knitr::opts_chunk$set(echo = TRUE)rm(list=ls())Estos son lso datos de temperatura
library(readxl)
Datos_NaEl<-Base_de_datos_Salinidad_Brassinoesteroides_1_ <- read_excel("d:/Users/Janus/Documents/Fisiologia vegetal basica/Base de datos Salinidad + Brassinoesteroides parte 2.xlsx")Datos_NaEl#1.1 Anova para variable temperatura
m1 <- aov(Temp~Trat, data = Datos_NaEl)
anova(m1)Analysis of Variance Table
Response: Temp
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 42.477 14.159 13.123 0.0004262 ***
Residuals 12 12.948 1.079
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para temperatura
shapiro.test(resid(m1))
Shapiro-Wilk normality test
data: resid(m1)
W = 0.98373, p-value = 0.9862#1.3 Prueba de homogeneidad de varianza para temp
library(car)
library(carData)leveneTest(Datos_NaEl$Temp~Datos_NaEl$Trat, center=mean)group coerced to factor.Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 2.0138 0.1658
12 #1.4 prueba de comparacion de promedio tukey
library(agricolae)
library(dplyr)m1tukey <-HSD.test(Datos_NaEl$Temp,Datos_NaEl$Trat, 12, 1.079, alpha = 0.05)
m1tukey$statistics
MSerror Df Mean CV MSD
1.079 12 19.88125 5.224768 2.180678
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$Temp std r Min Max Q25 Q50 Q75
T1 18.225 0.6800735 4 17.3 18.9 17.975 18.35 18.600
T2 21.950 0.9882645 4 20.9 23.1 21.275 21.90 22.575
T3 18.350 0.7416198 4 17.4 19.2 18.075 18.40 18.675
T4 21.000 1.5253415 4 19.3 22.8 20.050 20.95 21.900
$comparison
NULL
$groups
Datos_NaEl$Temp groups
T2 21.950 a
T4 21.000 a
T3 18.350 b
T1 18.225 b
attr(,"class")
[1] "group"datos de área foliar en adelante.
#1.1 Anova para variable Área foliar
m10 <- aov(Area_foliar~Trat, data = Datos_NaEl)
anova(m10)Analysis of Variance Table
Response: Area_foliar
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 550.34 183.446 27.845 1.088e-05 ***
Residuals 12 79.06 6.588
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para Área foliar
shapiro.test(resid(m10))
Shapiro-Wilk normality test
data: resid(m10)
W = 0.9256, p-value = 0.2075#1.3 Prueba de homogeneidad de varianza para Área foliar
library(car)
library(carData)m10var<-leveneTest(Datos_NaEl$Area_foliar~Datos_NaEl$Trat, center=mean)group coerced to factor.m10varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.1719 0.3611
12 #1.4 prueba de comparacion de promedio tukey para Área foliar
library(agricolae)
library(dplyr)m10tukey <-HSD.test(Datos_NaEl$Area_foliar,Datos_NaEl$Trat, 12, 6.588, alpha = 0.05)
m10tukey$statistics
MSerror Df Mean CV MSD
6.588 12 59.42406 4.319311 5.388372
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$Area_foliar std r Min Max Q25 Q50
T1 66.57400 1.953636 4 64.925 69.223 65.20250 66.0740
T2 52.04325 1.303762 4 50.238 53.351 51.73575 52.2920
T3 63.53700 2.607883 4 59.999 66.252 62.73875 63.9485
T4 55.54200 3.746375 4 50.356 58.346 54.01225 56.7330
Q75
T1 67.44550
T2 52.59950
T3 64.74675
T4 58.26275
$comparison
NULL
$groups
Datos_NaEl$Area_foliar groups
T1 66.57400 a
T3 63.53700 a
T4 55.54200 b
T2 52.04325 b
attr(,"class")
[1] "group"#Peso fresco en hojas
#1.1 Anova para variable de peso fresco en hojas
m11<- aov(Hojas_Peso_fresco~Trat, data = Datos_NaEl)
anova(m11)Analysis of Variance Table
