Tarea Agosto 24
Emanuel Santos
27/8/2021
Modelo EFECTOS FIJOS
library(readxl)## Warning: package 'readxl' was built under R version 4.0.5df=Datos_Tarea_Abril_24 <- read_excel("D:/Users/Usuario/Desktop/Trabajos Diseno/Datos Tarea Abril 24.xlsx");df## # A tibble: 600 x 4
## AMBIENTE GENOTIPO REPETICIONES RENDIMIENTO
## <chr> <chr> <chr> <dbl>
## 1 A-1 G-1 R-I 382.
## 2 A-1 G-1 R-II 239.
## 3 A-1 G-2 R-I 334.
## 4 A-1 G-2 R-II 376.
## 5 A-1 G-3 R-I 227.
## 6 A-1 G-3 R-II 112.
## 7 A-1 G-4 R-I 284.
## 8 A-1 G-4 R-II 330.
## 9 A-1 G-5 R-I 335.
## 10 A-1 G-5 R-II 251.
## # ... with 590 more rowsAmb= df$AMBIENTE; as.factor(Amb)## [1] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1
## [16] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1
## [31] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1
## [46] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1
## [61] A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2
## [76] A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2
## [91] A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2
## [106] A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2 A-2
## [121] A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3
## [136] A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3
## [151] A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3
## [166] A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3
## [181] A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4
## [196] A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4
## [211] A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4
## [226] A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4
## [241] A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5
## [256] A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5
## [271] A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5
## [286] A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5 A-5
## [301] A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6
## [316] A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6
## [331] A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6
## [346] A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6
## [361] A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7
## [376] A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7
## [391] A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7
## [406] A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7
## [421] A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8
## [436] A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8
## [451] A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8
## [466] A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8 A-8
## [481] A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9
## [496] A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9
## [511] A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9
## [526] A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9
## [541] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## [556] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## [571] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## [586] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## Levels: A-1 A-10 A-2 A-3 A-4 A-5 A-6 A-7 A-8 A-9Gen= df$GENOTIPO; as.factor(Gen)## [1] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [16] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [31] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [46] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [61] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [76] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [91] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [106] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [121] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [136] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [151] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [166] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [181] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [196] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [211] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [226] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [241] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [256] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [271] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [286] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [301] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [316] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [331] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [346] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [361] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [376] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [391] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [406] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [421] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [436] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [451] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [466] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [481] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [496] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [511] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [526] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## [541] G-1 G-1 G-2 G-2 G-3 G-3 G-4 G-4 G-5 G-5 G-6 G-6 G-7 G-7 G-8
## [556] G-8 G-9 G-9 G-10 G-10 G-11 G-11 G-12 G-12 G-13 G-13 G-14 G-14 G-15 G-15
## [571] G-16 G-16 G-17 G-17 G-18 G-18 G-19 G-19 G-20 G-20 G-21 G-21 G-22 G-22 G-23
## [586] G-23 G-24 G-24 G-25 G-25 G-26 G-26 G-27 G-27 G-28 G-28 G-29 G-29 G-30 G-30
## 30 Levels: G-1 G-10 G-11 G-12 G-13 G-14 G-15 G-16 G-17 G-18 G-19 G-2 ... G-9Rep=df$REPETICIONES; as.factor(Rep)## [1] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [16] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [31] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [46] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [61] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [76] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [91] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [106] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [121] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [136] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [151] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [166] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [181] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [196] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [211] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [226] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [241] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [256] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [271] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [286] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [301] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [316] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [331] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [346] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [361] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [376] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [391] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [406] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [421] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [436] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [451] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [466] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [481] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [496] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [511] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [526] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [541] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [556] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [571] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [586] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## Levels: R-I R-IIRend= df$RENDIMIENTOlibrary(collapsibleTree)
collapsibleTreeSummary(df, hierarchy=c ("AMBIENTE", "GENOTIPO", "REPETICIONES", "RENDIMIENTO"))boxplot( Rend~Gen,las=2, df)
boxplot( Rend~Amb,las=2, df)
medias=tapply(Rend, list(Gen, Amb), mean)
desviacion=tapply(Rend, list(Gen, Amb), sd)
cv=100*desviacion/medias#Analisis de Varianza
mod_EF=aov(Rend~Gen*Amb,df)
summary(mod_EF)## Df Sum Sq Mean Sq F value Pr(>F)
