Unveiling the aluminum tolerance by Tithonia diversifolia grown in acid soil: insights from morphological, anatomical, and nutritional analysis
Vagner S. Ovani
05/04/2022
DATA
data2=read.csv("D:\\Armazenamento\\DATA R\\Ensaio 2\\data2.csv")
data2$propagacao=as.factor(data2$propagacao)
data2$saturacao=as.factor(data2$saturacao)
str(data2)'data.frame': 32 obs. of 57 variables:
$ ID : chr "V1" "V18" "V4" "V19" ...
$ propagacao : Factor w/ 2 levels "Estaquia","Semente": 1 1 1 1 2 2 2 2 1 1 ...
$ saturacao : Factor w/ 4 levels "19","39","52",..: 1 1 1 1 1 1 1 1 2 2 ...
$ rep : int 1 2 3 4 1 2 3 4 1 2 ...
$ V. : num 18 23 15 20 18 22 14 22 39 42 ...
$ m. : num 46 32 54 38 50 35 56 37 11 12 ...
$ pH : num 4.3 4.28 4.13 4.21 4.17 4.26 4.12 4.24 4.72 4.77 ...
$ Al : num 5.8 4 5.8 4.8 6.3 4.3 6 4.5 1.8 2 ...
$ H.Al : num 30.4 28 29.5 31.1 28.3 ...
$ sb : num 6.8 8.3 5 7.8 6.4 8 4.7 7.6 14.3 14.8 ...
$ Ca : num 4 3.8 2.7 3.7 3.7 3.8 2.4 4 9 9.6 ...
$ Mg : num 2.5 3.9 2 3.5 2.4 3.6 1.9 3.2 4.9 4.9 ...
$ V.final : num 17 16 NA 15 32 17 25 25 19 18 ...
$ m.final : num 51 50 NA 53 23 49 31 33 41 51 ...
$ pH.final : num 4 3.89 NA 3.87 4.28 3.98 4.43 4.13 3.96 3.89 ...
$ Al.final : num 7.8 8 NA 7.5 4.5 7.5 4.3 5.8 6 7.8 ...
$ H.Al.final : num 36 40 NA 36.4 31.8 37.6 28.9 34.2 36.4 35.7 ...
$ sb.final : num 7.5 7.9 NA 6.6 14.9 7.7 9.7 11.5 8.8 7.6 ...
$ Ca.final : num 5.3 5.2 NA 4.5 6.7 4.7 5.6 5.1 7.2 6.5 ...
$ Mg.final : num 1.3 2 NA 1.5 2.2 2.1 1.5 2.5 0.9 0.8 ...
$ K.final : num 0.87 0.69 NA 0.59 5.97 0.87 2.61 3.93 0.65 0.32 ...
$ P.final : num 207 122 NA 110 265 ...
$ Folha : num 30.6 28.8 28 28 NA ...
$ Caule : num 47.5 52.3 48.4 57.5 NA ...
$ Senescente : num 22 18.9 23.6 14.6 NA ...
$ Raizes : num 10.2 20.7 20.9 13.3 NA ...
$ Altura : num 37.7 43.3 42 53.3 11 ...
$ Perfilhos : num 19.33 24.67 17.67 19.33 0.67 ...
$ MF.corte1 : num 27.5 28.8 25.1 33 12.7 ...
$ MF.corte2 : num 20.9 21.8 21.4 24.4 NA ...
$ MF.corte3 : num 27.69 32.87 27.57 31.58 1.68 ...
$ MF.accumulation: num 76.1 83.5 74.1 89 NA ...
$ MF.mean : num 25.4 27.9 24.7 29.7 7.2 ...
$ DM105 : num 911 901 913 902 NA ...
$ MM : num 125 126 120 127 NA ...
$ MO : num 875 874 880 873 NA ...
$ EE : num 59.4 51.3 56.7 49.7 NA ...
$ FDN : num 599 579 627 543 NA ...
$ aFDNom : num 588 566 618 534 NA ...
$ FDA : num 327 346 360 366 NA ...
$ aFDAom : num 316 333 352 357 NA ...
$ LDA : num 121 134 128 124 NA ...
$ aLDAom : num 110 121 120 114 NA ...
$ PB : num 188 162 188 194 NA ...
$ Nitrogênio : num 30 26 30 31 NA 37 44 26 21 23 ...
$ Fósforo : num 3.8 3.4 4.4 3.4 NA 3.9 4.4 3.2 3.3 3.6 ...
$ Potássio : num 14 19 27 11 NA 17 18 15 20 18 ...
$ Cálcio : num 8 8 9 10 NA 8 7 9 11 13 ...
$ Magnésio : num 2.2 2.8 2.3 3.2 NA 2.9 1.9 2.7 3.7 3.8 ...
$ Enxofre : num 1.2 1.2 1.5 1.2 NA 1.2 1.6 1.2 1.4 1 ...
$ Boro : num 137 157 138 170 NA 154 164 141 134 132 ...
$ Cobre : num 9 7 8 7 NA 9 12 5 9 10 ...
$ Ferro : num 314 330 239 298 NA 282 306 284 324 275 ...
$ Manganês : num 236 372 244 328 NA 481 361 315 205 163 ...
$ Zinco : num 170 155 169 173 NA 268 296 152 112 102 ...
$ SPAD : num 30.6 30.7 30.3 28.7 NA ...
$ Al.1 : num 291 NA 232 NA NA ...data2biomass=read.csv("D:\\Armazenamento\\DATA R\\Ensaio 2\\Assay2_biomass.csv")
biomass$propagacao=as.factor(biomass$propagacao)
biomass$Cycle=as.factor(biomass$Cycle)
biomass$Perfilhos=as.numeric(biomass$Perfilhos)
str(biomass)'data.frame': 96 obs. of 9 variables:
$ ID : chr "V1" "V18" "V4" "V19" ...
$ propagacao: Factor w/ 2 levels "Estaquia","Semente": 1 1 1 1 2 2 2 2 1 1 ...
$ saturacao : int 19 19 19 19 19 19 19 19 39 39 ...
$ rep : int 1 2 3 4 1 2 3 4 1 2 ...
$ Cycle : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ Altura : int 39 50 43 56 33 40 15 33 77 60 ...
$ Perfilhos : num 11 12 10 10 2 2 2 2 10 8 ...
$ MF.MV : num 154.6 155.8 144.5 185.5 46.2 ...
$ MF.MS : num 27.5 28.8 25.1 33 12.7 ...biomassSOIL PROPERTIES
1 - V.final
#model
mod1 = aov(V.final~propagacao*saturacao, data = data2)
hist(rstandard(mod1))
shapiro.test(resid(mod1))
Shapiro-Wilk normality test
data: resid(mod1)
W = 0.98004, p-value = 0.8138#Tukey
library(emmeans)
medias1=emmeans(mod1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias1.1=emmeans(mod1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias1) propagacao emmean SE df lower.CL upper.CL
Estaquia 24.8 1.021 23 22.6 26.9
Semente 29.1 0.981 23 27.1 31.2
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias1.1) saturacao emmean SE df lower.CL upper.CL
19 20.4 1.50 23 17.3 23.5
39 24.1 1.39 23 21.3 27.0
52 26.4 1.39 23 23.5 29.2
63 36.9 1.39 23 34.0 39.7
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 111.3 111.3 7.232 0.0131 *
saturacao 3 1124.4 374.8 24.351 2.53e-07 ***
propagacao:saturacao 3 62.7 20.9 1.358 0.2804
Residuals 23 354.0 15.4
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesslibrary(multcompView)
citation("multcompView")To cite package ‘multcompView’ in publications use:
Graves S, Piepho H, Dorai-Raj LSwhfS (2023). _multcompView: Visualizations of Paired
Comparisons_. R package version 0.1-9,
<https://CRAN.R-project.org/package=multcompView>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {multcompView: Visualizations of Paired Comparisons},
author = {Spencer Graves and Hans-Peter Piepho and Luciano Selzer with help from Sundar Dorai-Raj},
year = {2023},
note = {R package version 0.1-9},
url = {https://CRAN.R-project.org/package=multcompView},
}
ATTENTION: This citation information has been auto-generated from the package
DESCRIPTION file and may need manual editing, see ‘help("citation")’.tukey1 = TukeyHSD(mod1)
print(tukey1) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = V.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 3.791667 0.8749011 6.708432 0.0130969
$saturacao
diff lwr upr p adj
39-19 3.395833 -2.2230018 9.014668 0.3603510
52-19 5.645833 0.0269982 11.264668 0.0486130
63-19 16.145833 10.5269982 21.764668 0.0000003
52-39 2.250000 -3.1783105 7.678310 0.6650139
63-39 12.750000 7.3216895 18.178310 0.0000070
63-52 10.500000 5.0716895 15.928310 0.0001083
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 8.75 -1.2120724 18.712072 0.1141999
Estaquia:39-Estaquia:19 5.50 -4.4620724 15.462072 0.6042243
Semente:39-Estaquia:19 10.75 0.7879276 20.712072 0.0282059
Estaquia:52-Estaquia:19 9.25 -0.7120724 19.212072 0.0819912
Semente:52-Estaquia:19 11.50 1.5379276 21.462072 0.0160484
Estaquia:63-Estaquia:19 20.25 10.2879276 30.212072 0.0000166
Semente:63-Estaquia:19 21.50 11.5379276 31.462072 0.0000065
Estaquia:39-Semente:19 -3.25 -12.4730868 5.973087 0.9321091
Semente:39-Semente:19 2.00 -7.2230868 11.223087 0.9953989
Estaquia:52-Semente:19 0.50 -8.7230868 9.723087 0.9999996
Semente:52-Semente:19 2.75 -6.4730868 11.973087 0.9711717
Estaquia:63-Semente:19 11.50 2.2769132 20.723087 0.0078803
Semente:63-Semente:19 12.75 3.5269132 21.973087 0.0027161
Semente:39-Estaquia:39 5.25 -3.9730868 14.473087 0.5689015
Estaquia:52-Estaquia:39 3.75 -5.4730868 12.973087 0.8690912
Semente:52-Estaquia:39 6.00 -3.2230868 15.223087 0.4077928
Estaquia:63-Estaquia:39 14.75 5.5269132 23.973087 0.0004843
Semente:63-Estaquia:39 16.00 6.7769132 25.223087 0.0001658
Estaquia:52-Semente:39 -1.50 -10.7230868 7.723087 0.9992473
Semente:52-Semente:39 0.75 -8.4730868 9.973087 0.9999928
Estaquia:63-Semente:39 9.50 0.2769132 18.723087 0.0403455
Semente:63-Semente:39 10.75 1.5269132 19.973087 0.0147409
Semente:52-Estaquia:52 2.25 -6.9730868 11.473087 0.9906954
Estaquia:63-Estaquia:52 11.00 1.7769132 20.223087 0.0119810
Semente:63-Estaquia:52 12.25 3.0269132 21.473087 0.0041686
Estaquia:63-Semente:52 8.75 -0.4730868 17.973087 0.0714830
Semente:63-Semente:52 10.00 0.7769132 19.223087 0.0271543
Semente:63-Estaquia:63 1.25 -7.9730868 10.473087 0.9997720tukey.cld1 = multcompLetters4(mod1, tukey1)
print(tukey.cld1)$propagacao
Semente Estaquia
"a" "b"
$saturacao
63 52 39 19
"a" "b" "bc" "c"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Semente:52 Semente:39 Estaquia:52 Semente:19 Estaquia:39 Estaquia:19
"a" "ab" "bc" "c" "cd" "cd" "cd" "d" 2 - m.final
#model
mod2 = aov(m.final~propagacao*saturacao, data = data2)
hist(rstandard(mod2))
shapiro.test(resid(mod2))
Shapiro-Wilk normality test
data: resid(mod2)
W = 0.94695, p-value = 0.1285#Tukey
medias2=emmeans(mod2,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias2.1=emmeans(mod2,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias2) propagacao emmean SE df lower.CL upper.CL
Estaquia 35.7 1.66 23 32.3 39.1
Semente 28.1 1.59 23 24.8 31.4
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias2.1) saturacao emmean SE df lower.CL upper.CL
19 42.7 2.43 23 37.6 47.7
39 35.5 2.25 23 30.8 40.2
52 31.4 2.25 23 26.7 36.0
63 18.0 2.25 23 13.3 22.7
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod2) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 337.7 337.7 8.343 0.00829 **
saturacao 3 2346.3 782.1 19.323 1.76e-06 ***
propagacao:saturacao 3 265.0 88.3 2.183 0.11746
Residuals 23 930.9 40.5
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey2 = TukeyHSD(mod2)
print(tukey2) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = m.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -6.604167 -11.3341 -1.87423 0.0082929
$saturacao
diff lwr upr p adj
39-19 -6.400298 -15.51201 2.711416 0.2381970
52-19 -10.525298 -19.63701 -1.413584 0.0194955
63-19 -23.900298 -33.01201 -14.788584 0.0000012
52-39 -4.125000 -12.92775 4.677752 0.5741358
63-39 -17.500000 -26.30275 -8.697248 0.0000755
63-52 -13.375000 -22.17775 -4.572248 0.0017915
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -17.33333 -33.488203 -1.1784635 0.0295125
Estaquia:39-Estaquia:19 -12.08333 -28.238203 4.0715365 0.2496591
Semente:39-Estaquia:19 -19.58333 -35.738203 -3.4284635 0.0103062
Estaquia:52-Estaquia:19 -17.58333 -33.738203 -1.4284635 0.0263255
Semente:52-Estaquia:19 -22.33333 -38.488203 -6.1784635 0.0027150
Estaquia:63-Estaquia:19 -32.83333 -48.988203 -16.6784635 0.0000166
Semente:63-Estaquia:19 -33.83333 -49.988203 -17.6784635 0.0000104
Estaquia:39-Semente:19 5.25000 -9.706503 20.2065032 0.9333722
Semente:39-Semente:19 -2.25000 -17.206503 12.7065032 0.9995468
Estaquia:52-Semente:19 -0.25000 -15.206503 14.7065032 1.0000000
Semente:52-Semente:19 -5.00000 -19.956503 9.9565032 0.9476713
Estaquia:63-Semente:19 -15.50000 -30.456503 -0.5434968 0.0385480
Semente:63-Semente:19 -16.50000 -31.456503 -1.5434968 0.0235878
Semente:39-Estaquia:39 -7.50000 -22.456503 7.4565032 0.7067841
Estaquia:52-Estaquia:39 -5.50000 -20.456503 9.4565032 0.9167603
Semente:52-Estaquia:39 -10.25000 -25.206503 4.7065032 0.3459336
Estaquia:63-Estaquia:39 -20.75000 -35.706503 -5.7934968 0.0026115
Semente:63-Estaquia:39 -21.75000 -36.706503 -6.7934968 0.0015356
Estaquia:52-Semente:39 2.00000 -12.956503 16.9565032 0.9997914
Semente:52-Semente:39 -2.75000 -17.706503 12.2065032 0.9983491
Estaquia:63-Semente:39 -13.25000 -28.206503 1.7065032 0.1087348
Semente:63-Semente:39 -14.25000 -29.206503 0.7065032 0.0695222
Semente:52-Estaquia:52 -4.75000 -19.706503 10.2065032 0.9597479
Estaquia:63-Estaquia:52 -15.25000 -30.206503 -0.2934968 0.0434761
Semente:63-Estaquia:52 -16.25000 -31.206503 -1.2934968 0.0267063
Estaquia:63-Semente:52 -10.50000 -25.456503 4.4565032 0.3182512
Semente:63-Semente:52 -11.50000 -26.456503 3.4565032 0.2221409
Semente:63-Estaquia:63 -1.00000 -15.956503 13.9565032 0.9999981tukey.cld2 = multcompLetters4(mod2, tukey2)
print(tukey.cld2)$propagacao
Estaquia Semente
"a" "b"
$saturacao
19 39 52 63
"a" "ab" "b" "c"
$`propagacao:saturacao`
Estaquia:19 Estaquia:39 Semente:19 Estaquia:52 Semente:39 Semente:52 Estaquia:63 Semente:63
"a" "ab" "b" "b" "bc" "bc" "c" "c" 3 - pH.final
#model
mod3 = aov(pH.final~propagacao*saturacao, data = data2)
hist(rstandard(mod3))
shapiro.test(resid(mod3))
Shapiro-Wilk normality test
data: resid(mod3)
W = 0.96168, p-value = 0.323#Tukey
medias3=emmeans(mod3,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias3.1=emmeans(mod3,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias3) propagacao emmean SE df lower.CL upper.CL
Estaquia 4.07 0.0367 23 3.99 4.14
Semente 4.18 0.0353 23 4.11 4.25
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias3.1) saturacao emmean SE df lower.CL upper.CL
19 4.06 0.0539 23 3.95 4.17
39 4.10 0.0499 23 3.99 4.20
52 4.04 0.0499 23 3.94 4.14
63 4.29 0.0499 23 4.19 4.40
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod3) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.0879 0.08795 4.419 0.04670 *
saturacao 3 0.3105 0.10351 5.200 0.00688 **
propagacao:saturacao 3 0.0780 0.02601 1.307 0.29623
Residuals 23 0.4578 0.01990
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey3 = TukeyHSD(mod3)
print(tukey3) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = pH.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 0.1065833 0.00169244 0.2114742 0.0467021
$saturacao
diff lwr upr p adj
39-19 0.02225595 -0.179805073 0.2243170 0.9899044
52-19 -0.03649405 -0.238555073 0.1655670 0.9582928
63-19 0.21850595 0.016444927 0.4205670 0.0307545
52-39 -0.05875000 -0.253959496 0.1364595 0.8383251
63-39 0.19625000 0.001040504 0.3914595 0.0484635
63-52 0.25500000 0.059790504 0.4502095 0.0073953
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.2850 -0.07324980 0.6432498 0.1904642
Estaquia:39-Estaquia:19 0.1225 -0.23574980 0.4807498 0.9414202
Semente:39-Estaquia:19 0.2325 -0.12574980 0.5907498 0.4106783
Estaquia:52-Estaquia:19 0.0875 -0.27074980 0.4457498 0.9906304
Semente:52-Estaquia:19 0.1500 -0.20824980 0.5082498 0.8517105
Estaquia:63-Estaquia:19 0.3700 0.01175020 0.7282498 0.0395462
Semente:63-Estaquia:19 0.3775 0.01925020 0.7357498 0.0339736
Estaquia:39-Semente:19 -0.1625 -0.49417486 0.1691749 0.7291373
Semente:39-Semente:19 -0.0525 -0.38417486 0.2791749 0.9993685
Estaquia:52-Semente:19 -0.1975 -0.52917486 0.1341749 0.5152171
Semente:52-Semente:19 -0.1350 -0.46667486 0.1966749 0.8684848
Estaquia:63-Semente:19 0.0850 -0.24667486 0.4166749 0.9876091
Semente:63-Semente:19 0.0925 -0.23917486 0.4241749 0.9800010
Semente:39-Estaquia:39 0.1100 -0.22167486 0.4416749 0.9497332
Estaquia:52-Estaquia:39 -0.0350 -0.36667486 0.2966749 0.9999574
Semente:52-Estaquia:39 0.0275 -0.30417486 0.3591749 0.9999918
Estaquia:63-Estaquia:39 0.2475 -0.08417486 0.5791749 0.2520744
Semente:63-Estaquia:39 0.2550 -0.07667486 0.5866749 0.2222304
Estaquia:52-Semente:39 -0.1450 -0.47667486 0.1866749 0.8230605
Semente:52-Semente:39 -0.0825 -0.41417486 0.2491749 0.9895768
Estaquia:63-Semente:39 0.1375 -0.19417486 0.4691749 0.8577793
Semente:63-Semente:39 0.1450 -0.18667486 0.4766749 0.8230605
Semente:52-Estaquia:52 0.0625 -0.26917486 0.3941749 0.9980723
Estaquia:63-Estaquia:52 0.2825 -0.04917486 0.6141749 0.1352802
Semente:63-Estaquia:52 0.2900 -0.04167486 0.6216749 0.1171664
Estaquia:63-Semente:52 0.2200 -0.11167486 0.5516749 0.3844683
Semente:63-Semente:52 0.2275 -0.10417486 0.5591749 0.3449293
Semente:63-Estaquia:63 0.0075 -0.32417486 0.3391749 1.0000000tukey.cld3 = multcompLetters4(mod3, tukey3)
print(tukey.cld3)$propagacao
Semente Estaquia
"a" "b"
$saturacao
63 39 19 52
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Semente:19 Semente:39 Semente:52 Estaquia:39 Estaquia:52 Estaquia:19
"a" "a" "ab" "ab" "ab" "ab" "ab" "b" 4 - Al.final
#model
mod4 = aov(Al.final~propagacao*saturacao, data = data2)
hist(rstandard(mod4))
shapiro.test(resid(mod4))
Shapiro-Wilk normality test
data: resid(mod4)
W = 0.925, p-value = 0.0321mod4.1 = aov(Al.final^0.8~propagacao*saturacao, data = data2)
hist(rstandard(mod4.1))
shapiro.test(resid(mod4.1))
Shapiro-Wilk normality test
data: resid(mod4.1)
W = 0.93375, p-value = 0.05549#Tukey
medias4=emmeans(mod4,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias4.1=emmeans(mod4,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias4) propagacao emmean SE df lower.CL upper.CL
Estaquia 5.67 0.235 23 5.18 6.15
Semente 4.92 0.225 23 4.46 5.39
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias4.1) saturacao emmean SE df lower.CL upper.CL
19 6.65 0.344 23 5.93 7.36
39 5.80 0.319 23 5.14 6.46
52 5.15 0.319 23 4.49 5.81
63 3.59 0.319 23 2.93 4.25
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod4.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.887 0.887 3.329 0.0811 .
