 33.177369 8 16.00000 1.542044e-08
##
## $`Ho: Profiles have equal levels`
## Df Sum Sq Mean Sq F value Pr(>F)
## group 4 280.3 70.08 31.22 2.42e-08 ***
## Residuals 20 44.9 2.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $`Ho: Profiles are flat`
## F df1 df2 p-value
## 1 84.90767 8 13 5.775056e-10
#print(mod05) ;summary(mod05)