## Gen 29 301157 10385 1.479 0.0579 .
## Amb 9 116404 12934 1.842 0.0605 .
## Gen:Amb 261 2064697 7911 1.126 0.1593
## Residuals 300 2106909 7023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Revisión de Supuestos
#Comparacion de las medias
library(agricolae)## Warning: package 'agricolae' was built under R version 4.0.5HSD_test = with(df, HSD.test (Rend, Gen, DFerror = 300,MSerror = 7023));HSD_test## $statistics
## MSerror Df Mean CV MSD
## 7023 300 255.8198 32.75874 100.3317
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Gen 30 5.354168 0.05
##
## $means
## Rend std r Min Max Q25 Q50 Q75
## G-1 252.3530 88.36506 20 109.7232 398.7274 186.2293 241.1374 334.5241
## G-10 230.4625 70.49721 20 115.9032 377.3492 179.7311 224.3873 263.1520
## G-11 243.8173 77.34631 20 129.2062 366.9118 180.3537 248.7594 312.2486
## G-12 247.7825 77.56010 20 123.6854 395.7793 175.6409 241.9431 303.9079
## G-13 258.9541 76.26803 20 109.2837 370.5741 222.7210 278.7118 322.9629
## G-14 258.6905 59.14638 20 119.1900 350.5966 226.4519 267.6428 298.7968
## G-15 262.0002 105.79515 20 104.2024 393.7101 153.6218 280.3827 359.4638
## G-16 263.9343 86.35256 20 100.5310 399.0844 174.4736 284.5164 327.4697
## G-17 264.8086 85.75643 20 105.4750 388.1619 215.3783 271.4789 330.5345
## G-18 236.0538 87.47470 20 117.9998 368.4408 150.5112 235.7219 297.3228
## G-19 238.1230 99.56912 20 106.4547 390.1944 149.3713 231.0251 330.0012
## G-2 249.2863 103.60580 20 110.7028 398.8922 142.6260 266.2465 331.5027
## G-20 267.9293 94.70728 20 104.7426 391.5036 202.0180 295.6496 337.8842
## G-21 248.5919 85.31584 20 102.7192 390.3958 197.9209 238.5418 295.1987
## G-22 260.9130 88.93772 20 103.1953 374.4560 206.0190 258.8260 336.4467
## G-23 276.9201 77.75550 20 111.9480 383.0988 220.2742 292.4680 321.7177
## G-24 238.1463 103.96577 20 108.4323 383.5383 122.4631 245.6145 328.0831
## G-25 262.7670 87.09145 20 101.2543 396.2828 198.0743 264.4887 334.9818
## G-26 283.7204 81.14577 20 132.4747 396.3836 231.3066 313.0360 346.6872
## G-27 217.0423 91.08048 20 104.7609 383.2728 154.8853 178.8385 296.9703
## G-28 233.2366 73.88042 20 128.8491 375.9392 170.8686 239.9838 278.4074
## G-29 242.1940 95.61551 20 122.0923 386.0836 160.9325 203.4669 340.3493
## G-3 243.9679 98.50812 20 110.2084 390.7987 181.6652 232.0231 342.3452
## G-30 276.4925 92.72791 20 109.2929 398.6084 202.8352 305.3133 334.5676
## G-4 295.0925 79.50112 20 135.8074 397.2167 237.7453 299.3301 363.4602
## G-5 207.6771 73.00571 20 116.0497 368.4774 153.7751 191.5143 252.4079
## G-6 298.1664 104.47512 20 101.2818 399.2676 193.4118 355.6139 373.6938
## G-7 305.1856 70.94373 20 140.8887 396.7681 272.2091 310.5365 366.6349
## G-8 238.1532 79.46704 20 103.0213 345.2681 161.5871 249.0387 311.5825
## G-9 272.1308 83.63831 20 121.9001 393.6552 202.6406 290.1105 330.1866
##
## $comparison
## NULL
##
## $groups
## Rend groups
## G-7 305.1856 a
## G-6 298.1664 a
## G-4 295.0925 a
## G-26 283.7204 a
## G-23 276.9201 a
## G-30 276.4925 a
## G-9 272.1308 a
## G-20 267.9293 a
## G-17 264.8086 a
## G-16 263.9343 a
## G-25 262.7670 a
## G-15 262.0002 a
## G-22 260.9130 a
## G-13 258.9541 a
## G-14 258.6905 a
## G-1 252.3530 a
## G-2 249.2863 a
## G-21 248.5919 a
## G-12 247.7825 a
## G-3 243.9679 a
## G-11 243.8173 a
## G-29 242.1940 a
## G-8 238.1532 a
## G-24 238.1463 a
## G-19 238.1230 a
## G-18 236.0538 a
## G-28 233.2366 a
## G-10 230.4625 a
## G-27 217.0423 a
## G-5 207.6771 a
##
## attr(,"class")
## [1] "group"#Normalidad de los Resultados
res=mod_EF$residuals
