Análisis datos Eliana
Joaquin R & Kevin Q
6/12/2021
Brotes o tallos/planta
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Brotes = mean(`Brotes o tallos/planta`),
desv_Brotes = sd(`Brotes o tallos/planta`),
mediana_Brotes = median(`Brotes o tallos/planta`),
minimo_Brotes = min(`Brotes o tallos/planta`),
maximo_Brotes = max(`Brotes o tallos/planta`),
cv = (desv_Brotes/media_Brotes),
p75 = quantile(`Brotes o tallos/planta`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Brotes o tallos/planta`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Brotes o tallos/planta`),
desv = sd(`Brotes o tallos/planta`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Brotes o tallos/planta` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9.239 1.1548 9.382 8.97e-09 ***
## Residuals 72 8.862 0.1231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Brotes o tallos/planta` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 0.10409035 -0.42481620 0.63299691 0.9993731
## 3-1 -0.71448921 -1.24339577 -0.18558266 0.0015417
## 4-1 0.16849817 -0.36040839 0.69740472 0.9828239
## 5-1 -0.12799684 -0.65690339 0.40090971 0.9972494
## 6-1 -0.63882784 -1.16773439 -0.10992128 0.0070858
## 7-1 0.05402930 -0.47487725 0.58293586 0.9999957
## 8-1 -0.29658798 -0.82549453 0.23231857 0.6862834
## 9-1 0.32137011 -0.20753644 0.85027666 0.5868881
## 3-2 -0.81857957 -1.34748612 -0.28967301 0.0001578
## 4-2 0.06440781 -0.46449874 0.59331437 0.9999833
## 5-2 -0.23208719 -0.76099375 0.29681936 0.8929527
## 6-2 -0.74291819 -1.27182475 -0.21401164 0.0008429
## 7-2 -0.05006105 -0.57896760 0.47884550 0.9999977
## 8-2 -0.40067833 -0.92958489 0.12822822 0.2879693
## 9-2 0.21727976 -0.31162680 0.74618631 0.9239832
## 4-3 0.88298738 0.35408083 1.41189394 0.0000355
## 5-3 0.58649237 0.05758582 1.11539893 0.0187644
## 6-3 0.07566138 -0.45324518 0.60456793 0.9999424
## 7-3 0.76851852 0.23961196 1.29742507 0.0004832
## 8-3 0.41790123 -0.11100532 0.94680779 0.2369311
## 9-3 1.03585932 0.50695277 1.56476588 0.0000009
## 5-4 -0.29649501 -0.82540156 0.23241155 0.6866452
## 6-4 -0.80732601 -1.33623256 -0.27841945 0.0002036
## 7-4 -0.11446886 -0.64337542 0.41443769 0.9987523
## 8-4 -0.46508615 -0.99399270 0.06382041 0.1298279
## 9-4 0.15287194 -0.37603461 0.68177850 0.9908218
## 6-5 -0.51083100 -1.03973755 0.01807555 0.0665103
## 7-5 0.18202614 -0.34688041 0.71093270 0.9723323
## 8-5 -0.16859114 -0.69749769 0.36031541 0.9827641
## 9-5 0.44936695 -0.07953960 0.97827350 0.1603103
## 7-6 0.69285714 0.16395059 1.22176370 0.0024150
## 8-6 0.34223986 -0.18666670 0.87114641 0.5018655
## 9-6 0.96019795 0.43129139 1.48910450 0.0000056
## 8-7 -0.35061728 -0.87952384 0.17828927 0.4683378
## 9-7 0.26734081 -0.26156575 0.79624736 0.7926876
## 9-8 0.61795809 0.08905154 1.14686464 0.0105396lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Brotes o tallos/planta` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 5.974 2.9871 19.21 1.64e-07 ***
## Residuals 78 12.126 0.1555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Brotes o tallos/planta` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 -0.3324639 -0.5888633 -0.07606447 0.0075490
## 25-13 -0.6652302 -0.9216296 -0.40883080 0.0000001
## 25-18 -0.3327663 -0.5891657 -0.07636693 0.0074870lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Brotes o tallos/planta` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9.239 1.1548 38.391 < 2e-16 ***
## Tiempo 2 5.974 2.9871 99.304 < 2e-16 ***
## Tratamiento:Tiempo 16 1.264 0.0790 2.625 0.00422 **
## Residuals 54 1.624 0.0301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)El mejor modelo:
library(broom)##
## Attaching package: 'broom'## The following object is masked from 'package:RVAideMemoire':
##
## bootstraptidy(Mod_3)Estimación de medias, errores estándar e intervalos de confianza:
library(emmeans)
emmeans(Mod_3, specs = c("Tratamiento", "Tiempo"))## Tratamiento Tiempo emmean SE df lower.CL upper.CL
## 1 13 5.87 0.1 54 5.67 6.07
## 2 13 6.00 0.1 54 5.80 6.20
## 3 13 4.78 0.1 54 4.58 4.98
## 4 13 5.77 0.1 54 5.57 5.98
## 5 13 5.31 0.1 54 5.11 5.51
## 6 13 4.91 0.1 54 4.71 5.11
## 7 13 5.62 0.1 54 5.42 5.83
## 8 13 5.36 0.1 54 5.16 5.56
## 9 13 6.00 0.1 54 5.80 6.20
## 1 18 5.40 0.1 54 5.20 5.60
## 2 18 5.23 0.1 54 5.03 5.43
## 3 18 4.67 0.1 54 4.47 4.87
## 4 18 5.43 0.1 54 5.23 5.63
## 5 18 5.23 0.1 54 5.03 5.43
## 6 18 4.65 0.1 54 4.45 4.85
## 7 18 5.46 0.1 54 5.26 5.66
## 8 18 5.00 0.1 54 4.80 5.20
## 9 18 5.56 0.1 54 5.36 5.77
## 1 25 4.65 0.1 54 4.45 4.85
## 2 25 5.00 0.1 54 4.80 5.20
## 3 25 4.33 0.1 54 4.13 4.53
## 4 25 5.23 0.1 54 5.02 5.43
## 5 25 5.00 0.1 54 4.80 5.20
## 6 25 4.44 0.1 54 4.24 4.64
## 7 25 5.00 0.1 54 4.80 5.20
## 8 25 4.67 0.1 54 4.47 4.87
## 9 25 5.32 0.1 54 5.12 5.52
##
## Confidence level used: 0.95Tamaño del efecto - Eta cuadrado:
library(effectsize)
eta_squared(Mod_3)Tamaño del efecto - Índice de Cohen:
cohens_f(Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Brotes o tallos/planta` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Brotes o tallos/planta` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Brotes o tallos/planta` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- Las respuestas de los brotes dependieron significativamente del tratamiento, y del tiempo, pero no de la interacción entre estos dos
Anova permutacional
- Las respuestas de los brotes dependieron significativamente del tratamiento, y del tiempo, pero no de la interacción entre estos dos. Al igual que los anovas convencionales planteados.
Longitud tallo
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_LonTallo = mean(`Longitud tallo`),
desv_LonTallo = sd(`Longitud tallo`),
mediana_LonTallo = median(`Longitud tallo`),
minimo_LonTallo = min(`Longitud tallo`),
maximo_LonTallo = max(`Longitud tallo`),
cv = (desv_LonTallo/media_LonTallo),
p75 = quantile(`Longitud tallo`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Longitud tallo`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Longitud tallo`),
desv = sd(`Longitud tallo`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Longitud tallo` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 168 21.1 0.015 1
## Residuals 72 101573 1410.7TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Longitud tallo` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -1.5892094 -58.21331 55.03489 1.0000000
## 3-1 0.4634717 -56.16063 57.08757 1.0000000
## 4-1 1.7655678 -54.85853 58.38967 1.0000000
## 5-1 2.9272642 -53.69684 59.55136 1.0000000
## 6-1 2.6153439 -54.00876 59.23944 1.0000000
## 7-1 1.0535714 -55.57053 57.67767 1.0000000
## 8-1 0.6791887 -55.94491 57.30329 1.0000000
## 9-1 3.0309593 -53.59314 59.65506 1.0000000
## 3-2 2.0526811 -54.57142 58.67678 1.0000000
## 4-2 3.3547772 -53.26932 59.97888 0.9999999
## 5-2 4.5164736 -52.10763 61.14057 0.9999994
## 6-2 4.2045533 -52.41955 60.82865 0.9999997
## 7-2 2.6427808 -53.98132 59.26688 1.0000000
## 8-2 2.2683981 -54.35570 58.89250 1.0000000
## 9-2 4.6201687 -52.00393 61.24427 0.9999993
## 4-3 1.3020961 -55.32200 57.92620 1.0000000
## 5-3 2.4637925 -54.16031 59.08789 1.0000000
## 6-3 2.1518722 -54.47223 58.77597 1.0000000
## 7-3 0.5900997 -56.03400 57.21420 1.0000000
## 8-3 0.2157170 -56.40838 56.83982 1.0000000
## 9-3 2.5674876 -54.05661 59.19159 1.0000000
## 5-4 1.1616965 -55.46240 57.78580 1.0000000
## 6-4 0.8497761 -55.77432 57.47388 1.0000000
## 7-4 -0.7119963 -57.33610 55.91210 1.0000000
## 8-4 -1.0863791 -57.71048 55.53772 1.0000000
## 9-4 1.2653916 -55.35871 57.88949 1.0000000
## 6-5 -0.3119203 -56.93602 56.31218 1.0000000
## 7-5 -1.8736928 -58.49779 54.75041 1.0000000
## 8-5 -2.2480755 -58.87218 54.37602 1.0000000
## 9-5 0.1036951 -56.52040 56.72780 1.0000000
## 7-6 -1.5617725 -58.18587 55.06233 1.0000000
## 8-6 -1.9361552 -58.56026 54.68794 1.0000000
## 9-6 0.4156154 -56.20848 57.03972 1.0000000
## 8-7 -0.3743827 -56.99848 56.24972 1.0000000
## 9-7 1.9773879 -54.64671 58.60149 1.0000000
## 9-8 2.3517706 -54.27233 58.97587 1.0000000lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Longitud tallo` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 101140 50570 6561 <2e-16 ***
## Residuals 78 601 8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Longitud tallo` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 42.59086 40.78545 44.39626 0
## 25-13 86.55205 84.74665 88.35746 0
## 25-18 43.96120 42.15579 45.76660 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Longitud tallo` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 168 21 7.679 8.32e-07 ***
## Tiempo 2 101140 50570 18447.494 < 2e-16 ***
## Tratamiento:Tiempo 16 285 18 6.493 8.63e-08 ***
## Residuals 54 148 3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Longitud tallo` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Longitud tallo` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Longitud tallo` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta de la longitud del tallo está en función de la interacción de tratamiento con el tiempo.
Anova permutacional
- La respuesta de la longitud del tallo está en función de la interacción de tratamiento con el tiempo, corroborando el anova convencional.