saturacao 3 12.066 4.022 15.098 1.2e-05 ***
propagacao:saturacao 3 1.708 0.569 2.137 0.1232
Residuals 23 6.127 0.266
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey4 = TukeyHSD(mod4.1)
print(tukey4) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Al.final^0.8 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.3384637 -0.7221834 0.04525596 0.0810671
$saturacao
diff lwr upr p adj
39-19 -0.3942806 -1.133475 0.34491409 0.4674264
52-19 -0.7622127 -1.501407 -0.02301802 0.0416129
63-19 -1.6923371 -2.431532 -0.95314244 0.0000103
52-39 -0.3679321 -1.082062 0.34619776 0.4967529
63-39 -1.2980565 -2.012186 -0.58392665 0.0002378
63-52 -0.9301244 -1.644254 -0.21599454 0.0075827
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -1.24518233 -2.5557583 0.06539369 0.0708023
Estaquia:39-Estaquia:19 -1.03098525 -2.3415613 0.27959077 0.2005996
Semente:39-Estaquia:19 -1.13228943 -2.4428654 0.17828660 0.1252156
Estaquia:52-Estaquia:19 -1.30030116 -2.6108772 0.01027486 0.0528485
Semente:52-Estaquia:19 -1.59883775 -2.9094138 -0.28826172 0.0097093
Estaquia:63-Estaquia:19 -2.36861464 -3.6791907 -1.05803862 0.0000939
Semente:63-Estaquia:19 -2.39077310 -3.7013491 -1.08019707 0.0000823
Estaquia:39-Semente:19 0.21419707 -0.9991605 1.42755470 0.9987253
Semente:39-Semente:19 0.11289290 -1.1004647 1.32625053 0.9999819
Estaquia:52-Semente:19 -0.05511883 -1.2684765 1.15823879 0.9999999
Semente:52-Semente:19 -0.35365542 -1.5670130 0.85970221 0.9745065
Estaquia:63-Semente:19 -1.12343231 -2.3367899 0.08992531 0.0834590
Semente:63-Semente:19 -1.14559077 -2.3589484 0.06776685 0.0737445
Semente:39-Estaquia:39 -0.10130417 -1.3146618 1.11205345 0.9999914
Estaquia:52-Estaquia:39 -0.26931591 -1.4826735 0.94404172 0.9947005
Semente:52-Estaquia:39 -0.56785249 -1.7812101 0.64550513 0.7700911
Estaquia:63-Estaquia:39 -1.33762939 -2.5509870 -0.12427176 0.0237252
Semente:63-Estaquia:39 -1.35978784 -2.5731455 -0.14643022 0.0206966
Estaquia:52-Semente:39 -0.16801173 -1.3813694 1.04534589 0.9997372
Semente:52-Semente:39 -0.46654832 -1.6799059 0.74680930 0.8977221
Estaquia:63-Semente:39 -1.23632522 -2.4496828 -0.02296759 0.0436929
Semente:63-Semente:39 -1.25848367 -2.4718413 -0.04512605 0.0383111
Semente:52-Estaquia:52 -0.29853659 -1.5118942 0.91482104 0.9902191
Estaquia:63-Estaquia:52 -1.06831348 -2.2816711 0.14504414 0.1126397
Semente:63-Estaquia:52 -1.09047194 -2.3038296 0.12288569 0.0999917
Estaquia:63-Semente:52 -0.76977690 -1.9831345 0.44358073 0.4382096
Semente:63-Semente:52 -0.79193535 -2.0052930 0.42142227 0.4038336
Semente:63-Estaquia:63 -0.02215846 -1.2355161 1.19119917 1.0000000tukey.cld4 = multcompLetters4(mod4.1, tukey4)
print(tukey.cld4)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
19 39 52 63
"a" "ab" "b" "c"
$`propagacao:saturacao`
Estaquia:19 Estaquia:39 Semente:39 Semente:19 Estaquia:52 Semente:52 Estaquia:63 Semente:63
"a" "ab" "ab" "abc" "abc" "bc" "c" "c" 5 - H.Al.final
#model
mod5 = aov(H.Al.final~propagacao*saturacao, data = data2)
hist(rstandard(mod5))
shapiro.test(resid(mod5))
Shapiro-Wilk normality test
data: resid(mod5)
W = 0.988, p-value = 0.9742#Tukey
medias5=emmeans(mod5,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias5.1=emmeans(mod5,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias5) propagacao emmean SE df lower.CL upper.CL
Estaquia 32.9 0.621 23 31.6 34.1
Semente 31.8 0.597 23 30.5 33.0
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias5.1) saturacao emmean SE df lower.CL upper.CL
19 35.3 0.911 23 33.4 37.2
39 33.9 0.844 23 32.1 35.6
52 31.7 0.844 23 30.0 33.4
63 28.3 0.844 23 26.6 30.1
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod5) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 4.91 4.91 0.863 0.363
saturacao 3 200.55 66.85 11.740 7.24e-05 ***
propagacao:saturacao 3 26.23 8.74 1.535 0.232
Residuals 23 130.96 5.69
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey5 = TukeyHSD(mod5)
print(tukey5) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = H.Al.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.7966667 -2.570742 0.9774084 0.3625615
$saturacao
diff lwr upr p adj
39-19 -1.167619 -4.585184 2.24994573 0.7809214
52-19 -3.342619 -6.760184 0.07494573 0.0567953
63-19 -6.705119 -10.122684 -3.28755427 0.0000899
52-39 -2.175000 -5.476681 1.12668125 0.2885387
63-39 -5.537500 -8.839181 -2.23581875 0.0006162
63-52 -3.362500 -6.664181 -0.06081875 0.0448702
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -4.341667 -10.400935 1.71760128 0.2953974
Estaquia:39-Estaquia:19 -3.291667 -9.350935 2.76760128 0.6224438
Semente:39-Estaquia:19 -3.891667 -9.950935 2.16760128 0.4232790
Estaquia:52-Estaquia:19 -5.741667 -11.800935 0.31760128 0.0720340
Semente:52-Estaquia:19 -5.791667 -11.850935 0.26760128 0.0680688
Estaquia:63-Estaquia:19 -9.416667 -15.475935 -3.35739872 0.0006935
Semente:63-Estaquia:19 -8.841667 -14.900935 -2.78239872 0.0014760
Estaquia:39-Semente:19 1.050000 -4.559792 6.65979205 0.9981524
Semente:39-Semente:19 0.450000 -5.159792 6.05979205 0.9999934
Estaquia:52-Semente:19 -1.400000 -7.009792 4.20979205 0.9893737
Semente:52-Semente:19 -1.450000 -7.059792 4.15979205 0.9869844
Estaquia:63-Semente:19 -5.075000 -10.684792 0.53479205 0.0961477
Semente:63-Semente:19 -4.500000 -10.109792 1.10979205 0.1831455
Semente:39-Estaquia:39 -0.600000 -6.209792 5.00979205 0.9999533
Estaquia:52-Estaquia:39 -2.450000 -8.059792 3.15979205 0.8237697
Semente:52-Estaquia:39 -2.500000 -8.109792 3.10979205 0.8091046
Estaquia:63-Estaquia:39 -6.125000 -11.734792 -0.51520795 0.0256668
Semente:63-Estaquia:39 -5.550000 -11.159792 0.05979205 0.0539082
Estaquia:52-Semente:39 -1.850000 -7.459792 3.75979205 0.9511530
Semente:52-Semente:39 -1.900000 -7.509792 3.70979205 0.9441350
Estaquia:63-Semente:39 -5.525000 -11.134792 0.08479205 0.0556233
Semente:63-Semente:39 -4.950000 -10.559792 0.65979205 0.1112448
Semente:52-Estaquia:52 -0.050000 -5.659792 5.55979205 1.0000000
Estaquia:63-Estaquia:52 -3.675000 -9.284792 1.93479205 0.3993715
Semente:63-Estaquia:52 -3.100000 -8.709792 2.50979205 0.6031704
Estaquia:63-Semente:52 -3.625000 -9.234792 1.98479205 0.4159000
Semente:63-Semente:52 -3.050000 -8.659792 2.55979205 0.6215244
Semente:63-Estaquia:63 0.575000 -5.034792 6.18479205 0.9999650tukey.cld5 = multcompLetters4(mod5, tukey5)
print(tukey.cld5)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
19 39 52 63
"a" "a" "a" "b"
$`propagacao:saturacao`
Estaquia:19 Estaquia:39 Semente:39 Semente:19 Estaquia:52 Semente:52 Semente:63 Estaquia:63
"a" "ab" "abc" "abc" "abc" "abc" "bc" "c" 6 - sb.final
#model
mod6 = aov(sb.final~propagacao*saturacao, data = data2)
hist(rstandard(mod6))
shapiro.test(resid(mod6))
Shapiro-Wilk normality test
data: resid(mod6)
W = 0.98781, p-value = 0.9722#Tukey
medias6=emmeans(mod6,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias6.1=emmeans(mod6,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias6) propagacao emmean SE df lower.CL upper.CL
Estaquia 10.8 0.477 23 9.85 11.8
Semente 13.1 0.458 23 12.11 14.0
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias6.1) saturacao emmean SE df lower.CL upper.CL
19 9.14 0.700 23 7.69 10.6
39 10.71 0.648 23 9.37 12.1
52 11.46 0.648 23 10.12 12.8
63 16.48 0.648 23 15.13 17.8
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod6) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 30.84 30.84 9.174 0.00597 **
saturacao 3 228.56 76.19 22.663 4.68e-07 ***
propagacao:saturacao 3 8.30 2.77 0.823 0.49461
Residuals 23 77.32 3.36
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey6 = TukeyHSD(mod6)
print(tukey6) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = sb.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 1.995833 0.6327059 3.358961 0.0059714
$saturacao
diff lwr upr p adj
39-19 1.45506 -1.1708588 4.080978 0.4348209
52-19 2.20506 -0.4208588 4.830978 0.1217687
63-19 7.21756 4.5916412 9.843478 0.0000006
52-39 0.75000 -1.7868781 3.286878 0.8452953
63-39 5.76250 3.2256219 8.299378 0.0000116
63-52 5.01250 2.4756219 7.549378 0.0000819
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 3.616667 -1.0390294 8.272363 0.2123227
Estaquia:39-Estaquia:19 1.941667 -2.7140294 6.597363 0.8541459
Semente:39-Estaquia:19 4.816667 0.1609706 9.472363 0.0390424
Estaquia:52-Estaquia:19 3.466667 -1.1890294 8.122363 0.2542969
Semente:52-Estaquia:19 4.791667 0.1359706 9.447363 0.0405842
Estaquia:63-Estaquia:19 8.591667 3.9359706 13.247363 0.0000698
Semente:63-Estaquia:19 9.691667 5.0359706 14.347363 0.0000115
Estaquia:39-Semente:19 -1.675000 -5.9853370 2.635337 0.8927216
Semente:39-Semente:19 1.200000 -3.1103370 5.510337 0.9801945
Estaquia:52-Semente:19 -0.150000 -4.4603370 4.160337 1.0000000
Semente:52-Semente:19 1.175000 -3.1353370 5.485337 0.9823933
Estaquia:63-Semente:19 4.975000 0.6646630 9.285337 0.0160699
Semente:63-Semente:19 6.075000 1.7646630 10.385337 0.0021925
Semente:39-Estaquia:39 2.875000 -1.4353370 7.185337 0.3778350
Estaquia:52-Estaquia:39 1.525000 -2.7853370 5.835337 0.9307688
Semente:52-Estaquia:39 2.850000 -1.4603370 7.160337 0.3882574
Estaquia:63-Estaquia:39 6.650000 2.3396630 10.960337 0.0007587
Semente:63-Estaquia:39 7.750000 3.4396630 12.060337 0.0001009
Estaquia:52-Semente:39 -1.350000 -5.6603370 2.960337 0.9625653
Semente:52-Semente:39 -0.025000 -4.3353370 4.285337 1.0000000
Estaquia:63-Semente:39 3.775000 -0.5353370 8.085337 0.1160774
Semente:63-Semente:39 4.875000 0.5646630 9.185337 0.0191515
Semente:52-Estaquia:52 1.325000 -2.9853370 5.635337 0.9660744
Estaquia:63-Estaquia:52 5.125000 0.8146630 9.435337 0.0123220
Semente:63-Estaquia:52 6.225000 1.9146630 10.535337 0.0016629
Estaquia:63-Semente:52 3.800000 -0.5103370 8.110337 0.1118111
Semente:63-Semente:52 4.900000 0.5896630 9.210337 0.0183321
Semente:63-Estaquia:63 1.100000 -3.2103370 5.410337 0.9879057tukey.cld6 = multcompLetters4(mod6, tukey6)
print(tukey.cld6)$propagacao
Semente Estaquia
"a" "b"
$saturacao
63 52 39 19
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Semente:39 Semente:52 Semente:19 Estaquia:52 Estaquia:39 Estaquia:19
"a" "ab" "bc" "bc" "cd" "cd" "cd" "d" 7 - Ca.final
#model
mod7 = aov(Ca.final~propagacao*saturacao, data = data2)
hist(rstandard(mod7))
shapiro.test(resid(mod7))
Shapiro-Wilk normality test
data: resid(mod7)
W = 0.97628, p-value = 0.7033#Tukey
medias7=emmeans(mod7,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias7.1=emmeans(mod7,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias7) propagacao emmean SE df lower.CL upper.CL
Estaquia 8.56 0.252 23 8.03 9.08
Semente 9.29 0.242 23 8.79 9.80
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias7.1) saturacao emmean SE df lower.CL upper.CL
19 5.26 0.370 23 4.50 6.03
39 8.05 0.343 23 7.34 8.76
52 9.39 0.343 23 8.68 10.10
63 13.00 0.343 23 12.29 13.71
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod7) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1.94 1.94 2.063 0.164
saturacao 3 234.52 78.17 83.200 1.7e-12 ***
propagacao:saturacao 3 0.31 0.10 0.111 0.953
Residuals 23 21.61 0.94
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey7 = TukeyHSD(mod7)
print(tukey7) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Ca.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 0.5004167 -0.2202381 1.221071 0.1643357
$saturacao
diff lwr upr p adj
39-19 2.785744 1.397480112 4.174008 0.0000667
52-19 4.123244 2.734980112 5.511508 0.0000002
63-19 7.735744 6.347480112 9.124008 0.0000000
52-39 1.337500 -0.003690381 2.678690 0.0508103
63-39 4.950000 3.608809619 6.291190 0.0000000
63-52 3.612500 2.271309619 4.953690 0.0000008
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.525 -1.93636173 2.986362 0.9958397
Estaquia:39-Estaquia:19 2.525 0.06363827 4.986362 0.0415745
Semente:39-Estaquia:19 3.575 1.11363827 6.036362 0.0015574
Estaquia:52-Estaquia:19 4.000 1.53863827 6.461362 0.0003944
Semente:52-Estaquia:19 4.775 2.31363827 7.236362 0.0000336
Estaquia:63-Estaquia:19 7.700 5.23863827 10.161362 0.0000000
Semente:63-Estaquia:19 8.300 5.83863827 10.761362 0.0000000
Estaquia:39-Semente:19 2.000 -0.27877817 4.278778 0.1146931
Semente:39-Semente:19 3.050 0.77122183 5.328778 0.0038451
Estaquia:52-Semente:19 3.475 1.19622183 5.753778 0.0008746
Semente:52-Semente:19 4.250 1.97122183 6.528778 0.0000599
Estaquia:63-Semente:19 7.175 4.89622183 9.453778 0.0000000
Semente:63-Semente:19 7.775 5.49622183 10.053778 0.0000000
Semente:39-Estaquia:39 1.050 -1.22877817 3.328778 0.7830351
Estaquia:52-Estaquia:39 1.475 -0.80377817 3.753778 0.4138703
Semente:52-Estaquia:39 2.250 -0.02877817 4.528778 0.0546604
Estaquia:63-Estaquia:39 5.175 2.89622183 7.453778 0.0000028
Semente:63-Estaquia:39 5.775 3.49622183 8.053778 0.0000004
Estaquia:52-Semente:39 0.425 -1.85377817 2.703778 0.9981937
Semente:52-Semente:39 1.200 -1.07877817 3.478778 0.6564868
Estaquia:63-Semente:39 4.125 1.84622183 6.403778 0.0000918
Semente:63-Semente:39 4.725 2.44622183 7.003778 0.0000122
Semente:52-Estaquia:52 0.775 -1.50377817 3.053778 0.9429740
Estaquia:63-Estaquia:52 3.700 1.42122183 5.978778 0.0003989
Semente:63-Estaquia:52 4.300 2.02122183 6.578778 0.0000505
Estaquia:63-Semente:52 2.925 0.64622183 5.203778 0.0059176
Semente:63-Semente:52 3.525 1.24622183 5.803778 0.0007345
Semente:63-Estaquia:63 0.600 -1.67877817 2.878778 0.9855330tukey.cld7 = multcompLetters4(mod7, tukey7)
print(tukey.cld7)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
63 52 39 19
"a" "b" "b" "c"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Semente:52 Estaquia:52 Semente:39 Estaquia:39 Semente:19 Estaquia:19
"a" "a" "b" "b" "b" "bc" "cd" "d" 8 - Mg.final
#model
mod8 = aov(Mg.final~propagacao*saturacao, data = data2)
hist(rstandard(mod8))
shapiro.test(resid(mod8))
Shapiro-Wilk normality test
data: resid(mod8)
W = 0.98935, p-value = 0.9859#Tukey
medias8=emmeans(mod8,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias8.1=emmeans(mod8,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias8) propagacao emmean SE df lower.CL upper.CL
Estaquia 1.72 0.172 23 1.36 2.07
Semente 2.51 0.165 23 2.16 2.85
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias8.1) saturacao emmean SE df lower.CL upper.CL
19 1.84 0.252 23 1.32 2.36
39 1.95 0.233 23 1.47 2.43
52 1.61 0.233 23 1.13 2.10
63 3.05 0.233 23 2.57 3.53
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod8) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 4.705 4.705 10.790 0.00325 **
saturacao 3 9.861 3.287 7.538 0.00110 **
propagacao:saturacao 3 0.867 0.289 0.663 0.58328
Residuals 23 10.030 0.436
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey8 = TukeyHSD(mod8)
print(tukey8) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Mg.final ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 0.7795833 0.2886185 1.270548 0.0032473
$saturacao
diff lwr upr p adj
39-19 0.134256 -0.8115349 1.0800468 0.9789365
52-19 -0.203244 -1.1490349 0.7425468 0.9326591
63-19 1.234256 0.2884651 2.1800468 0.0074579
52-39 -0.337500 -1.2512208 0.5762208 0.7383586
63-39 1.100000 0.1862792 2.0137208 0.0143284
63-52 1.437500 0.5237792 2.3512208 0.0012458
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.475 -1.20186665 2.1518666 0.9781987
Estaquia:39-Estaquia:19 -0.325 -2.00186665 1.3518666 0.9977000
Semente:39-Estaquia:19 1.025 -0.65186665 2.7018666 0.4834853
Estaquia:52-Estaquia:19 -0.300 -1.97686665 1.3768666 0.9986122
Semente:52-Estaquia:19 0.325 -1.35186665 2.0018666 0.9977000
Estaquia:63-Estaquia:19 1.100 -0.57686665 2.7768666 0.3977559
Semente:63-Estaquia:19 1.800 0.12313335 3.4768666 0.0294078
Estaquia:39-Semente:19 -0.800 -2.35247685 0.7524768 0.6792883
Semente:39-Semente:19 0.550 -1.00247685 2.1024768 0.9303209
Estaquia:52-Semente:19 -0.775 -2.32747685 0.7774768 0.7111945
Semente:52-Semente:19 -0.150 -1.70247685 1.4024768 0.9999766
Estaquia:63-Semente:19 0.625 -0.92747685 2.1774768 0.8745824
Semente:63-Semente:19 1.325 -0.22747685 2.8774768 0.1338102
Semente:39-Estaquia:39 1.350 -0.20247685 2.9024768 0.1208083
Estaquia:52-Estaquia:39 0.025 -1.52747685 1.5774768 1.0000000
Semente:52-Estaquia:39 0.650 -0.90247685 2.2024768 0.8517349
Estaquia:63-Estaquia:39 1.425 -0.12747685 2.9774768 0.0880585
Semente:63-Estaquia:39 2.125 0.57252315 3.6774768 0.0030254
Estaquia:52-Semente:39 -1.325 -2.87747685 0.2274768 0.1338102
Semente:52-Semente:39 -0.700 -2.25247685 0.8524768 0.8002300
Estaquia:63-Semente:39 0.075 -1.47747685 1.6274768 0.9999998
Semente:63-Semente:39 0.775 -0.77747685 2.3274768 0.7111945
Semente:52-Estaquia:52 0.625 -0.92747685 2.1774768 0.8745824
Estaquia:63-Estaquia:52 1.400 -0.15247685 2.9524768 0.0979956
Semente:63-Estaquia:52 2.100 0.54752315 3.6524768 0.0034362
Estaquia:63-Semente:52 0.775 -0.77747685 2.3274768 0.7111945
Semente:63-Semente:52 1.475 -0.07747685 3.0274768 0.0708064
Semente:63-Estaquia:63 0.700 -0.85247685 2.2524768 0.8002300tukey.cld8 = multcompLetters4(mod8, tukey8)
print(tukey.cld8)$propagacao
Semente Estaquia
"a" "b"
$saturacao
63 39 19 52
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Semente:39 Semente:19 Semente:52 Estaquia:19 Estaquia:52 Estaquia:39
"a" "ab" "ab" "ab" "ab" "b" "b" "b" BIOMASSA PRODUCTION
9 - MF.mean
#model
mod9 = aov(MF.mean~propagacao*saturacao, data = data2)
hist(rstandard(mod9))
shapiro.test(resid(mod9))
Shapiro-Wilk normality test
data: resid(mod9)
W = 0.9314, p-value = 0.04286summary(mod9) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 509.2 509.2 38.853 1.93e-06 ***
saturacao 3 450.9 150.3 11.468 7.36e-05 ***
propagacao:saturacao 3 105.3 35.1 2.678 0.0698 .
