Tarea Agosto 10
Emanuel Santos
15/8/2021
DiseƱo Parcelas Dividas
library(readxl)## Warning: package 'readxl' was built under R version 4.0.5df = Datos_Tarea_Abril_10 <- read_excel("D:/Users/Usuario/Desktop/Trabajos Diseno/Datos Tarea Abril 10.xlsx");df ## # A tibble: 72 x 4
## Nitrogeno Variedad Repeticion Rendimiento
## <dbl> <chr> <chr> <dbl>
## 1 0 IR8 I 4.43
## 2 0 IR8 II 4.48
## 3 0 IR8 III 3.85
## 4 0 IR5 I 3.94
## 5 0 IR5 II 5.31
## 6 0 IR5 III 3.66
## 7 0 C4 - 63 I 3.46
## 8 0 C4 - 63 II 2.94
## 9 0 C4 - 63 III 3.14
## 10 0 Peta I 4.13
## # ... with 62 more rowsNivel=Datos_Tarea_Abril_10$Nitrogeno
Nivel=as.factor(Nivel)
Var=Datos_Tarea_Abril_10$Variedad
Rep=Datos_Tarea_Abril_10$Repeticion
Rep = as.factor(Rep)
Rend=Datos_Tarea_Abril_10$Rendimientolibrary(collapsibleTree)
collapsibleTreeSummary(df, hierarchy = c('Nitrogeno','Variedad', 'Repeticion', 'Rendimiento'))library(lattice)
bwplot(Rend~Var|Nivel+Rep,Datos_Tarea_Abril_10,xlab="",pch=20)
medias = tapply(Rend, list(Var,Nivel),mean); medias## 0 60 90 120 150 180
## C4 - 63 3.183333 5.442667 5.994 6.014000 6.687333 6.065333
## IR5 4.306000 5.982000 6.259 6.895000 6.950667 6.540333
## IR8 4.252667 5.672000 6.400 6.732667 7.563333 8.700667
## Peta 4.481333 4.816000 4.812 3.816000 2.046667 1.880667desviacion = tapply(Rend, list(Var,Nivel),sd); desviacion## 0 60 90 120 150 180
## C4 - 63 0.2624525 0.6257454 0.2605763 0.3114290 0.3806328 0.9168737
## IR5 0.8844275 0.4704211 0.3369822 0.2972087 0.6334172 0.7415891
## IR8 0.3495445 0.6701313 0.3144773 0.3004818 0.2791726 0.2154654
## Peta 0.3550005 0.3265088 0.8315455 1.1414570 0.6801539 0.4490984cv = (desviacion*100)/medias; cv## 0 60 90 120 150 180
## C4 - 63 8.244582 11.497037 4.347285 5.178400 5.691847 15.116625
## IR5 20.539422 7.863943 5.383962 4.310496 9.113042 11.338704
## IR8 8.219418 11.814727 4.913709 4.463043 3.691132 2.476424
## Peta 7.921760 6.779668 17.280664 29.912394 33.232275 23.879742#Analisis de Varanza
mod_ParcelasDiv= aov(Rend ~ Var*Nivel + Error(Rep:Var),data = df)## Warning in aov(Rend ~ Var * Nivel + Error(Rep:Var), data = df): Error() model is
## singularsummary(mod_ParcelasDiv)##
## Error: Rep:Var
## Df Sum Sq Mean Sq F value Pr(>F)
## Var 3 89.89 29.963 106.6 8.66e-07 ***
## Residuals 8 2.25 0.281
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Error: Within
## Df Sum Sq Mean Sq F value Pr(>F)
## Nivel 5 30.43 6.086 18.96 1.25e-09 ***
## Var:Nivel 15 69.34 4.623 14.40 1.21e-11 ***
## Residuals 40 12.84 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mod_2 = aov(Rend ~ Var*Nivel + Rep:Var,data = df)
summary(mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Var 3 89.89 29.963 93.359 < 2e-16 ***
## Nivel 5 30.43 6.086 18.962 1.25e-09 ***
## Var:Nivel 15 69.34 4.623 14.404 1.21e-11 ***
## Var:Rep 8 2.25 0.281 0.876 0.545
## Residuals 40 12.84 0.321
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Analisis de Medias