Diámetro tallo
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_DiaTallo = mean(`Diametro tallo`),
desv_DiaTallo = sd(`Diametro tallo`),
mediana_DiaTallo = median(`Diametro tallo`),
minimo_DiaTallo = min(`Diametro tallo`),
maximo_DiaTallo = max(`Diametro tallo`),
cv = (desv_DiaTallo/media_DiaTallo),
p75 = quantile(`Diametro tallo`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Diametro tallo`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Diametro tallo`),
desv = sd(`Diametro tallo`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Diametro tallo` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.0417 0.005212 0.851 0.561
## Residuals 72 0.4408 0.006122TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Diametro tallo` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 0.0003357753 -0.11762304 0.11829459 1.0000000
## 3-1 -0.0241351241 -0.14209394 0.09382369 0.9991679
## 4-1 0.0114774115 -0.10648141 0.12943623 0.9999971
## 5-1 0.0300099655 -0.08794885 0.14796878 0.9961111
## 6-1 0.0127391127 -0.10521971 0.13069793 0.9999934
## 7-1 0.0370115995 -0.08094722 0.15497042 0.9843989
## 8-1 0.0575362909 -0.06042253 0.17549511 0.8227210
## 9-1 0.0275980193 -0.09036080 0.14555684 0.9978293
## 3-2 -0.0244708995 -0.14242972 0.09348792 0.9990803
## 4-2 0.0111416361 -0.10681718 0.12910045 0.9999977
## 5-2 0.0296741902 -0.08828463 0.14763301 0.9964010
## 6-2 0.0124033374 -0.10555548 0.13036216 0.9999947
## 7-2 0.0366758242 -0.08128299 0.15463464 0.9852770
## 8-2 0.0572005155 -0.06075830 0.17515933 0.8273359
## 9-2 0.0272622439 -0.09069657 0.14522106 0.9980087
## 4-3 0.0356125356 -0.08234628 0.15357135 0.9878086
## 5-3 0.0541450897 -0.06381373 0.17210391 0.8663722
## 6-3 0.0368742369 -0.08108458 0.15483306 0.9847629
## 7-3 0.0611467236 -0.05681210 0.17910554 0.7693947
## 8-3 0.0816714150 -0.03628740 0.19963023 0.4079252
## 9-3 0.0517331434 -0.06622568 0.16969196 0.8932426
## 5-4 0.0185325540 -0.09942626 0.13649137 0.9998827
## 6-4 0.0012617013 -0.11669712 0.11922052 1.0000000
## 7-4 0.0255341880 -0.09242463 0.14349301 0.9987506
## 8-4 0.0460588794 -0.07189994 0.16401770 0.9423774
## 9-4 0.0161206078 -0.10183821 0.13407943 0.9999594
## 6-5 -0.0172708528 -0.13522967 0.10068797 0.9999313
## 7-5 0.0070016340 -0.11095718 0.12496045 0.9999999
## 8-5 0.0275263253 -0.09043249 0.14548514 0.9978687
## 9-5 -0.0024119463 -0.12037077 0.11554687 1.0000000
## 7-6 0.0242724868 -0.09368633 0.14223131 0.9991330
## 8-6 0.0447971781 -0.07316164 0.16275600 0.9507056
## 9-6 0.0148589065 -0.10309991 0.13281773 0.9999783
## 8-7 0.0205246914 -0.09743413 0.13848351 0.9997476
## 9-7 -0.0094135802 -0.12737240 0.10854524 0.9999994
## 9-8 -0.0299382716 -0.14789709 0.08802055 0.9961745lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Diametro tallo` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 0.3306 0.16528 84.85 <2e-16 ***
## Residuals 78 0.1519 0.00195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Diametro tallo` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 -0.14489845 -0.173598211 -0.11619868 0.0000000
## 25-13 -0.12361067 -0.152310430 -0.09491090 0.0000000
## 25-18 0.02128778 -0.007411984 0.04998755 0.1856561lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Diametro tallo` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.0417 0.00521 3.877 0.00114 **
## Tiempo 2 0.3306 0.16528 122.938 < 2e-16 ***
## Tratamiento:Tiempo 16 0.0376 0.00235 1.750 0.06457 .
## Residuals 54 0.0726 0.00134
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Diametro tallo` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Diametro tallo` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Diametro tallo` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del diámetro del tallo está en función del tiempo, con una significancia baja del tratamiento.
Anova permutacional
- A diferencia del anova convencional, el diámetro del tallo está en razón de los factores tiempo y tratamiento, pero no de su interacción.
Peso fresco
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_PesoF = mean(`peso fresco (g)`),
desv_PesoF = sd(`peso fresco (g)`),
mediana_PesoF = median(`peso fresco (g)`),
minimo_PesoF = min(`peso fresco (g)`),
maximo_PesoF = max(`peso fresco (g)`),
cv = (desv_PesoF/media_PesoF),
p75 = quantile(`peso fresco (g)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `peso fresco (g)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`peso fresco (g)`),
desv = sd(`peso fresco (g)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`peso fresco (g)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 57083 7135 0.346 0.945
## Residuals 72 1484433 20617TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `peso fresco (g)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -23.313333 -239.7804 193.1537 0.9999936
## 3-1 -30.941111 -247.4082 185.5260 0.9999428
## 4-1 24.168889 -192.2982 240.6360 0.9999915
## 5-1 -29.133333 -245.6004 187.3337 0.9999639
## 6-1 2.308889 -214.1582 218.7760 1.0000000
## 7-1 35.385556 -181.0815 251.8526 0.9998418
## 8-1 -21.540000 -238.0071 194.9271 0.9999965
## 9-1 41.083333 -175.3837 257.5504 0.9995196
## 3-2 -7.627778 -224.0949 208.8393 1.0000000
## 4-2 47.482222 -168.9849 263.9493 0.9986264
## 5-2 -5.820000 -222.2871 210.6471 1.0000000
## 6-2 25.622222 -190.8449 242.0893 0.9999866
## 7-2 58.698889 -157.7682 275.1660 0.9939972
## 8-2 1.773333 -214.6937 218.2404 1.0000000
## 9-2 64.396667 -152.0704 280.8637 0.9889185
## 4-3 55.110000 -161.3571 271.5771 0.9960923
## 5-3 1.807778 -214.6593 218.2749 1.0000000
## 6-3 33.250000 -183.2171 249.7171 0.9999011
## 7-3 66.326667 -150.1404 282.7937 0.9865913
## 8-3 9.401111 -207.0660 225.8682 1.0000000
## 9-3 72.024444 -144.4426 288.4915 0.9774849
## 5-4 -53.302222 -269.7693 163.1649 0.9968965
## 6-4 -21.860000 -238.3271 194.6071 0.9999961
## 7-4 11.216667 -205.2504 227.6837 1.0000000
## 8-4 -45.708889 -262.1760 170.7582 0.9989548
## 9-4 16.914444 -199.5526 233.3815 0.9999995
## 6-5 31.442222 -185.0249 247.9093 0.9999353
## 7-5 64.518889 -151.9482 280.9860 0.9887812
## 8-5 7.593333 -208.8737 224.0604 1.0000000
## 9-5 70.216667 -146.2504 286.6837 0.9807679
## 7-6 33.076667 -183.3904 249.5437 0.9999049
## 8-6 -23.848889 -240.3160 192.6182 0.9999923
## 9-6 38.774444 -177.6926 255.2415 0.9996867
## 8-7 -56.925556 -273.3926 159.5415 0.9951227
## 9-7 5.697778 -210.7693 222.1649 1.0000000
## 9-8 62.623333 -153.8437 279.0904 0.9907667lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`peso fresco (g)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 1019862 509931 76.25 <2e-16 ***
## Residuals 78 521654 6688
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `peso fresco (g)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 210.0489 156.869838 263.2279 0.0000000
## 25-13 258.5452 205.366134 311.7242 0.0000000
## 25-18 48.4963 -4.682755 101.6753 0.0811814lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`peso fresco (g)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 57083 7135 0.987 0.457
## Tiempo 2 1019862 509931 70.507 8.77e-16 ***
## Tratamiento:Tiempo 16 74023 4626 0.640 0.837
## Residuals 54 390548 7232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`peso fresco (g)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Brotes o tallos/planta` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Brotes o tallos/planta` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Peso fresco está en función del tiempo.
Anova permutacional
- La respuesta del Peso fresco está en función del tiempo, corrobora el anova convencional.
Peso seco
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_PesoS = mean(`peso seco (g)`),
desv_PesoS = sd(`peso seco (g)`),
mediana_PesoS = median(`peso seco (g)`),
minimo_PesoS = min(`peso seco (g)`),
maximo_PesoS = max(`peso seco (g)`),
cv = (desv_PesoS/media_PesoS),
p75 = quantile(`peso seco (g)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `peso seco (g)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`peso seco (g)`),
desv = sd(`peso seco (g)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`peso seco (g)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 682 85.2 0.189 0.992
## Residuals 72 32460 450.8TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `peso seco (g)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -3.2131778 -35.22318 28.79683 0.9999963
## 3-1 -0.4832556 -32.49326 31.52675 1.0000000
## 4-1 2.1024889 -29.90751 34.11249 0.9999999
## 5-1 -2.7101556 -34.72016 29.29985 0.9999990
## 6-1 2.3528778 -29.65713 34.36288 0.9999997
## 7-1 5.8424111 -26.16759 37.85241 0.9996399
## 8-1 0.1425889 -31.86741 32.15259 1.0000000
## 9-1 4.7168778 -27.29313 36.72688 0.9999278
## 3-2 2.7299222 -29.28008 34.73993 0.9999990
## 4-2 5.3156667 -26.69434 37.32567 0.9998220
## 5-2 0.5030222 -31.50698 32.51303 1.0000000
## 6-2 5.5660556 -26.44395 37.57606 0.9997488
## 7-2 9.0555889 -22.95441 41.06559 0.9920339
## 8-2 3.3557667 -28.65424 35.36577 0.9999948
## 9-2 7.9300556 -24.07995 39.94006 0.9967630
## 4-3 2.5857444 -29.42426 34.59575 0.9999993
## 5-3 -2.2269000 -34.23690 29.78310 0.9999998
## 6-3 2.8361333 -29.17387 34.84614 0.9999986
## 7-3 6.3256667 -25.68434 38.33567 0.9993540
## 8-3 0.6258444 -31.38416 32.63585 1.0000000
## 9-3 5.2001333 -26.80987 37.21014 0.9998491
## 5-4 -4.8126444 -36.82265 27.19736 0.9999159
## 6-4 0.2503889 -31.75961 32.26039 1.0000000
## 7-4 3.7399222 -28.27008 35.74993 0.9999879
## 8-4 -1.9599000 -33.96990 30.05010 0.9999999
## 9-4 2.6143889 -29.39561 34.62439 0.9999993
## 6-5 5.0630333 -26.94697 37.07304 0.9998766
## 7-5 8.5525667 -23.45744 40.56257 0.9945679
## 8-5 2.8527444 -29.15726 34.86275 0.9999985
## 9-5 7.4270333 -24.58297 39.43704 0.9979530
## 7-6 3.4895333 -28.52047 35.49954 0.9999929
## 8-6 -2.2102889 -34.22029 29.79971 0.9999998
## 9-6 2.3640000 -29.64600 34.37400 0.9999997
## 8-7 -5.6998222 -37.70983 26.31018 0.9997002
## 9-7 -1.1255333 -33.13554 30.88447 1.0000000
## 9-8 4.5742889 -27.43571 36.58429 0.9999429lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`peso seco (g)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 26697 13348 161.6 <2e-16 ***
## Residuals 78 6445 83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `peso seco (g)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 27.94244 22.03149 33.85339 0
## 25-13 43.93077 38.01982 49.84172 0
## 25-18 15.98833 10.07738 21.89928 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`peso seco (g)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 682 85 0.941 0.491
## Tiempo 2 26697 13348 147.437 <2e-16 ***
## Tratamiento:Tiempo 16 874 55 0.603 0.867
## Residuals 54 4889 91
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`peso seco (g)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`peso seco (g)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`peso seco (g)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Peso seco está en función del tiempo.
Anova permutacional
- La respuesta del Peso seco está en función del tiempo, corrobora el anova convencional.
