Comparacion de tres densidades de siembra para papa criolla

\[H_o: \mu_{rto1}=\mu_{rto2}=\mu_{rto3}\] \[H_a= H_o~es~falsa\]

set.seed(7234)
rto30= rnorm(80,3,0.3)
rto35= rnorm(80,3.2,0.29)
rto40= rnorm(80,3.5,0.32)
rtot= c(rto30,rto35,rto40)
densidad<-gl(3,80,240, labels = c('d30','d35','d40'))
df1=data.frame(rtot,densidad)

Analisis descriptivo

medias6= tapply(df1$rtot,df1$densidad,mean);medias6
##      d30      d35      d40 
## 3.015935 3.192775 3.493202
dv6= tapply(df1$rtot,df1$densidad, sd);dv6
##       d30       d35       d40 
## 0.3541551 0.3032317 0.3728287
cv6= 100*(dv6/medias6); cv6
##       d30       d35       d40 
## 11.742797  9.497435 10.672979
boxplot(df1$rtot~df1$densidad)
points(c(1,2,3), medias6,col= "red", pch= 16)

kg_d= 80*medias6; kg_d
##      d30      d35      d40 
## 241.2748 255.4220 279.4562

library(ggplot2) library(plyr) medias <- ddply(df_rto, “densidad”, summarise, grp.mean=mean(rto)) p<-ggplot(df_rto, aes(x=rto, color=densidad)) + geom_histogram(aes(y=..density..), colour=“black”, fill=“white”)+ geom_density(aes(fill=densidad), alpha=.2) + geom_vline(data=medias, aes(xintercept=grp.mean, color=densidad), linetype=“dashed”)

##Asumiendo

Igresos a 1500 por kg de papa criolla

ing= 1500*kg_d; ing
##      d30      d35      d40 
## 361912.2 383133.0 419184.2

Costos de manejo

costos= c(d30= 160000, d35=180000, d40=200000); costos
##    d30    d35    d40 
## 160000 180000 200000
ingresos= ing-costos; ingresos
##      d30      d35      d40 
## 201912.2 203133.0 219184.2

#Tabla del analisis de varianza

mod6=aov(formula = rtot~densidad, data= df1)
res_mod6= summary(mod6); res_mod6
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## densidad      2  9.315   4.658   39.21 1.95e-15 ***
## Residuals   237 28.154   0.119                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
p_value= unlist(res_mod6)[9]; p_value
##     Pr(>F)1 
## 1.95092e-15
f_cal= unlist(res_mod6)[7];f_cal
## F value1 
## 39.20714
pv_cal= pf(f_cal, 2, 237, lower.tail = F, ); pv_cal
##    F value1 
## 1.95092e-15
ifelse(p_value<0.05, "rechazar Ho", "No rechazar Ho")
##       Pr(>F)1 
## "rechazar Ho"

#Tukey-HSD

res_mod6
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## densidad      2  9.315   4.658   39.21 1.95e-15 ***
## Residuals   237 28.154   0.119                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(mod6, 'densidad')
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = rtot ~ densidad, data = df1)
## 
## $densidad
##              diff        lwr       upr     p adj
## d35-d30 0.1768399 0.04830937 0.3053703 0.0038301
## d40-d30 0.4772670 0.34873656 0.6057975 0.0000000
## d40-d35 0.3004272 0.17189671 0.4289577 0.0000003
a30= 80*(0.5*0.3); a30
## [1] 12
a35= 80*(0.5*0.35); a35
## [1] 14
a40= 80*(0.5*0.4); a40
## [1] 16
p30= 12/(0.5*0.3); p30
## [1] 80
p35= 12/(0.5*0.35); p35
## [1] 68.57143
p40= 12/(0.5*0.4); p40
## [1] 60
rto_30_12 = sample(rto30, size = p30, replace = F)
rto_35_12 = sample(rto35, size = round(p35), replace = F)
rto_40_12 = sample(rto40, size = round(p40), replace = F)

df_rto_12 = data.frame(densidad = rep(c('d30','d35','d40'), 
                                      c(p30, round(p35), p40)),
                       rto = c(rto_30_12, rto_35_12, rto_40_12))
head(df_rto_12)
##   densidad      rto
## 1      d30 3.037740
## 2      d30 3.059726
## 3      d30 3.141929
## 4      d30 2.744815
## 5      d30 3.691797
## 6      d30 2.939161
boxplot(df_rto_12$rto~df_rto_12$densidad, varwidth = T)

###Analisis de varianza desvalanceado

mod7= anova(lm(rto~densidad, df_rto_12)); mod7
## Analysis of Variance Table
## 
## Response: rto
##            Df Sum Sq Mean Sq F value    Pr(>F)    
## densidad    2  7.686  3.8430  33.562 2.417e-13 ***
## Residuals 206 23.588  0.1145                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##Omitiendo el desvalanceo

mod7_b= aov(formula = rto~densidad, data = df_rto_12)
summary(mod7_b)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## densidad      2  7.686   3.843   33.56 2.42e-13 ***
## Residuals   206 23.588   0.115                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1