Diseño en parcelas divididas

con minimo dos factores, es como hacer el experimento dos veces, con dos aleatorizaciones. cuando los estudios tienene que ver riego de fertilizacion, densidad de siembra, maquinaria y labranza

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 = sample_frac(xy)
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  5 1 V1 R1  r1 V1R1r1
## 2  6 3 V1 R1  r2 V1R1r2
## 3  6 4 V1 R2  r1 V1R2r1
## 4  3 1 V1 R2  r2 V1R2r2
## 5  6 1 V1 R3  r1 V1R3r1
## 6  1 4 V1 R3  r2 V1R3r2
## 7  4 4 V2 R1  r1 V2R1r1
## 8  1 3 V2 R1  r2 V2R1r2
## 9  2 4 V2 R2  r1 V2R2r1
## 10 6 2 V2 R2  r2 V2R2r2
## 11 3 3 V2 R3  r1 V2R3r1
## 12 1 1 V2 R3  r2 V2R3r2
## 13 2 3 V3 R1  r1 V3R1r1
## 14 3 2 V3 R1  r2 V3R1r2
## 15 2 2 V3 R2  r1 V3R2r1
## 16 5 2 V3 R2  r2 V3R2r2
## 17 4 1 V3 R3  r1 V3R3r1
## 18 2 1 V3 R3  r2 V3R3r2
## 19 4 2 V4 R1  r1 V4R1r1
## 20 1 2 V4 R1  r2 V4R1r2
## 21 5 3 V4 R2  r1 V4R2r1
## 22 3 4 V4 R2  r2 V4R2r2
## 23 4 3 V4 R3  r1 V4R3r1
## 24 5 4 V4 R3  r2 V4R3r2
library(ggplot2)
ggplot(xy)+
  aes(x,y,label=name)+
  geom_tile(fill='blue', color='white')+
  geom_text(color='white')

library(dplyr)
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))
library(ggplot2)
ggplot(data)+
  aes(x,y, label=name, fill=f1)+
  geom_tile(color='white')+
  geom_text()+
  facet_wrap(~f2, scales = 'free')+
  theme(axis.text = element_blank())

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()

no siempre la variedad 2 es la mejor, depende tambien de la interaccion

library(lme4)
## Loading required package: Matrix
# mod1 = lmer(biom ~ f2*f1 + (1|f2), data)
mod1 = aov(biom ~ f2 * f1 + Error(f1), data)
summary(mod1)
## 
## Error: f1
##    Df Sum Sq Mean Sq
## f1  3  8.431    2.81
## 
## Error: Within
##           Df Sum Sq Mean Sq F value Pr(>F)
## f2         2  13.33   6.664   2.106  0.164
## f2:f1      6  24.86   4.143   1.309  0.325
## Residuals 12  37.97   3.164