TAC
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_TAC = mean(`TAC (g*semana)`),
desv_TAC = sd(`TAC (g*semana)`),
mediana_TAC = median(`TAC (g*semana)`),
minimo_TAC = min(`TAC (g*semana)`),
maximo_TAC = max(`TAC (g*semana)`),
cv = (desv_TAC/media_TAC),
p75 = quantile(`TAC (g*semana)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `TAC (g*semana)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`TAC (g*semana)`),
desv = sd(`TAC (g*semana)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`TAC (g*semana)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9.5 1.185 0.207 0.989
## Residuals 72 411.3 5.712TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `TAC (g*semana)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -0.11908111 -3.722218 3.484056 1.0000000
## 3-1 0.33935329 -3.263784 3.942490 0.9999977
## 4-1 0.14053159 -3.462605 3.743669 1.0000000
## 5-1 -0.13384548 -3.736983 3.469292 1.0000000
## 6-1 0.35043099 -3.252706 3.953568 0.9999971
## 7-1 0.58957313 -3.013564 4.192710 0.9998407
## 8-1 0.49122250 -3.111915 4.094360 0.9999601
## 9-1 0.97095440 -2.632183 4.574091 0.9942454
## 3-2 0.45843440 -3.144703 4.061571 0.9999766
## 4-2 0.25961270 -3.343524 3.862750 0.9999997
## 5-2 -0.01476437 -3.617901 3.588373 1.0000000
## 6-2 0.46951210 -3.133625 4.072649 0.9999718
## 7-2 0.70865425 -2.894483 4.311791 0.9993761
## 8-2 0.61030361 -2.992833 4.213441 0.9997935
## 9-2 1.09003552 -2.513102 4.693173 0.9876468
## 4-3 -0.19882171 -3.801959 3.404315 1.0000000
## 5-3 -0.47319877 -4.076336 3.129938 0.9999701
## 6-3 0.01107770 -3.592059 3.614215 1.0000000
## 7-3 0.25021984 -3.352917 3.853357 0.9999998
## 8-3 0.15186921 -3.451268 3.755006 1.0000000
## 9-3 0.63160111 -2.971536 4.234738 0.9997332
## 5-4 -0.27437706 -3.877514 3.328760 0.9999996
## 6-4 0.20989940 -3.393238 3.813036 0.9999999
## 7-4 0.44904155 -3.154095 4.052179 0.9999800
## 8-4 0.35069091 -3.252446 3.953828 0.9999971
## 9-4 0.83042282 -2.772714 4.433560 0.9980476
## 6-5 0.48427647 -3.118861 4.087414 0.9999643
## 7-5 0.72341861 -2.879718 4.326556 0.9992748
## 8-5 0.62506798 -2.978069 4.228205 0.9997531
## 9-5 1.10479988 -2.498337 4.707937 0.9865305
## 7-6 0.23914214 -3.363995 3.842279 0.9999999
## 8-6 0.14079151 -3.462346 3.743929 1.0000000
## 9-6 0.62052341 -2.982614 4.223660 0.9997662
## 8-7 -0.09835063 -3.701488 3.504786 1.0000000
## 9-7 0.38138127 -3.221756 3.984518 0.9999944
## 9-8 0.47973190 -3.123405 4.082869 0.9999668lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`TAC (g*semana)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 207.3 103.63 37.86 3.23e-12 ***
## Residuals 78 213.5 2.74
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `TAC (g*semana)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 3.475783 2.3999570 4.551609 0.0000000
## 25-13 0.171343 -0.9044828 1.247169 0.9233682
## 25-18 -3.304440 -4.3802656 -2.228614 0.0000000lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`TAC (g*semana)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9.48 1.19 0.37 0.932
## Tiempo 2 207.27 103.63 32.39 5.7e-10 ***
## Tratamiento:Tiempo 16 31.25 1.95 0.61 0.862
## Residuals 54 172.77 3.20
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`TAC (g*semana)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`TAC (g*semana)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`TAC (g*semana)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del TAC está en función del tiempo.
Anova permutacional
- La respuesta del TAC está en función del tiempo, corrobora el anova convencional.
TCR
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_TCR = mean(`TCR (g*g-1*sem-1)`),
desv_TCR = sd(`TCR (g*g-1*sem-1)`),
mediana_TCR = median(`TCR (g*g-1*sem-1)`),
minimo_TCR = min(`TCR (g*g-1*sem-1)`),
maximo_TCR = max(`TCR (g*g-1*sem-1)`),
cv = (desv_TCR/media_TCR),
p75 = quantile(`TCR (g*g-1*sem-1)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `TCR (g*g-1*sem-1)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`TCR (g*g-1*sem-1)`),
desv = sd(`TCR (g*g-1*sem-1)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`TCR (g*g-1*sem-1)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 3.98 0.4975 0.65 0.733
## Residuals 72 55.11 0.7654TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `TCR (g*g-1*sem-1)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -0.01250466 -1.3314478 1.3064385 1.0000000
## 3-1 0.11373650 -1.2052066 1.4326796 0.9999989
## 4-1 0.31372970 -1.0052134 1.6326728 0.9975619
## 5-1 0.26964302 -1.0493001 1.5885861 0.9991729
## 6-1 -0.02349479 -1.3424379 1.2954483 1.0000000
## 7-1 0.23316973 -1.0857734 1.5521128 0.9997159
## 8-1 0.47393740 -0.8450057 1.7928805 0.9642055
## 9-1 0.65990479 -0.6590383 1.9788479 0.8016295
## 3-2 0.12624116 -1.1927020 1.4451843 0.9999974
## 4-2 0.32623436 -0.9927088 1.6451775 0.9967983
## 5-2 0.28214768 -1.0367954 1.6010908 0.9988524
## 6-2 -0.01099013 -1.3299333 1.3079530 1.0000000
## 7-2 0.24567439 -1.0732687 1.5646175 0.9995816
## 8-2 0.48644206 -0.8325011 1.8053852 0.9583245
## 9-2 0.67240945 -0.6465337 1.9913526 0.7849649
## 4-3 0.19999320 -1.1189499 1.5189363 0.9999103
## 5-3 0.15590652 -1.1630366 1.4748496 0.9999867
## 6-3 -0.13723129 -1.4561744 1.1817118 0.9999951
## 7-3 0.11943323 -1.1995099 1.4383763 0.9999983
## 8-3 0.36020090 -0.9587422 1.6791440 0.9937040
## 9-3 0.54616829 -0.7727748 1.8651114 0.9206882
## 5-4 -0.04408668 -1.3630298 1.2748564 1.0000000
## 6-4 -0.33722449 -1.6561676 0.9817186 0.9959760
## 7-4 -0.08055998 -1.3995031 1.2383831 0.9999999
## 8-4 0.16020770 -1.1587354 1.4791508 0.9999836
## 9-4 0.34617509 -0.9727680 1.6651182 0.9951869
## 6-5 -0.29313781 -1.6120809 1.0258053 0.9984913
## 7-5 -0.03647329 -1.3554164 1.2824698 1.0000000
## 8-5 0.20429438 -1.1146487 1.5232375 0.9998947
## 9-5 0.39026177 -0.9286813 1.7092049 0.9893003
## 7-6 0.25666452 -1.0622786 1.5756076 0.9994227
## 8-6 0.49743219 -0.8215109 1.8163753 0.9526135
## 9-6 0.68339958 -0.6355435 2.0023427 0.7698220
## 8-7 0.24076767 -1.0781754 1.5597108 0.9996395
## 9-7 0.42673506 -0.8922081 1.7456782 0.9810741
## 9-8 0.18596739 -1.1329757 1.5049105 0.9999484lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`TCR (g*g-1*sem-1)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 12.54 6.270 10.51 9.12e-05 ***
## Residuals 78 46.55 0.597
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `TCR (g*g-1*sem-1)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 0.4315903 -0.07076334 0.93394391 0.1066071
## 25-13 -0.5304903 -1.03284396 -0.02813671 0.0360421
## 25-18 -0.9620806 -1.46443424 -0.45972700 0.0000520lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`TCR (g*g-1*sem-1)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 3.98 0.497 0.862 0.554172
## Tiempo 2 12.54 6.270 10.858 0.000109 ***
## Tratamiento:Tiempo 16 11.39 0.712 1.233 0.274782
## Residuals 54 31.18 0.577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`TCR (g*g-1*sem-1)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`TCR (g*g-1*sem-1)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`TCR (g*g-1*sem-1)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del TCR está en función del tiempo.
Anova permutacional
- La respuesta del TCR está en función del tiempo, corrobora el anova convencional.
Da
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Da = mean(`Da (gr/cc)`),
desv_Da = sd(`Da (gr/cc)`),
mediana_Da = median(`Da (gr/cc)`),
minimo_Da = min(`Da (gr/cc)`),
maximo_Da = max(`Da (gr/cc)`),
cv = (desv_Da/media_Da),
p75 = quantile(`Da (gr/cc)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Da (gr/cc)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Da (gr/cc)`),
desv = sd(`Da (gr/cc)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Da (gr/cc)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.00091 0.0001135 0.057 1
## Residuals 72 0.14294 0.0019853TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Da (gr/cc)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 0.0053590395 -0.06181310 0.07253118 0.9999994
## 3-1 0.0026859699 -0.06448617 0.06985811 1.0000000
## 4-1 0.0023623352 -0.06480981 0.06953448 1.0000000
## 5-1 -0.0023059322 -0.06947808 0.06486621 1.0000000
## 6-1 -0.0014709981 -0.06864314 0.06570115 1.0000000
## 7-1 0.0042933145 -0.06287883 0.07146546 0.9999999
## 8-1 0.0080435028 -0.05912864 0.07521565 0.9999853
## 9-1 0.0063027307 -0.06086941 0.07347487 0.9999978
## 3-2 -0.0026730697 -0.06984521 0.06449907 1.0000000
## 4-2 -0.0029967043 -0.07016885 0.06417544 1.0000000
## 5-2 -0.0076649718 -0.07483712 0.05950717 0.9999899
## 6-2 -0.0068300377 -0.07400218 0.06034211 0.9999959
## 7-2 -0.0010657250 -0.06823787 0.06610642 1.0000000
## 8-2 0.0026844633 -0.06448768 0.06985661 1.0000000
## 9-2 0.0009436911 -0.06622845 0.06811584 1.0000000
## 4-3 -0.0003236347 -0.06749578 0.06684851 1.0000000
## 5-3 -0.0049919021 -0.07216405 0.06218024 0.9999996
## 6-3 -0.0041569680 -0.07132911 0.06301518 0.9999999
## 7-3 0.0016073446 -0.06556480 0.06877949 1.0000000
## 8-3 0.0053575330 -0.06181461 0.07252968 0.9999994
## 9-3 0.0036167608 -0.06355538 0.07078890 1.0000000
## 5-4 -0.0046682674 -0.07184041 0.06250388 0.9999998
## 6-4 -0.0038333333 -0.07100548 0.06333881 1.0000000
## 7-4 0.0019309793 -0.06524116 0.06910312 1.0000000
## 8-4 0.0056811676 -0.06149098 0.07285331 0.9999990
## 9-4 0.0039403955 -0.06323175 0.07111254 0.9999999
## 6-5 0.0008349341 -0.06633721 0.06800708 1.0000000
## 7-5 0.0065992467 -0.06057290 0.07377139 0.9999969
## 8-5 0.0103494350 -0.05682271 0.07752158 0.9998988
## 9-5 0.0086086629 -0.05856348 0.07578081 0.9999752
## 7-6 0.0057643126 -0.06140783 0.07293646 0.9999989
## 8-6 0.0095145009 -0.05765764 0.07668664 0.9999466
## 9-6 0.0077737288 -0.05939842 0.07494587 0.9999887
## 8-7 0.0037501883 -0.06342196 0.07092233 1.0000000
## 9-7 0.0020094162 -0.06516273 0.06918156 1.0000000
## 9-8 -0.0017407721 -0.06891292 0.06543137 1.0000000lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Da (gr/cc)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 0.07801 0.03900 46.21 5.79e-14 ***
## Residuals 78 0.06584 0.00084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Da (gr/cc)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 -0.066624890 -0.08551751 -0.04773227 0.0000000
## 25-13 -0.001615631 -0.02050825 0.01727699 0.9772551
## 25-18 0.065009259 0.04611664 0.08390188 0.0000000lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Da (gr/cc)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.00091 0.00011 0.125 0.998
## Tiempo 2 0.07801 0.03900 42.812 7.26e-12 ***
## Tratamiento:Tiempo 16 0.01573 0.00098 1.079 0.396
## Residuals 54 0.04920 0.00091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Da (gr/cc)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Da (gr/cc)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Da (gr/cc)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta de la Da está en función del tiempo, cabe resaltar la nula significancia del tratamiento sobre la respuesta.