Residuals 24 314.5 13.1
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1#means and sd
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, uniondata_summary9 = group_by(data2, saturacao) %>%
summarise(sd = sd(MF.mean, na.rm = TRUE),
biomass.mean = mean(MF.mean, na.rm = TRUE))%>%
arrange(desc(biomass.mean))
print(data_summary9)
data_summary9.1 = group_by(data2, propagacao) %>%
summarise(sd = sd(MF.mean, na.rm = TRUE),
biomass.mean = mean(MF.mean, na.rm = TRUE))%>%
arrange(desc(biomass.mean))
print(data_summary9.1)
library(multcompView)
#tukey
tukey9 = TukeyHSD(mod9)
print(tukey9) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = MF.mean ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -7.978125 -10.61977 -5.336478 1.9e-06
$saturacao
diff lwr upr p adj
39-19 7.92000 2.926654 12.913346 0.0010927
52-19 9.07875 4.085404 14.072096 0.0002204
63-19 8.83500 3.841654 13.828346 0.0003087
52-39 1.15875 -3.834596 6.152096 0.9179214
63-39 0.91500 -4.078346 5.908346 0.9569858
63-52 -0.24375 -5.237096 4.749596 0.9991027
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -13.4150 -21.8930389 -4.9369611 0.0005173
Estaquia:39-Estaquia:19 5.7450 -2.7330389 14.2230389 0.3622628
Semente:39-Estaquia:19 -3.3200 -11.7980389 5.1580389 0.8912196
Estaquia:52-Estaquia:19 4.4500 -4.0280389 12.9280389 0.6639560
Semente:52-Estaquia:19 0.2925 -8.1855389 8.7705389 1.0000000
Estaquia:63-Estaquia:19 4.7650 -3.7130389 13.2430389 0.5877694
Semente:63-Estaquia:19 -0.5100 -8.9880389 7.9680389 0.9999991
Estaquia:39-Semente:19 19.1600 10.6819611 27.6380389 0.0000025
Semente:39-Semente:19 10.0950 1.6169611 18.5730389 0.0119702
Estaquia:52-Semente:19 17.8650 9.3869611 26.3430389 0.0000080
Semente:52-Semente:19 13.7075 5.2294611 22.1855389 0.0003910
Estaquia:63-Semente:19 18.1800 9.7019611 26.6580389 0.0000060
Semente:63-Semente:19 12.9050 4.4269611 21.3830389 0.0008428
Semente:39-Estaquia:39 -9.0650 -17.5430389 -0.5869611 0.0301833
Estaquia:52-Estaquia:39 -1.2950 -9.7730389 7.1830389 0.9995172
Semente:52-Estaquia:39 -5.4525 -13.9305389 3.0255389 0.4251916
Estaquia:63-Estaquia:39 -0.9800 -9.4580389 7.4980389 0.9999242
Semente:63-Estaquia:39 -6.2550 -14.7330389 2.2230389 0.2662086
Estaquia:52-Semente:39 7.7700 -0.7080389 16.2480389 0.0892826
Semente:52-Semente:39 3.6125 -4.8655389 12.0905389 0.8433944
Estaquia:63-Semente:39 8.0850 -0.3930389 16.5630389 0.0692879
Semente:63-Semente:39 2.8100 -5.6680389 11.2880389 0.9511217
Semente:52-Estaquia:52 -4.1575 -12.6355389 4.3205389 0.7320200
Estaquia:63-Estaquia:52 0.3150 -8.1630389 8.7930389 1.0000000
Semente:63-Estaquia:52 -4.9600 -13.4380389 3.5180389 0.5404267
Estaquia:63-Semente:52 4.4725 -4.0055389 12.9505389 0.6585784
Semente:63-Semente:52 -0.8025 -9.2805389 7.6755389 0.9999804
Semente:63-Estaquia:63 -5.2750 -13.7530389 3.2030389 0.4656448tukey.cld9 = multcompLetters4(mod9, tukey9)
print(tukey.cld9)$propagacao
Estaquia Semente
"a" "b"
$saturacao
52 63 39 19
"a" "a" "a" "b"
$`propagacao:saturacao`
Estaquia:39 Estaquia:63 Estaquia:52 Semente:52 Estaquia:19 Semente:63 Semente:39 Semente:19
"a" "ab" "ab" "ab" "ab" "ab" "b" "c" cld9 = as.data.frame.list(tukey.cld9$`saturacao`)
data_summary9$Tukey = cld9$Letters
print(data_summary9)library(ggplot2)
#plot
plt1=ggplot(data_summary9, aes(x=as.factor(saturacao), y=biomass.mean, fill=saturacao))+
geom_col(position = "dodge")+
geom_errorbar(aes(ymin=biomass.mean-sd, ymax=biomass.mean+sd), width=.2, position=position_dodge(.9))+
geom_text(aes(label=Tukey), position = position_dodge(0.9), size = 4,
vjust=-0.9, hjust=-0.9, colour = "Black")+
scale_x_discrete(name="BCS levels (%)")+
scale_y_continuous(breaks = seq(0, 45, 5), name= "Shoot biomass production (g/pot)")+
coord_cartesian(ylim = c(0,45))+scale_fill_manual(values = c("grey80", "grey60", "grey40","grey20"))
plt1
plt1=plt1+theme(legend.key=element_blank(),
axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
panel.background = element_rect(fill = 'transparent',size = 0.5, colour = "black",linetype = "solid"),
legend.position = "none",
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate("text", size=5, x=3, y=4, color= "white",label="-0.82%")+
annotate("text", size=5, x=2, y=4, color= "white",label="-3.93%")+
annotate("text", size=5, x=1, y=4, color= "white",label="-31.0%*")+
annotate("text", size=4, x=4, y=45,label="P-value")+
annotate("text",size=3, x=4, y=43,label= "PM = <.001")+
annotate("text",size=3, x=4, y=41, label= "BCS = <.001")+
annotate("text",size=3, x=4, y=39,label= "P*B = 0.070")Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.plt1
10- Roots biomass
#model
mod10 = aov(Raizes~propagacao*saturacao, data = data2)
hist(rstandard(mod10))
shapiro.test(resid(mod10))
Shapiro-Wilk normality test
data: resid(mod10)
W = 0.9658, p-value = 0.4315summary(mod10) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 378.6 378.6 7.079 0.0143 *
saturacao 3 599.6 199.9 3.738 0.0261 *
propagacao:saturacao 3 197.0 65.7 1.228 0.3233
Residuals 22 1176.5 53.5
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingness#means and sd
data_summary10 = group_by(data2, saturacao) %>%
summarise(sd = sd(Raizes, na.rm = TRUE),
Root = mean(Raizes, na.rm = TRUE))%>%
arrange(desc(Root))
print(data_summary10)
data_summary10.1 = group_by(data2, propagacao) %>%
summarise(sd = sd(Raizes, na.rm = TRUE),
Root = mean(Raizes, na.rm = TRUE))%>%
arrange(desc(Root))
print(data_summary10.1)
#tukey
tukey10 = TukeyHSD(mod10)
print(tukey10) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Raizes ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -7.120625 -12.67083 -1.570423 0.0142822
$saturacao
diff lwr upr p adj
39-19 6.544687 -4.4222064 17.511581 0.3691285
52-19 11.532187 0.5652936 22.499081 0.0369178
63-19 11.374687 0.4077936 22.341581 0.0402045
52-39 4.987500 -5.1658708 15.140871 0.5339830
63-39 4.830000 -5.3233708 14.983371 0.5599003
63-52 -0.157500 -10.3108708 9.995871 0.9999704
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -10.7150 -31.8596898 10.429690 0.6921137
Estaquia:39-Estaquia:19 9.1875 -8.0770669 26.452067 0.6407814
Semente:39-Estaquia:19 -5.6150 -22.8795669 11.649567 0.9532277
Estaquia:52-Estaquia:19 10.2175 -7.0470669 27.482067 0.5183178
Semente:52-Estaquia:19 3.3300 -13.9345669 20.594567 0.9976836
Estaquia:63-Estaquia:19 7.2175 -10.0470669 24.482067 0.8496787
Semente:63-Estaquia:19 6.0150 -11.2495669 23.279567 0.9340982
Estaquia:39-Semente:19 19.9025 -1.2421898 41.047190 0.0749498
Semente:39-Semente:19 5.1000 -16.0446898 26.244690 0.9909963
Estaquia:52-Semente:19 20.9325 -0.2121898 42.077190 0.0536367
Semente:52-Semente:19 14.0450 -7.0996898 35.189690 0.3792297
Estaquia:63-Semente:19 17.9325 -3.2121898 39.077190 0.1373865
Semente:63-Semente:19 16.7300 -4.4146898 37.874690 0.1936295
Semente:39-Estaquia:39 -14.8025 -32.0670669 2.462067 0.1296135
Estaquia:52-Estaquia:39 1.0300 -16.2345669 18.294567 0.9999991
Semente:52-Estaquia:39 -5.8575 -23.1220669 11.407067 0.9421509
Estaquia:63-Estaquia:39 -1.9700 -19.2345669 15.294567 0.9999247
Semente:63-Estaquia:39 -3.1725 -20.4370669 14.092067 0.9982918
Estaquia:52-Semente:39 15.8325 -1.4320669 33.097067 0.0881326
Semente:52-Semente:39 8.9450 -8.3195669 26.209567 0.6693344
Estaquia:63-Semente:39 12.8325 -4.4320669 30.097067 0.2537318
Semente:63-Semente:39 11.6300 -5.6345669 28.894567 0.3626692
Semente:52-Estaquia:52 -6.8875 -24.1520669 10.377067 0.8768359
Estaquia:63-Estaquia:52 -3.0000 -20.2645669 14.264567 0.9988024
Semente:63-Estaquia:52 -4.2025 -21.4670669 13.062067 0.9904990
Estaquia:63-Semente:52 3.8875 -13.3770669 21.152067 0.9940140
Semente:63-Semente:52 2.6850 -14.5795669 19.949567 0.9994142
Semente:63-Estaquia:63 -1.2025 -18.4670669 16.062067 0.9999974tukey.cld10 = multcompLetters4(mod10, tukey10)
print(tukey.cld10)$propagacao
Estaquia Semente
"a" "b"
$saturacao
52 63 39 19
"a" "a" "ab" "b"
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:52 Estaquia:39 Estaquia:63 Semente:63 Semente:52 Estaquia:19 Semente:39 Semente:19
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:52 TRUE
Estaquia:39 TRUE
Estaquia:63 TRUE
Semente:63 TRUE
Semente:52 TRUE
Estaquia:19 TRUE
Semente:39 TRUE
Semente:19 TRUEcld10 = as.data.frame.list(tukey.cld10$`saturacao`)
data_summary10$Tukey = cld10$Letters
print(data_summary10)
#plot
plt2=ggplot(data_summary10, aes(x=as.factor(saturacao), y=Root, fill=saturacao))+
geom_col(position = "dodge")+
geom_errorbar(aes(ymin=Root-sd, ymax=Root+sd), width=.2, position=position_dodge(.9))+
geom_text(aes(label=Tukey), position = position_dodge(0.9), size = 4,
vjust=-0.9, hjust=-0.9, colour = "Black")+
scale_x_discrete(name="BCS levels (%)")+
scale_y_continuous(breaks = seq(0, 45, 5), name= "Root biomass production (g/pot)")+
coord_cartesian(ylim = c(0,45))+scale_fill_manual(values = c("grey80", "grey60", "grey40","grey20"))
plt2
plt2=plt2+theme(legend.key=element_blank(),
axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
panel.background = element_rect(fill = 'transparent',size = 0.5, colour = "black",linetype = "solid"),
legend.position = "none",
axis.title.x = element_text(size = 13),
axis.title.y = element_text(size = 13),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate("text", size=5, x=3, y=4, color= "white",label="-0.65%")+
annotate("text", size=5, x=2, y=4, color= "white",label="-21.7%")+
annotate("text", size=5, x=1, y=4, color= "white",label="-44.9%*")+
annotate("text", size=4, x=4, y=45,label="P-value")+
annotate("text",size=3, x=4, y=43,label= "PM = 0.014")+
annotate("text",size=3, x=4, y=41, label= "BCS = 0.026")+
annotate("text",size=3, x=4, y=39,label= "P*B = 0.323")
plt2
library(cowplot)
side.by.side = plot_grid(plt1,plt2,labels = c("A", "B"), ncol = 1, nrow = 2, align = "v")
save_plot("shoot and root.pdf",side.by.side, ncol = 1, nrow = 2) 
11- Branches
#Outlier
boxplot(data2$Perfilhos)
#model
mod11 = aov(Perfilhos~propagacao*saturacao, data = data2)
hist(rstandard(mod11))
shapiro.test(rstandard(mod11))
Shapiro-Wilk normality test
data: rstandard(mod11)
W = 0.95792, p-value = 0.2407#Tukey
medias11=emmeans(mod11,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias11.1=emmeans(mod11,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias11) propagacao emmean SE df lower.CL upper.CL
Estaquia 20.29 0.82 24 18.60 22.0
Semente 9.73 0.82 24 8.04 11.4
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias11.1) saturacao emmean SE df lower.CL upper.CL
19 12.1 1.16 24 9.69 14.5
39 16.7 1.16 24 14.27 19.1
52 16.4 1.16 24 13.98 18.8
63 14.9 1.16 24 12.52 17.3
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod11) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 892.6 892.6 82.936 2.96e-09 ***
saturacao 3 105.4 35.1 3.264 0.0388 *
propagacao:saturacao 3 104.3 34.8 3.229 0.0402 *
Residuals 24 258.3 10.8
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1tukey11 = TukeyHSD(mod11)
print(tukey11) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Perfilhos ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -10.56312 -12.95705 -8.169204 0
$saturacao
diff lwr upr p adj
39-19 4.58250 0.05741559 9.107584 0.0463909
52-19 4.29000 -0.23508441 8.815084 0.0675547
63-19 2.83125 -1.69383441 7.356334 0.3327589
52-39 -0.29250 -4.81758441 4.232584 0.9979286
63-39 -1.75125 -6.27633441 2.773834 0.7120139
63-52 -1.45875 -5.98383441 3.066334 0.8104046
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -16.3325 -24.01549344 -8.6495066 0.0000069
Estaquia:39-Estaquia:19 1.5000 -6.18299344 9.1829934 0.9976733
Semente:39-Estaquia:19 -8.6675 -16.35049344 -0.9845066 0.0193785
Estaquia:52-Estaquia:19 -0.6675 -8.35049344 7.0154934 0.9999891
Semente:52-Estaquia:19 -7.0850 -14.76799344 0.5979934 0.0859300
Estaquia:63-Estaquia:19 -0.6675 -8.35049344 7.0154934 0.9999891
Semente:63-Estaquia:19 -10.0025 -17.68549344 -2.3195066 0.0049832
Estaquia:39-Semente:19 17.8325 10.14950656 25.5154934 0.0000016
Semente:39-Semente:19 7.6650 -0.01799344 15.3479934 0.0508432
Estaquia:52-Semente:19 15.6650 7.98200656 23.3479934 0.0000136
Semente:52-Semente:19 9.2475 1.56450656 16.9304934 0.0108252
Estaquia:63-Semente:19 15.6650 7.98200656 23.3479934 0.0000136
Semente:63-Semente:19 6.3300 -1.35299344 14.0129934 0.1619319
Semente:39-Estaquia:39 -10.1675 -17.85049344 -2.4845066 0.0041977
Estaquia:52-Estaquia:39 -2.1675 -9.85049344 5.5154934 0.9792810
Semente:52-Estaquia:39 -8.5850 -16.26799344 -0.9020066 0.0210259
Estaquia:63-Estaquia:39 -2.1675 -9.85049344 5.5154934 0.9792810
Semente:63-Estaquia:39 -11.5025 -19.18549344 -3.8195066 0.0010327
Estaquia:52-Semente:39 8.0000 0.31700656 15.6829934 0.0370962
Semente:52-Semente:39 1.5825 -6.10049344 9.2654934 0.9967537
Estaquia:63-Semente:39 8.0000 0.31700656 15.6829934 0.0370962
Semente:63-Semente:39 -1.3350 -9.01799344 6.3479934 0.9988880
Semente:52-Estaquia:52 -6.4175 -14.10049344 1.2654934 0.1509672
Estaquia:63-Estaquia:52 0.0000 -7.68299344 7.6829934 1.0000000
Semente:63-Estaquia:52 -9.3350 -17.01799344 -1.6520066 0.0099033
Estaquia:63-Semente:52 6.4175 -1.26549344 14.1004934 0.1509672
Semente:63-Semente:52 -2.9175 -10.60049344 4.7654934 0.9054237
Semente:63-Estaquia:63 -9.3350 -17.01799344 -1.6520066 0.0099033tukey.cld11 = multcompLetters4(mod11, tukey11)
print(tukey.cld11)$propagacao
Estaquia Semente
"a" "b"
$saturacao
39 52 63 19
"a" "ab" "ab" "b"
$`propagacao:saturacao`
Estaquia:39 Estaquia:19 Estaquia:52 Estaquia:63 Semente:52 Semente:39 Semente:63 Semente:19
"a" "ab" "ab" "ab" "bc" "cd" "cd" "d" 12- Leaves
#Outlier
boxplot(data2$Folha)
#model
mod12 = aov(Folha~propagacao*saturacao, data = data2)
hist(rstandard(mod12))
shapiro.test(rstandard(mod12))
Shapiro-Wilk normality test
data: rstandard(mod12)
W = 0.98135, p-value = 0.8601#Tukey
medias12=emmeans(mod12,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias12.1=emmeans(mod12,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias12) propagacao emmean SE df lower.CL upper.CL
Estaquia 26.3 0.873 22 24.5 28.1
Semente 25.9 0.976 22 23.9 27.9
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias12.1) saturacao emmean SE df lower.CL upper.CL
19 27.1 1.51 22 24.0 30.3
39 23.7 1.23 22 21.1 26.3
52 25.9 1.23 22 23.3 28.4
63 27.7 1.23 22 25.1 30.3
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod12) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.64 0.640 0.052 0.821
saturacao 3 81.76 27.254 2.234 0.113
propagacao:saturacao 3 22.76 7.587 0.622 0.608
Residuals 22 268.35 12.198
2 observations deleted due to missingness13- Stems
#Outlier
boxplot(data2$Caule)
#model
mod13 = aov(Caule~propagacao*saturacao, data = data2)
hist(rstandard(mod13))
shapiro.test(rstandard(mod13))
Shapiro-Wilk normality test
data: rstandard(mod13)
W = 0.95364, p-value = 0.2114#Tukey
medias13=emmeans(mod13,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias13.1=emmeans(mod13,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias13) propagacao emmean SE df lower.CL upper.CL
Estaquia 56.3 1.28 22 53.6 58.9
Semente 49.4 1.43 22 46.5 52.4
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias13.1) saturacao emmean SE df lower.CL upper.CL
19 43.5 2.21 22 38.9 48.1
39 56.2 1.80 22 52.5 60.0
52 54.9 1.80 22 51.1 58.6
63 56.8 1.80 22 53.0 60.5
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod13) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 178.1 178.06 6.833 0.01584 *
saturacao 3 567.1 189.02 7.254 0.00147 **
propagacao:saturacao 3 200.4 66.81 2.564 0.08066 .
