Analisis yuca
Pedro Lizarazo
2024-05-20
Data
library(readxl)
data1 <- read_excel("H:/Unidades compartidas/Ensayo yuca inoculacion/Datos_yuca_Lavega.xlsx",
sheet = "fenotipage 2")
str(data1)## tibble [440 × 9] (S3: tbl_df/tbl/data.frame)
## $ F : POSIXct[1:440], format: "2024-02-28" "2024-02-28" ...
## $ M : num [1:440] 1 1 1 1 1 1 1 1 1 1 ...
## $ Variedad: chr [1:440] "V28" "V28" "V28" "V28" ...
## $ Inoculo : chr [1:440] "No" "No" "No" "No" ...
## $ INT : chr [1:440] "V28-No" "V28-No" "V28-No" "V28-No" ...
## $ PL : num [1:440] 1 2 3 4 5 6 7 8 9 10 ...
## $ UDPC : num [1:440] 0 0 0 0 0 NA 0 0 0 0 ...
## $ DIATA : num [1:440] 3.5 3.34 3.3 2.56 3.11 2.74 3.16 3.15 3.03 3.28 ...
## $ ALTPL : num [1:440] 35 42.5 49.9 32.5 37 40.4 41 36.8 32.5 41.5 ...Diametro de tallo
library(lattice)
# Interaccion variedad por tiempo
bwplot(DIATA ~ Variedad | as.factor(M), data=data1)
# Interaccion inoculo por tiempo
bwplot(DIATA ~ Inoculo | as.factor(M), data=data1)
# Interaccion variedad por inoculo por tiempo
xyplot(DIATA ~ as.factor(M) | Variedad ,
data=data1, group = Inoculo,
type = c("p", "g", "smooth"),
xlab = "Muestreo", ylab = "Diametro de tallo (cm)")
# Altura de planta
library(lattice)
# Interaccion variedad por tiempo
bwplot(ALTPL ~ Variedad | as.factor(M), data=data1)
# Interaccion inoculo por tiempo
bwplot(ALTPL ~ Inoculo | as.factor(M), data=data1)
# Interaccion variedad por inoculo por tiempo
xyplot(ALTPL ~ as.factor(M) | Variedad ,
data=data1, group = Inoculo,
type = c("p", "g", "smooth"),
xlab = "Muestreo", ylab = "Altura de planta (cm)")
# Ultimo muestreo
#
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionM_fin <- data1 %>% filter(., M==11) %>%
filter(., !is.na(DIATA)) %>%
mutate(., Variedad=as.factor(Variedad), Inoculo=as.factor(Inoculo), INT=as.factor(INT) )
M_fin## # A tibble: 36 × 9
## F M Variedad Inoculo INT PL UDPC DIATA ALTPL
## <dttm> <dbl> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 2024-05-08 00:00:00 11 V28 No V28-No 1 0 6.41 61
## 2 2024-05-08 00:00:00 11 V28 No V28-No 2 0 6.46 73
## 3 2024-05-08 00:00:00 11 V28 No V28-No 3 0 6.36 79.5
## 4 2024-05-08 00:00:00 11 V28 No V28-No 4 0 6.35 75
## 5 2024-05-08 00:00:00 11 V28 No V28-No 5 0 6.36 71
## 6 2024-05-08 00:00:00 11 V28 Si V28-Si 7 2 3.73 45.5
## 7 2024-05-08 00:00:00 11 V28 Si V28-Si 8 3 3.35 49
## 8 2024-05-08 00:00:00 11 V28 Si V28-Si 9 4 2.57 38.5
## 9 2024-05-08 00:00:00 11 V28 Si V28-Si 10 4 3.16 50
## 10 2024-05-08 00:00:00 11 JA08 No JA08-No 1 0 7.38 52
## # ℹ 26 more rowsEstadistica descriptiva (ver tendencias, observar datos atípicos)
Diametro de tallo
library(ggplot2)
#
ggplot(M_fin, aes(x=Inoculo, y=DIATA, fill=Inoculo) )+
geom_boxplot()+
facet_wrap(~Variedad, scales="free_x")+
theme_bw()+
