Diseños que se tendrán para el segundo parcial

Trabajo (Quiz)

Parcelas divididas

  • Debe tener dos factores como minimo. Igualmente requiere 2 aleatorizaciones.

  • Se usa en riego, fertilizacion, densidad de siembra, maquinaria- labranza

#Completamente al azar
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
xy = expand.grid(x = seq(6), y = seq(4))
xy$f1 = gl(4,6,24, paste0('V', 1:4))
xy$f2 = gl(3,2,24, paste0('R', 1:3))
xy$rep = gl(2,1,24, paste0('r', 1:2))
xy$name = paste0(xy$f1, xy$f2, xy$rep)
xy
##    x y f1 f2 rep   name
## 1  1 1 V1 R1  r1 V1R1r1
## 2  2 1 V1 R1  r2 V1R1r2
## 3  3 1 V1 R2  r1 V1R2r1
## 4  4 1 V1 R2  r2 V1R2r2
## 5  5 1 V1 R3  r1 V1R3r1
## 6  6 1 V1 R3  r2 V1R3r2
## 7  1 2 V2 R1  r1 V2R1r1
## 8  2 2 V2 R1  r2 V2R1r2
## 9  3 2 V2 R2  r1 V2R2r1
## 10 4 2 V2 R2  r2 V2R2r2
## 11 5 2 V2 R3  r1 V2R3r1
## 12 6 2 V2 R3  r2 V2R3r2
## 13 1 3 V3 R1  r1 V3R1r1
## 14 2 3 V3 R1  r2 V3R1r2
## 15 3 3 V3 R2  r1 V3R2r1
## 16 4 3 V3 R2  r2 V3R2r2
## 17 5 3 V3 R3  r1 V3R3r1
## 18 6 3 V3 R3  r2 V3R3r2
## 19 1 4 V4 R1  r1 V4R1r1
## 20 2 4 V4 R1  r2 V4R1r2
## 21 3 4 V4 R2  r1 V4R2r1
## 22 4 4 V4 R2  r2 V4R2r2
## 23 5 4 V4 R3  r1 V4R3r1
## 24 6 4 V4 R3  r2 V4R3r2
library(ggplot2)

ggplot(xy)+
  aes(x,y, label = name, fill=f2)+
  geom_tile(color = 'white')+
  geom_text(color = 'white')+
  labs(title = 'Completamente al azar')

#Parcelas Divididas

xy = expand.grid(y = seq(4), x = seq(6))
f2 = gl(3, 8, 24, paste0('R',1:3))
lf1 = paste0('V',1:4)
f1 = c(sample(lf1),sample(lf1),
       sample(lf1),sample(lf1),
       sample(lf1),sample(lf1))
rep = rep(rep(paste0('r',1:2), each=4), 3)

data = data.frame(xy, f1, f2, rep)
data$name = with(data, paste0(f1, rep))

Modelo

\[Y_{ijk} = \mu + \alpha_{i} + n_k(i) + \beta_j + (\alpha\beta)_{ij} + \epsilon_k(ij)\]

set.seed(123)
data$biom = rnorm(24,8,2)

Analisis descriptivo

ggplot(data)+
  aes(f2, biom)+
  geom_boxplot()

ggplot(data)+
  aes(f1, biom)+
  geom_boxplot()

ggplot(data)+
  aes(f2, biom, fill=f1)+
  geom_boxplot()

library(lme4)
## Loading required package: Matrix
mod1 = aov(biom ~ f2 * f1 + Error(f2:rep),data)
## Warning in aov(biom ~ f2 * f1 + Error(f2:rep), data): Error() model is singular
summary(mod1)
## 
## Error: f2:rep
##           Df Sum Sq Mean Sq F value Pr(>F)  
## f2         2 13.327   6.664   8.078  0.062 .
## Residuals  3  2.475   0.825                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Error: Within
##           Df Sum Sq Mean Sq F value Pr(>F)
## f1         3  10.14   3.382   0.779  0.535
## f2:f1      6  19.58   3.264   0.752  0.624
## Residuals  9  39.06   4.340