Anova permutacional
- La respuesta del PT está en función del tiempo, pero con una significancia baja, corrobora el anova convencional.
PT
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_PT = mean(`PT (%)`),
desv_PT = sd(`PT (%)`),
mediana_PT = median(`PT (%)`),
minimo_PT = min(`PT (%)`),
maximo_PT = max(`PT (%)`),
cv = (desv_PT/media_PT),
p75 = quantile(`PT (%)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `PT (%)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`PT (%)`),
desv = sd(`PT (%)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`PT (%)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 263 32.84 0.355 0.94
## Residuals 72 6655 92.44TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `PT (%)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -1.359472693 -15.853768 13.134822 0.9999978
## 3-1 2.221092279 -12.273203 16.715387 0.9999029
## 4-1 -0.761902072 -15.256197 13.732393 1.0000000
## 5-1 -2.683333333 -17.177628 11.810962 0.9996000
## 6-1 0.008841808 -14.485453 14.503137 1.0000000
## 7-1 -1.657156309 -16.151451 12.837139 0.9999897
## 8-1 2.999340866 -11.494954 17.493636 0.9990969
## 9-1 1.706629002 -12.787666 16.200924 0.9999871
## 3-2 3.580564972 -10.913730 18.074860 0.9968262
## 4-2 0.597570621 -13.896724 15.091866 1.0000000
## 5-2 -1.323860640 -15.818156 13.170434 0.9999982
## 6-2 1.368314501 -13.125981 15.862610 0.9999977
## 7-2 -0.297683616 -14.791979 14.196611 1.0000000
## 8-2 4.358813559 -10.135481 18.853109 0.9881133
## 9-2 3.066101695 -11.428193 17.560397 0.9989412
## 4-3 -2.982994350 -17.477289 11.511301 0.9991319
## 5-3 -4.904425612 -19.398721 9.589869 0.9750399
## 6-3 -2.212250471 -16.706545 12.282045 0.9999058
## 7-3 -3.878248588 -18.372544 10.616046 0.9945145
## 8-3 0.778248588 -13.716046 15.272544 1.0000000
## 9-3 -0.514463277 -15.008758 13.979832 1.0000000
## 5-4 -1.921431262 -16.415726 12.572864 0.9999679
## 6-4 0.770743879 -13.723551 15.265039 1.0000000
## 7-4 -0.895254237 -15.389549 13.599041 0.9999999
## 8-4 3.761242938 -10.733052 18.255538 0.9955458
## 9-4 2.468531073 -12.025764 16.962826 0.9997849
## 6-5 2.692175141 -11.802120 17.186470 0.9995902
## 7-5 1.026177024 -13.468118 15.520472 0.9999998
## 8-5 5.682674200 -8.811621 20.176969 0.9410560
## 9-5 4.389962335 -10.104333 18.884257 0.9875541
## 7-6 -1.665998117 -16.160293 12.828297 0.9999893
## 8-6 2.990499058 -11.503796 17.484794 0.9991160
## 9-6 1.697787194 -12.796508 16.192082 0.9999876
## 8-7 4.656497175 -9.837798 19.150792 0.9818934
## 9-7 3.363785311 -11.130510 17.858080 0.9979496
## 9-8 -1.292711864 -15.787007 13.201583 0.9999985lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`PT (%)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 833 416.6 5.34 0.00671 **
## Residuals 78 6085 78.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `PT (%)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 -7.734338 -13.4778681 -1.990807 0.0052974
## 25-13 -2.676190 -8.4197199 3.067341 0.5088296
## 25-18 5.058148 -0.6853822 10.801679 0.0955578lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`PT (%)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 263 32.8 0.334 0.9490
## Tiempo 2 833 416.6 4.238 0.0195 *
## Tratamiento:Tiempo 16 514 32.1 0.327 0.9917
## Residuals 54 5308 98.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`PT (%)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`PT (%)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`PT (%)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del PT está en función del tiempo, pero con una significancia no tal alta.
Anova permutacional
- La respuesta del PT está en función del tiempo, pero con una significancia baja,menor al del anova convencional.
Retención
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Ret = mean(`Retencion (%)`),
desv_Ret = sd(`Retencion (%)`),
mediana_Ret = median(`Retencion (%)`),
minimo_Ret = min(`Retencion (%)`),
maximo_Ret = max(`Retencion (%)`),
cv = (desv_Ret/media_Ret),
p75 = quantile(`Retencion (%)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Retencion (%)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Retencion (%)`),
desv = sd(`Retencion (%)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Retencion (%)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 680 84.97 0.812 0.594
## Residuals 72 7534 104.63TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Retencion (%)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -1.6110301 -17.031950 13.809890 0.9999949
## 3-1 -8.7557463 -24.176666 6.665174 0.6717306
## 4-1 -5.9397232 -21.360643 9.481197 0.9466109
## 5-1 0.7222235 -14.698696 16.143143 1.0000000
## 6-1 -4.5384763 -19.959396 10.882444 0.9896681
## 7-1 -5.7994593 -21.220379 9.621461 0.9533740
## 8-1 -5.2590901 -20.680010 10.161830 0.9738177
## 9-1 -3.5413784 -18.962298 11.879541 0.9980965
## 3-2 -7.1447162 -22.565636 8.276204 0.8602765
## 4-2 -4.3286931 -19.749613 11.092227 0.9924356
## 5-2 2.3332536 -13.087666 17.754174 0.9999118
## 6-2 -2.9274462 -18.348366 12.493474 0.9995187
## 7-2 -4.1884292 -19.609349 11.232491 0.9939315
## 8-2 -3.6480600 -19.068980 11.772860 0.9976537
## 9-2 -1.9303483 -17.351268 13.490572 0.9999793
## 4-3 2.8160231 -12.604897 18.236943 0.9996386
## 5-3 9.4779698 -5.942950 24.898890 0.5717480
## 6-3 4.2172700 -11.203650 19.638190 0.9936451
## 7-3 2.9562870 -12.464633 18.377207 0.9994828
## 8-3 3.4966562 -11.924264 18.917576 0.9982604
## 9-3 5.2143678 -10.206552 20.635288 0.9751448
## 5-4 6.6619467 -8.758973 22.082867 0.9011070
## 6-4 1.4012469 -14.019673 16.822167 0.9999983
## 7-4 0.1402638 -15.280656 15.561184 1.0000000
## 8-4 0.6806330 -14.740287 16.101553 1.0000000
## 9-4 2.3983447 -13.022575 17.819265 0.9998914
## 6-5 -5.2606998 -20.681620 10.160220 0.9737690
## 7-5 -6.5216828 -21.942603 8.899237 0.9113942
## 8-5 -5.9813137 -21.402234 9.439606 0.9444814
## 9-5 -4.2636020 -19.684522 11.157318 0.9931624
## 7-6 -1.2609830 -16.681903 14.159937 0.9999993
## 8-6 -0.7206138 -16.141534 14.700306 1.0000000
## 9-6 0.9970979 -14.423822 16.418018 0.9999999
## 8-7 0.5403692 -14.880551 15.961289 1.0000000
## 9-7 2.2580809 -13.162839 17.679001 0.9999312
## 9-8 1.7177117 -13.703208 17.138632 0.9999916lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Retencion (%)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 1754 877.2 10.59 8.51e-05 ***
## Residuals 78 6459 82.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Retencion (%)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 7.228496 1.311147 13.145845 0.0126202
## 25-13 -4.019817 -9.937166 1.897532 0.2420553
## 25-18 -11.248313 -17.165662 -5.330964 0.0000590lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Retencion (%)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 680 85.0 0.987 0.456159
## Tiempo 2 1754 877.2 10.193 0.000175 ***
## Tratamiento:Tiempo 16 1132 70.8 0.822 0.655618
## Residuals 54 4647 86.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Retencion (%)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Retencion (%)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Retencion (%)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Retención está en función del tiempo.
Anova permutacional
- La respuesta del Retención está en función del tiempo, corrobora el anova convencional.
Respiración
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Resp = mean(`Respiración (mg CO2)`),
desv_Resp = sd(`Respiración (mg CO2)`),
mediana_Resp = median(`Respiración (mg CO2)`),
minimo_Resp = min(`Respiración (mg CO2)`),
maximo_Resp = max(`Respiración (mg CO2)`),
cv = (desv_Resp/media_Resp),
p75 = quantile(`Respiración (mg CO2)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Respiración (mg CO2)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Respiración (mg CO2)`),
desv = sd(`Respiración (mg CO2)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Respiración (mg CO2)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.96 0.1200 0.277 0.971
## Residuals 72 31.15 0.4326TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Respiración (mg CO2)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -3.666667e-02 -1.0282456 0.9549123 1.0000000
## 3-1 7.333333e-02 -0.9182456 1.0649123 0.9999997
## 4-1 2.077778e-01 -0.7838011 1.1993567 0.9990112
## 5-1 1.222222e-02 -0.9793567 1.0038011 1.0000000
## 6-1 -3.666667e-02 -1.0282456 0.9549123 1.0000000
## 7-1 2.933333e-01 -0.6982456 1.2849123 0.9893157
## 8-1 -1.222222e-02 -1.0038011 0.9793567 1.0000000
## 9-1 8.555556e-02 -0.9060234 1.0771345 0.9999989
## 3-2 1.100000e-01 -0.8815789 1.1015789 0.9999919
## 4-2 2.444444e-01 -0.7471345 1.2360234 0.9968716
## 5-2 4.888889e-02 -0.9426900 1.0404678 1.0000000
## 6-2 -2.220446e-16 -0.9915789 0.9915789 1.0000000
## 7-2 3.300000e-01 -0.6615789 1.3215789 0.9774534
## 8-2 2.444444e-02 -0.9671345 1.0160234 1.0000000
## 9-2 1.222222e-01 -0.8693567 1.1138011 0.9999816
## 4-3 1.344444e-01 -0.8571345 1.1260234 0.9999618
## 5-3 -6.111111e-02 -1.0526900 0.9304678 0.9999999
## 6-3 -1.100000e-01 -1.1015789 0.8815789 0.9999919
## 7-3 2.200000e-01 -0.7715789 1.2115789 0.9985097
## 8-3 -8.555556e-02 -1.0771345 0.9060234 0.9999989
## 9-3 1.222222e-02 -0.9793567 1.0038011 1.0000000
## 5-4 -1.955556e-01 -1.1871345 0.7960234 0.9993635
## 6-4 -2.444444e-01 -1.2360234 0.7471345 0.9968716
## 7-4 8.555556e-02 -0.9060234 1.0771345 0.9999989
## 8-4 -2.200000e-01 -1.2115789 0.7715789 0.9985097
## 9-4 -1.222222e-01 -1.1138011 0.8693567 0.9999816
## 6-5 -4.888889e-02 -1.0404678 0.9426900 1.0000000
## 7-5 2.811111e-01 -0.7104678 1.2726900 0.9919214
## 8-5 -2.444444e-02 -1.0160234 0.9671345 1.0000000
## 9-5 7.333333e-02 -0.9182456 1.0649123 0.9999997
## 7-6 3.300000e-01 -0.6615789 1.3215789 0.9774534
## 8-6 2.444444e-02 -0.9671345 1.0160234 1.0000000
## 9-6 1.222222e-01 -0.8693567 1.1138011 0.9999816
## 8-7 -3.055556e-01 -1.2971345 0.6860234 0.9860946
## 9-7 -2.077778e-01 -1.1993567 0.7838011 0.9990112
## 9-8 9.777778e-02 -0.8938011 1.0893567 0.9999968lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Respiración (mg CO2)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 11.06 5.529 20.49 7.07e-08 ***
## Residuals 78 21.05 0.270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Respiración (mg CO2)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 0.9003704 0.5625548 1.23818597 0.0000000
## 25-13 0.5296296 0.1918140 0.86744523 0.0009893
## 25-18 -0.3707407 -0.7085563 -0.03292514 0.0280276lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Respiración (mg CO2)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 0.960 0.120 0.436 0.894
## Tiempo 2 11.058 5.529 20.071 3.04e-07 ***
## Tratamiento:Tiempo 16 5.216 0.326 1.183 0.311
## Residuals 54 14.875 0.275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Respiración (mg CO2)` ~ Tratamiento,
data = data,
nperm = 10000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Respiración (mg CO2)` ~ Tiempo,
data = data,
nperm = 10000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Respiración (mg CO2)` ~ Tiempo * Tratamiento,
data = data,
nperm = 10000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Respiración está en función del tiempo.