labs(x="Tratamiento", y="Diámetro de tallo (cm)")+
scale_fill_manual(values = c("No" = "lightblue", "Si" = "pink4"))
## Altura
#
ggplot(M_fin, aes(x=Inoculo, y=ALTPL, fill=Inoculo) )+
geom_boxplot()+
facet_wrap(~Variedad, scales="free_x")+
theme_bw()+
labs(x="Tratamiento", y="Altura (cm)")+
scale_fill_manual(values = c("No" = "lightblue", "Si" = "pink4"))
Análisis de varianza (ANOVA)
Anova para el diametro
# ANOVA
aov_dia <- aov( DIATA ~ Variedad * Inoculo, data=M_fin)
summary(aov_dia)## Df Sum Sq Mean Sq F value Pr(>F)
## Variedad 3 13.53 4.51 15.68 3.55e-06 ***
## Inoculo 1 32.71 32.71 113.68 2.29e-11 ***
## Variedad:Inoculo 3 9.17 3.06 10.62 7.81e-05 ***
## Residuals 28 8.06 0.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Supuestos del modelo
Residuales <- residuals(aov_dia)
res_estan <- Residuales/sd(Residuales)
# Test normalidad
shapiro.test(res_estan)##
## Shapiro-Wilk normality test
##
## data: res_estan
## W = 0.97855, p-value = 0.6963# Test de homocedasticidad
bartlett.test( res_estan ~ M_fin$Variedad )##
## Bartlett test of homogeneity of variances
##
## data: res_estan by M_fin$Variedad
## Bartlett's K-squared = 3.7676, df = 3, p-value = 0.2877bartlett.test( res_estan ~ M_fin$Inoculo )##
## Bartlett test of homogeneity of variances
##
## data: res_estan by M_fin$Inoculo
## Bartlett's K-squared = 3.6653, df = 1, p-value = 0.05556Anova para la altura
# ANOVA
aov_alt <- aov( ALTPL ~ Variedad * Inoculo, data=M_fin)
summary(aov_alt)## Df Sum Sq Mean Sq F value Pr(>F)
## Variedad 3 3300 1100.0 30.36 6.04e-09 ***
## Inoculo 1 369 369.3 10.19 0.00347 **
## Variedad:Inoculo 3 1463 487.8 13.46 1.27e-05 ***
## Residuals 28 1014 36.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Supuestos del modelo
Residuales <- residuals(aov_alt)
res_estan <- Residuales/sd(Residuales)
# Test normalidad
shapiro.test(res_estan)##
## Shapiro-Wilk normality test
##
## data: res_estan
## W = 0.96155, p-value = 0.2399# Test de homocedasticidad
bartlett.test( res_estan ~ M_fin$Variedad )##
## Bartlett test of homogeneity of variances
##
## data: res_estan by M_fin$Variedad
## Bartlett's K-squared = 9.6083, df = 3, p-value = 0.02221bartlett.test( res_estan ~ M_fin$Inoculo )##
## Bartlett test of homogeneity of variances
##
## data: res_estan by M_fin$Inoculo
## Bartlett's K-squared = 0.08824, df = 1, p-value = 0.7664Pruebas poshoc
Diametro
aov_int_dia <- aov( DIATA ~ INT, data=M_fin )
library(agricolae)
LSD.test(y=aov_int_dia, trt="INT", group=TRUE, console=TRUE)##