Response: Hojas_Peso_fresco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 5.4673 1.82242 28.939 8.901e-06 ***
Residuals 12 0.7557 0.06297
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para peso fresco en hojas
shapiro.test(resid(m11))
Shapiro-Wilk normality test
data: resid(m11)
W = 0.97295, p-value = 0.8839#1.3 Prueba de homogeneidad de varianza para peso fresco en hojas
library(car)
library(carData)m11var<-leveneTest(Datos_NaEl$Hojas_Peso_fresco~Datos_NaEl$Trat, center=mean)group coerced to factor.m11varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 2.5854 0.1017
12 #1.4 prueba de comparacion de promedio tukey para peso fresco en hojas
library(agricolae)
library(dplyr)m11tukey <-HSD.test(Datos_NaEl$Hojas_Peso_fresco,Datos_NaEl$Trat, 12, 0.06297, alpha = 0.05)
m11tukey$statistics
MSerror Df Mean CV MSD
0.06297 12 3.9295 6.386009 0.5268022
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$Hojas_Peso_fresco std r Min Max Q25 Q50
T1 4.68800 0.3633492 4 4.223 5.023 4.49150 4.753
T2 3.09075 0.1257971 4 2.936 3.219 3.01850 3.104
T3 4.17550 0.2728864 4 3.902 4.510 3.98525 4.145
T4 3.76375 0.1719852 4 3.520 3.923 3.72400 3.806
Q75
T1 4.94950
T2 3.17625
T3 4.33525
T4 3.84575
$comparison
NULL
$groups
Datos_NaEl$Hojas_Peso_fresco groups
T1 4.68800 a
T3 4.17550 ab
T4 3.76375 b
T2 3.09075 c
attr(,"class")
[1] "group"#Hojas peso seco
#1.1 Anova para variable Hojas de peso seco
m12 <- aov(Hojas_Peso_seco~Trat, data = Datos_NaEl)
anova(m12)Analysis of Variance Table
Response: Hojas_Peso_seco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 0.057738 0.019246 12.662 0.0005002 ***
Residuals 12 0.018240 0.001520
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para hojas de peso seco
shapiro.test(resid(m12))
Shapiro-Wilk normality test
data: resid(m12)
W = 0.96767, p-value = 0.7997#1.3 Prueba de homogeneidad de varianza para Hojas de peso seco
library(car)
library(carData)m12var<-leveneTest(Datos_NaEl$Hojas_Peso_seco~Datos_NaEl$Trat, center=mean)group coerced to factor.m12varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 2.1742 0.1441
12 #1.4 prueba de comparacion de promedio tukey para Hojas de peso seco
library(agricolae)
library(dplyr)m12tukey <-HSD.test(Datos_NaEl$Hojas_Peso_seco,Datos_NaEl$Trat, 12, 0.001520, alpha = 0.05)
m12tukey$statistics
MSerror Df Mean CV MSD
0.00152 12 0.30575 12.75133 0.08184696
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$Hojas_Peso_seco std r Min Max Q25 Q50
T1 0.37125 0.05314367 4 0.311 0.432 0.33800 0.3710
T2 0.20800 0.02080064 4 0.189 0.236 0.19425 0.2035
T3 0.32775 0.04495461 4 0.285 0.386 0.29700 0.3200
T4 0.31600 0.02831960 4 0.286 0.347 0.29575 0.3155
Q75
T1 0.40425
T2 0.21725
T3 0.35075
T4 0.33575
$comparison
NULL
$groups
Datos_NaEl$Hojas_Peso_seco groups
T1 0.37125 a
T3 0.32775 a
T4 0.31600 a
T2 0.20800 b
attr(,"class")
[1] "group"#CRA Peso fresco
#1.1 Anova para variable CRA de peso fresco
m13<- aov(CRA_Peso_fresco~Trat, data = Datos_NaEl)
anova(m13)Analysis of Variance Table
Response: CRA_Peso_fresco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 5.2125e-06 1.7375e-06 1.9085 0.1821
Residuals 12 1.0925e-05 9.1042e-07 #1.2 Pueba normaliada para CRA peso fresco
shapiro.test(resid(m13))
Shapiro-Wilk normality test
data: resid(m13)
