Tarea Julio 23

#Diseño Factorial Completo en Arreglo Completamente al azar

set.seed(123)

#Peso inicial de cubos
pic = runif(120, 0.9, 1.3)
pic = round(sort.int(pic, 6), 2)

#Laboratorio
lab = gl(2, 10, 120, c('biol','alim'))

#Tiempo de Coccion
tiemp = gl(3, 40, 120, c(0, 10, 12))

#Vierdades
varie = gl(2, 20, 120, c('var1', 'var2'))

#Compuestos Fenolicos
cf = c(rnorm(40, 20, 1),
       rnorm(40, 12, 1.2),
       rnorm(40, 16, 1.15))

df = data.frame(lab, varie, tiemp, cf, pic)
head(df)

##    lab varie tiemp       cf  pic
## 1 biol  var1     0 20.37964 0.90
## 2 biol  var1     0 19.49768 0.91
## 3 biol  var1     0 19.66679 0.92
## 4 biol  var1     0 18.98142 0.92
## 5 biol  var1     0 18.92821 0.92
## 6 biol  var1     0 20.30353 0.92

library(collapsibleTree)
collapsibleTreeSummary(
  df,
  hierarchy = c('lab','varie','tiemp', "cf"))

ANCOVA

mod1 = aov(cf ~ lab*varie + tiemp + pic, df)
summary(mod1)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## lab           1    0.1     0.1   0.067 0.7964    
## varie         1    5.4     5.4   4.620 0.0337 *  
## tiemp         2 1399.9   700.0 600.082 <2e-16 ***
## pic           1    0.5     0.5   0.467 0.4959    
## lab:varie     1    0.1     0.1   0.056 0.8142    
## Residuals   113  131.8     1.2                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANCOVA - Interacciones

mod2 = aov(cf ~ lab*varie + tiemp + pic, df)
summary(mod2)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## lab           1    0.1     0.1   0.067 0.7964    
## varie         1    5.4     5.4   4.620 0.0337 *  
## tiemp         2 1399.9   700.0 600.082 <2e-16 ***
## pic           1    0.5     0.5   0.467 0.4959    
## lab:varie     1    0.1     0.1   0.056 0.8142    
## Residuals   113  131.8     1.2                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA 1

mod3 = aov(cf ~ tiemp*varie, df)
summary(mod3)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## tiemp         2 1399.9   700.0 602.599 <2e-16 ***
## varie         1    5.4     5.4   4.639 0.0334 *  
## tiemp:varie   2    0.1     0.0   0.033 0.9678    
## Residuals   114  132.4     1.2                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA 2

mod4 = aov(cf ~ lab*varie + tiemp, df)
summary(mod4)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## lab           1    0.1     0.1   0.067 0.7959    
## varie         1    5.4     5.4   4.642 0.0333 *  
## tiemp         2 1399.9   700.0 602.906 <2e-16 ***
## lab:varie     1    0.1     0.1   0.056 0.8127    
## Residuals   114  132.4     1.2                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

lattice::bwplot(df$cf ~ df$tiemp | df$varie)

Eficiencia de bloqueo

H = 0/132.4;H

## [1] 0