## Study: aov_int_dia ~ "INT"
##
## LSD t Test for DIATA
##
## Mean Square Error: 0.2877059
##
## INT, means and individual ( 95 %) CI
##
## DIATA std r se LCL UCL Min Max Q25 Q50
## AN04-No 6.9640 0.68027935 5 0.2398774 6.472633 7.455367 6.03 7.53 6.4600 7.290
## AN04-Si 6.1700 0.48067661 5 0.2398774 5.678633 6.661367 5.66 6.84 5.7500 6.250
## JA08-No 7.1400 0.37155080 5 0.2398774 6.648633 7.631367 6.77 7.66 6.8700 7.020
## JA08-Si 4.1250 1.32228968 2 0.3792795 3.348081 4.901919 3.19 5.06 3.6575 4.125
## T28-No 6.8540 0.24419255 5 0.2398774 6.362633 7.345367 6.47 7.05 6.7600 6.950
## T28-Si 5.4740 0.71251667 5 0.2398774 4.982633 5.965367 4.64 6.56 5.1000 5.470
## V28-No 6.3880 0.04658326 5 0.2398774 5.896633 6.879367 6.35 6.46 6.3600 6.360
## V28-Si 3.2025 0.48369240 4 0.2681911 2.653135 3.751865 2.57 3.73 3.0125 3.255
## Q75
## AN04-No 7.5100
## AN04-Si 6.3500
## JA08-No 7.3800
## JA08-Si 4.5925
## T28-No 7.0400
## T28-Si 5.6000
## V28-No 6.4100
## V28-Si 3.4450
##
## Alpha: 0.05 ; DF Error: 28
## Critical Value of t: 2.048407
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## DIATA groups
## JA08-No 7.1400 a
## AN04-No 6.9640 ab
## T28-No 6.8540 abc
## V28-No 6.3880 bc
## AN04-Si 6.1700 c
## T28-Si 5.4740 d
## JA08-Si 4.1250 e
## V28-Si 3.2025 eAltura
aov_int_alt <- aov( ALTPL ~ INT, data=M_fin )
library(agricolae)
LSD.test(y=aov_int_alt, trt="INT", group=TRUE, console=TRUE)##
## Study: aov_int_alt ~ "INT"
##
## LSD t Test for ALTPL
##
## Mean Square Error: 36.22636
##
## INT, means and individual ( 95 %) CI
##
## ALTPL std r se LCL UCL Min Max Q25 Q50 Q75
## AN04-No 66.10 8.855789 5 2.691704 60.58629 71.61371 52.0 76.5 66.50 67.00 68.50
## AN04-Si 60.50 7.818248 5 2.691704 54.98629 66.01371 49.5 69.5 56.00 63.00 64.50
## JA08-No 52.80 2.588436 5 2.691704 47.28629 58.31371 50.0 56.0 51.00 52.00 55.00
## JA08-Si 48.00 1.414214 2 4.255958 39.28207 56.71793 47.0 49.0 47.50 48.00 48.50
## T28-No 73.90 4.321458 5 2.691704 68.38629 79.41371 69.5 79.0 69.50 74.50 77.00
## T28-Si 82.88 4.560373 5 2.691704 77.36629 88.39371 77.0 89.5 81.00 82.90 84.00
## V28-No 71.90 6.859300 5 2.691704 66.38629 77.41371 61.0 79.5 71.00 73.00 75.00
## V28-Si 45.75 5.204165 4 3.009417 39.58549 51.91451 38.5 50.0 43.75 47.25 49.25
##
## Alpha: 0.05 ; DF Error: 28
## Critical Value of t: 2.048407
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ALTPL groups
## T28-Si 82.88 a
## T28-No 73.90 b
## V28-No 71.90 bc
## AN04-No 66.10 cd
## AN04-Si 60.50 de
## JA08-No 52.80 ef
## JA08-Si 48.00 f
## V28-Si 45.75 fGrafico de interaccion
Diametro
library(phia)## Warning: package 'phia' was built under R version 4.4.1## Cargando paquete requerido: car## Cargando paquete requerido: carData##
## Adjuntando el paquete: 'car'## The following object is masked from 'package:dplyr':
##
## recodegrafica_diametro <- interactionMeans(aov_dia)
plot(grafica_diametro)
Altura
grafica_altura <- interactionMeans(aov_alt)
plot(grafica_altura)