W = 0.89742, p-value = 0.07309#1.3 Prueba de homogeneidad de varianza para CRA de peso fresco
library(car)
library(carData)m13var<-leveneTest(Datos_NaEl$CRA_Peso_fresco~Datos_NaEl$Trat, center=mean)group coerced to factor.m13varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 0.5748 0.6424
12 #1.4 prueba de comparacion de promedio tukey para CRA de peso fresco
library(agricolae)
library(dplyr)m13tukey <-HSD.test(Datos_NaEl$CRA_Peso_fresco,Datos_NaEl$Trat, 12, 0.00000091042, alpha = 0.05)
m13tukey$statistics
MSerror Df Mean CV MSD
9.1042e-07 12 0.0165625 5.760962 0.002003095
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$CRA_Peso_fresco std r Min Max Q25 Q50
T1 0.016050 0.0010630146 4 0.0145 0.0169 0.015850 0.0164
T2 0.017325 0.0012500000 4 0.0156 0.0185 0.016875 0.0176
T3 0.015975 0.0005737305 4 0.0152 0.0165 0.015725 0.0161
T4 0.016900 0.0007874008 4 0.0158 0.0176 0.016625 0.0171
Q75
T1 0.016600
T2 0.018050
T3 0.016350
T4 0.017375
$comparison
NULL
$groups
Datos_NaEl$CRA_Peso_fresco groups
T2 0.017325 a
T4 0.016900 a
T1 0.016050 a
T3 0.015975 a
attr(,"class")
[1] "group"#minima significativa
kruskallcd<-LSD.test(Datos_NaEl$CRA_Peso_fresco, Datos_NaEl$Trat, 12, 0.00000091042, alpha=0.05)
kruskallcd$statistics
MSerror Df Mean CV t.value LSD
9.1042e-07 12 0.0165625 5.760962 2.178813 0.001470029
$parameters
test p.ajusted name.t ntr alpha
Fisher-LSD none Datos_NaEl$Trat 4 0.05
$means
Datos_NaEl$CRA_Peso_fresco std r LCL UCL Min
T1 0.016050 0.0010630146 4 0.01501053 0.01708947 0.0145
T2 0.017325 0.0012500000 4 0.01628553 0.01836447 0.0156
T3 0.015975 0.0005737305 4 0.01493553 0.01701447 0.0152
T4 0.016900 0.0007874008 4 0.01586053 0.01793947 0.0158
Max Q25 Q50 Q75
T1 0.0169 0.015850 0.0164 0.016600
T2 0.0185 0.016875 0.0176 0.018050
T3 0.0165 0.015725 0.0161 0.016350
T4 0.0176 0.016625 0.0171 0.017375
$comparison
NULL
$groups
Datos_NaEl$CRA_Peso_fresco groups
T2 0.017325 a
T4 0.016900 a
T1 0.016050 a
T3 0.015975 a
attr(,"class")
[1] "group"#CRA a peso de a saturacion #CRA Peso seco
#1.1 Anova para variable CRA de peso saturacion
m20<- aov(CRA_Peso_a_saturación~Trat, data = Datos_NaEl)
anova(m20)Analysis of Variance Table
Response: CRA_Peso_a_saturación
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 1.1839e-04 3.9462e-05 14.904 0.0002381 ***
Residuals 12 3.1773e-05 2.6480e-06
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para CRA peso saturacion
shapiro.test(resid(m20))
Shapiro-Wilk normality test
data: resid(m20)
W = 0.91756, p-value = 0.1541#1.3 Prueba de homogeneidad de varianza para CRA de peso seco
library(car)
library(carData)m20var<-leveneTest(Datos_NaEl$CRA_Peso_a_saturación~Datos_NaEl$Trat, center=mean)group coerced to factor.m20varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.7024 0.2194
12 #1.4 prueba de comparacion de promedio tukey para CRA de peso seco
library(agricolae)
library(dplyr)m20tukey <-HSD.test(Datos_NaEl$CRA_Peso_a_saturación,Datos_NaEl$Trat, 12, 0.000002648, alpha = 0.05)
m20tukey$statistics
MSerror Df Mean CV MSD
2.648e-06 12 0.02165625 7.514079 0.003416172
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$CRA_Peso_a_saturación std r Min Max Q25
T1 0.018825 0.000801561 4 0.0178 0.0196 0.01840
T2 0.025875 0.002574717 4 0.0237 0.0296 0.02460