shapiro.test(res)##
## Shapiro-Wilk normality test
##
## data: res
## W = 0.99429, p-value = 0.02373plot(mod_EF,1)
plot(mod_EF,2)
Modelo EFECTOS ALEATORIOS
library(readxl)
df_2 = Datos_Tarea_Abril_24 <- read_excel("D:/Users/Usuario/Desktop/Trabajos Diseno/Datos Tarea Abril 24.xlsx",
sheet = "M. Efectos Aleatorios");df_2## # A tibble: 40 x 4
## AMBIENTE GENOTIPO REPETICIONES RENDIMIENTO
## <chr> <chr> <chr> <dbl>
## 1 A-1 G-1 R-I 382.
## 2 A-1 G-1 R-II 239.
## 3 A-1 G-8 R-I 327.
## 4 A-1 G-8 R-II 309.
## 5 A-1 G-15 R-I 377.
## 6 A-1 G-15 R-II 371.
## 7 A-1 G-22 R-I 231.
## 8 A-1 G-22 R-II 217.
## 9 A-1 G-29 R-I 182.
## 10 A-1 G-29 R-II 243.
## # ... with 30 more rowsAmb_2= df_2$AMBIENTE; as.factor(Amb_2)## [1] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-4 A-4 A-4 A-4 A-4
## [16] A-4 A-4 A-4 A-4 A-4 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7
## [31] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## Levels: A-1 A-10 A-4 A-7Gen_2= df_2$GENOTIPO; as.factor(Gen_2)## [1] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15
## [16] G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## [31] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## Levels: G-1 G-15 G-22 G-29 G-8Rep_2=df_2$REPETICIONES; as.factor(Rep_2)## [1] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [16] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [31] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## Levels: R-I R-IIRend_2= df_2$RENDIMIENTOlibrary(collapsibleTree)
collapsibleTreeSummary(df_2, hierarchy=c ("AMBIENTE", "GENOTIPO", "REPETICIONES", "RENDIMIENTO"))library(lattice)
boxplot( Rend_2~Gen_2,las=3, df_2)
boxplot( Rend_2~Amb_2,las=3, df_2)
library(lattice)
bwplot(Rend_2~Gen_2|Amb_2,las=2, df_2)
library(lme4)## Warning: package 'lme4' was built under R version 4.0.5## Loading required package: Matrixmod_2 = lmer(Rend_2 ~ (1 | Gen_2) + (1 | Amb_2) + (1 | Amb_2:Gen_2), data = df_2);mod_2## boundary (singular) fit: see ?isSingular## Linear mixed model fit by REML ['lmerMod']
## Formula: Rend_2 ~ (1 | Gen_2) + (1 | Amb_2) + (1 | Amb_2:Gen_2)
## Data: df_2
## REML criterion at convergence: 468.4289
## Random effects:
## Groups Name Std.Dev.
## Amb_2:Gen_2 (Intercept) 41.24144
## Gen_2 (Intercept) 0.01912
## Amb_2 (Intercept) 0.00000
## Residual 85.26540
## Number of obs: 40, groups: Amb_2:Gen_2, 20; Gen_2, 5; Amb_2, 4
## Fixed Effects:
## (Intercept)
## 254.1
## optimizer (nloptwrap) convergence code: 0 (OK) ; 0 optimizer warnings; 1 lme4 warningssummary(mod_2)## Linear mixed model fit by REML ['lmerMod']
## Formula: Rend_2 ~ (1 | Gen_2) + (1 | Amb_2) + (1 | Amb_2:Gen_2)
## Data: df_2
##
## REML criterion at convergence: 468.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3023 -0.9192 -0.1079 0.8820 1.3677