Residuals 22 573.3 26.06
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey13 = TukeyHSD(mod13)
print(tukey13) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Caule ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -4.883304 -8.757532 -1.009075 0.0158449
$saturacao
diff lwr upr p adj
39-19 10.890551 3.235289 18.545812 0.0035302
52-19 9.515551 1.860289 17.170812 0.0113062
63-19 11.453051 3.797789 19.108312 0.0021744
52-39 -1.375000 -8.462395 5.712395 0.9485857
63-39 0.562500 -6.524895 7.649895 0.9961071
63-52 1.937500 -5.149895 9.024895 0.8717948
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -15.7475 -30.507206 -0.9877936 0.0310025
Estaquia:39-Estaquia:19 7.3725 -4.678750 19.4237499 0.4783360
Semente:39-Estaquia:19 2.2825 -9.768750 14.3337499 0.9979334
Estaquia:52-Estaquia:19 6.7250 -5.326250 18.7762499 0.5875629
Semente:52-Estaquia:19 0.1800 -11.871250 12.2312499 1.0000000
Estaquia:63-Estaquia:19 5.4000 -6.651250 17.4512499 0.8014839
Semente:63-Estaquia:19 5.3800 -6.671250 17.4312499 0.8043089
Estaquia:39-Semente:19 23.1200 8.360294 37.8797064 0.0006742
Semente:39-Semente:19 18.0300 3.270294 32.7897064 0.0097849
Estaquia:52-Semente:19 22.4725 7.712794 37.2322064 0.0009490
Semente:52-Semente:19 15.9275 1.167794 30.6872064 0.0283702
Estaquia:63-Semente:19 21.1475 6.387794 35.9072064 0.0019114
Semente:63-Semente:19 21.1275 6.367794 35.8872064 0.0019317
Semente:39-Estaquia:39 -5.0900 -17.141250 6.9612499 0.8431620
Estaquia:52-Estaquia:39 -0.6475 -12.698750 11.4037499 0.9999996
Semente:52-Estaquia:39 -7.1925 -19.243750 4.8587499 0.5081938
Estaquia:63-Estaquia:39 -1.9725 -14.023750 10.0787499 0.9991843
Semente:63-Estaquia:39 -1.9925 -14.043750 10.0587499 0.9991295
Estaquia:52-Semente:39 4.4425 -7.608750 16.4937499 0.9137572
Semente:52-Semente:39 -2.1025 -14.153750 9.9487499 0.9987714
Estaquia:63-Semente:39 3.1175 -8.933750 15.1687499 0.9864897
Semente:63-Semente:39 3.0975 -8.953750 15.1487499 0.9869786
Semente:52-Estaquia:52 -6.5450 -18.596250 5.5062499 0.6183409
Estaquia:63-Estaquia:52 -1.3250 -13.376250 10.7262499 0.9999414
Semente:63-Estaquia:52 -1.3450 -13.396250 10.7062499 0.9999351
Estaquia:63-Semente:52 5.2200 -6.831250 17.2712499 0.8262488
Semente:63-Semente:52 5.2000 -6.851250 17.2512499 0.8289057
Semente:63-Estaquia:63 -0.0200 -12.071250 12.0312499 1.0000000tukey.cld13 = multcompLetters4(mod13, tukey13)
print(tukey.cld13)$propagacao
Estaquia Semente
"a" "b"
$saturacao
63 39 52 19
"a" "a" "a" "b"
$`propagacao:saturacao`
Estaquia:39 Estaquia:52 Estaquia:63 Semente:63 Semente:39 Semente:52 Estaquia:19 Semente:19
"a" "a" "a" "a" "a" "a" "a" "b" 14- Dead material
#Outlier
boxplot(data2$Senescente)
#model
mod14 = aov(Senescente^0.8~propagacao*saturacao, data = data2)
mod14.1 = aov(Senescente~propagacao*saturacao, data = data2)
hist(rstandard(mod14))
shapiro.test(rstandard(mod14))
Shapiro-Wilk normality test
data: rstandard(mod14)
W = 0.93734, p-value = 0.07714#Tukey
medias14=emmeans(mod14.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias14.1=emmeans(mod14.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias14) propagacao emmean SE df lower.CL upper.CL
Estaquia 17.4 1.77 22 13.8 21.1
Semente 24.7 1.98 22 20.6 28.8
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias14.1) saturacao emmean SE df lower.CL upper.CL
19 29.3 3.06 22 23.0 35.7
39 20.1 2.50 22 14.9 25.3
52 19.3 2.50 22 14.1 24.5
63 15.5 2.50 22 10.3 20.7
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod14) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 35.39 35.39 4.053 0.0565 .
saturacao 3 81.89 27.30 3.126 0.0464 *
propagacao:saturacao 3 48.32 16.11 1.845 0.1686
Residuals 22 192.12 8.73
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey14 = TukeyHSD(mod14)
print(tukey14) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Senescente^0.8 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 2.177185 -0.06563339 4.420004 0.0564903
$saturacao
diff lwr upr p adj
39-19 -2.8280545 -7.259741 1.6036317 0.3128077
52-19 -3.1665560 -7.598242 1.2651302 0.2241320
63-19 -4.8266146 -9.258301 -0.3949283 0.0294582
52-39 -0.3385015 -4.441446 3.7644427 0.9956344
63-39 -1.9985600 -6.101504 2.1043842 0.5408309
63-52 -1.6600585 -5.763003 2.4428857 0.6792843
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 7.63566139 -0.9088392 16.1801620 0.1027888
Estaquia:39-Estaquia:19 -1.10170603 -8.0782615 5.8748495 0.9993531
Semente:39-Estaquia:19 1.26176637 -5.7147891 8.2383219 0.9984553
Estaquia:52-Estaquia:19 -1.25066519 -8.2272207 5.7258903 0.9985395
Semente:52-Estaquia:19 0.73372252 -6.2428330 7.7102780 0.9999566
Estaquia:63-Estaquia:19 -1.91230090 -8.8888564 5.0642546 0.9812614
Semente:63-Estaquia:19 -1.92475884 -8.9013144 5.0517967 0.9805709
Estaquia:39-Semente:19 -8.73736742 -17.2818680 -0.1928668 0.0426334
Semente:39-Semente:19 -6.37389502 -14.9183956 2.1706056 0.2500507
Estaquia:52-Semente:19 -8.88632659 -17.4308272 -0.3418260 0.0376462
Semente:52-Semente:19 -6.90193888 -15.4464395 1.6425617 0.1756439
Estaquia:63-Semente:19 -9.54796229 -18.0924629 -1.0034617 0.0214070
Semente:63-Semente:19 -9.56042023 -18.1049208 -1.0159196 0.0211771
Semente:39-Estaquia:39 2.36347240 -4.6130831 9.3400279 0.9425768
Estaquia:52-Estaquia:39 -0.14895917 -7.1255147 6.8275963 1.0000000
Semente:52-Estaquia:39 1.83542854 -5.1411270 8.8119841 0.9851270
Estaquia:63-Estaquia:39 -0.81059487 -7.7871504 6.1659606 0.9999150
Semente:63-Estaquia:39 -0.82305281 -7.7996083 6.1535027 0.9999058
Estaquia:52-Semente:39 -2.51243157 -9.4889871 4.4641240 0.9227323
Semente:52-Semente:39 -0.52804386 -7.5045994 6.4485117 0.9999954
Estaquia:63-Semente:39 -3.17406727 -10.1506228 3.8024882 0.7895946
Semente:63-Semente:39 -3.18652521 -10.1630807 3.7900303 0.7864593
Semente:52-Estaquia:52 1.98438771 -4.9921678 8.9609432 0.9770033
Estaquia:63-Estaquia:52 -0.66163570 -7.6381912 6.3149198 0.9999785
Semente:63-Estaquia:52 -0.67409365 -7.6506492 6.3024619 0.9999756
Estaquia:63-Semente:52 -2.64602341 -9.6225789 4.3305321 0.9016725
Semente:63-Semente:52 -2.65848136 -9.6350369 4.3180742 0.8995506
Semente:63-Estaquia:63 -0.01245794 -6.9890135 6.9640976 1.0000000tukey.cld14 = multcompLetters4(mod14, tukey14)
print(tukey.cld14)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
19 39 52 63
"a" "ab" "ab" "b"
$`propagacao:saturacao`
Semente:19 Semente:39 Semente:52 Estaquia:19 Estaquia:39 Estaquia:52 Estaquia:63 Semente:63
"a" "ab" "ab" "ab" "b" "b" "b" "b" BROMATOLOGICAL ANALYSIS
15- Dried matter
#Outlier
boxplot(data2$DM105)
#model
mod15 = aov(DM105~propagacao*saturacao, data = data2)
hist(rstandard(mod15))
shapiro.test(rstandard(mod15))
Shapiro-Wilk normality test
data: rstandard(mod15)
W = 0.97205, p-value = 0.577#Tukey
medias15=emmeans(mod15,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias15.1=emmeans(mod15,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias15) propagacao emmean SE df lower.CL upper.CL
Estaquia 906.7 1.118 23 904.4 909.0
Semente 902.7 1.164 23 900.3 905.1
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias15.1) saturacao emmean SE df lower.CL upper.CL
19 903.5 1.708 23 900.0 907.0
39 905.3 1.582 23 902.0 908.6
52 905.9 1.582 23 902.6 909.1
63 904.1 1.582 23 900.9 907.4
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod15) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 111.9 111.93 5.593 0.0268 *
saturacao 3 23.6 7.86 0.393 0.7594
propagacao:saturacao 3 23.6 7.88 0.394 0.7588
Residuals 23 460.3 20.01
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey15 = TukeyHSD(mod15)
print(tukey15) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = DM105 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -3.80225 -7.128136 -0.4763643 0.0268459
$saturacao
diff lwr upr p adj
39-19 1.6214107 -4.785550 8.028372 0.8958907
52-19 2.1839107 -4.223050 8.590872 0.7820936
63-19 0.4389107 -5.968050 6.845872 0.9975122
52-39 0.5625000 -5.627213 6.752213 0.9942588
63-39 -1.1825000 -7.372213 5.007213 0.9512357
63-52 -1.7450000 -7.934713 4.444713 0.8626344
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -6.2850 -17.644403 5.074403 0.6017664
Estaquia:39-Estaquia:19 0.9625 -9.554263 11.479263 0.9999838
Semente:39-Estaquia:19 -3.6500 -14.166763 6.866763 0.9369600
Estaquia:52-Estaquia:19 0.9950 -9.521763 11.511763 0.9999797
Semente:52-Estaquia:19 -2.5575 -13.074263 7.959263 0.9908664
Estaquia:63-Estaquia:19 -1.8325 -12.349263 8.684263 0.9988272
Semente:63-Estaquia:19 -3.2200 -13.736763 7.296763 0.9667834
Estaquia:39-Semente:19 7.2475 -4.111903 18.606903 0.4313345
Semente:39-Semente:19 2.6350 -8.724403 13.994403 0.9930936
Estaquia:52-Semente:19 7.2800 -4.079403 18.639403 0.4259029
Semente:52-Semente:19 3.7275 -7.631903 15.086903 0.9523746
Estaquia:63-Semente:19 4.4525 -6.906903 15.811903 0.8885030
Semente:63-Semente:19 3.0650 -8.294403 14.424403 0.9833803
Semente:39-Estaquia:39 -4.6125 -15.129263 5.904263 0.8207719
Estaquia:52-Estaquia:39 0.0325 -10.484263 10.549263 1.0000000
Semente:52-Estaquia:39 -3.5200 -14.036763 6.996763 0.9473548
Estaquia:63-Estaquia:39 -2.7950 -13.311763 7.721763 0.9847448
Semente:63-Estaquia:39 -4.1825 -14.699263 6.334263 0.8811014
Estaquia:52-Semente:39 4.6450 -5.871763 15.161763 0.8157093
Semente:52-Semente:39 1.0925 -9.424263 11.609263 0.9999617
Estaquia:63-Semente:39 1.8175 -8.699263 12.334263 0.9988872
Semente:63-Semente:39 0.4300 -10.086763 10.946763 0.9999999
Semente:52-Estaquia:52 -3.5525 -14.069263 6.964263 0.9448720
Estaquia:63-Estaquia:52 -2.8275 -13.344263 7.689263 0.9837127
Semente:63-Estaquia:52 -4.2150 -14.731763 6.301763 0.8769992
Estaquia:63-Semente:52 0.7250 -9.791763 11.241763 0.9999977
Semente:63-Semente:52 -0.6625 -11.179263 9.854263 0.9999988
Semente:63-Estaquia:63 -1.3875 -11.904263 9.129263 0.9998093tukey.cld15 = multcompLetters4(mod15, tukey15)
print(tukey.cld15)$propagacao
Estaquia Semente
"a" "b"
$saturacao
$saturacao$Letters
52 39 63 19
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
52 TRUE
39 TRUE
63 TRUE
19 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:52 Estaquia:39 Estaquia:19 Estaquia:63 Semente:52 Semente:63 Semente:39 Semente:19
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:52 TRUE
Estaquia:39 TRUE
Estaquia:19 TRUE
Estaquia:63 TRUE
Semente:52 TRUE
Semente:63 TRUE
Semente:39 TRUE
Semente:19 TRUE16- Ash
#Outlier
boxplot(data2$MM)
#model
mod16 = aov(MM~propagacao*saturacao, data = data2)
mod16.1 = aov(MM^0.9~propagacao*saturacao, data = data2)
hist(rstandard(mod16.1))
shapiro.test(rstandard(mod16.1))
Shapiro-Wilk normality test
data: rstandard(mod16.1)
W = 0.93235, p-value = 0.05082#Tukey
medias16=emmeans(mod16,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias16.1=emmeans(mod16,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias16) propagacao emmean SE df lower.CL upper.CL
Estaquia 125 2.86 23 119 131
Semente 137 2.98 23 131 143
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias16.1) saturacao emmean SE df lower.CL upper.CL
19 136 4.38 23 126 145
39 121 4.05 23 113 130
52 130 4.05 23 121 138
63 138 4.05 23 130 146
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod16.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 315.1 315.06 7.945 0.00974 **
saturacao 3 375.4 125.15 3.156 0.04411 *
propagacao:saturacao 3 88.5 29.50 0.744 0.53688
Residuals 23 912.0 39.65
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey16 = TukeyHSD(mod16.1)
print(tukey16) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = MM^0.9 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 6.379255 1.697526 11.06098 0.0097414
$saturacao
diff lwr upr p adj
39-19 -7.394551 -16.4133988 1.624297 0.1349422
52-19 -2.694503 -11.7133510 6.324345 0.8412175
63-19 1.731335 -7.2875125 10.750183 0.9505782
52-39 4.700048 -4.0129869 13.413083 0.4578320
63-39 9.125886 0.4128516 17.838921 0.0377675
63-52 4.425839 -4.2871963 13.138873 0.5086310
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 12.3871316 -3.603089 28.377352 0.2149605
Estaquia:39-Estaquia:19 -4.7095884 -19.513656 10.094479 0.9593909
Semente:39-Estaquia:19 1.4493499 -13.354718 16.253418 0.9999744
Estaquia:52-Estaquia:19 0.2978294 -14.506238 15.101897 1.0000000
Semente:52-Estaquia:19 5.8420277 -8.962040 20.646096 0.8851059
Estaquia:63-Estaquia:19 6.0836527 -8.720415 20.887721 0.8629739
Semente:63-Estaquia:19 8.9078814 -5.896186 23.711949 0.5025819
Estaquia:39-Semente:19 -17.0967200 -33.086941 -1.106499 0.0303374
Semente:39-Semente:19 -10.9377817 -26.928003 5.052439 0.3481297
Estaquia:52-Semente:19 -12.0893021 -28.079523 3.900919 0.2387638
Semente:52-Semente:19 -6.5451039 -22.535325 9.445117 0.8652741
Estaquia:63-Semente:19 -6.3034788 -22.293700 9.686742 0.8856400
Semente:63-Semente:19 -3.4792501 -19.469471 12.510971 0.9953027
Semente:39-Estaquia:39 6.1589383 -8.645130 20.963006 0.8556431
Estaquia:52-Estaquia:39 5.0074179 -9.796650 19.811486 0.9445025
Semente:52-Estaquia:39 10.5516161 -4.252452 25.355684 0.3012539
Estaquia:63-Estaquia:39 10.7932411 -4.010827 25.597309 0.2765205
Semente:63-Estaquia:39 13.6174699 -1.186598 28.421538 0.0869123
Estaquia:52-Semente:39 -1.1515205 -15.955588 13.652547 0.9999947
Semente:52-Semente:39 4.3926778 -10.411390 19.196746 0.9719158
Estaquia:63-Semente:39 4.6343028 -10.169765 19.438371 0.9626642
Semente:63-Semente:39 7.4585315 -7.345536 22.262599 0.7021382
Semente:52-Estaquia:52 5.5441983 -9.259870 20.348266 0.9093611
Estaquia:63-Estaquia:52 5.7858233 -9.018245 20.589891 0.8899419
Semente:63-Estaquia:52 8.6100520 -6.194016 23.414120 0.5434697
Estaquia:63-Semente:52 0.2416250 -14.562443 15.045693 1.0000000
Semente:63-Semente:52 3.0658537 -11.738214 17.869922 0.9965287
Semente:63-Estaquia:63 2.8242287 -11.979839 17.628297 0.9979170tukey.cld16 = multcompLetters4(mod16.1, tukey16)
print(tukey.cld16)$propagacao
Semente Estaquia
"a" "b"
$saturacao
63 19 52 39
"a" "ab" "ab" "b"
$`propagacao:saturacao`
Semente:19 Semente:63 Estaquia:63 Semente:52 Semente:39 Estaquia:52 Estaquia:19 Estaquia:39
"a" "ab" "ab" "ab" "ab" "ab" "ab" "b" 17- Crude protein
#Outlier
boxplot(data2$PB)
#model
mod17 = aov(PB~propagacao*saturacao, data = data2)
hist(rstandard(mod17))
shapiro.test(rstandard(mod17))
Shapiro-Wilk normality test
data: rstandard(mod17)
W = 0.97048, p-value = 0.5524#Tukey
medias17=emmeans(mod17,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias17.1=emmeans(mod17,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias17) propagacao emmean SE df lower.CL upper.CL
Estaquia 157 6.36 22 144 171
Semente 167 6.87 22 153 181
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias17.1) saturacao emmean SE df lower.CL upper.CL
19 203 9.72 22 183 223
39 155 9.00 22 136 173
52 152 9.00 22 133 170
63 140 9.72 22 120 160
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod17) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 449 449 0.694 0.41382
saturacao 3 14867 4956 7.654 0.00111 **
propagacao:saturacao 3 2211 737 1.138 0.35553
Residuals 22 14245 647
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey17 = TukeyHSD(mod17)
print(tukey17) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = PB ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 7.756696 -11.55567 27.06906 0.4138198
$saturacao
diff lwr upr p adj
39-19 -45.86655 -82.43601 -9.297093 0.0105256
52-19 -48.99155 -85.56101 -12.422093 0.0060700
63-19 -59.82143 -97.59020 -22.052656 0.0012147
52-39 -3.12500 -38.45445 32.204452 0.9946378
63-39 -13.95488 -50.52434 22.614578 0.7168939
63-52 -10.82988 -47.39934 25.739578 0.8432845
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 40.10417 -24.78254 104.990874 0.4659177
Estaquia:39-Estaquia:19 -28.12500 -88.19842 31.948418 0.7659539
Semente:39-Estaquia:19 -28.12500 -88.19842 31.948418 0.7659539
Estaquia:52-Estaquia:19 -32.81250 -92.88592 27.260918 0.6119411
Semente:52-Estaquia:19 -29.68750 -89.76092 30.385918 0.7168250
Estaquia:63-Estaquia:19 -40.62500 -100.69842 19.448418 0.3581243
Semente:63-Estaquia:19 -45.31250 -110.19921 19.574207 0.3211836
Estaquia:39-Semente:19 -68.22917 -133.11587 -3.342459 0.0346581
Semente:39-Semente:19 -68.22917 -133.11587 -3.342459 0.0346581
Estaquia:52-Semente:19 -72.91667 -137.80337 -8.029959 0.0204285
Semente:52-Semente:19 -69.79167 -134.67837 -4.904959 0.0291092
Estaquia:63-Semente:19 -80.72917 -145.61587 -15.842459 0.0082349
Semente:63-Semente:19 -85.41667 -154.78347 -16.049859 0.0090798
Semente:39-Estaquia:39 0.00000 -60.07342 60.073418 1.0000000
Estaquia:52-Estaquia:39 -4.68750 -64.76092 55.385918 0.9999943
Semente:52-Estaquia:39 -1.56250 -61.63592 58.510918 1.0000000
Estaquia:63-Estaquia:39 -12.50000 -72.57342 47.573418 0.9962939
Semente:63-Estaquia:39 -17.18750 -82.07421 47.699207 0.9845407
Estaquia:52-Semente:39 -4.68750 -64.76092 55.385918 0.9999943
Semente:52-Semente:39 -1.56250 -61.63592 58.510918 1.0000000
Estaquia:63-Semente:39 -12.50000 -72.57342 47.573418 0.9962939
Semente:63-Semente:39 -17.18750 -82.07421 47.699207 0.9845407
Semente:52-Estaquia:52 3.12500 -56.94842 63.198418 0.9999997
Estaquia:63-Estaquia:52 -7.81250 -67.88592 52.260918 0.9998194
Semente:63-Estaquia:52 -12.50000 -77.38671 52.386707 0.9977014
Estaquia:63-Semente:52 -10.93750 -71.01092 49.135918 0.9983887
Semente:63-Semente:52 -15.62500 -80.51171 49.261707 0.9910817
Semente:63-Estaquia:63 -4.68750 -69.57421 60.199207 0.9999967tukey.cld17 = multcompLetters4(mod17, tukey17)
print(tukey.cld17)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
19 39 52 63
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:19 Estaquia:19 Estaquia:39 Semente:39 Semente:52 Estaquia:52 Estaquia:63 Semente:63
"a" "ab" "b" "b" "b" "b" "b" "b" 18- Ether extract
#Outlier
boxplot(data2$EE)
#model
mod18 = aov(EE~propagacao*saturacao, data = data2)
hist(rstandard(mod18))
shapiro.test(rstandard(mod18))
Shapiro-Wilk normality test
data: rstandard(mod18)
W = 0.96887, p-value = 0.4886#Tukey
medias18=emmeans(mod18,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias18.1=emmeans(mod18,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias18) propagacao emmean SE df lower.CL upper.CL
Estaquia 46.7 0.981 23 44.7 48.7
Semente 44.3 1.021 23 42.2 46.4
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias18.1) saturacao emmean SE df lower.CL upper.CL
19 53.2 1.50 23 50.1 56.3
39 47.7 1.39 23 44.8 50.5
52 43.9 1.39 23 41.0 46.7
63 37.2 1.39 23 34.3 40.1
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod18) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 65.0 65.0 4.221 0.0515 .