T3 0.019775 0.001004573 4 0.0189 0.0212 0.01920
T4 0.022150 0.001519868 4 0.0209 0.0242 0.02105
Q50 Q75
T1 0.01895 0.019375
T2 0.02510 0.026375
T3 0.01950 0.020075
T4 0.02175 0.022850
$comparison
NULL
$groups
Datos_NaEl$CRA_Peso_a_saturación groups
T2 0.025875 a
T4 0.022150 b
T3 0.019775 b
T1 0.018825 b
attr(,"class")
[1] "group"#CRA Peso seco
#1.1 Anova para variable CRA de peso seco
m14<- aov(CRA_Peso_seco~Trat, data = Datos_NaEl)
anova(m14)Analysis of Variance Table
Response: CRA_Peso_seco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 0.0e+00 0.0000e+00 0 1
Residuals 12 7.6e-07 6.3333e-08 #1.2 Pueba normaliada para CRA peso seco
shapiro.test(resid(m14))
Shapiro-Wilk normality test
data: resid(m14)
W = 0.77651, p-value = 0.00135#1.3 Prueba de homogeneidad de varianza para CRA de peso seco
library(car)
library(carData)m14var<-leveneTest(Datos_NaEl$CRA_Peso_seco~Datos_NaEl$Trat, center=mean)group coerced to factor.m14varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 0 1
12 #1.4 prueba de comparacion de promedio tukey para CRA de peso seco
library(agricolae)
library(dplyr)m14tukey <-HSD.test(Datos_NaEl$CRA_Peso_seco,Datos_NaEl$Trat, 12, 0.0000000633, alpha = 0.05)
m14tukey$statistics
MSerror Df Mean CV MSD
6.33e-08 12 0.00125 20.12759 0.0005281808
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$CRA_Peso_seco std r Min Max Q25 Q50
T1 0.00125 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T2 0.00125 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T3 0.00125 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T4 0.00125 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
Q75
T1 0.00135
T2 0.00135
T3 0.00135
T4 0.00135
$comparison
NULL
$groups
Datos_NaEl$CRA_Peso_seco groups
T1 0.00125 a
T2 0.00125 a
T3 0.00125 a
T4 0.00125 a
attr(,"class")
[1] "group"##kruskal
kruskal2<-kruskal(Datos_NaEl$CRA_Peso_seco, Datos_NaEl$Trat, alpha = 0.05)
kruskal2$statistics
Chisq Df p.chisq t.value MSD
0 3 1 2.178813 7.547629
$parameters
test p.ajusted name.t ntr alpha
Kruskal-Wallis none Datos_NaEl$Trat 4 0.05
$means
Datos_NaEl.CRA_Peso_seco rank std r Min Max Q25 Q50
T1 0.00125 8.5 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T2 0.00125 8.5 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T3 0.00125 8.5 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
T4 0.00125 8.5 0.0002516611 4 9e-04 0.0015 0.0012 0.0013
Q75
T1 0.00135
T2 0.00135
T3 0.00135
T4 0.00135
$comparison
NULL
$groups
Datos_NaEl$CRA_Peso_seco groups
T1 8.5 a
T2 8.5 a
T3 8.5 a
T4 8.5 a
attr(,"class")
[1] "group"#CRA
#1.1 Anova para variable CRA
m15<- aov(CRA~Trat, data = Datos_NaEl)
anova(m15)Analysis of Variance Table
Response: CRA
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 760.15 253.383 16.477 0.0001486 ***
Residuals 12 184.53 15.378
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para CRA peso fresco
shapiro.test(resid(m15))
Shapiro-Wilk normality test
data: resid(m15)
W = 0.91972, p-value = 0.1669#1.3 Prueba de homogeneidad de varianza para CRA de peso fresco
library(car)
library(carData)m15var<-leveneTest(Datos_NaEl$CRA~Datos_NaEl$Trat, center=mean)group coerced to factor.m15varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 2.6384 0.09733 .