Anova permutacional
- La respuesta del Respiración está en función del tiempo, corrobora el anova convencional.
Nitrógeno (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_N = mean(`kg/ha N`),
desv_N = sd(`kg/ha N`),
mediana_N = median(`kg/ha N`),
minimo_N = min(`kg/ha N`),
maximo_N = max(`kg/ha N`),
cv = (desv_N/media_N),
p75 = quantile(`kg/ha N`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha N`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha N`),
desv = sd(`kg/ha N`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha N` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 343401 42925 0.196 0.991
## Residuals 72 15773875 219082TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha N` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -102.815411 -808.4515 602.8207 0.9999338
## 3-1 -12.629840 -718.2659 693.0062 1.0000000
## 4-1 -41.071561 -746.7076 664.5645 0.9999999
## 5-1 -47.133401 -752.7695 658.5027 0.9999998
## 6-1 63.812346 -641.8237 769.4484 0.9999984
## 7-1 123.656688 -581.9794 829.2928 0.9997338
## 8-1 21.824545 -683.8115 727.4606 1.0000000
## 9-1 65.060447 -640.5756 770.6965 0.9999981
## 3-2 90.185571 -615.4505 795.8217 0.9999757
## 4-2 61.743850 -643.8922 767.3799 0.9999987
## 5-2 55.682010 -649.9541 761.3181 0.9999994
## 6-2 166.627757 -539.0083 872.2638 0.9976833
## 7-2 226.472098 -479.1640 932.1082 0.9820051
## 8-2 124.639956 -580.9961 830.2760 0.9997176
## 9-2 167.875858 -537.7602 873.5119 0.9975588
## 4-3 -28.441721 -734.0778 677.1944 1.0000000
## 5-3 -34.503561 -740.1396 671.1325 1.0000000
## 6-3 76.442186 -629.1939 782.0783 0.9999933
## 7-3 136.286527 -569.3496 841.9226 0.9994537
## 8-3 34.454385 -671.1817 740.0905 1.0000000
## 9-3 77.690287 -627.9458 783.3264 0.9999924
## 5-4 -6.061840 -711.6979 699.5742 1.0000000
## 6-4 104.883907 -600.7522 810.5200 0.9999229
## 7-4 164.728248 -540.9078 870.3643 0.9978628
## 8-4 62.896106 -642.7400 768.5322 0.9999985
## 9-4 106.132008 -599.5041 811.7681 0.9999157
## 6-5 110.945747 -594.6903 816.5818 0.9998820
## 7-5 170.790089 -534.8460 876.4262 0.9972466
## 8-5 68.957946 -636.6781 774.5940 0.9999970
## 9-5 112.193848 -593.4422 817.8299 0.9998716
## 7-6 59.844342 -645.7917 765.4804 0.9999990
## 8-6 -41.987801 -747.6239 663.6483 0.9999999
## 9-6 1.248101 -704.3880 706.8842 1.0000000
## 8-7 -101.832142 -807.4682 603.8039 0.9999384
## 9-7 -58.596240 -764.2323 647.0398 0.9999992
## 9-8 43.235902 -662.4002 748.8720 0.9999999lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha N` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 13905509 6952755 245.2 <2e-16 ***
## Residuals 78 2211767 28356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha N` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 691.3891 581.8878 800.8903 0
## 25-13 989.1338 879.6326 1098.6351 0
## 25-18 297.7448 188.2435 407.2460 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha N` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 343401 42925 1.504 0.178
## Tiempo 2 13905509 6952755 243.612 <2e-16 ***
## Tratamiento:Tiempo 16 327188 20449 0.717 0.765
## Residuals 54 1541178 28540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha N` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha N` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha N` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Nitrógeno está en función del tiempo.
Anova permutacional
- La respuesta del Nitrógeno está en función del tiempo, corrobora el anova convencional.
Fósforo (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_P = mean(`kg/ha P`),
desv_P = sd(`kg/ha P`),
mediana_P = median(`kg/ha P`),
minimo_P = min(`kg/ha P`),
maximo_P = max(`kg/ha P`),
cv = (desv_P/media_P),
p75 = quantile(`kg/ha P`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha P`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha P`),
desv = sd(`kg/ha P`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha P` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 6472 809 1.682 0.118
## Residuals 72 34632 481TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha P` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -21.3446180 -54.408357 11.71912 0.5050913
## 3-1 -5.3766401 -38.440379 27.68710 0.9998479
## 4-1 7.3604332 -25.703305 40.42417 0.9984737
## 5-1 0.1493369 -32.914402 33.21308 1.0000000
## 6-1 5.1469463 -27.916792 38.21068 0.9998906
## 7-1 8.0715877 -24.992151 41.13533 0.9970771
## 8-1 -7.5479678 -40.611707 25.51577 0.9981750
## 9-1 6.1548959 -26.908843 39.21863 0.9995834
## 3-2 15.9679779 -17.095761 49.03172 0.8304996
## 4-2 28.7050512 -4.358687 61.76879 0.1406966
## 5-2 21.4939549 -11.569784 54.55769 0.4954605
## 6-2 26.4915643 -6.572174 59.55530 0.2209979
## 7-2 29.4162057 -3.647533 62.47994 0.1203535
## 8-2 13.7966501 -19.267089 46.86039 0.9174133
## 9-2 27.4995139 -5.564225 60.56325 0.1811246
## 4-3 12.7370733 -20.326665 45.80081 0.9465682
## 5-3 5.5259770 -27.537762 38.58972 0.9998132
## 6-3 10.5235864 -22.540152 43.58733 0.9829243
## 7-3 13.4482278 -19.615511 46.51197 0.9279407
## 8-3 -2.1713278 -35.235066 30.89241 0.9999999
## 9-3 11.5315360 -21.532203 44.59527 0.9700236
## 5-4 -7.2110963 -40.274835 25.85264 0.9986816
## 6-4 -2.2134869 -35.277226 30.85025 0.9999998
## 7-4 0.7111545 -32.352584 33.77489 1.0000000
## 8-4 -14.9084011 -47.972140 18.15534 0.8774667
## 9-4 -1.2055373 -34.269276 31.85820 1.0000000
## 6-5 4.9976094 -28.066129 38.06135 0.9999125
## 7-5 7.9222508 -25.141488 40.98599 0.9974342
## 8-5 -7.6973047 -40.761043 25.36643 0.9979040
## 9-5 6.0055590 -27.058180 39.06930 0.9996526
## 7-6 2.9246414 -30.139097 35.98838 0.9999986
## 8-6 -12.6949141 -45.758653 20.36882 0.9475555
## 9-6 1.0079496 -32.055789 34.07169 1.0000000
## 8-7 -15.6195556 -48.683294 17.44418 0.8468698
## 9-7 -1.9166918 -34.980431 31.14705 1.0000000
## 9-8 13.7028637 -19.360875 46.76660 0.9203396lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha P` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 12781 6391 17.6 4.92e-07 ***
## Residuals 78 28323 363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha P` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 18.18017 5.78880778 30.57153 0.0021685
## 25-13 30.58875 18.19738578 42.98010 0.0000003
## 25-18 12.40858 0.01721846 24.79994 0.0496043lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha P` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 6472 809 2.680 0.0148 *
## Tiempo 2 12781 6391 21.166 1.63e-07 ***
## Tratamiento:Tiempo 16 5547 347 1.148 0.3381
## Residuals 54 16304 302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha P` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha P` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha P` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Fósforo está en función del tiempo.
Anova permutacional
- La respuesta del Fósforo está en función del tiempo, corrobora el anova convencional.
Potasio (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_K = mean(`kg/ha K`),
desv_K = sd(`kg/ha K`),
mediana_K = median(`kg/ha K`),
minimo_K = min(`kg/ha K`),
maximo_K = max(`kg/ha K`),
cv = (desv_K/media_K),
p75 = quantile(`kg/ha K`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha K`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha K`),
desv = sd(`kg/ha K`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha K` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 142001 17750 1.106 0.369
## Residuals 72 1155463 16048TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha K` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -57.485106 -248.46589 133.4957 0.9880433
## 3-1 -43.482108 -234.46290 147.4987 0.9982091
## 4-1 54.304044 -136.67674 245.2848 0.9917607
## 5-1 31.916296 -159.06449 222.8971 0.9998133
## 6-1 67.485859 -123.49493 258.4666 0.9675838
## 7-1 58.849843 -132.13095 249.8306 0.9860961
## 8-1 10.456208 -180.52458 201.4370 1.0000000
## 9-1 35.097122 -155.88367 226.0779 0.9996212
## 3-2 14.002998 -176.97779 204.9838 0.9999997
## 4-2 111.789150 -79.19164 302.7699 0.6347135
## 5-2 89.401402 -101.57939 280.3822 0.8532789
## 6-2 124.970964 -66.00982 315.9518 0.4863559
## 7-2 116.334949 -74.64584 307.3157 0.5835777
## 8-2 67.941314 -123.03948 258.9221 0.9662627
## 9-2 92.582228 -98.39856 283.5630 0.8275724
## 4-3 97.786152 -93.19464 288.7669 0.7809897
## 5-3 75.398404 -115.58239 266.3792 0.9387471
## 6-3 110.967966 -80.01282 301.9488 0.6438516
## 7-3 102.331951 -88.64884 293.3127 0.7362891
## 8-3 53.938316 -137.04247 244.9191 0.9921214
## 9-3 78.579230 -112.40156 269.5600 0.9233470
## 5-4 -22.387748 -213.36854 168.5930 0.9999876
## 6-4 13.181815 -177.79897 204.1626 0.9999998
## 7-4 4.545799 -186.43499 195.5266 1.0000000
## 8-4 -43.847836 -234.82862 147.1330 0.9980997
## 9-4 -19.206922 -210.18771 171.7739 0.9999962
## 6-5 35.569563 -155.41123 226.5504 0.9995819
## 7-5 26.933547 -164.04724 217.9143 0.9999484
## 8-5 -21.460088 -212.44088 169.5207 0.9999910
## 9-5 3.180826 -187.79996 194.1616 1.0000000
## 7-6 -8.636016 -199.61680 182.3448 1.0000000
## 8-6 -57.029650 -248.01044 133.9511 0.9886436
## 9-6 -32.388737 -223.36953 158.5921 0.9997916
## 8-7 -48.393635 -239.37442 142.5872 0.9962166
## 9-7 -23.752721 -214.73351 167.2281 0.9999803
## 9-8 24.640914 -166.33988 215.6217 0.9999739lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha K` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 717181 358591 48.2 2.35e-14 ***
## Residuals 78 580283 7440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha K` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 190.89305 134.80515 246.98095 0.0000000
## 25-13 207.30937 151.22148 263.39727 0.0000000
## 25-18 16.41632 -39.67157 72.50422 0.7645929lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha K` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 142001 17750 3.173 0.00509 **
## Tiempo 2 717181 358591 64.097 5.5e-15 ***
## Tratamiento:Tiempo 16 136177 8511 1.521 0.12623
## Residuals 54 302105 5595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha K` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha K` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha K` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Potasio está en función del tiempo.