##
## Random effects:
## Groups Name Variance Std.Dev.
## Amb_2:Gen_2 (Intercept) 1.701e+03 41.24144
## Gen_2 (Intercept) 3.657e-04 0.01912
## Amb_2 (Intercept) 0.000e+00 0.00000
## Residual 7.270e+03 85.26540
## Number of obs: 40, groups: Amb_2:Gen_2, 20; Gen_2, 5; Amb_2, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 254.07 16.33 15.55
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularvar_Amb_Gen_2 = 41.24144**2
var_Amb_2 = 0.01912**2
var_Gen_2 = 0.00**2
var_Res_2 = 85.26540**2
var_tot_2 = var_Amb_Gen_2 + var_Amb_2 + var_Gen_2 + var_Res_2; var_tot_2## [1] 8971.045100 * var_Amb_2/var_tot_2## [1] 4.075048e-06100 * var_Gen_2/var_tot_2## [1] 0mod_EA=aov(Rend_2~Gen_2*Amb_2,df_2)
summary(mod_EA)## Df Sum Sq Mean Sq F value Pr(>F)
## Gen_2 4 42027 10507 1.445 0.256
## Amb_2 3 16479 5493 0.756 0.532
## Gen_2:Amb_2 12 144261 12022 1.654 0.155
## Residuals 20 145404 7270library(agricolae)
HSD_test = with(df_2, HSD.test (Rend_2, Gen_2, DFerror = 20,MSerror = 7270))
HSD_test## $statistics
## MSerror Df Mean CV MSD
## 7270 20 254.0749 33.55872 127.5714
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Gen_2 5 4.231857 0.05
##
## $means
## Rend_2 std r Min Max Q25 Q50 Q75
## G-1 294.5498 85.60984 8 181.1548 381.5332 228.2853 303.0015 375.3739
## G-15 207.7013 114.82511 8 104.2024 377.2576 123.9189 146.9268 295.6244
## G-22 278.8572 75.56017 8 159.7125 374.4560 227.4430 283.2026 338.3168
## G-29 262.9654 96.09835 8 147.0595 372.5150 174.4369 264.9968 354.4282
## G-8 226.3009 90.97627 8 109.9704 327.3507 133.8183 237.9284 312.4638
##
## $comparison
## NULL
##
## $groups
## Rend_2 groups
## G-1 294.5498 a
## G-22 278.8572 a
## G-29 262.9654 a
## G-8 226.3009 a
## G-15 207.7013 a
##
## attr(,"class")
## [1] "group"with(df_2, interaction.plot(x.factor = Gen_2, trace.factor = Amb_2, response = Rend_2))
#Modelo EFECTOS MIXTOS
library(readxl)
df_3 = Datos_Tarea_Abril_24 <- read_excel("D:/Users/Usuario/Desktop/Trabajos Diseno/Datos Tarea Abril 24.xlsx",