12
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.4 prueba de comparacion de promedio tukey para CRA
library(agricolae)
library(dplyr)m15tukey <-HSD.test(Datos_NaEl$CRA,Datos_NaEl$Trat, 12, 15.378, alpha = 0.05)
m15tukey$statistics
MSerror Df Mean CV MSD
15.378 12 76.08813 5.153865 8.23248
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$CRA std r Min Max Q25 Q50 Q75
T1 84.20468 3.542397 4 80.47337 87.86127 81.59375 84.24203 86.85296
T2 65.56335 5.423975 4 60.59322 71.42857 61.14134 65.11580 69.53782
T3 79.58223 3.087446 4 75.37688 82.60870 78.42755 80.17166 81.32633
T4 75.00228 3.163968 4 71.17904 78.57143 73.27945 75.12933 76.85216
$comparison
NULL
$groups
Datos_NaEl$CRA groups
T1 84.20468 a
T3 79.58223 ab
T4 75.00228 b
T2 65.56335 c
attr(,"class")
[1] "group"#Raíz tuberosa longitud
#1.1 Anova para variable Raíz tuberosa longitud
m16<- aov(RT_Longitud~Trat, data = Datos_NaEl)
anova(m16)Analysis of Variance Table
Response: RT_Longitud
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 46.25 15.4167 5.5224 0.01288 *
Residuals 12 33.50 2.7917
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para Raíz tuberosa longitud
shapiro.test(resid(m16))
Shapiro-Wilk normality test
data: resid(m16)
W = 0.94539, p-value = 0.4204#1.3 Prueba de homogeneidad de varianza para Raíz tuberosa longitud
library(car)
library(carData)m16var<-leveneTest(Datos_NaEl$RT_Longitud~Datos_NaEl$Trat, center=mean)group coerced to factor.m16varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.503 0.2639
12 #1.4 prueba de comparacion de promedio tukey para Raíz tuberosa longitud
library(agricolae)
library(dplyr)m16tukey <-HSD.test(Datos_NaEl$RT_Longitud,Datos_NaEl$Trat, 12, 2.7917, alpha = 0.05)
m16tukey$statistics
MSerror Df Mean CV MSD
2.7917 12 15.125 11.04686 3.507641
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$RT_Longitud std r Min Max Q25 Q50 Q75
T1 16.50 1.290994 4 15 18 15.75 16.5 17.25
T2 12.50 2.516611 4 10 16 11.50 12.0 13.00
T3 16.75 0.500000 4 16 17 16.75 17.0 17.00
T4 14.75 1.707825 4 13 17 13.75 14.5 15.50
$comparison
NULL
$groups
Datos_NaEl$RT_Longitud groups
T3 16.75 a
T1 16.50 a
T4 14.75 ab
T2 12.50 b
attr(,"class")
[1] "group"#Raíz tuberosa diametro
#1.1 Anova para variable Raíz tuberosa diametro
m17<- aov(RT_Diámetro~Trat, data = Datos_NaEl)
anova(m17)Analysis of Variance Table
Response: RT_Diámetro
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 79.25 26.417 9.1884 0.001963 **
Residuals 12 34.50 2.875
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para Raíz tuberosa diametro
shapiro.test(resid(m17))
Shapiro-Wilk normality test
data: resid(m17)
W = 0.98893, p-value = 0.9985#1.3 Prueba de homogeneidad de varianza para Raíz tuberosa diametro
library(car)
library(carData)m17var<-leveneTest(Datos_NaEl$RT_Diámetro~Datos_NaEl$Trat, center=mean)group coerced to factor.m17varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.1818 0.3577
12 #1.4 prueba de comparacion de promedio tukey para Raíz tuberosa longitud
library(agricolae)
library(dplyr)m17tukey <-HSD.test(Datos_NaEl$RT_Longitud,Datos_NaEl$Trat, 12, 2.875, alpha = 0.05)
m17tukey$statistics
MSerror Df Mean CV MSD
2.875 12 15.125 11.21046 3.559587
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$RT_Longitud std r Min Max Q25 Q50 Q75
T1 16.50 1.290994 4 15 18 15.75 16.5 17.25