Anova permutacional
- La respuesta del Potasio está en función del tiempo, corrobora el anova convencional.
Sodio (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Na = mean(`kg/ha Na`),
desv_Na = sd(`kg/ha Na`),
mediana_Na = median(`kg/ha Na`),
minimo_Na = min(`kg/ha Na`),
maximo_Na = max(`kg/ha Na`),
cv = (desv_Na/media_Na),
p75 = quantile(`kg/ha Na`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha Na`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha Na`),
desv = sd(`kg/ha Na`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha Na` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 23343 2918 0.074 1
## Residuals 72 2842257 39476TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Na` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -14.357434 -313.8895 285.1746 1.0000000
## 3-1 -2.467420 -301.9994 297.0646 1.0000000
## 4-1 -20.014052 -319.5461 279.5180 0.9999998
## 5-1 -15.548796 -315.0808 283.9832 1.0000000
## 6-1 -24.714328 -324.2464 274.8177 0.9999992
## 7-1 0.202453 -299.3296 299.7345 1.0000000
## 8-1 9.120862 -290.4112 308.6529 1.0000000
## 9-1 34.210201 -265.3218 333.7422 0.9999898
## 3-2 11.890014 -287.6420 311.4220 1.0000000
## 4-2 -5.656618 -305.1886 293.8754 1.0000000
## 5-2 -1.191362 -300.7234 298.3407 1.0000000
## 6-2 -10.356893 -309.8889 289.1751 1.0000000
## 7-2 14.559887 -284.9721 314.0919 1.0000000
## 8-2 23.478297 -276.0537 323.0103 0.9999995
## 9-2 48.567635 -250.9644 348.0997 0.9998512
## 4-3 -17.546632 -317.0787 281.9854 0.9999999
## 5-3 -13.081377 -312.6134 286.4506 1.0000000
## 6-3 -22.246908 -321.7789 277.2851 0.9999997
## 7-3 2.669873 -296.8622 302.2019 1.0000000
## 8-3 11.588282 -287.9437 311.1203 1.0000000
## 9-3 36.677621 -262.8544 336.2096 0.9999826
## 5-4 4.465256 -295.0668 303.9973 1.0000000
## 6-4 -4.700275 -304.2323 294.8318 1.0000000
## 7-4 20.216505 -279.3155 319.7485 0.9999998
## 8-4 29.134914 -270.3971 328.6669 0.9999971
## 9-4 54.224253 -245.3078 353.7563 0.9996611
## 6-5 -9.165531 -308.6976 290.3665 1.0000000
## 7-5 15.751249 -283.7808 315.2833 1.0000000
## 8-5 24.669659 -274.8624 324.2017 0.9999992
## 9-5 49.758997 -249.7730 349.2910 0.9998215
## 7-6 24.916781 -274.6152 324.4488 0.9999991
## 8-6 33.835190 -265.6968 333.3672 0.9999907
## 9-6 58.924528 -240.6075 358.4566 0.9993750
## 8-7 8.918409 -290.6136 308.4504 1.0000000
## 9-7 34.007748 -265.5243 333.5398 0.9999903
## 9-8 25.089338 -274.4427 324.6214 0.9999991lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha Na` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 2702575 1351288 646.5 <2e-16 ***
## Residuals 78 163025 2090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Na` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 16.02689 -13.70188 45.75565 0.4061339
## 25-13 395.24777 365.51901 424.97653 0.0000000
## 25-18 379.22088 349.49212 408.94965 0.0000000lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha Na` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 23343 2918 1.757 0.1064
## Tiempo 2 2702575 1351288 813.603 <2e-16 ***
## Tratamiento:Tiempo 16 49995 3125 1.881 0.0434 *
## Residuals 54 89687 1661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha Na` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha Na` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha Na` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Sodio está en función del tiempo. Cabe resaltar la nula afectación del tratamiento sobre la respuesta de Sodio.
Anova permutacional
- La respuesta del Sodio está en función del tiempo, corrobora el anova convencional. A diferencia del anova convencional se tiene una significancia relativa del tratamiento sobre la repsuesta de Sodio.
Calcio (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Ca = mean(`kg/ha Ca`),
desv_Ca = sd(`kg/ha Ca`),
mediana_Ca = median(`kg/ha Ca`),
minimo_Ca = min(`kg/ha Ca`),
maximo_Ca = max(`kg/ha Ca`),
cv = (desv_Ca/media_Ca),
p75 = quantile(`kg/ha Ca`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha Ca`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha Ca`),
desv = sd(`kg/ha Ca`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha Ca` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 48481 6060 0.141 0.997
## Residuals 72 3104378 43116TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Ca` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -31.91917974 -344.9585 281.1201 0.9999958
## 3-1 -4.91650194 -317.9558 308.1228 1.0000000
## 4-1 15.73048848 -297.3088 328.7698 1.0000000
## 5-1 -6.51112308 -319.5504 306.5282 1.0000000
## 6-1 29.85584742 -283.1834 342.8951 0.9999975
## 7-1 29.88171438 -283.1576 342.9210 0.9999975
## 8-1 13.05275790 -299.9865 326.0920 1.0000000
## 9-1 57.11158446 -255.9277 370.1509 0.9996410
## 3-2 27.00267780 -286.0366 340.0420 0.9999989
## 4-2 47.64966822 -265.3896 360.6890 0.9999077
## 5-2 25.40805666 -287.6312 338.4473 0.9999993
## 6-2 61.77502716 -251.2643 374.8143 0.9993605
## 7-2 61.80089412 -251.2384 374.8402 0.9993586
## 8-2 44.97193764 -268.0674 358.0112 0.9999405
## 9-2 89.03076420 -224.0085 402.0701 0.9917483
## 4-3 20.64699042 -292.3923 333.6863 0.9999999
## 5-3 -1.59462114 -314.6339 311.4447 1.0000000
## 6-3 34.77234936 -278.2669 347.8116 0.9999918
## 7-3 34.79821632 -278.2411 347.8375 0.9999918
## 8-3 17.96925984 -295.0700 331.0086 1.0000000
## 9-3 62.02808640 -251.0112 375.0674 0.9993412
## 5-4 -22.24161156 -335.2809 290.7977 0.9999998
## 6-4 14.12535894 -298.9139 327.1646 1.0000000
## 7-4 14.15122590 -298.8881 327.1905 1.0000000
## 8-4 -2.67773058 -315.7170 310.3616 1.0000000
## 9-4 41.38109598 -271.6582 354.4204 0.9999685
## 6-5 36.36697050 -276.6723 349.4063 0.9999884
## 7-5 36.39283746 -276.6465 349.4321 0.9999883
## 8-5 19.56388098 -293.4754 332.6032 0.9999999
## 9-5 63.62270754 -249.4166 376.6620 0.9992074
## 7-6 0.02586696 -313.0134 313.0652 1.0000000
## 8-6 -16.80308952 -329.8424 296.2362 1.0000000
## 9-6 27.25573704 -285.7836 340.2950 0.9999988
## 8-7 -16.82895648 -329.8682 296.2103 1.0000000
## 9-7 27.22987008 -285.8094 340.2692 0.9999988
## 9-8 44.05882656 -268.9805 357.0981 0.9999491lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha Ca` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 2815658 1407829 325.7 <2e-16 ***
## Residuals 78 337201 4323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Ca` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 280.6219 237.8662 323.3775 0
## 25-13 452.3429 409.5872 495.0986 0
## 25-18 171.7210 128.9654 214.4767 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha Ca` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 48481 6060 1.482 0.185
## Tiempo 2 2815658 1407829 344.333 <2e-16 ***
## Tratamiento:Tiempo 16 67937 4246 1.039 0.434
## Residuals 54 220783 4089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha Ca` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha Ca` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha Ca` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Calcio está en función del tiempo.
Anova permutacional
- La respuesta del Calcio está en función del tiempo, corrobora el anova convencional.
Magnesio (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Mg = mean(`kg/ha Mg`),
desv_Mg = sd(`kg/ha Mg`),
mediana_Mg = median(`kg/ha Mg`),
minimo_Mg = min(`kg/ha Mg`),
maximo_Mg = max(`kg/ha Mg`),
cv = (desv_Mg/media_Mg),
p75 = quantile(`kg/ha Mg`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `kg/ha Mg`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`kg/ha Mg`),
desv = sd(`kg/ha Mg`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`kg/ha Mg` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 50269 6284 0.141 0.997
## Residuals 72 3200358 44449TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Mg` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -32.097575 -349.9393 285.7441 0.9999961
## 3-1 -4.028753 -321.8704 313.8129 1.0000000
## 4-1 14.282701 -303.5590 332.1244 1.0000000
## 5-1 -8.785595 -326.6273 309.0561 1.0000000
## 6-1 24.254720 -293.5870 342.0964 0.9999996
## 7-1 33.409497 -284.4322 351.2512 0.9999947
## 8-1 10.878317 -306.9634 328.7200 1.0000000
## 9-1 58.974771 -258.8669 376.8165 0.9995933
## 3-2 28.068822 -289.7729 345.9105 0.9999986
## 4-2 46.380276 -271.4614 364.2220 0.9999330
## 5-2 23.311980 -294.5297 341.1537 0.9999997
## 6-2 56.352295 -261.4894 374.1940 0.9997097
## 7-2 65.507072 -252.3346 383.3488 0.9991229
## 8-2 42.975891 -274.8658 360.8176 0.9999626
## 9-2 91.072346 -226.7693 408.9140 0.9913322
## 4-3 18.311454 -299.5302 336.1531 1.0000000
## 5-3 -4.756842 -322.5985 313.0848 1.0000000
## 6-3 28.283473 -289.5582 346.1252 0.9999986
## 7-3 37.438250 -280.4034 355.2799 0.9999871
## 8-3 14.907069 -302.9346 332.7488 1.0000000
## 9-3 63.003524 -254.8382 380.8452 0.9993393
## 5-4 -23.068296 -340.9100 294.7734 0.9999997
## 6-4 9.972019 -307.8697 327.8137 1.0000000
## 7-4 19.126796 -298.7149 336.9685 0.9999999
## 8-4 -3.404385 -321.2461 314.4373 1.0000000
## 9-4 44.692070 -273.1496 362.5338 0.9999495
## 6-5 33.040315 -284.8014 350.8820 0.9999951
## 7-5 42.195092 -275.6466 360.0368 0.9999675
## 8-5 19.663912 -298.1778 337.5056 0.9999999
## 9-5 67.760366 -250.0813 385.6021 0.9988803
## 7-6 9.154777 -308.6869 326.9965 1.0000000
## 8-6 -13.376404 -331.2181 304.4653 1.0000000
## 9-6 34.720051 -283.1216 352.5617 0.9999928
## 8-7 -22.531181 -340.3729 295.3105 0.9999998
## 9-7 25.565274 -292.2764 343.4070 0.9999993
## 9-8 48.096455 -269.7452 365.9381 0.9999117lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`kg/ha Mg` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 2914122 1457061 337.7 <2e-16 ***
## Residuals 78 336505 4314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `kg/ha Mg` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 288.0106 245.2991 330.7221 0
## 25-13 459.7316 417.0201 502.4431 0
## 25-18 171.7210 129.0095 214.4326 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`kg/ha Mg` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 50269 6284 1.54 0.165
## Tiempo 2 2914122 1457061 357.16 <2e-16 ***
## Tratamiento:Tiempo 16 65939 4121 1.01 0.461
## Residuals 54 220297 4080
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`kg/ha Mg` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`kg/ha Mg` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`kg/ha Mg` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Magnesio está en función del tiempo.