saturacao 3 1023.9 341.3 22.166 5.65e-07 ***
propagacao:saturacao 3 32.9 11.0 0.713 0.5541
Residuals 23 354.1 15.4
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey18 = TukeyHSD(mod18)
print(tukey18) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = EE ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -2.897375 -5.814743 0.01999338 0.0514627
$saturacao
diff lwr upr p adj
39-19 -5.50108 -11.121077 0.1189161 0.0565432
52-19 -9.30483 -14.924827 -3.6848339 0.0007130
63-19 -15.98733 -21.607327 -10.3673339 0.0000003
52-39 -3.80375 -9.233182 1.6256824 0.2401897
63-39 -10.48625 -15.915682 -5.0568176 0.0001104
63-52 -6.68250 -12.111932 -1.2530676 0.0120625
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -2.078333 -12.042465 7.88579795 0.9963725
Estaquia:39-Estaquia:19 -6.932500 -16.157493 2.29249302 0.2448632
Semente:39-Estaquia:19 -6.265000 -15.489993 2.95999302 0.3565306
Estaquia:52-Estaquia:19 -8.832500 -18.057493 0.39249302 0.0673150
Semente:52-Estaquia:19 -11.972500 -21.197493 -2.74750698 0.0052923
Estaquia:63-Estaquia:19 -14.617500 -23.842493 -5.39250698 0.0005443
Semente:63-Estaquia:19 -19.552500 -28.777493 -10.32750698 0.0000086
Estaquia:39-Semente:19 -4.854167 -14.818298 5.10996461 0.7344406
Semente:39-Semente:19 -4.186667 -14.150798 5.77746461 0.8495214
Estaquia:52-Semente:19 -6.754167 -16.718298 3.20996461 0.3587549
Semente:52-Semente:19 -9.894167 -19.858298 0.06996461 0.0525444
Estaquia:63-Semente:19 -12.539167 -22.503298 -2.57503539 0.0072022
Semente:63-Semente:19 -17.474167 -27.438298 -7.51003539 0.0001428
Semente:39-Estaquia:39 0.667500 -8.557493 9.89249302 0.9999968
Estaquia:52-Estaquia:39 -1.900000 -11.124993 7.32499302 0.9966441
Semente:52-Estaquia:39 -5.040000 -14.264993 4.18499302 0.6160736
Estaquia:63-Estaquia:39 -7.685000 -16.909993 1.53999302 0.1519445
Semente:63-Estaquia:39 -12.620000 -21.844993 -3.39500698 0.0030436
Estaquia:52-Semente:39 -2.567500 -11.792493 6.65749302 0.9802263
Semente:52-Semente:39 -5.707500 -14.932493 3.51749302 0.4687589
Estaquia:63-Semente:39 -8.352500 -17.577493 0.87249302 0.0956704
Semente:63-Semente:39 -13.287500 -22.512493 -4.06250698 0.0017142
Semente:52-Estaquia:52 -3.140000 -12.364993 6.08499302 0.9427357
Estaquia:63-Estaquia:52 -5.785000 -15.009993 3.43999302 0.4523306
Semente:63-Estaquia:52 -10.720000 -19.944993 -1.49500698 0.0151382
Estaquia:63-Semente:52 -2.645000 -11.869993 6.57999302 0.9766948
Semente:63-Semente:52 -7.580000 -16.804993 1.64499302 0.1628989
Semente:63-Estaquia:63 -4.935000 -14.159993 4.28999302 0.6394369tukey.cld18 = multcompLetters4(mod18, tukey18)
print(tukey.cld18)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
19 39 52 63
"a" "ab" "b" "c"
$`propagacao:saturacao`
Estaquia:19 Semente:19 Semente:39 Estaquia:39 Estaquia:52 Semente:52 Estaquia:63 Semente:63
"a" "ab" "abc" "abc" "abc" "bcd" "cd" "d" 12- aNDFom
#Outlier
boxplot(data2$aFDNom)
#model
mod18 = aov(aFDNom~propagacao*saturacao, data = data2)
hist(rstandard(mod18))
shapiro.test(rstandard(mod18))
Shapiro-Wilk normality test
data: rstandard(mod18)
W = 0.99042, p-value = 0.9921#Tukey
medias18=emmeans(mod18,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias18.1=emmeans(mod18,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias18) propagacao emmean SE df lower.CL upper.CL
Estaquia 598 10.2 23 577 619
Semente 562 10.6 23 540 583
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias18.1) saturacao emmean SE df lower.CL upper.CL
19 569 15.5 23 537 602
39 585 14.4 23 555 615
52 594 14.4 23 564 624
63 571 14.4 23 541 601
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod18) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 10399 10399 6.286 0.0197 *
saturacao 3 3473 1158 0.700 0.5618
propagacao:saturacao 3 4453 1484 0.897 0.4576
Residuals 23 38052 1654
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey18 = TukeyHSD(mod18)
print(tukey18) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = aFDNom ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -36.65025 -66.89068 -6.40982 0.0196818
$saturacao
diff lwr upr p adj
39-19 17.154839 -41.10010 75.40978 0.8467975
52-19 26.212339 -32.04260 84.46728 0.6055289
63-19 3.112339 -55.14260 61.36728 0.9988129
52-39 9.057500 -47.22212 65.33712 0.9698586
63-39 -14.042500 -70.32212 42.23712 0.8996740
63-52 -23.100000 -79.37962 33.17962 0.6717277
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -14.2875 -117.57223 88.99723 0.9997390
Estaquia:39-Estaquia:19 35.9825 -59.64058 131.60558 0.9073406
Semente:39-Estaquia:19 -19.1550 -114.77808 76.46808 0.9971744
Estaquia:52-Estaquia:19 23.2450 -72.37808 118.86808 0.9908870
Semente:52-Estaquia:19 11.6975 -83.92558 107.32058 0.9998848
Estaquia:63-Estaquia:19 27.0875 -68.53558 122.71058 0.9781956
Semente:63-Estaquia:19 -38.3450 -133.96808 57.27808 0.8767139
Estaquia:39-Semente:19 50.2700 -53.01473 153.55473 0.7353003
Semente:39-Semente:19 -4.8675 -108.15223 98.41723 0.9999998
Estaquia:52-Semente:19 37.5325 -65.75223 140.81723 0.9213034
Semente:52-Semente:19 25.9850 -77.29973 129.26973 0.9888636
Estaquia:63-Semente:19 41.3750 -61.90973 144.65973 0.8772632
Semente:63-Semente:19 -24.0575 -127.34223 79.22723 0.9929219
Semente:39-Estaquia:39 -55.1375 -150.76058 40.48558 0.5537008
Estaquia:52-Estaquia:39 -12.7375 -108.36058 82.88558 0.9997967
Semente:52-Estaquia:39 -24.2850 -119.90808 71.33808 0.9882397
Estaquia:63-Estaquia:39 -8.8950 -104.51808 86.72808 0.9999819
Semente:63-Estaquia:39 -74.3275 -169.95058 21.29558 0.2117507
Estaquia:52-Semente:39 42.4000 -53.22308 138.02308 0.8128438
Semente:52-Semente:39 30.8525 -64.77058 126.47558 0.9563198
Estaquia:63-Semente:39 46.2425 -49.38058 141.86558 0.7412217
Semente:63-Semente:39 -19.1900 -114.81308 76.43308 0.9971422
Semente:52-Estaquia:52 -11.5475 -107.17058 84.07558 0.9998943
Estaquia:63-Estaquia:52 3.8425 -91.78058 99.46558 0.9999999
Semente:63-Estaquia:52 -61.5900 -157.21308 34.03308 0.4198411
Estaquia:63-Semente:52 15.3900 -80.23308 111.01308 0.9992965
Semente:63-Semente:52 -50.0425 -145.66558 45.58058 0.6631136
Semente:63-Estaquia:63 -65.4325 -161.05558 30.19058 0.3477112tukey.cld18 = multcompLetters4(mod18, tukey18)
print(tukey.cld18)$propagacao
Estaquia Semente
"a" "b"
$saturacao
$saturacao$Letters
52 39 63 19
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
52 TRUE
39 TRUE
63 TRUE
19 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:39 Estaquia:63 Estaquia:52 Semente:52 Estaquia:19 Semente:19 Semente:39 Semente:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:39 TRUE
Estaquia:63 TRUE
Estaquia:52 TRUE
Semente:52 TRUE
Estaquia:19 TRUE
Semente:19 TRUE
Semente:39 TRUE
Semente:63 TRUE19- ADFom
#Outlier
boxplot(data2$aFDAom)
#model
mod19 = aov(aFDAom~propagacao*saturacao, data = data2)
hist(rstandard(mod19))
shapiro.test(rstandard(mod19))
Shapiro-Wilk normality test
data: rstandard(mod19)
W = 0.92687, p-value = 0.03607#Tukey
medias19=emmeans(mod19,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias19.1=emmeans(mod19,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias19) propagacao emmean SE df lower.CL upper.CL
Estaquia 377 6.19 23 365 390
Semente 363 6.44 23 350 377
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias19.1) saturacao emmean SE df lower.CL upper.CL
19 362 9.45 23 342 381
39 361 8.75 23 343 379
52 379 8.75 23 361 397
63 380 8.75 23 362 399
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod19) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1835 1834.5 2.994 0.0970 .
saturacao 3 3216 1072.1 1.750 0.1848
propagacao:saturacao 3 8339 2779.8 4.537 0.0122 *
Residuals 23 14091 612.7
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey19 = TukeyHSD(mod19)
print(tukey19) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = aFDAom ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -15.3935 -33.79588 3.008877 0.0969524
$saturacao
diff lwr upr p adj
39-19 3.684893 -31.76531 39.13509 0.9914787
52-19 21.243643 -14.20656 56.69384 0.3675944
63-19 23.017393 -12.43281 58.46759 0.3003260
52-39 17.558750 -16.68940 51.80690 0.5008744
63-39 19.332500 -14.91565 53.58065 0.4188889
63-52 1.773750 -32.47440 36.02190 0.9989180
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 44.221667 -18.630766 107.07410 0.3157663
Estaquia:39-Estaquia:19 42.757500 -15.432545 100.94755 0.2681904
Semente:39-Estaquia:19 0.317500 -57.872545 58.50755 1.0000000
Estaquia:52-Estaquia:19 51.780000 -6.410045 109.97005 0.1059800
Semente:52-Estaquia:19 26.412500 -31.777545 84.60255 0.7950586
Estaquia:63-Estaquia:19 57.152500 -1.037545 115.34255 0.0566924
Semente:63-Estaquia:19 24.587500 -33.602545 82.77755 0.8459700
Estaquia:39-Semente:19 -1.464167 -64.316599 61.38827 1.0000000
Semente:39-Semente:19 -43.904167 -106.756599 18.94827 0.3239443
Estaquia:52-Semente:19 7.558333 -55.294099 70.41077 0.9998973
Semente:52-Semente:19 -17.809167 -80.661599 45.04327 0.9781636
Estaquia:63-Semente:19 12.930833 -49.921599 75.78327 0.9966671
Semente:63-Semente:19 -19.634167 -82.486599 43.21827 0.9630734
Semente:39-Estaquia:39 -42.440000 -100.630045 15.75005 0.2761354
Estaquia:52-Estaquia:39 9.022500 -49.167545 67.21255 0.9994479
Semente:52-Estaquia:39 -16.345000 -74.535045 41.84505 0.9791910
Estaquia:63-Estaquia:39 14.395000 -43.795045 72.58505 0.9899044
Semente:63-Estaquia:39 -18.170000 -76.360045 40.02005 0.9631555
Estaquia:52-Semente:39 51.462500 -6.727545 109.65255 0.1098106
Semente:52-Semente:39 26.095000 -32.095045 84.28505 0.8043958
Estaquia:63-Semente:39 56.835000 -1.355045 115.02505 0.0588975
Semente:63-Semente:39 24.270000 -33.920045 82.46005 0.8541021
Semente:52-Estaquia:52 -25.367500 -83.557545 32.82255 0.8250497
Estaquia:63-Estaquia:52 5.372500 -52.817545 63.56255 0.9999828
Semente:63-Estaquia:52 -27.192500 -85.382545 30.99755 0.7713502
Estaquia:63-Semente:52 30.740000 -27.450045 88.93005 0.6530892
Semente:63-Semente:52 -1.825000 -60.015045 56.36505 1.0000000
Semente:63-Estaquia:63 -32.565000 -90.755045 25.62505 0.5886779tukey.cld19 = multcompLetters4(mod19, tukey19)
print(tukey.cld19)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
$saturacao$Letters
63 52 39 19
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
63 TRUE
52 TRUE
39 TRUE
19 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:63 Estaquia:52 Semente:19 Estaquia:39 Semente:52 Semente:63 Semente:39 Estaquia:19
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:63 TRUE
Estaquia:52 TRUE
Semente:19 TRUE
Estaquia:39 TRUE
Semente:52 TRUE
Semente:63 TRUE
Semente:39 TRUE
Estaquia:19 TRUE20- aLDAom
#Outlier
boxplot(data2$aLDAom)
#model
mod20 = aov(aLDAom~propagacao*saturacao, data = data2)
hist(rstandard(mod20))
shapiro.test(rstandard(mod20))
Shapiro-Wilk normality test
data: rstandard(mod20)
W = 0.97223, p-value = 0.5825#Tukey
medias20=emmeans(mod20,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias20.1=emmeans(mod20,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias20) propagacao emmean SE df lower.CL upper.CL
Estaquia 111 3.56 23 104 119
Semente 118 3.71 23 110 125
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias20.1) saturacao emmean SE df lower.CL upper.CL
19 139 5.44 23 127.8 150
39 112 5.04 23 101.2 122
52 102 5.04 23 91.1 112
63 106 5.04 23 95.7 117
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod20) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 97 96.7 0.476 0.496942
saturacao 3 5185 1728.5 8.515 0.000551 ***
propagacao:saturacao 3 3921 1307.2 6.440 0.002514 **
Residuals 23 4669 203.0
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
1 observation deleted due to missingnesstukey20 = TukeyHSD(mod20)
print(tukey20) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = aLDAom ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 3.534417 -7.058081 14.12691 0.4969423
$saturacao
diff lwr upr p adj
39-19 -24.42407 -44.82937 -4.018759 0.0149735
52-19 -34.52782 -54.93312 -14.122509 0.0005569
63-19 -29.94782 -50.35312 -9.542509 0.0025370
52-39 -10.10375 -29.81715 9.609649 0.5011357
63-39 -5.52375 -25.23715 14.189649 0.8647372
63-52 4.58000 -15.13340 24.293399 0.9169263
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 45.36083 9.182669 81.53900 0.0074657
Estaquia:39-Estaquia:19 -3.99250 -37.486972 29.50197 0.9999032
Semente:39-Estaquia:19 -5.47000 -38.964472 28.02447 0.9992268
Estaquia:52-Estaquia:19 -14.35000 -47.844472 19.14447 0.8369378
Semente:52-Estaquia:19 -15.32000 -48.814472 18.17447 0.7889917
Estaquia:63-Estaquia:19 -1.72250 -35.216972 31.77197 0.9999997
Semente:63-Estaquia:19 -18.78750 -52.281972 14.70697 0.5860341
Estaquia:39-Semente:19 -49.35333 -85.531498 -13.17517 0.0031377
Semente:39-Semente:19 -50.83083 -87.008998 -14.65267 0.0022703
Estaquia:52-Semente:19 -59.71083 -95.888998 -23.53267 0.0003226
Semente:52-Semente:19 -60.68083 -96.858998 -24.50267 0.0002609
Estaquia:63-Semente:19 -47.08333 -83.261498 -10.90517 0.0051450
Semente:63-Semente:19 -64.14833 -100.326498 -27.97017 0.0001226
Semente:39-Estaquia:39 -1.47750 -34.971972 32.01697 0.9999999
Estaquia:52-Estaquia:39 -10.35750 -43.851972 23.13697 0.9649937
Semente:52-Estaquia:39 -11.32750 -44.821972 22.16697 0.9445483
Estaquia:63-Estaquia:39 2.27000 -31.224472 35.76447 0.9999979
Semente:63-Estaquia:39 -14.79500 -48.289472 18.69947 0.8156452
Estaquia:52-Semente:39 -8.88000 -42.374472 24.61447 0.9849548
Semente:52-Semente:39 -9.85000 -43.344472 23.64447 0.9732418
Estaquia:63-Semente:39 3.74750 -29.746972 37.24197 0.9999367
Semente:63-Semente:39 -13.31750 -46.811972 20.17697 0.8812269
Semente:52-Estaquia:52 -0.97000 -34.464472 32.52447 1.0000000
Estaquia:63-Estaquia:52 12.62750 -20.866972 46.12197 0.9065348
Semente:63-Estaquia:52 -4.43750 -37.931972 29.05697 0.9998039
Estaquia:63-Semente:52 13.59750 -19.896972 47.09197 0.8699577
Semente:63-Semente:52 -3.46750 -36.961972 30.02697 0.9999626
Semente:63-Estaquia:63 -17.06500 -50.559472 16.42947 0.6909101tukey.cld20 = multcompLetters4(mod20, tukey20)
print(tukey.cld20)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
19 39 63 52
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:19 Estaquia:19 Estaquia:63 Estaquia:39 Semente:39 Estaquia:52 Semente:52 Semente:63
"a" "b" "b" "b" "b" "b" "b" "b" CHEMICAL ANALYSIS
21- Fosforo
#Outlier
boxplot(data2$Fósforo)
#model
mod21 = aov(Fósforo~propagacao*saturacao, data = data2)
hist(rstandard(mod21))
shapiro.test(rstandard(mod21))
Shapiro-Wilk normality test
data: rstandard(mod21)
W = 0.96256, p-value = 0.3596#Tukey
medias21=emmeans(mod21,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias21.1=emmeans(mod21,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias21) propagacao emmean SE df lower.CL upper.CL
Estaquia 3.58 0.103 22 3.37 3.8
Semente 3.37 0.111 22 3.14 3.6
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias21.1) saturacao emmean SE df lower.CL upper.CL
19 3.79 0.158 22 3.46 4.12
39 3.54 0.146 22 3.24 3.84
52 3.30 0.146 22 3.00 3.60
63 3.28 0.158 22 2.95 3.61
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod21) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.399 0.3993 2.347 0.140
saturacao 3 1.176 0.3921 2.305 0.105
propagacao:saturacao 3 0.440 0.1466 0.861 0.476
Residuals 22 3.743 0.1702
2 observations deleted due to missingnesstukey21 = TukeyHSD(mod21)
print(tukey21) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Fósforo ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.23125 -0.5443166 0.08181655 0.1398046
$saturacao
diff lwr upr p adj
39-19 -0.23169643 -0.8245121 0.3611193 0.7019020
52-19 -0.46919643 -1.0620121 0.1236193 0.1550368
63-19 -0.50000000 -1.1122574 0.1122574 0.1365410
52-39 -0.23750000 -0.8102144 0.3352144 0.6625213
63-39 -0.26830357 -0.8611193 0.3245121 0.5988110
63-52 -0.03080357 -0.6236193 0.5620121 0.9988946
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.083333333 -0.9685242 1.1351909 0.9999937
Estaquia:39-Estaquia:19 0.075000000 -0.8988309 1.0488309 0.9999948
Semente:39-Estaquia:19 -0.500000000 -1.4738309 0.4738309 0.6787489
Estaquia:52-Estaquia:19 -0.325000000 -1.2988309 0.6488309 0.9467128
Semente:52-Estaquia:19 -0.575000000 -1.5488309 0.3988309 0.5210914
Estaquia:63-Estaquia:19 -0.425000000 -1.3988309 0.5488309 0.8209558
Semente:63-Estaquia:19 -0.516666667 -1.5685242 0.5351909 0.7226286
Estaquia:39-Semente:19 -0.008333333 -1.0601909 1.0435242 1.0000000
Semente:39-Semente:19 -0.583333333 -1.6351909 0.4685242 0.5946955
Estaquia:52-Semente:19 -0.408333333 -1.4601909 0.6435242 0.8907949
Semente:52-Semente:19 -0.658333333 -1.7101909 0.3935242 0.4506937
Estaquia:63-Semente:19 -0.508333333 -1.5601909 0.5435242 0.7378167
Semente:63-Semente:19 -0.600000000 -1.7244830 0.5244830 0.6378777
Semente:39-Estaquia:39 -0.575000000 -1.5488309 0.3988309 0.5210914
Estaquia:52-Estaquia:39 -0.400000000 -1.3738309 0.5738309 0.8603965
Semente:52-Estaquia:39 -0.650000000 -1.6238309 0.3238309 0.3734868
Estaquia:63-Estaquia:39 -0.500000000 -1.4738309 0.4738309 0.6787489
Semente:63-Estaquia:39 -0.591666667 -1.6435242 0.4601909 0.5783612
Estaquia:52-Semente:39 0.175000000 -0.7988309 1.1488309 0.9985168
Semente:52-Semente:39 -0.075000000 -1.0488309 0.8988309 0.9999948
Estaquia:63-Semente:39 0.075000000 -0.8988309 1.0488309 0.9999948
Semente:63-Semente:39 -0.016666667 -1.0685242 1.0351909 1.0000000
Semente:52-Estaquia:52 -0.250000000 -1.2238309 0.7238309 0.9870682
Estaquia:63-Estaquia:52 -0.100000000 -1.0738309 0.8738309 0.9999631
Semente:63-Estaquia:52 -0.191666667 -1.2435242 0.8601909 0.9983803
Estaquia:63-Semente:52 0.150000000 -0.8238309 1.1238309 0.9994498
Semente:63-Semente:52 0.058333333 -0.9935242 1.1101909 0.9999995
Semente:63-Estaquia:63 -0.091666667 -1.1435242 0.9601909 0.9999879tukey.cld21 = multcompLetters4(mod21, tukey21)
print(tukey.cld21)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
$saturacao$Letters
19 39 52 63
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
19 TRUE
39 TRUE
52 TRUE
63 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Semente:19 Estaquia:39 Estaquia:19 Estaquia:52 Estaquia:63 Semente:39 Semente:63 Semente:52
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Semente:19 TRUE
Estaquia:39 TRUE
Estaquia:19 TRUE
Estaquia:52 TRUE
Estaquia:63 TRUE
Semente:39 TRUE
Semente:63 TRUE
Semente:52 TRUE22- Potassio
#Outlier
boxplot(data2$Potássio)
#model
mod22 = aov(Potássio~propagacao*saturacao, data = data2)
hist(rstandard(mod22))
shapiro.test(rstandard(mod22))
Shapiro-Wilk normality test
data: rstandard(mod22)
W = 0.94884, p-value = 0.1574#Tukey
medias22=emmeans(mod22,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias22.1=emmeans(mod22,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias22) propagacao emmean SE df lower.CL upper.CL
Estaquia 17.1 1.03 22 15.0 19.3
Semente 15.0 1.11 22 12.6 17.3
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias22.1) saturacao emmean SE df lower.CL upper.CL
19 17.2 1.58 22 13.9 20.5
39 17.2 1.46 22 14.2 20.3
52 15.8 1.46 22 12.7 18.8
63 14.0 1.58 22 10.7 17.2
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod22) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 31.5 31.488 1.848 0.188
saturacao 3 46.1 15.354 0.901 0.456