sheet = "M. Efecto Mixto");df_3## # A tibble: 100 x 4
## AMBIENTE GENOTIPO REPETICIONES RENDIMIENTO
## <chr> <chr> <chr> <dbl>
## 1 A-1 G-1 R-I 382.
## 2 A-1 G-1 R-II 239.
## 3 A-1 G-8 R-I 327.
## 4 A-1 G-8 R-II 309.
## 5 A-1 G-15 R-I 377.
## 6 A-1 G-15 R-II 371.
## 7 A-1 G-22 R-I 231.
## 8 A-1 G-22 R-II 217.
## 9 A-1 G-29 R-I 182.
## 10 A-1 G-29 R-II 243.
## # ... with 90 more rowsAmb_3= df_3$AMBIENTE; as.factor(Amb_3)## [1] A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-1 A-2 A-2 A-2 A-2 A-2
## [16] A-2 A-2 A-2 A-2 A-2 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3 A-3
## [31] A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-4 A-5 A-5 A-5 A-5 A-5
## [46] A-5 A-5 A-5 A-5 A-5 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6 A-6
## [61] A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-7 A-8 A-8 A-8 A-8 A-8
## [76] A-8 A-8 A-8 A-8 A-8 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9 A-9
## [91] A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10 A-10
## Levels: A-1 A-10 A-2 A-3 A-4 A-5 A-6 A-7 A-8 A-9Gen_3= df_3$GENOTIPO; as.factor(Gen_3)## [1] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15
## [16] G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## [31] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15
## [46] G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## [61] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15
## [76] G-15 G-22 G-22 G-29 G-29 G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## [91] G-1 G-1 G-8 G-8 G-15 G-15 G-22 G-22 G-29 G-29
## Levels: G-1 G-15 G-22 G-29 G-8Rep_3=df_3$REPETICIONES; as.factor(Rep_3)## [1] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [16] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [31] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [46] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [61] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I
## [76] R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## [91] R-I R-II R-I R-II R-I R-II R-I R-II R-I R-II
## Levels: R-I R-IIRend_3= df_3$RENDIMIENTOlibrary(collapsibleTree)
collapsibleTreeSummary(df_3, hierarchy=c ("AMBIENTE", "GENOTIPO", "REPETICIONES", "RENDIMIENTO"))boxplot( Rend_3~Gen_3,las= 3, df_3)
boxplot( Rend_3~Amb_3,las= 3, df_3)
bwplot(Rend_3~Gen_3|Amb_3,las=3, df_3)
mod_3 = lmer(Rend_3 ~ Amb_3 + (1 | Gen_3) + (1 | Gen_3:Amb_3), data = df_3)## boundary (singular) fit: see ?isSingularsummary(mod_3)## Linear mixed model fit by REML ['lmerMod']
## Formula: Rend_3 ~ Amb_3 + (1 | Gen_3) + (1 | Gen_3:Amb_3)
## Data: df_3
##
## REML criterion at convergence: 1086.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.74771 -0.81875 0.01101 0.74386 1.79158
##
## Random effects:
## Groups Name Variance Std.Dev.
## Gen_3:Amb_3 (Intercept) 1.535e+03 3.917e+01
## Gen_3 (Intercept) 1.951e-11 4.417e-06
## Residual 6.704e+03 8.188e+01
## Number of obs: 100, groups: Gen_3:Amb_3, 50; Gen_3, 5
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 287.779 31.262 9.205
## Amb_3A-10 -48.361 44.211 -1.094
## Amb_3A-2 -10.112 44.211 -0.229
## Amb_3A-3 -42.076 44.211 -0.952
## Amb_3A-4 -50.834 44.211 -1.150
## Amb_3A-5 -20.280 44.211 -0.459
## Amb_3A-6 -107.719 44.211 -2.436
## Amb_3A-7 -35.620 44.211 -0.806
## Amb_3A-8 -46.317 44.211 -1.048
## Amb_3A-9 -5.241 44.211 -0.119
##
## Correlation of Fixed Effects:
## (Intr) A_3A-1 A_3A-2 A_3A-3 A_3A-4 A_3A-5 A_3A-6 A_3A-7 A_3A-8
## Amb_3A-10 -0.707
## Amb_3A-2 -0.707 0.500
## Amb_3A-3 -0.707 0.500 0.500
## Amb_3A-4 -0.707 0.500 0.500 0.500
## Amb_3A-5 -0.707 0.500 0.500 0.500 0.500
## Amb_3A-6 -0.707 0.500 0.500 0.500 0.500 0.500
## Amb_3A-7 -0.707 0.500 0.500 0.500 0.500 0.500 0.500
## Amb_3A-8 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## Amb_3A-9 -0.707 0.500 0.500 0.500 0.500 0.500 0.500 0.500 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingularvar_Amb_Gen_3 = 3.917e+01**2
var_Amb_3 = 0.000**2
var_Gen_3 = 4.417e-06**2
var_Res_3 = 8.188e+01**2
var_tot_3 = var_Amb_Gen_3 + var_Amb_3 + var_Gen_3 + var_Res_3; var_tot_3## [1] 8238.623100 * var_Amb_3/var_tot_3## [1] 0100 * var_Gen_3/var_tot_3## [1] 2.368101e-13