Anova permutacional
- La respuesta del Magnesio está en función del tiempo, corrobora el anova convencional.
Bóro (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_B = mean(`g/ha B`),
desv_B = sd(`g/ha B`),
mediana_B = median(`g/ha B`),
minimo_B = min(`g/ha B`),
maximo_B = max(`g/ha B`),
cv = (desv_B/media_B),
p75 = quantile(`g/ha B`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `g/ha B`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`g/ha B`),
desv = sd(`g/ha B`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`g/ha B` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 1368740 171092 0.134 0.997
## Residuals 72 91644388 1272839TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `g/ha B` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -119.97419 -1820.818 1580.870 0.9999998
## 3-1 -138.02545 -1838.870 1562.819 0.9999993
## 4-1 -63.31386 -1764.158 1637.530 1.0000000
## 5-1 -190.93016 -1891.774 1509.914 0.9999911
## 6-1 -12.69064 -1713.535 1688.154 1.0000000
## 7-1 209.35417 -1491.490 1910.198 0.9999818
## 8-1 -85.61199 -1786.456 1615.232 1.0000000
## 9-1 179.73340 -1521.111 1880.578 0.9999944
## 3-2 -18.05126 -1718.895 1682.793 1.0000000
## 4-2 56.66033 -1644.184 1757.504 1.0000000
## 5-2 -70.95597 -1771.800 1629.888 1.0000000
## 6-2 107.28355 -1593.561 1808.128 0.9999999
## 7-2 329.32836 -1371.516 2030.173 0.9994435
## 8-2 34.36220 -1666.482 1735.206 1.0000000
## 9-2 299.70759 -1401.137 2000.552 0.9997226
## 4-3 74.71159 -1626.133 1775.556 1.0000000
## 5-3 -52.90471 -1753.749 1647.939 1.0000000
## 6-3 125.33481 -1575.509 1826.179 0.9999997
## 7-3 347.37962 -1353.465 2048.224 0.9991787
## 8-3 52.41346 -1648.431 1753.258 1.0000000
## 9-3 317.75885 -1383.085 2018.603 0.9995722
## 5-4 -127.61630 -1828.460 1573.228 0.9999996
## 6-4 50.62322 -1650.221 1751.467 1.0000000
## 7-4 272.66803 -1428.176 1973.512 0.9998634
## 8-4 -22.29813 -1723.142 1678.546 1.0000000
## 9-4 243.04726 -1457.797 1943.891 0.9999429
## 6-5 178.23952 -1522.605 1879.084 0.9999948
## 7-5 400.28433 -1300.560 2101.128 0.9977375
## 8-5 105.31817 -1595.526 1806.162 0.9999999
## 9-5 370.66356 -1330.181 2071.508 0.9986888
## 7-6 222.04481 -1478.799 1922.889 0.9999714
## 8-6 -72.92135 -1773.765 1627.923 1.0000000
## 9-6 192.42404 -1508.420 1893.268 0.9999906
## 8-7 -294.96616 -1995.810 1405.878 0.9997537
## 9-7 -29.62077 -1730.465 1671.223 1.0000000
## 9-8 265.34539 -1435.499 1966.190 0.9998888lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`g/ha B` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 79158516 39579258 222.8 <2e-16 ***
## Residuals 78 13854612 177623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `g/ha B` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 1610.1758 1336.1152 1884.236 0
## 25-13 2371.3560 2097.2954 2645.417 0
## 25-18 761.1802 487.1196 1035.241 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`g/ha B` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 1368740 171092 1.080 0.391
## Tiempo 2 79158516 39579258 249.822 <2e-16 ***
## Tratamiento:Tiempo 16 3930669 245667 1.551 0.116
## Residuals 54 8555204 158430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`g/ha B` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`g/ha B` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`g/ha B` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Boro está en función del tiempo.
Anova permutacional
- La respuesta del Boro está en función del tiempo, corrobora el anova convencional.
Zinc (kg/ha)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Zn = mean(`g/ha Zn`),
desv_Zn = sd(`g/ha Zn`),
mediana_Zn = median(`g/ha Zn`),
minimo_Zn = min(`g/ha Zn`),
maximo_Zn = max(`g/ha Zn`),
cv = (desv_Zn/media_Zn),
p75 = quantile(`g/ha Zn`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `g/ha Zn`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`g/ha Zn`),
desv = sd(`g/ha Zn`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`g/ha Zn` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9.62e+06 1202533 0.408 0.912
## Residuals 72 2.12e+08 2943830TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `g/ha Zn` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -587.63206 -3174.261 1998.997 0.9982367
## 3-1 -354.07474 -2940.704 2232.554 0.9999589
## 4-1 12.30474 -2574.324 2598.934 1.0000000
## 5-1 -176.63877 -2763.268 2409.990 0.9999998
## 6-1 -25.12357 -2611.753 2561.506 1.0000000
## 7-1 188.76611 -2397.863 2775.395 0.9999997
## 8-1 -246.53566 -2833.165 2340.093 0.9999975
## 9-1 704.23361 -1882.396 3290.863 0.9938328
## 3-2 233.55733 -2353.072 2820.186 0.9999984
## 4-2 599.93681 -1986.692 3186.566 0.9979583
## 5-2 410.99329 -2175.636 2997.622 0.9998723
## 6-2 562.50849 -2024.121 3149.138 0.9987086
## 7-2 776.39818 -1810.231 3363.027 0.9882578
## 8-2 341.09640 -2245.533 2927.726 0.9999691
## 9-2 1291.86568 -1294.763 3878.495 0.8031592
## 4-3 366.37948 -2220.250 2953.009 0.9999466
## 5-3 177.43597 -2409.193 2764.065 0.9999998
## 6-3 328.95117 -2257.678 2915.580 0.9999766
## 7-3 542.84085 -2043.788 3129.470 0.9990002
## 8-3 107.53908 -2479.090 2694.168 1.0000000
## 9-3 1058.30835 -1528.321 3644.938 0.9256165
## 5-4 -188.94352 -2775.573 2397.686 0.9999997
## 6-4 -37.42831 -2624.057 2549.201 1.0000000
## 7-4 176.46137 -2410.168 2763.091 0.9999998
## 8-4 -258.84040 -2845.470 2327.789 0.9999964
## 9-4 691.92887 -1894.700 3278.558 0.9945240
## 6-5 151.51520 -2435.114 2738.144 0.9999999
## 7-5 365.40489 -2221.224 2952.034 0.9999477
## 8-5 -69.89689 -2656.526 2516.732 1.0000000
## 9-5 880.87238 -1705.757 3467.502 0.9740445
## 7-6 213.88969 -2372.739 2800.519 0.9999992
## 8-6 -221.41209 -2808.041 2365.217 0.9999989
## 9-6 729.35718 -1857.272 3315.986 0.9922054
## 8-7 -435.30177 -3021.931 2151.327 0.9998033
## 9-7 515.46750 -2071.162 3102.097 0.9993132
## 9-8 950.76927 -1635.860 3537.398 0.9591312lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`g/ha Zn` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 178672046 89336023 162.4 <2e-16 ***
## Residuals 78 42903949 550051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `g/ha Zn` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 2832.4809 2350.20229 3314.760 0.000000
## 25-13 3393.3630 2911.08443 3875.642 0.000000
## 25-18 560.8821 78.60353 1043.161 0.018545lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`g/ha Zn` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 9620261 1202533 2.580 0.0183 *
## Tiempo 2 178672046 89336023 191.696 <2e-16 ***
## Tratamiento:Tiempo 16 8118130 507383 1.089 0.3881
## Residuals 54 25165558 466029
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`g/ha Zn` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`g/ha Zn` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`g/ha Zn` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del Zinc está en función del tiempo, con una pequeña significancia del tratamiento, el cual puede interferir en el resultado
Anova permutacional
- La respuesta del zinc está en función del tiempo, corrobora el anova convencional
Nitrato sav (ppm)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_NO3 = mean(`NO3_sav (ppm)`),
desv_NO3 = sd(`NO3_sav (ppm)`),
mediana_NO3 = median(`NO3_sav (ppm)`),
minimo_NO3 = min(`NO3_sav (ppm)`),
maximo_NO3 = max(`NO3_sav (ppm)`),
cv = (desv_NO3/media_NO3),
p75 = quantile(`NO3_sav (ppm)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `NO3_sav (ppm)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`NO3_sav (ppm)`),
desv = sd(`NO3_sav (ppm)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`NO3_sav (ppm)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 4960114 620014 0.437 0.895
## Residuals 72 102262778 1420316TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `NO3_sav (ppm)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -197.777778 -1994.4563 1598.901 0.9999924
## 3-1 -692.222222 -2488.9007 1104.456 0.9465284
## 4-1 -353.333333 -2150.0119 1443.345 0.9993765
## 5-1 -202.222222 -1998.9007 1594.456 0.9999909
## 6-1 220.000000 -1576.6785 2016.679 0.9999826
## 7-1 -255.555556 -2052.2341 1541.123 0.9999449
## 8-1 40.000000 -1756.6785 1836.679 1.0000000
## 9-1 -293.333333 -2090.0119 1503.345 0.9998433
## 3-2 -494.444444 -2291.1230 1302.234 0.9933719
## 4-2 -155.555556 -1952.2341 1641.123 0.9999988
## 5-2 -4.444444 -1801.1230 1792.234 1.0000000
## 6-2 417.777778 -1378.9007 2214.456 0.9979214
## 7-2 -57.777778 -1854.4563 1738.901 1.0000000
## 8-2 237.777778 -1558.9007 2034.456 0.9999683
## 9-2 -95.555556 -1892.2341 1701.123 1.0000000
## 4-3 338.888889 -1457.7896 2135.567 0.9995410
## 5-3 490.000000 -1306.6785 2286.679 0.9937615
## 6-3 912.222222 -884.4563 2708.901 0.7886777
## 7-3 436.666667 -1360.0119 2233.345 0.9971660
## 8-3 732.222222 -1064.4563 2528.901 0.9271689
## 9-3 398.888889 -1397.7896 2195.567 0.9985027
## 5-4 151.111111 -1645.5674 1947.790 0.9999991
## 6-4 573.333333 -1223.3452 2370.012 0.9826435
## 7-4 97.777778 -1698.9007 1894.456 1.0000000
## 8-4 393.333333 -1403.3452 2190.012 0.9986454
## 9-4 60.000000 -1736.6785 1856.679 1.0000000
## 6-5 422.222222 -1374.4563 2218.901 0.9977606
## 7-5 -53.333333 -1850.0119 1743.345 1.0000000
## 8-5 242.222222 -1554.4563 2038.901 0.9999634
## 9-5 -91.111111 -1887.7896 1705.567 1.0000000
## 7-6 -475.555556 -2272.2341 1321.123 0.9949030
## 8-6 -180.000000 -1976.6785 1616.679 0.9999963
## 9-6 -513.333333 -2310.0119 1283.345 0.9914948
## 8-7 295.555556 -1501.1230 2092.234 0.9998342
## 9-7 -37.777778 -1834.4563 1758.901 1.0000000
## 9-8 -333.333333 -2130.0119 1463.345 0.9995936lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`NO3_sav (ppm)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 55743351 27871675 42.23 3.74e-13 ***
## Residuals 78 51479541 659994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `NO3_sav (ppm)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 2008.8889 1480.6058 2537.1720 0.0000000
## 25-13 739.6296 211.3466 1267.9127 0.0035886
## 25-18 -1269.2593 -1797.5423 -740.9762 0.0000005lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`NO3_sav (ppm)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 4960114 620014 0.964 0.474
## Tiempo 2 55743351 27871675 43.319 5.97e-12 ***
## Tratamiento:Tiempo 16 11775294 735956 1.144 0.342
## Residuals 54 34744133 643410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`NO3_sav (ppm)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`NO3_sav (ppm)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`NO3_sav (ppm)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del nitrato está en función del tiempo
Anova permutacional