propagacao:saturacao 3 13.8 4.594 0.270 0.847
Residuals 22 374.8 17.038
2 observations deleted due to missingnesstukey22 = TukeyHSD(mod22)
print(tukey22) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Potássio ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -2.053571 -5.186327 1.079184 0.1877742
$saturacao
diff lwr upr p adj
39-19 0.1109694 -5.821146 6.043084 0.9999480
52-19 -1.3890306 -7.321146 4.543084 0.9143473
63-19 -3.0000000 -9.126662 3.126662 0.5365671
52-39 -1.5000000 -7.230968 4.230968 0.8853186
63-39 -3.1109694 -9.043084 2.821146 0.4795339
63-52 -1.6109694 -7.543084 4.321146 0.8739405
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -1.0833333 -11.608932 9.442265 0.9999625
Estaquia:39-Estaquia:19 0.2500000 -9.494810 9.994810 1.0000000
Semente:39-Estaquia:19 -1.2500000 -10.994810 8.494810 0.9998352
Estaquia:52-Estaquia:19 -1.2500000 -10.994810 8.494810 0.9998352
Semente:52-Estaquia:19 -2.7500000 -12.494810 6.994810 0.9779791
Estaquia:63-Estaquia:19 -1.5000000 -11.244810 8.244810 0.9994522
Semente:63-Estaquia:19 -6.0833333 -16.608932 4.442265 0.5465791
Estaquia:39-Semente:19 1.3333333 -9.192265 11.858932 0.9998484
Semente:39-Semente:19 -0.1666667 -10.692265 10.358932 1.0000000
Estaquia:52-Semente:19 -0.1666667 -10.692265 10.358932 1.0000000
Semente:52-Semente:19 -1.6666667 -12.192265 8.858932 0.9993416
Estaquia:63-Semente:19 -0.4166667 -10.942265 10.108932 0.9999999
Semente:63-Semente:19 -5.0000000 -16.252338 6.252338 0.8078153
Semente:39-Estaquia:39 -1.5000000 -11.244810 8.244810 0.9994522
Estaquia:52-Estaquia:39 -1.5000000 -11.244810 8.244810 0.9994522
Semente:52-Estaquia:39 -3.0000000 -12.744810 6.744810 0.9648084
Estaquia:63-Estaquia:39 -1.7500000 -11.494810 7.994810 0.9985231
Semente:63-Estaquia:39 -6.3333333 -16.858932 4.192265 0.4983752
Estaquia:52-Semente:39 0.0000000 -9.744810 9.744810 1.0000000
Semente:52-Semente:39 -1.5000000 -11.244810 8.244810 0.9994522
Estaquia:63-Semente:39 -0.2500000 -9.994810 9.494810 1.0000000
Semente:63-Semente:39 -4.8333333 -15.358932 5.692265 0.7821261
Semente:52-Estaquia:52 -1.5000000 -11.244810 8.244810 0.9994522
Estaquia:63-Estaquia:52 -0.2500000 -9.994810 9.494810 1.0000000
Semente:63-Estaquia:52 -4.8333333 -15.358932 5.692265 0.7821261
Estaquia:63-Semente:52 1.2500000 -8.494810 10.994810 0.9998352
Semente:63-Semente:52 -3.3333333 -13.858932 7.192265 0.9592069
Semente:63-Estaquia:63 -4.5833333 -15.108932 5.942265 0.8225405tukey.cld22 = multcompLetters4(mod22, tukey22)
print(tukey.cld22)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
$saturacao$Letters
19 39 52 63
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
19 TRUE
39 TRUE
52 TRUE
63 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:39 Estaquia:19 Semente:19 Semente:39 Estaquia:52 Estaquia:63 Semente:52 Semente:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:39 TRUE
Estaquia:19 TRUE
Semente:19 TRUE
Semente:39 TRUE
Estaquia:52 TRUE
Estaquia:63 TRUE
Semente:52 TRUE
Semente:63 TRUE23- Calcio
#Outlier
boxplot(data2$Cálcio)
#model
mod23 = aov(Cálcio~propagacao*saturacao, data = data2)
hist(rstandard(mod23))
shapiro.test(rstandard(mod23))
Shapiro-Wilk normality test
data: rstandard(mod23)
W = 0.95688, p-value = 0.2572#Tukey
medias23=emmeans(mod23,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias23.1=emmeans(mod23,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias23) propagacao emmean SE df lower.CL upper.CL
Estaquia 13.0 0.457 22 12.1 13.9
Semente 12.1 0.493 22 11.1 13.2
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias23.1) saturacao emmean SE df lower.CL upper.CL
19 8.38 0.698 22 6.93 9.82
39 11.88 0.646 22 10.54 13.21
52 13.75 0.646 22 12.41 15.09
63 16.29 0.698 22 14.84 17.74
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod23) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 5.49 5.49 1.644 0.213
saturacao 3 232.14 77.38 23.187 5.26e-07 ***
propagacao:saturacao 3 4.16 1.39 0.416 0.743
Residuals 22 73.42 3.34
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey23 = TukeyHSD(mod23)
print(tukey23) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Cálcio ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.8571429 -2.243595 0.5293089 0.2131452
$saturacao
diff lwr upr p adj
39-19 3.507653 0.8822995 6.133007 0.0062142
52-19 5.382653 2.7572995 8.008007 0.0000557
63-19 7.857143 5.1456894 10.568596 0.0000003
52-39 1.875000 -0.6613325 4.411332 0.1997680
63-39 4.349490 1.7241363 6.974843 0.0007483
63-52 2.474490 -0.1508637 5.099843 0.0694354
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -0.75000000 -5.40827387 3.908274 0.9992666
Estaquia:39-Estaquia:19 3.50000000 -0.81272357 7.812724 0.1717191
Semente:39-Estaquia:19 2.75000000 -1.56272357 7.062724 0.4281485
Estaquia:52-Estaquia:19 6.00000000 1.68727643 10.312724 0.0026420
Semente:52-Estaquia:19 4.00000000 -0.31272357 8.312724 0.0822149
Estaquia:63-Estaquia:19 7.50000000 3.18727643 11.812724 0.0001773
Semente:63-Estaquia:19 7.58333333 2.92505947 12.241607 0.0004181
Estaquia:39-Semente:19 4.25000000 -0.40827387 8.908274 0.0909158
Semente:39-Semente:19 3.50000000 -1.15827387 8.158274 0.2427658
Estaquia:52-Semente:19 6.75000000 2.09172613 11.408274 0.0016839
Semente:52-Semente:19 4.75000000 0.09172613 9.408274 0.0435143
Estaquia:63-Semente:19 8.25000000 3.59172613 12.908274 0.0001387
Semente:63-Semente:19 8.33333333 3.35342910 13.313238 0.0002940
Semente:39-Estaquia:39 -0.75000000 -5.06272357 3.562724 0.9987963
Estaquia:52-Estaquia:39 2.50000000 -1.81272357 6.812724 0.5430490
Semente:52-Estaquia:39 0.50000000 -3.81272357 4.812724 0.9999162
Estaquia:63-Estaquia:39 4.00000000 -0.31272357 8.312724 0.0822149
Semente:63-Estaquia:39 4.08333333 -0.57494053 8.741607 0.1147541
Estaquia:52-Semente:39 3.25000000 -1.06272357 7.562724 0.2397916
Semente:52-Semente:39 1.25000000 -3.06272357 5.562724 0.9745144
Estaquia:63-Semente:39 4.75000000 0.43727643 9.062724 0.0240986
Semente:63-Semente:39 4.83333333 0.17505947 9.491607 0.0383059
Semente:52-Estaquia:52 -2.00000000 -6.31272357 2.312724 0.7739945
Estaquia:63-Estaquia:52 1.50000000 -2.81272357 5.812724 0.9346429
Semente:63-Estaquia:52 1.58333333 -3.07494053 6.241607 0.9416267
Estaquia:63-Semente:52 3.50000000 -0.81272357 7.812724 0.1717191
Semente:63-Semente:52 3.58333333 -1.07494053 8.241607 0.2196816
Semente:63-Estaquia:63 0.08333333 -4.57494053 4.741607 1.0000000tukey.cld23 = multcompLetters4(mod23, tukey23)
print(tukey.cld23)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
63 52 39 19
"a" "ab" "b" "c"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Estaquia:52 Semente:52 Estaquia:39 Semente:39 Estaquia:19 Semente:19
"a" "a" "ab" "abc" "abcd" "bcd" "cd" "d" 24- Magnesio
#Outlier
boxplot(data2$Magnésio)
#model
mod24 = aov(Magnésio~propagacao*saturacao, data = data2)
hist(rstandard(mod24))
shapiro.test(rstandard(mod24))
Shapiro-Wilk normality test
data: rstandard(mod24)
W = 0.97967, p-value = 0.8166#Tukey
medias24=emmeans(mod24,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias24.1=emmeans(mod24,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias24) propagacao emmean SE df lower.CL upper.CL
Estaquia 3.54 0.121 22 3.29 3.79
Semente 3.51 0.131 22 3.24 3.78
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias24.1) saturacao emmean SE df lower.CL upper.CL
19 2.56 0.185 22 2.18 2.95
39 3.46 0.171 22 3.11 3.82
52 3.73 0.171 22 3.37 4.08
63 4.34 0.185 22 3.95 4.72
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod24) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.015 0.015 0.064 0.803
saturacao 3 10.774 3.591 15.364 1.3e-05 ***
propagacao:saturacao 3 1.610 0.537 2.296 0.106
Residuals 22 5.142 0.234
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey24 = TukeyHSD(mod24)
print(tukey24) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Magnésio ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.04464286 -0.4115824 0.3222967 0.8031415
$saturacao
diff lwr upr p adj
39-19 0.8942602 0.1994317 1.5890887 0.0085311
52-19 1.1567602 0.4619317 1.8515887 0.0007091
63-19 1.7142857 0.9966699 2.4319015 0.0000064
52-39 0.2625000 -0.4087681 0.9337681 0.7015587
63-39 0.8200255 0.1251970 1.5148540 0.0168094
63-52 0.5575255 -0.1373030 1.2523540 0.1467785
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -0.125 -1.35786305 1.1078631 0.9999661
Estaquia:39-Estaquia:19 1.125 -0.01640939 2.2664094 0.0552837
Semente:39-Estaquia:19 0.550 -0.59140939 1.6914094 0.7404943
Estaquia:52-Estaquia:19 1.175 0.03359061 2.3164094 0.0406028
Semente:52-Estaquia:19 1.025 -0.11640939 2.1664094 0.0998976
Estaquia:63-Estaquia:19 1.350 0.20859061 2.4914094 0.0131349
Semente:63-Estaquia:19 2.075 0.84213695 3.3078631 0.0002728
Estaquia:39-Semente:19 1.250 0.01713695 2.4828631 0.0453361
Semente:39-Semente:19 0.675 -0.55786305 1.9078631 0.6092648
Estaquia:52-Semente:19 1.300 0.06713695 2.5328631 0.0339301
Semente:52-Semente:19 1.150 -0.08286305 2.3828631 0.0793466
Estaquia:63-Semente:19 1.475 0.24213695 2.7078631 0.0118464
Semente:63-Semente:19 2.200 0.88201396 3.5179860 0.0003037
Semente:39-Estaquia:39 -0.575 -1.71640939 0.5664094 0.6980316
Estaquia:52-Estaquia:39 0.050 -1.09140939 1.1914094 0.9999999
Semente:52-Estaquia:39 -0.100 -1.24140939 1.0414094 0.9999875
Estaquia:63-Estaquia:39 0.225 -0.91640939 1.3664094 0.9973468
Semente:63-Estaquia:39 0.950 -0.28286305 2.1828631 0.2180411
Estaquia:52-Semente:39 0.625 -0.51640939 1.7664094 0.6091359
Semente:52-Semente:39 0.475 -0.66640939 1.6164094 0.8525074
Estaquia:63-Semente:39 0.800 -0.34140939 1.9414094 0.3170657
Semente:63-Semente:39 1.525 0.29213695 2.7578631 0.0087009
Semente:52-Estaquia:52 -0.150 -1.29140939 0.9914094 0.9998064
Estaquia:63-Estaquia:52 0.175 -0.96640939 1.3164094 0.9994661
Semente:63-Estaquia:52 0.900 -0.33286305 2.1328631 0.2725406
Estaquia:63-Semente:52 0.325 -0.81640939 1.4664094 0.9768704
Semente:63-Semente:52 1.050 -0.18286305 2.2828631 0.1343425
Semente:63-Estaquia:63 0.725 -0.50786305 1.9578631 0.5259461tukey.cld24 = multcompLetters4(mod24, tukey24)
print(tukey.cld24)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
63 52 39 19
"a" "ab" "b" "c"
$`propagacao:saturacao`
Semente:63 Estaquia:63 Estaquia:52 Estaquia:39 Semente:52 Semente:39 Estaquia:19 Semente:19
"a" "ab" "ab" "abc" "abcd" "bcd" "cd" "d" 25- Enxofre
#Outlier
boxplot(data2$Enxofre)
#model
mod25 = aov(Enxofre~propagacao*saturacao, data = data2)
hist(rstandard(mod25))
shapiro.test(rstandard(mod25))
Shapiro-Wilk normality test
data: rstandard(mod25)
W = 0.97964, p-value = 0.816#Tukey
medias25=emmeans(mod25,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias25.1=emmeans(mod25,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias25) propagacao emmean SE df lower.CL upper.CL
Estaquia 1.24 0.0391 22 1.16 1.32
Semente 1.17 0.0422 22 1.08 1.25
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias25.1) saturacao emmean SE df lower.CL upper.CL
19 1.30 0.0597 22 1.18 1.43
39 1.25 0.0553 22 1.14 1.36
52 1.12 0.0553 22 1.01 1.24
63 1.13 0.0597 22 1.01 1.25
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod25) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.0400 0.04002 1.636 0.214
saturacao 3 0.1551 0.05170 2.113 0.128
propagacao:saturacao 3 0.0562 0.01873 0.766 0.526
Residuals 22 0.5383 0.02447
2 observations deleted due to missingnesstukey25 = TukeyHSD(mod25)
print(tukey25) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Enxofre ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.07321429 -0.1919368 0.04550822 0.21425
$saturacao
diff lwr upr p adj
39-19 -0.04477041 -0.2695807 0.18003984 0.9447429
52-19 -0.16977041 -0.3945807 0.05503984 0.1851868
63-19 -0.15714286 -0.3893259 0.07504016 0.2653819
52-39 -0.12500000 -0.3421873 0.09218733 0.4000927
63-39 -0.11237245 -0.3371827 0.11243779 0.5196632
63-52 0.01262755 -0.2121827 0.23743779 0.9986057
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.05833333 -0.3405568 0.4572235 0.9996081
Estaquia:39-Estaquia:19 0.00000000 -0.3693005 0.3693005 1.0000000
Semente:39-Estaquia:19 -0.05000000 -0.4193005 0.3193005 0.9997642
Estaquia:52-Estaquia:19 -0.10000000 -0.4693005 0.2693005 0.9824973
Semente:52-Estaquia:19 -0.20000000 -0.5693005 0.1693005 0.6214931
Estaquia:63-Estaquia:19 -0.05000000 -0.4193005 0.3193005 0.9997642
Semente:63-Estaquia:19 -0.24166667 -0.6405568 0.1572235 0.4900925
Estaquia:39-Semente:19 -0.05833333 -0.4572235 0.3405568 0.9996081
Semente:39-Semente:19 -0.10833333 -0.5072235 0.2905568 0.9822036
Estaquia:52-Semente:19 -0.15833333 -0.5572235 0.2405568 0.8795132
Semente:52-Semente:19 -0.25833333 -0.6572235 0.1405568 0.4094373
Estaquia:63-Semente:19 -0.10833333 -0.5072235 0.2905568 0.9822036
Semente:63-Semente:19 -0.30000000 -0.7264315 0.1264315 0.3128705
Semente:39-Estaquia:39 -0.05000000 -0.4193005 0.3193005 0.9997642
Estaquia:52-Estaquia:39 -0.10000000 -0.4693005 0.2693005 0.9824973
Semente:52-Estaquia:39 -0.20000000 -0.5693005 0.1693005 0.6214931
Estaquia:63-Estaquia:39 -0.05000000 -0.4193005 0.3193005 0.9997642
Semente:63-Estaquia:39 -0.24166667 -0.6405568 0.1572235 0.4900925
Estaquia:52-Semente:39 -0.05000000 -0.4193005 0.3193005 0.9997642
Semente:52-Semente:39 -0.15000000 -0.5193005 0.2193005 0.8668981
Estaquia:63-Semente:39 0.00000000 -0.3693005 0.3693005 1.0000000
Semente:63-Semente:39 -0.19166667 -0.5905568 0.2072235 0.7430664
Semente:52-Estaquia:52 -0.10000000 -0.4693005 0.2693005 0.9824973
Estaquia:63-Estaquia:52 0.05000000 -0.3193005 0.4193005 0.9997642
Semente:63-Estaquia:52 -0.14166667 -0.5405568 0.2572235 0.9276944
Estaquia:63-Semente:52 0.15000000 -0.2193005 0.5193005 0.8668981
Semente:63-Semente:52 -0.04166667 -0.4405568 0.3572235 0.9999585
Semente:63-Estaquia:63 -0.19166667 -0.5905568 0.2072235 0.7430664tukey.cld25 = multcompLetters4(mod25, tukey25)
print(tukey.cld25)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
$saturacao$Letters
19 39 63 52
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
19 TRUE
39 TRUE
63 TRUE
52 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Semente:19 Estaquia:19 Estaquia:39 Semente:39 Estaquia:63 Estaquia:52 Semente:52 Semente:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Semente:19 TRUE
Estaquia:19 TRUE
Estaquia:39 TRUE
Semente:39 TRUE
Estaquia:63 TRUE
Estaquia:52 TRUE
Semente:52 TRUE
Semente:63 TRUE26- Boro
#Outlier
boxplot(data2$Boro)
#model
mod26 = aov(Boro~propagacao*saturacao, data = data2)
hist(rstandard(mod26))
shapiro.test(rstandard(mod26))
Shapiro-Wilk normality test
data: rstandard(mod26)
W = 0.96432, p-value = 0.3973#Tukey
medias26=emmeans(mod26,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias26.1=emmeans(mod26,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias26) propagacao emmean SE df lower.CL upper.CL
Estaquia 142 3.23 22 136 149
Semente 143 3.49 22 136 150
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias26.1) saturacao emmean SE df lower.CL upper.CL
19 152 4.94 22 142 162
39 139 4.57 22 130 149
52 141 4.57 22 131 150
63 139 4.94 22 129 150
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod26) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1 0.5 0.003 0.957
saturacao 3 780 259.9 1.556 0.228
propagacao:saturacao 3 145 48.2 0.289 0.833
Residuals 22 3674 167.0
2 observations deleted due to missingnesstukey26 = TukeyHSD(mod26)
print(tukey26) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Boro ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 0.2589286 -9.549549 10.06741 0.9568341
$saturacao
diff lwr upr p adj
39-19 -12.4649235 -31.03803 6.108188 0.2720229
52-19 -10.8399235 -29.41303 7.733188 0.3881288
63-19 -12.5714286 -31.75366 6.610798 0.2910409
52-39 1.6250000 -16.31833 19.568330 0.9942519
63-39 -0.1065051 -18.67962 18.466606 0.9999985
63-52 -1.7315051 -20.30462 16.841606 0.9937397
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 2.500000 -30.45504 35.45504 0.9999953
Estaquia:39-Estaquia:19 -12.750000 -43.26044 17.76044 0.8499224
Semente:39-Estaquia:19 -10.000000 -40.51044 20.51044 0.9513530
Estaquia:52-Estaquia:19 -6.500000 -37.01044 24.01044 0.9957188
Semente:52-Estaquia:19 -13.000000 -43.51044 17.51044 0.8374243
Estaquia:63-Estaquia:19 -13.500000 -44.01044 17.01044 0.8109694
Semente:63-Estaquia:19 -8.833333 -41.78838 24.12171 0.9834703
Estaquia:39-Semente:19 -15.250000 -48.20504 17.70504 0.7757797
Semente:39-Semente:19 -12.500000 -45.45504 20.45504 0.9016359
Estaquia:52-Semente:19 -9.000000 -41.95504 23.95504 0.9816418
Semente:52-Semente:19 -15.500000 -48.45504 17.45504 0.7619966
Estaquia:63-Semente:19 -16.000000 -48.95504 16.95504 0.7335537
Semente:63-Semente:19 -11.333333 -46.56376 23.89709 0.9557738
Semente:39-Estaquia:39 2.750000 -27.76044 33.26044 0.9999848
Estaquia:52-Estaquia:39 6.250000 -24.26044 36.76044 0.9966340
Semente:52-Estaquia:39 -0.250000 -30.76044 30.26044 1.0000000
Estaquia:63-Estaquia:39 -0.750000 -31.26044 29.76044 1.0000000
Semente:63-Estaquia:39 3.916667 -29.03838 36.87171 0.9999010
Estaquia:52-Semente:39 3.500000 -27.01044 34.01044 0.9999220
Semente:52-Semente:39 -3.000000 -33.51044 27.51044 0.9999725
Estaquia:63-Semente:39 -3.500000 -34.01044 27.01044 0.9999220
Semente:63-Semente:39 1.166667 -31.78838 34.12171 1.0000000
Semente:52-Estaquia:52 -6.500000 -37.01044 24.01044 0.9957188
Estaquia:63-Estaquia:52 -7.000000 -37.51044 23.51044 0.9933037
Semente:63-Estaquia:52 -2.333333 -35.28838 30.62171 0.9999971
Estaquia:63-Semente:52 -0.500000 -31.01044 30.01044 1.0000000
Semente:63-Semente:52 4.166667 -28.78838 37.12171 0.9998503
Semente:63-Estaquia:63 4.666667 -28.28838 37.62171 0.9996828tukey.cld26 = multcompLetters4(mod26, tukey26)
print(tukey.cld26)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
$saturacao$Letters
19 52 39 63
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
19 TRUE
52 TRUE
39 TRUE
63 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Semente:19 Estaquia:19 Estaquia:52 Semente:63 Semente:39 Estaquia:39 Semente:52 Estaquia:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Semente:19 TRUE
Estaquia:19 TRUE
Estaquia:52 TRUE
Semente:63 TRUE
Semente:39 TRUE
Estaquia:39 TRUE
Semente:52 TRUE
Estaquia:63 TRUE27- Cobre
#Outlier
boxplot(data2$Cobre)
#model
mod27 = aov(Cobre~propagacao*saturacao, data = data2)
mod27.1 = aov(Cobre^0.5~propagacao*saturacao, data = data2)
hist(rstandard(mod27))
shapiro.test(rstandard(mod27.1))
Shapiro-Wilk normality test
data: rstandard(mod27.1)
W = 0.93165, p-value = 0.05432#Tukey
medias27=emmeans(mod27,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias27.1=emmeans(mod27,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias27) propagacao emmean SE df lower.CL upper.CL
Estaquia 7.50 0.359 22 6.76 8.24
Semente 7.12 0.388 22 6.32 7.93
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias27.1) saturacao emmean SE df lower.CL upper.CL
19 8.21 0.548 22 7.07 9.35
39 7.88 0.508 22 6.82 8.93
52 7.12 0.508 22 6.07 8.18
63 6.04 0.548 22 4.90 7.18
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod27.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.0585 0.05845 0.872 0.3605
saturacao 3 0.6719 0.22395 3.342 0.0378 *