T2 12.50 2.516611 4 10 16 11.50 12.0 13.00
T3 16.75 0.500000 4 16 17 16.75 17.0 17.00
T4 14.75 1.707825 4 13 17 13.75 14.5 15.50
$comparison
NULL
$groups
Datos_NaEl$RT_Longitud groups
T3 16.75 a
T1 16.50 a
T4 14.75 ab
T2 12.50 b
attr(,"class")
[1] "group"#Raíz tuberosa peso fresco
#1.1 Anova para variable Raíz tuberosa peso fresco
m18<- aov(RT_Peso_fresco~Trat, data = Datos_NaEl)
anova(m18)Analysis of Variance Table
Response: RT_Peso_fresco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 13.7767 4.5922 52.062 3.749e-07 ***
Residuals 12 1.0585 0.0882
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para Raíz tuberosa peso fresco
shapiro.test(resid(m18))
Shapiro-Wilk normality test
data: resid(m18)
W = 0.92893, p-value = 0.2345#1.3 Prueba de homogeneidad de varianza para Raíz tuberosa peso fresco
library(car)
library(carData)m18var<-leveneTest(Datos_NaEl$RT_Peso_fresco~Datos_NaEl$Trat, center=mean)group coerced to factor.m18varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.5923 0.2428
12 #1.4 prueba de comparacion de promedio tukey para Raíz tuberosa peso fresco
library(agricolae)
library(dplyr)m18tukey <-HSD.test(Datos_NaEl$RT_Peso_fresco,Datos_NaEl$Trat, 12, 0.0882, alpha = 0.05)
m18tukey$statistics
MSerror Df Mean CV MSD
0.0882 12 2.6625 11.15436 0.6234692
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$RT_Peso_fresco std r Min Max Q25 Q50
T1 3.47075 0.2578648 4 3.122 3.734 3.37850 3.5135
T2 1.08900 0.1629438 4 0.922 1.298 0.98650 1.0680
T3 3.15050 0.1493106 4 2.927 3.238 3.14300 3.2185
T4 2.93975 0.4873297 4 2.251 3.361 2.78425 3.0735
Q75
T1 3.60575
T2 1.17050
T3 3.22600
T4 3.22900
$comparison
NULL
$groups
Datos_NaEl$RT_Peso_fresco groups
T1 3.47075 a
T3 3.15050 a
T4 2.93975 a
T2 1.08900 b
attr(,"class")
[1] "group"#Raíz tuberosa peso seco
#1.1 Anova para variable Raíz tuberosa peso seco
m19<- aov(RT_Peso_seco~Trat, data = Datos_NaEl)
anova(m19)Analysis of Variance Table
Response: RT_Peso_seco
Df Sum Sq Mean Sq F value Pr(>F)
Trat 3 0.255710 0.085237 39.772 1.64e-06 ***
Residuals 12 0.025718 0.002143
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#1.2 Pueba normaliada para Raíz tuberosa peso seco
shapiro.test(resid(m19))
Shapiro-Wilk normality test
data: resid(m19)
W = 0.96667, p-value = 0.7822#1.3 Prueba de homogeneidad de varianza para Raíz tuberosa peso fresco
library(car)
library(carData)m19var<-leveneTest(Datos_NaEl$RT_Peso_seco~Datos_NaEl$Trat, center=mean)group coerced to factor.m19varLevene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 3 1.4348 0.2813
12 #1.4 prueba de comparacion de promedio tukey para Raíz tuberosa peso seco
library(agricolae)
library(dplyr)m19tukey <-HSD.test(Datos_NaEl$RT_Peso_seco,Datos_NaEl$Trat, 12, 0.002143, alpha = 0.05)
m19tukey$statistics
MSerror Df Mean CV MSD
0.002143 12 0.2714375 17.05459 0.09718334
$parameters
test name.t ntr StudentizedRange alpha
Tukey Datos_NaEl$Trat 4 4.19866 0.05
$means
Datos_NaEl$RT_Peso_seco std r Min Max Q25 Q50 Q75
T1 0.40550 0.06477911 4 0.342 0.493 0.36750 0.3935 0.4315
T2 0.06800 0.02294922 4 0.039 0.095 0.06000 0.0690 0.0770
T3 0.33900 0.03787699 4 0.293 0.385 0.32300 0.3390 0.3550
T4 0.27325 0.04914180 4 0.229 0.331 0.23425 0.2665 0.3055
$comparison
NULL
$groups
Datos_NaEl$RT_Peso_seco groups
T1 0.40550 a
T3 0.33900 ab
T4 0.27325 b
T2 0.06800 c
attr(,"class")
[1] "group"