- La respuesta del nitrato está en función del tiempo, corrobora el anova convencional
Calcio sav (ppm)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_Ca_s = mean(`Ca_sav (ppm)`),
desv_Ca_s = sd(`Ca_sav (ppm)`),
mediana_Ca_s = median(`Ca_sav (ppm)`),
minimo_Ca_s = min(`Ca_sav (ppm)`),
maximo_Ca_s = max(`Ca_sav (ppm)`),
cv = (desv_Ca_s/media_Ca_s),
p75 = quantile(`Ca_sav (ppm)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `Ca_sav (ppm)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`Ca_sav (ppm)`),
desv = sd(`Ca_sav (ppm)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`Ca_sav (ppm)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 5204 650.5 0.226 0.985
## Residuals 72 206971 2874.6TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Ca_sav (ppm)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -4.7777778 -85.60659 76.05104 0.9999999
## 3-1 17.7777778 -63.05104 98.60659 0.9985996
## 4-1 12.7777778 -68.05104 93.60659 0.9998771
## 5-1 19.2222222 -61.60659 100.05104 0.9975655
## 6-1 1.4444444 -79.38437 82.27326 1.0000000
## 7-1 14.5555556 -66.27326 95.38437 0.9996741
## 8-1 13.3333333 -67.49548 94.16215 0.9998307
## 9-1 11.7777778 -69.05104 92.60659 0.9999337
## 3-2 22.5555556 -58.27326 103.38437 0.9927266
## 4-2 17.5555556 -63.27326 98.38437 0.9987202
## 5-2 24.0000000 -56.82882 104.82882 0.9890547
## 6-2 6.2222222 -74.60659 87.05104 0.9999995
## 7-2 19.3333333 -61.49548 100.16215 0.9974652
## 8-2 18.1111111 -62.71770 98.93993 0.9984013
## 9-2 16.5555556 -64.27326 97.38437 0.9991615
## 4-3 -5.0000000 -85.82882 75.82882 0.9999999
## 5-3 1.4444444 -79.38437 82.27326 1.0000000
## 6-3 -16.3333333 -97.16215 64.49548 0.9992399
## 7-3 -3.2222222 -84.05104 77.60659 1.0000000
## 8-3 -4.4444444 -85.27326 76.38437 1.0000000
## 9-3 -6.0000000 -86.82882 74.82882 0.9999997
## 5-4 6.4444444 -74.38437 87.27326 0.9999994
## 6-4 -11.3333333 -92.16215 69.49548 0.9999506
## 7-4 1.7777778 -79.05104 82.60659 1.0000000
## 8-4 0.5555556 -80.27326 81.38437 1.0000000
## 9-4 -1.0000000 -81.82882 79.82882 1.0000000
## 6-5 -17.7777778 -98.60659 63.05104 0.9985996
## 7-5 -4.6666667 -85.49548 76.16215 1.0000000
## 8-5 -5.8888889 -86.71770 74.93993 0.9999997
## 9-5 -7.4444444 -88.27326 73.38437 0.9999981
## 7-6 13.1111111 -67.71770 93.93993 0.9998508
## 8-6 11.8888889 -68.93993 92.71770 0.9999288
## 9-6 10.3333333 -70.49548 91.16215 0.9999757
## 8-7 -1.2222222 -82.05104 79.60659 1.0000000
## 9-7 -2.7777778 -83.60659 78.05104 1.0000000
## 9-8 -1.5555556 -82.38437 79.27326 1.0000000lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`Ca_sav (ppm)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 163904 81952 132.4 <2e-16 ***
## Residuals 78 48270 619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `Ca_sav (ppm)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 110.14815 93.97151 126.32479 0
## 25-13 52.55556 36.37891 68.73220 0
## 25-18 -57.59259 -73.76923 -41.41595 0lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`Ca_sav (ppm)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 5204 650 1.041 0.418
## Tiempo 2 163904 81952 131.105 <2e-16 ***
## Tratamiento:Tiempo 16 9312 582 0.931 0.540
## Residuals 54 33755 625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`Ca_sav (ppm)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`Ca_sav (ppm)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`Ca_sav (ppm)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_3Resultados
Anova
- La respuesta del calcio está en función del tiempo
Anova permutacional
- La respuesta del calcio está en función del tiempo, corrobora el anova convencional
Potasio sav (ppm)
Estadística descriptiva
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media_K_s = mean(`K_sav(ppm)`),
desv_K_s = sd(`K_sav(ppm)`),
mediana_K_s = median(`K_sav(ppm)`),
minimo_K_s = min(`K_sav(ppm)`),
maximo_K_s = max(`K_sav(ppm)`),
cv = (desv_K_s/media_K_s),
p75 = quantile(`K_sav(ppm)`, probs = 0.75)) %>%
mutate_if(is.numeric, round, digits = 2) %>%
datatable(
rownames = FALSE,
extensions = c('Buttons'),
options = list(
dom = 'Brftip',
buttons = c('excel', 'pdf')
))Gráficos
Boxplot
data %>%
ggplot(aes(x = Tratamiento,
y = `K_sav(ppm)`,
fill = Tratamiento)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_boxplot(alpha = 0.6) +
theme_bw() +
theme(legend.position = "none") 
#scale_color_npg() #+ scale_fill_npg()Promedio + desviación estandar
data %>%
group_by(Tratamiento, Tiempo) %>%
summarise(media = mean(`K_sav(ppm)`),
desv = sd(`K_sav(ppm)`))%>%
ggplot(aes(x = Tratamiento,
y = media,
color = Tratamiento,
ymin = media - desv,
ymax = media + desv)) +
facet_wrap(~Tiempo, scales = "free", ncol = 3) +
geom_point() +
geom_errorbar(width = 0.1) +
theme_bw() +
theme(legend.position = "none")
Modelos Anova
Tratamiento
Mod_1 = aov(`K_sav(ppm)` ~ Tratamiento, data = data)
summary(Mod_1)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 2014891 251861 2.003 0.0581 .
## Residuals 72 9054311 125754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_1)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `K_sav(ppm)` ~ Tratamiento, data = data)
##
## $Tratamiento
## diff lwr upr p adj
## 2-1 -67.777778 -602.3905 466.83494 0.9999772
## 3-1 -100.000000 -634.6127 434.61272 0.9995684
## 4-1 -126.666667 -661.2794 407.94605 0.9976281
## 5-1 -150.000000 -684.6127 384.61272 0.9924581
## 6-1 -341.111111 -875.7238 193.50161 0.5211753
## 7-1 -426.666667 -961.2794 107.94605 0.2254292
## 8-1 -348.888889 -883.5016 185.72383 0.4900945
## 9-1 -444.444444 -979.0572 90.16828 0.1815786
## 3-2 -32.222222 -566.8349 502.39050 0.9999999
## 4-2 -58.888889 -593.5016 475.72383 0.9999923
## 5-2 -82.222222 -616.8349 452.39050 0.9999002
## 6-2 -273.333333 -807.9461 261.27939 0.7823366
## 7-2 -358.888889 -893.5016 175.72383 0.4508264
## 8-2 -281.111111 -815.7238 253.50161 0.7554794
## 9-2 -376.666667 -911.2794 157.94605 0.3839036
## 4-3 -26.666667 -561.2794 507.94605 1.0000000
## 5-3 -50.000000 -584.6127 484.61272 0.9999979
## 6-3 -241.111111 -775.7238 293.50161 0.8773299
## 7-3 -326.666667 -861.2794 207.94605 0.5794948
## 8-3 -248.888889 -783.5016 285.72383 0.8570422
## 9-3 -344.444444 -879.0572 190.16828 0.5078101
## 5-4 -23.333333 -557.9461 511.27939 1.0000000
## 6-4 -214.444444 -749.0572 320.16828 0.9331694
## 7-4 -300.000000 -834.6127 234.61272 0.6854658
## 8-4 -222.222222 -756.8349 312.39050 0.9190773
## 9-4 -317.777778 -852.3905 216.83494 0.6153286
## 6-5 -191.111111 -725.7238 343.50161 0.9652822
## 7-5 -276.666667 -811.2794 257.94605 0.7709884
## 8-5 -198.888889 -733.5016 335.72383 0.9561842
## 9-5 -294.444444 -829.0572 240.16828 0.7066613
## 7-6 -85.555556 -620.1683 449.05717 0.9998652
## 8-6 -7.777778 -542.3905 526.83494 1.0000000
## 9-6 -103.333333 -637.9461 431.27939 0.9994507
## 8-7 77.777778 -456.8349 612.39050 0.9999345
## 9-7 -17.777778 -552.3905 516.83494 1.0000000
## 9-8 -95.555556 -630.1683 439.05717 0.9996917lsd_test = LSD.test(Mod_1, "Tratamiento")
plot(lsd_test)
Tiempo
Mod_2 = aov(`K_sav(ppm)` ~ Tiempo, data = data)
summary(Mod_2)## Df Sum Sq Mean Sq F value Pr(>F)
## Tiempo 2 1069691 534846 4.172 0.019 *
## Residuals 78 9999511 128199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Mod_2)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = `K_sav(ppm)` ~ Tiempo, data = data)
##
## $Tiempo
## diff lwr upr p adj
## 18-13 268.51852 35.68862 501.3484 0.0197376
## 25-13 207.40741 -25.42249 440.2373 0.0906848
## 25-18 -61.11111 -293.94101 171.7188 0.8057044lsd_test = LSD.test(Mod_2, "Tiempo")
plot(lsd_test)
Tratamiento * Tiempo
Mod_3 = aov(`K_sav(ppm)` ~ Tratamiento * Tiempo, data = data)
summary(Mod_3)## Df Sum Sq Mean Sq F value Pr(>F)
## Tratamiento 8 2014891 251861 3.761 0.001451 **
## Tiempo 2 1069691 534846 7.987 0.000915 ***
## Tratamiento:Tiempo 16 4368686 273043 4.078 5.07e-05 ***
## Residuals 54 3615933 66962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Mejor modelo
AIC(Mod_1,Mod_2,Mod_3)Modelos Anova permutacional
Tratamiento
ModPer_1 = perm.anova(`K_sav(ppm)` ~ Tratamiento,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_1Tiempo
ModPer_2 = perm.anova(`K_sav(ppm)` ~ Tiempo,
data = data,
nperm = 1000)##
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|======================================================================| 100%ModPer_2Tiempo * Tratamiento
ModPer_3 = perm.anova(`K_sav(ppm)` ~ Tiempo * Tratamiento,
data = data,
nperm = 1000)##
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|================================================================= | 93%
|
|================================================================== | 94%
|
|================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 100%ModPer_3Resultados
Anova
- El tiempo y la interacción tienen una significancia similar, pero se explica la respuesta de Potasio es en razón de la interacción del tiempo con el tratamiento
Anova permutacional
- Con mayor significancia, la interacción entre el tiempo y el tratamiento, son los factores que definen la respuesta de Potasio