propagacao:saturacao 3 0.2880 0.09601 1.433 0.2602
Residuals 22 1.4745 0.06702
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey27 = TukeyHSD(mod27.1)
print(tukey27) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Cobre^0.5 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -0.08848001 -0.284964 0.108004 0.3604934
$saturacao
diff lwr upr p adj
39-19 -0.02963992 -0.4016975 0.342417656 0.9960636
52-19 -0.16132128 -0.5333789 0.210736300 0.6308416
63-19 -0.38879676 -0.7730562 -0.004537339 0.0466760
52-39 -0.13168136 -0.4911231 0.227760414 0.7412084
63-39 -0.35915683 -0.7312144 0.012900748 0.0610373
63-52 -0.22747548 -0.5995331 0.144582104 0.3485818
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 0.12007409 -0.5400831 0.78023132 0.9983988
Estaquia:39-Estaquia:19 0.17480054 -0.4363863 0.78598737 0.9763014
Semente:39-Estaquia:19 -0.14379974 -0.7549866 0.46738709 0.9922225
Estaquia:52-Estaquia:19 0.00000000 -0.6111868 0.61118683 1.0000000
Semente:52-Estaquia:19 -0.23236191 -0.8435487 0.37882492 0.9005971
Estaquia:63-Estaquia:19 -0.38384814 -0.9950350 0.22733870 0.4465192
Semente:63-Estaquia:19 -0.27532082 -0.9354781 0.38483641 0.8511695
Estaquia:39-Semente:19 0.05472645 -0.6054308 0.71488367 0.9999914
Semente:39-Semente:19 -0.26387383 -0.9240311 0.39628340 0.8757914
Estaquia:52-Semente:19 -0.12007409 -0.7802313 0.54008313 0.9983988
Semente:52-Semente:19 -0.35243600 -1.0125932 0.30772122 0.6372877
Estaquia:63-Semente:19 -0.50392223 -1.1640795 0.15623500 0.2271121
Semente:63-Semente:19 -0.39539492 -1.1011327 0.31034285 0.5829772
Semente:39-Estaquia:39 -0.31860028 -0.9297871 0.29258655 0.6629280
Estaquia:52-Estaquia:39 -0.17480054 -0.7859874 0.43638629 0.9763014
Semente:52-Estaquia:39 -0.40716245 -1.0183493 0.20402438 0.3757607
Estaquia:63-Estaquia:39 -0.55864868 -1.1698355 0.05253816 0.0899091
Semente:63-Estaquia:39 -0.45012136 -1.1102786 0.21003587 0.3485544
Estaquia:52-Semente:39 0.14379974 -0.4673871 0.75498657 0.9922225
Semente:52-Semente:39 -0.08856217 -0.6997490 0.52262466 0.9996310
Estaquia:63-Semente:39 -0.24004840 -0.8512352 0.37113843 0.8849881
Semente:63-Semente:39 -0.13152108 -0.7916783 0.52863614 0.9971661
Semente:52-Estaquia:52 -0.23236191 -0.8435487 0.37882492 0.9005971
Estaquia:63-Estaquia:52 -0.38384814 -0.9950350 0.22733870 0.4465192
Semente:63-Estaquia:52 -0.27532082 -0.9354781 0.38483641 0.8511695
Estaquia:63-Semente:52 -0.15148623 -0.7626731 0.45970061 0.9894430
Semente:63-Semente:52 -0.04295891 -0.7031161 0.61719832 0.9999984
Semente:63-Estaquia:63 0.10852731 -0.5516299 0.76868454 0.9991609tukey.cld27 = multcompLetters4(mod27.1, tukey27)
print(tukey.cld27)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
19 39 52 63
"a" "ab" "ab" "b"
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:39 Semente:19 Estaquia:19 Estaquia:52 Semente:39 Semente:52 Semente:63 Estaquia:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:39 TRUE
Semente:19 TRUE
Estaquia:19 TRUE
Estaquia:52 TRUE
Semente:39 TRUE
Semente:52 TRUE
Semente:63 TRUE
Estaquia:63 TRUE28- Ferro
#Outlier
boxplot(data2$Ferro)
#model
mod28 = aov(Ferro~propagacao*saturacao, data = data2)
hist(rstandard(mod28))
shapiro.test(rstandard(mod28))
Shapiro-Wilk normality test
data: rstandard(mod28)
W = 0.97832, p-value = 0.7794#Tukey
medias28=emmeans(mod28,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias28.1=emmeans(mod28,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias28) propagacao emmean SE df lower.CL upper.CL
Estaquia 281 11.7 22 257 306
Semente 265 12.6 22 238 291
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias28.1) saturacao emmean SE df lower.CL upper.CL
19 293 17.8 22 256 330
39 287 16.5 22 253 321
52 262 16.5 22 228 296
63 250 17.8 22 213 287
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod28) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 2501 2501 1.148 0.296
saturacao 3 9698 3233 1.484 0.246
propagacao:saturacao 3 9478 3159 1.450 0.255
Residuals 22 47933 2179
2 observations deleted due to missingnesstukey28 = TukeyHSD(mod28)
print(tukey28) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Ferro ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -18.30357 -53.72994 17.1228 0.29556
$saturacao
diff lwr upr p adj
39-19 -4.853316 -71.93588 62.22925 0.9970409
52-19 -30.228316 -97.31088 36.85425 0.6021484
63-19 -44.571429 -113.85400 24.71115 0.3061500
52-39 -25.375000 -90.18291 39.43291 0.7007576
63-39 -39.718112 -106.80068 27.36445 0.3758544
63-52 -14.343112 -81.42568 52.73945 0.9329050
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -4.58333333 -123.61071 114.44405 1.0000000
Estaquia:39-Estaquia:19 -4.50000000 -114.69794 105.69794 0.9999999
Semente:39-Estaquia:19 -11.75000000 -121.94794 98.44794 0.9999523
Estaquia:52-Estaquia:19 4.00000000 -106.19794 114.19794 1.0000000
Semente:52-Estaquia:19 -71.00000000 -181.19794 39.19794 0.4156451
Estaquia:63-Estaquia:19 -55.00000000 -165.19794 55.19794 0.7072196
Semente:63-Estaquia:19 -35.25000000 -154.27738 83.77738 0.9713674
Estaquia:39-Semente:19 0.08333333 -118.94405 119.11071 1.0000000
Semente:39-Semente:19 -7.16666667 -126.19405 111.86071 0.9999991
Estaquia:52-Semente:19 8.58333333 -110.44405 127.61071 0.9999967
Semente:52-Semente:19 -66.41666667 -185.44405 52.61071 0.5876424
Estaquia:63-Semente:19 -50.41666667 -169.44405 68.61071 0.8413087
Semente:63-Semente:19 -30.66666667 -157.91229 96.57896 0.9910382
Semente:39-Estaquia:39 -7.25000000 -117.44794 102.94794 0.9999983
Estaquia:52-Estaquia:39 8.50000000 -101.69794 118.69794 0.9999948
Semente:52-Estaquia:39 -66.50000000 -176.69794 43.69794 0.4948651
Estaquia:63-Estaquia:39 -50.50000000 -160.69794 59.69794 0.7837779
Semente:63-Estaquia:39 -30.75000000 -149.77738 88.27738 0.9865919
Estaquia:52-Semente:39 15.75000000 -94.44794 125.94794 0.9996629
Semente:52-Semente:39 -59.25000000 -169.44794 50.94794 0.6294944
Estaquia:63-Semente:39 -43.25000000 -153.44794 66.94794 0.8853528
Semente:63-Semente:39 -23.50000000 -142.52738 95.52738 0.9973209
Semente:52-Estaquia:52 -75.00000000 -185.19794 35.19794 0.3506771
Estaquia:63-Estaquia:52 -59.00000000 -169.19794 51.19794 0.6341467
Semente:63-Estaquia:52 -39.25000000 -158.27738 79.77738 0.9498324
Estaquia:63-Semente:52 16.00000000 -94.19794 126.19794 0.9996261
Semente:63-Semente:52 35.75000000 -83.27738 154.77738 0.9691237
Semente:63-Estaquia:63 19.75000000 -99.27738 138.77738 0.9991092tukey.cld28 = multcompLetters4(mod28, tukey28)
print(tukey.cld28)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
$saturacao$Letters
19 39 52 63
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
19 TRUE
39 TRUE
52 TRUE
63 TRUE
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:52 Estaquia:19 Estaquia:39 Semente:19 Semente:39 Semente:63 Estaquia:63 Semente:52
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:52 TRUE
Estaquia:19 TRUE
Estaquia:39 TRUE
Semente:19 TRUE
Semente:39 TRUE
Semente:63 TRUE
Estaquia:63 TRUE
Semente:52 TRUE29- Manganês
#Outlier
boxplot(data2$Manganês)
#model
mod29 = aov(Manganês~propagacao*saturacao, data = data2)
hist(rstandard(mod29))
shapiro.test(rstandard(mod29))
Shapiro-Wilk normality test
data: rstandard(mod29)
W = 0.96516, p-value = 0.4166#Tukey
medias29=emmeans(mod29,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias29.1=emmeans(mod29,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias29) propagacao emmean SE df lower.CL upper.CL
Estaquia 234 14.7 22 204 264
Semente 282 15.9 22 249 314
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias29.1) saturacao emmean SE df lower.CL upper.CL
19 340 22.4 22 294 387
39 241 20.8 22 198 284
52 234 20.8 22 191 277
63 216 22.4 22 169 262
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod29) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 13898 13898 4.032 0.05708 .
saturacao 3 63068 21023 6.099 0.00351 **
propagacao:saturacao 3 8514 2838 0.823 0.49503
Residuals 22 75834 3447
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey29 = TukeyHSD(mod29)
print(tukey29) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Manganês ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 43.14286 -1.416648 87.70236 0.0570849
$saturacao
diff lwr upr p adj
39-19 -96.18878 -180.5656 -11.81191 0.0215804
52-19 -102.56378 -186.9406 -18.18691 0.0134579
63-19 -121.57143 -208.7155 -34.42737 0.0042298
52-39 -6.37500 -87.8908 75.14080 0.9962731
63-39 -25.38265 -109.7595 58.99421 0.8371096
63-52 -19.00765 -103.3845 65.36921 0.9227552
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 90.666667 -59.04672 240.38004933 0.4905835
Estaquia:39-Estaquia:19 -82.750000 -221.35766 55.85765887 0.5078252
Semente:39-Estaquia:19 -25.750000 -164.35766 112.85765887 0.9981699
Estaquia:52-Estaquia:19 -59.000000 -197.60766 79.60765887 0.8380798
Semente:52-Estaquia:19 -62.250000 -200.85766 76.35765887 0.7997336
Estaquia:63-Estaquia:19 -102.250000 -240.85766 36.35765887 0.2615382
Semente:63-Estaquia:19 -56.666667 -206.38005 93.04671599 0.9025823
Estaquia:39-Semente:19 -173.416667 -323.13005 -23.70328401 0.0157849
Semente:39-Semente:19 -116.416667 -266.13005 33.29671599 0.2094784
Estaquia:52-Semente:19 -149.666667 -299.38005 0.04671599 0.0501096
Semente:52-Semente:19 -152.916667 -302.63005 -3.20328401 0.0429909
Estaquia:63-Semente:19 -192.916667 -342.63005 -43.20328401 0.0058546
Semente:63-Semente:19 -147.333333 -307.38367 12.71700499 0.0861120
Semente:39-Estaquia:39 57.000000 -81.60766 195.60765887 0.8596995
Estaquia:52-Estaquia:39 23.750000 -114.85766 162.35765887 0.9989051
Semente:52-Estaquia:39 20.500000 -118.10766 159.10765887 0.9995780
Estaquia:63-Estaquia:39 -19.500000 -158.10766 119.10765887 0.9996962
Semente:63-Estaquia:39 26.083333 -123.63005 175.79671599 0.9987822
Estaquia:52-Semente:39 -33.250000 -171.85766 105.35765887 0.9912798
Semente:52-Semente:39 -36.500000 -175.10766 102.10765887 0.9850472
Estaquia:63-Semente:39 -76.500000 -215.10766 62.10765887 0.6001735
Semente:63-Semente:39 -30.916667 -180.63005 118.79671599 0.9964627
Semente:52-Estaquia:52 -3.250000 -141.85766 135.35765887 1.0000000
Estaquia:63-Estaquia:52 -43.250000 -181.85766 95.35765887 0.9622320
Semente:63-Estaquia:52 2.333333 -147.38005 152.04671599 1.0000000
Estaquia:63-Semente:52 -40.000000 -178.60766 98.60765887 0.9751094
Semente:63-Semente:52 5.583333 -144.13005 155.29671599 1.0000000
Semente:63-Estaquia:63 45.583333 -104.13005 195.29671599 0.9668045tukey.cld29 = multcompLetters4(mod29, tukey29)
print(tukey.cld29)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
19 39 52 63
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:19 Estaquia:19 Semente:39 Semente:63 Estaquia:52 Semente:52 Estaquia:39 Estaquia:63
"a" "ab" "ab" "ab" "ab" "b" "b" "b" 30- Zinco
#Outlier
boxplot(data2$Zinco)
#model
mod30 = aov(Zinco~propagacao*saturacao, data = data2)
hist(rstandard(mod30))
shapiro.test(rstandard(mod30))
Shapiro-Wilk normality test
data: rstandard(mod30)
W = 0.96448, p-value = 0.401#Tukey
medias30=emmeans(mod30,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias30.1=emmeans(mod30,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias30) propagacao emmean SE df lower.CL upper.CL
Estaquia 126 9.26 22 107 145
Semente 159 10.00 22 139 180
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias30.1) saturacao emmean SE df lower.CL upper.CL
19 202.7 14.1 22 173.4 232
39 154.8 13.1 22 127.6 182
52 116.4 13.1 22 89.2 144
63 97.6 14.1 22 68.3 127
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod30) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 7225 7225 5.264 0.031689 *
saturacao 3 43443 14481 10.550 0.000168 ***
propagacao:saturacao 3 5419 1806 1.316 0.294409
Residuals 22 30198 1373
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey30 = TukeyHSD(mod30)
print(tukey30) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Zinco ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 31.10714 2.988399 59.22589 0.0316895
$saturacao
diff lwr upr p adj
39-19 -45.04337 -98.28838 8.201647 0.1173369
52-19 -83.41837 -136.66338 -30.173353 0.0013617
63-19 -101.71429 -156.70550 -46.723071 0.0002079
52-39 -38.37500 -89.81457 13.064571 0.1934667
63-39 -56.67092 -109.91593 -3.425904 0.0342000
63-52 -18.29592 -71.54093 34.949096 0.7762481
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 71.91667 -22.55818 166.391509 0.2298139
Estaquia:39-Estaquia:19 -31.75000 -119.21671 55.716708 0.9197732
Semente:39-Estaquia:19 7.75000 -79.71671 95.216708 0.9999865
Estaquia:52-Estaquia:19 -48.75000 -136.21671 38.716708 0.5889620
Semente:52-Estaquia:19 -52.00000 -139.46671 35.466708 0.5128666
Estaquia:63-Estaquia:19 -81.50000 -168.96671 5.966708 0.0798836
Semente:63-Estaquia:19 -56.75000 -151.22484 37.724843 0.5004196
Estaquia:39-Semente:19 -103.66667 -198.14151 -9.191824 0.0248358
Semente:39-Semente:19 -64.16667 -158.64151 30.308176 0.3530714
Estaquia:52-Semente:19 -120.66667 -215.14151 -26.191824 0.0063887
Semente:52-Semente:19 -123.91667 -218.39151 -29.441824 0.0048999
Estaquia:63-Semente:19 -153.41667 -247.89151 -58.941824 0.0004314
Semente:63-Semente:19 -128.66667 -229.66452 -27.668811 0.0065521
Semente:39-Estaquia:39 39.50000 -47.96671 126.966708 0.7954442
Estaquia:52-Estaquia:39 -17.00000 -104.46671 70.466708 0.9975704
Semente:52-Estaquia:39 -20.25000 -107.71671 67.216708 0.9929324
Estaquia:63-Estaquia:39 -49.75000 -137.21671 37.716708 0.5654057
Semente:63-Estaquia:39 -25.00000 -119.47484 69.474843 0.9846278
Estaquia:52-Semente:39 -56.50000 -143.96671 30.966708 0.4125405
Semente:52-Semente:39 -59.75000 -147.21671 27.716708 0.3463863
Estaquia:63-Semente:39 -89.25000 -176.71671 -1.783292 0.0432996
Semente:63-Semente:39 -64.50000 -158.97484 29.974843 0.3470569
Semente:52-Estaquia:52 -3.25000 -90.71671 84.216708 1.0000000
Estaquia:63-Estaquia:52 -32.75000 -120.21671 54.716708 0.9072948
Semente:63-Estaquia:52 -8.00000 -102.47484 86.474843 0.9999901
Estaquia:63-Semente:52 -29.50000 -116.96671 57.966708 0.9438312
Semente:63-Semente:52 -4.75000 -99.22484 89.724843 0.9999997
Semente:63-Estaquia:63 24.75000 -69.72484 119.224843 0.9854805tukey.cld30 = multcompLetters4(mod30, tukey30)
print(tukey.cld30)$propagacao
Semente Estaquia
"a" "b"
$saturacao
19 39 52 63
"a" "ab" "bc" "c"
$`propagacao:saturacao`
Semente:19 Semente:39 Estaquia:19 Estaquia:39 Estaquia:52 Semente:52 Semente:63 Estaquia:63
"a" "ab" "abc" "bc" "bc" "bc" "bc" "c" 31- Nitrogênio
#Outlier
boxplot(data2$Nitrogênio)
#model
mod31 = aov(Nitrogênio~propagacao*saturacao, data = data2)
hist(rstandard(mod31))
shapiro.test(rstandard(mod31))
Shapiro-Wilk normality test
data: rstandard(mod31)
W = 0.97048, p-value = 0.5524#Tukey
medias31=emmeans(mod31,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias31.1=emmeans(mod31,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias31) propagacao emmean SE df lower.CL upper.CL
Estaquia 25.2 1.02 22 23.1 27.3
Semente 26.7 1.10 22 24.4 29.0
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias31.1) saturacao emmean SE df lower.CL upper.CL
19 32.5 1.55 22 29.2 35.7
39 24.8 1.44 22 21.8 27.7
52 24.2 1.44 22 21.3 27.2
63 22.4 1.55 22 19.2 25.6
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod31) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 11.5 11.50 0.694 0.41382
saturacao 3 380.6 126.87 7.654 0.00111 **
propagacao:saturacao 3 56.6 18.86 1.138 0.35553
Residuals 22 364.7 16.58
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey31 = TukeyHSD(mod31)
print(tukey31) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Nitrogênio ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia 1.241071 -1.848907 4.33105 0.4138198
$saturacao
diff lwr upr p adj
39-19 -7.338648 -13.189761 -1.487535 0.0105256
52-19 -7.838648 -13.689761 -1.987535 0.0060700
63-19 -9.571429 -15.614432 -3.528425 0.0012147
52-39 -0.500000 -6.152712 5.152712 0.9946378
63-39 -2.232781 -8.083894 3.618332 0.7168939
63-52 -1.732781 -7.583894 4.118332 0.8432845
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 6.416667e+00 -3.965207 16.7985399 0.4659177
Estaquia:39-Estaquia:19 -4.500000e+00 -14.111747 5.1117469 0.7659539
Semente:39-Estaquia:19 -4.500000e+00 -14.111747 5.1117469 0.7659539
Estaquia:52-Estaquia:19 -5.250000e+00 -14.861747 4.3617469 0.6119411
Semente:52-Estaquia:19 -4.750000e+00 -14.361747 4.8617469 0.7168250
Estaquia:63-Estaquia:19 -6.500000e+00 -16.111747 3.1117469 0.3581243
Semente:63-Estaquia:19 -7.250000e+00 -17.631873 3.1318732 0.3211836
Estaquia:39-Semente:19 -1.091667e+01 -21.298540 -0.5347935 0.0346581
Semente:39-Semente:19 -1.091667e+01 -21.298540 -0.5347935 0.0346581
Estaquia:52-Semente:19 -1.166667e+01 -22.048540 -1.2847935 0.0204285
Semente:52-Semente:19 -1.116667e+01 -21.548540 -0.7847935 0.0291092
Estaquia:63-Semente:19 -1.291667e+01 -23.298540 -2.5347935 0.0082349
Semente:63-Semente:19 -1.366667e+01 -24.765356 -2.5679774 0.0090798
Semente:39-Estaquia:39 -3.552714e-15 -9.611747 9.6117469 1.0000000
Estaquia:52-Estaquia:39 -7.500000e-01 -10.361747 8.8617469 0.9999943
Semente:52-Estaquia:39 -2.500000e-01 -9.861747 9.3617469 1.0000000
Estaquia:63-Estaquia:39 -2.000000e+00 -11.611747 7.6117469 0.9962939
Semente:63-Estaquia:39 -2.750000e+00 -13.131873 7.6318732 0.9845407
Estaquia:52-Semente:39 -7.500000e-01 -10.361747 8.8617469 0.9999943
Semente:52-Semente:39 -2.500000e-01 -9.861747 9.3617469 1.0000000
Estaquia:63-Semente:39 -2.000000e+00 -11.611747 7.6117469 0.9962939
Semente:63-Semente:39 -2.750000e+00 -13.131873 7.6318732 0.9845407
Semente:52-Estaquia:52 5.000000e-01 -9.111747 10.1117469 0.9999997
Estaquia:63-Estaquia:52 -1.250000e+00 -10.861747 8.3617469 0.9998194
Semente:63-Estaquia:52 -2.000000e+00 -12.381873 8.3818732 0.9977014
Estaquia:63-Semente:52 -1.750000e+00 -11.361747 7.8617469 0.9983887
Semente:63-Semente:52 -2.500000e+00 -12.881873 7.8818732 0.9910817
Semente:63-Estaquia:63 -7.500000e-01 -11.131873 9.6318732 0.9999967tukey.cld31 = multcompLetters4(mod31, tukey31)
print(tukey.cld31)$propagacao
$propagacao$Letters
Semente Estaquia
"a" "a"
$propagacao$LetterMatrix
a
Semente TRUE
Estaquia TRUE
$saturacao
19 39 52 63
"a" "b" "b" "b"
$`propagacao:saturacao`
Semente:19 Estaquia:19 Estaquia:39 Semente:39 Semente:52 Estaquia:52 Estaquia:63 Semente:63
"a" "ab" "b" "b" "b" "b" "b" "b" 31.1- Aluminio
#Outlier
boxplot(data2$Al.1)
#model
mod31.1 = aov(Al.1~propagacao*saturacao, data = data2)
hist(rstandard(mod31.1))
shapiro.test(rstandard(mod31.1))
Shapiro-Wilk normality test
data: rstandard(mod31.1)
W = 0.96264, p-value = 0.7098#Tukey
medias31.1=emmeans(mod31.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias31.1.1=emmeans(mod31.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias31.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 278 29.3 8 210 346
Semente 104 29.3 8 36 171
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias31.1.1) saturacao emmean SE df lower.CL upper.CL
19 134 41.5 8 38.1 229
39 275 41.5 8 179.1 370
52 235 41.5 8 139.7 331
63 120 41.5 8 24.0 215
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod31.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 121841 121841 17.711 0.00296 **
saturacao 3 69398 23133 3.363 0.07559 .