library(agricolae)## Warning: package 'agricolae' was built under R version 4.0.5HSD_test = with(df, HSD.test (Rend, Nivel
, DFerror = 40,MSerror = 0.321));HSD_test## $statistics
## MSerror Df Mean CV MSD
## 0.321 40 5.478903 10.34091 0.6921034
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Nivel 6 4.231644 0.05
##
## $means
## Rend std r Min Max Q25 Q50 Q75
## 0 4.055833 0.6960603 12 2.944 5.314 3.6110 4.035 4.47900
## 120 5.864417 1.3902291 12 2.774 7.139 5.6030 6.416 6.75550
## 150 5.812000 2.3373706 12 1.414 7.848 5.4315 6.628 7.35550
## 180 5.796750 2.6364221 12 1.380 8.832 4.6720 6.117 7.65325
## 60 5.478167 0.6417666 12 4.604 6.502 5.0665 5.487 5.89450
## 90 5.866250 0.7771952 12 4.146 6.704 5.7390 6.045 6.28800
##
## $comparison
## NULL
##
## $groups
## Rend groups
## 90 5.866250 a
## 120 5.864417 a
## 150 5.812000 a
## 180 5.796750 a
## 60 5.478167 a
## 0 4.055833 b
##
## attr(,"class")
## [1] "group"HSD_test = with(df, HSD.test (Rend, Var, DFerror = 40,MSerror = 0.321));HSD_test## $statistics
## MSerror Df Mean CV MSD
## 0.321 40 5.478903 10.34091 0.5062138
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey Var 4 3.790685 0.05
##
## $means
## Rend std r Min Max Q25 Q50 Q75
## C4 - 63 5.564444 1.235991 18 2.944 7.122 5.4990 5.836 6.3010
## IR5 6.155500 1.048227 18 3.660 7.682 5.8950 6.365 6.6300
## IR8 6.553556 1.476074 18 3.850 8.832 5.5825 6.571 7.4865
## Peta 3.642111 1.396858 18 1.380 5.744 2.3775 4.136 4.6400
##
## $comparison
## NULL
##
## $groups
## Rend groups
## IR8 6.553556 a
## IR5 6.155500 a
## C4 - 63 5.564444 b
## Peta 3.642111 c
##
## attr(,"class")
## [1] "group"#EvaluaciĆ³n de Supuestos
TukeyHSD(mod_2, "Nivel");TukeyHSD## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Rend ~ Var * Nivel + Rep:Var, data = df)
##
## $Nivel
## diff lwr upr p adj
## 60-0 1.422333333 0.7302935 2.1143732 0.0000042
## 90-0 1.810416667 1.1183768 2.5024565 0.0000000
## 120-0 1.808583333 1.1165435 2.5006232 0.0000000
## 150-0 1.756166667 1.0641268 2.4482065 0.0000000
## 180-0 1.740916667 1.0488768 2.4329565 0.0000001
## 90-60 0.388083333 -0.3039565 1.0801232 0.5537702
## 120-60 0.386250000 -0.3057898 1.0782898 0.5588036
## 150-60 0.333833333 -0.3582065 1.0258732 0.7008666
## 180-60 0.318583333 -0.3734565 1.0106232 0.7398849
## 120-90 -0.001833333 -0.6938732 0.6902065 1.0000000
## 150-90 -0.054250000 -0.7462898 0.6377898 0.9998948
## 180-90 -0.069500000 -0.7615398 0.6225398 0.9996448
## 150-120 -0.052416667 -0.7444565 0.6396232 0.9999112
## 180-120 -0.067666667 -0.7597065 0.6243732 0.9996883
## 180-150 -0.015250000 -0.7072898 0.6767898 0.9999998## function (x, which, ordered = FALSE, conf.level = 0.95, ...)
## UseMethod("TukeyHSD")
## <bytecode: 0x0000000013a67db0>
## <environment: namespace:stats>shapiro.test(mod_2$residuals)##
## Shapiro-Wilk normality test
##
## data: mod_2$residuals
## W = 0.96954, p-value = 0.07792plot(mod_2,1)
plot(mod_2,2)