propagacao:saturacao 3 32699 10900 1.584 0.26763
Residuals 8 55035 6879
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingnesstukey31.1 = TukeyHSD(mod31.1)
print(tukey31.1) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = Al.1 ~ propagacao * saturacao, data = data2)
$propagacao
diff lwr upr p adj
Semente-Estaquia -174.5287 -270.1608 -78.89674 0.0029621
$saturacao
diff lwr upr p adj
39-19 141.0175 -46.79619 328.83119 0.1534512
52-19 101.6800 -86.13369 289.49369 0.3680189
63-19 -14.0300 -201.84369 173.78369 0.9947930
52-39 -39.3375 -227.15119 148.47619 0.9052396
63-39 -155.0475 -342.86119 32.76619 0.1099538
63-52 -115.7100 -303.52369 72.10369 0.2734255
$`propagacao:saturacao`
diff lwr upr p adj
Semente:19-Estaquia:19 -255.580 -583.78854 72.62854 0.1529104
Estaquia:39-Estaquia:19 34.975 -293.23354 363.18354 0.9997653
Semente:39-Estaquia:19 -8.520 -336.72854 319.68854 1.0000000
Estaquia:52-Estaquia:19 104.695 -223.51354 432.90354 0.8901983
Semente:52-Estaquia:19 -156.915 -485.12354 171.29354 0.5881948
Estaquia:63-Estaquia:19 -73.105 -401.31354 255.10354 0.9799365
Semente:63-Estaquia:19 -210.535 -538.74354 117.67354 0.2979961
Estaquia:39-Semente:19 290.555 -37.65354 618.76354 0.0892512
Semente:39-Semente:19 247.060 -81.14854 575.26854 0.1740608
Estaquia:52-Semente:19 360.275 32.06646 688.48354 0.0307899
Semente:52-Semente:19 98.665 -229.54354 426.87354 0.9144844
Estaquia:63-Semente:19 182.475 -145.73354 510.68354 0.4351998
Semente:63-Semente:19 45.045 -283.16354 373.25354 0.9988113
Semente:39-Estaquia:39 -43.495 -371.70354 284.71354 0.9990462
Estaquia:52-Estaquia:39 69.720 -258.48854 397.92854 0.9844386
Semente:52-Estaquia:39 -191.890 -520.09854 136.31854 0.3850384
Estaquia:63-Estaquia:39 -108.080 -436.28854 220.12854 0.8751409
Semente:63-Estaquia:39 -245.510 -573.71854 82.69854 0.1781909
Estaquia:52-Semente:39 113.215 -214.99354 441.42354 0.8504769
Semente:52-Semente:39 -148.395 -476.60354 179.81354 0.6423506
Estaquia:63-Semente:39 -64.585 -392.79354 263.62354 0.9897973
Semente:63-Semente:39 -202.015 -530.22354 126.19354 0.3356936
Semente:52-Estaquia:52 -261.610 -589.81854 66.59854 0.1394368
Estaquia:63-Estaquia:52 -177.800 -506.00854 150.40854 0.4615304
Semente:63-Estaquia:52 -315.230 -643.43854 12.97854 0.0610025
Estaquia:63-Semente:52 83.810 -244.39854 412.01854 0.9597289
Semente:63-Semente:52 -53.620 -381.82854 274.58854 0.9965384
Semente:63-Estaquia:63 -137.430 -465.63854 190.77854 0.7117419tukey.cld31.1 = multcompLetters4(mod31.1, tukey31.1)
print(tukey.cld31.1)$propagacao
Estaquia Semente
"a" "b"
$saturacao
$saturacao$Letters
39 52 19 63
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
39 TRUE
52 TRUE
19 TRUE
63 TRUE
$`propagacao:saturacao`
Estaquia:52 Estaquia:39 Estaquia:19 Semente:39 Estaquia:63 Semente:52 Semente:63 Semente:19
"a" "ab" "ab" "ab" "ab" "ab" "ab" "b" 32- Nutrient accumulation
DATA
library(tidyr)
Attaching package: ‘tidyr’
The following objects are masked from ‘package:Matrix’:
expand, pack, unpackcitation("tidyr")To cite package ‘tidyr’ in publications use:
Wickham H, Vaughan D, Girlich M (2024). _tidyr: Tidy Messy Data_. R package version
1.3.1, <https://CRAN.R-project.org/package=tidyr>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {tidyr: Tidy Messy Data},
author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich},
year = {2024},
note = {R package version 1.3.1},
url = {https://CRAN.R-project.org/package=tidyr},
}data2$ac.N=data2$MF.accumulation*data2$Nitrogênio/1000
data2$ac.P=data2$MF.accumulation*data2$Fósforo/1000
data2$ac.K=data2$MF.accumulation*data2$Potássio/1000
data2$ac.Ca=data2$MF.accumulation*data2$Cálcio/1000
data2$ac.Mg=data2$MF.accumulation*data2$Magnésio/1000
data2$ac.S=data2$MF.accumulation*data2$Enxofre/1000
data2$ac.B=data2$MF.accumulation*data2$Boro/1000
data2$ac.Cu=data2$MF.accumulation*data2$Cobre/1000
data2$ac.Fe=data2$MF.accumulation*data2$Ferro/1000
data2$ac.Mn=data2$MF.accumulation*data2$Manganês/1000
data2$ac.Zn=data2$MF.accumulation*data2$Zinco/1000
data2$ac.Al=data2$MF.accumulation*data2$Al.1/1000
ac.Macro=gather(data2[,-c(5:56)],variables,value,
ac.N,ac.P,ac.K,ac.Ca,ac.Mg,ac.S)
View(ac.Macro)
ac.Micro=gather(data2[,-c(5:56)],variables,value,
ac.B,ac.Cu,ac.Fe,ac.Mn,ac.Zn)
View(ac.Micro)MACRO - Anova
#MACRO
mod32.1 = aov(ac.N~propagacao*saturacao, data = data2)
summary(mod32.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1.8594 1.8594 16.012 0.000601 ***
saturacao 3 0.2346 0.0782 0.673 0.577503
propagacao:saturacao 3 0.2143 0.0714 0.615 0.612537
Residuals 22 2.5547 0.1161
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias32.1=emmeans(mod32.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias32.2=emmeans(mod32.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias32.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 2.29 0.0852 22 2.11 2.47
Semente 1.78 0.0920 22 1.59 1.97
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias32.2) saturacao emmean SE df lower.CL upper.CL
19 2.02 0.13 22 1.75 2.29
39 2.08 0.12 22 1.83 2.33
52 2.14 0.12 22 1.89 2.38
63 1.90 0.13 22 1.63 2.17
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 mod33.1 = aov(ac.P~propagacao*saturacao, data = data2)
summary(mod33.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.06755 0.06755 41.517 1.75e-06 ***
saturacao 3 0.01456 0.00485 2.983 0.0533 .
propagacao:saturacao 3 0.00943 0.00314 1.933 0.1538
Residuals 22 0.03580 0.00163
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias33.1=emmeans(mod33.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias33.2=emmeans(mod33.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias33.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 0.327 0.0101 22 0.306 0.348
Semente 0.230 0.0109 22 0.207 0.252
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias33.2) saturacao emmean SE df lower.CL upper.CL
19 0.240 0.0154 22 0.208 0.272
39 0.301 0.0143 22 0.271 0.331
52 0.290 0.0143 22 0.260 0.319
63 0.282 0.0154 22 0.250 0.314
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 mod34.1 = aov(ac.K~propagacao*saturacao, data = data2)
summary(mod34.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1.9596 1.9596 19.407 0.000224 ***
saturacao 3 0.6184 0.2061 2.042 0.137375
propagacao:saturacao 3 0.1233 0.0411 0.407 0.749403
Residuals 22 2.2214 0.1010
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias34.1=emmeans(mod34.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias34.2=emmeans(mod34.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias34.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 1.55 0.0794 22 1.390 1.72
Semente 1.02 0.0858 22 0.839 1.19
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias34.2) saturacao emmean SE df lower.CL upper.CL
19 1.09 0.121 22 0.843 1.35
39 1.47 0.112 22 1.237 1.70
52 1.38 0.112 22 1.146 1.61
63 1.20 0.121 22 0.948 1.45
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 mod35.1 = aov(ac.Ca~propagacao*saturacao, data = data2)
summary(mod35.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.7943 0.7943 25.086 5.16e-05 ***
saturacao 3 2.7992 0.9331 29.470 6.88e-08 ***
propagacao:saturacao 3 0.0132 0.0044 0.139 0.936
Residuals 22 0.6966 0.0317
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias35.1=emmeans(mod35.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias35.2=emmeans(mod35.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias35.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 1.204 0.0445 22 1.11 1.296
Semente 0.869 0.0480 22 0.77 0.969
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias35.2) saturacao emmean SE df lower.CL upper.CL
19 0.541 0.0680 22 0.400 0.682
39 1.008 0.0629 22 0.877 1.138
52 1.213 0.0629 22 1.082 1.343
63 1.385 0.0680 22 1.244 1.526
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 tukey35.1 = TukeyHSD(mod35.1)
tukey.cld35.1 = multcompLetters4(mod35.1, tukey35.1)
print(tukey.cld35.1)$propagacao
Estaquia Semente
"a" "b"
$saturacao
63 52 39 19
"a" "ab" "b" "c"
$`propagacao:saturacao`
Estaquia:63 Estaquia:52 Semente:63 Estaquia:39 Semente:52 Semente:39 Estaquia:19 Semente:19
"a" "ab" "abc" "abc" "bcd" "cde" "de" "e" mod36.1 = aov(ac.Mg~propagacao*saturacao, data = data2)
summary(mod36.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.04373 0.04373 15.385 0.000729 ***
saturacao 3 0.15225 0.05075 17.854 4.22e-06 ***
propagacao:saturacao 3 0.01689 0.00563 1.981 0.146307
Residuals 22 0.06253 0.00284
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias36.1=emmeans(mod36.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias36.2=emmeans(mod36.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias36.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 0.329 0.0133 22 0.301 0.356
Semente 0.251 0.0144 22 0.221 0.280
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias36.2) saturacao emmean SE df lower.CL upper.CL
19 0.167 0.0204 22 0.125 0.209
39 0.296 0.0188 22 0.257 0.335
52 0.329 0.0188 22 0.290 0.368
63 0.366 0.0204 22 0.324 0.409
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 tukey36.1 = TukeyHSD(mod36.1)
tukey.cld36.1 = multcompLetters4(mod36.1, tukey36.1)
print(tukey.cld36.1)$propagacao
Estaquia Semente
"a" "b"
$saturacao
63 52 39 19
"a" "a" "a" "b"
$`propagacao:saturacao`
Estaquia:63 Estaquia:39 Semente:63 Estaquia:52 Semente:52 Semente:39 Estaquia:19 Semente:19
"a" "a" "a" "a" "ab" "bc" "bc" "c" mod37.1 = aov(ac.S~propagacao*saturacao, data = data2)
summary(mod37.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.008405 0.008405 32.746 9.35e-06 ***
saturacao 3 0.002148 0.000716 2.790 0.0645 .
propagacao:saturacao 3 0.000346 0.000115 0.449 0.7207
Residuals 22 0.005647 0.000257
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias37.1=emmeans(mod37.1,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias37.2=emmeans(mod37.1,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias37.1) propagacao emmean SE df lower.CL upper.CL
Estaquia 0.1135 0.00401 22 0.1052 0.1218
Semente 0.0788 0.00433 22 0.0698 0.0877
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias37.2) saturacao emmean SE df lower.CL upper.CL
19 0.0818 0.00612 22 0.0691 0.0945
39 0.1057 0.00566 22 0.0939 0.1174
52 0.0990 0.00566 22 0.0872 0.1107
63 0.0981 0.00612 22 0.0854 0.1108
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 MICRO - Anova
print(tukey.cld43.1)
$propagacao
Estaquia Semente
"a" "b"
$saturacao
$saturacao$Letters
39 52 63 19
"a" "a" "a" "a"
$saturacao$LetterMatrix
a
39 TRUE
52 TRUE
63 TRUE
19 TRUE
$`propagacao:saturacao`
Estaquia:52 Estaquia:39 Estaquia:19 Semente:39 Estaquia:63 Semente:52 Semente:63 Semente:19
"a" "ab" "abc" "abc" "abc" "abc" "bc" "c" PLOTS
33- V%, m% and pH
library(ggplot2)
p1=ggplot(data2, aes(x=as.numeric(saturacao)))+
geom_line(aes(y=V.,color="V (%)"), stat="summary",fun="mean", size=1, linetype = 1)+
geom_line(aes(y=m.,color="m (%)"), stat="summary",fun="mean", size=1, linetype = 7)+
geom_line(aes(y=pH*8,color="pH"), stat="summary",fun="mean", size=1, linetype = 1)+
geom_line(aes(y=Al*8,color="Al+3"), stat="summary",fun="mean", size=1, linetype = 1)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.p1 
p1=p1+scale_x_discrete(name ="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
scale_y_continuous(breaks = seq(0, 70, 5), name= "V (%); m (%)", sec.axis = sec_axis(~./8, name="pH; Al+3 (mmolc dm3)", breaks = seq(0, 9, 0.5)))+
scale_colour_manual(" ", values=c("gold4", "firebrick3","steelblue","blue4"))+
coord_cartesian(ylim = c(0,70))
p1
p1=p1+theme(legend.key=element_blank(),legend.title=element_blank(),legend.box="h", axis.line = element_line(colour = "black", size = 1, linetype = "solid"),panel.background = element_rect(fill = "transparent"),legend.background = element_rect(fill = "transparent", size=0.5, linetype="solid",colour ="black"), legend.position = c(0.1, 0.80))Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2 3.5.0.
Please use the `legend.position.inside` argument of `theme()` instead.p1
library(cowplot)
save_plot("V.pdf",p1, ncol = 1, nrow = 1) 
35- Branches evolution
cut1=biomass[-c(33:96),]
cut2=biomass[-c(1:32,65:96),]
cut3=biomass[-c(1:64,69,71,72),]
mod35.1 = aov(Perfilhos^0.8~propagacao*saturacao, data = cut1)
hist(rstandard(mod35.1))
shapiro.test(rstandard(mod35.1))
Shapiro-Wilk normality test
data: rstandard(mod35.1)
W = 0.94252, p-value = 0.08827summary(mod35.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 119.90 119.90 126.039 7.05e-12 ***
saturacao 1 1.52 1.52 1.593 0.217
propagacao:saturacao 1 0.97 0.97 1.024 0.320
Residuals 28 26.64 0.95
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1mod35.2 = aov(Perfilhos~propagacao*saturacao, data = cut2)
hist(rstandard(mod35.2))
shapiro.test(rstandard(mod35.2))
Shapiro-Wilk normality test
data: rstandard(mod35.2)
W = 0.98437, p-value = 0.9118summary(mod35.2) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 586.5 586.5 66.462 7.1e-09 ***
saturacao 1 4.7 4.7 0.529 0.4729
propagacao:saturacao 1 47.9 47.9 5.429 0.0272 *
Residuals 28 247.1 8.8
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1mod35.3 = aov(Perfilhos~propagacao*saturacao, data = cut3)
hist(rstandard(mod35.3))
shapiro.test(rstandard(mod35.3))
Shapiro-Wilk normality test
data: rstandard(mod35.3)
W = 0.95042, p-value = 0.1877summary(mod35.3) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1100.9 1100.9 21.767 8.87e-05 ***
saturacao 1 0.6 0.6 0.012 0.915
propagacao:saturacao 1 3.3 3.3 0.065 0.801
Residuals 25 1264.5 50.6
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1library(dplyr)
data_summary = group_by(biomass[-c(69,71,72),], propagacao, Cycle) %>%
summarise(mean=mean(Perfilhos),
sd=sd(Perfilhos))`summarise()` has grouped output by 'propagacao'. You can override using the `.groups` argument.print(data_summary)
#Plot
library(ggplot2)
p3=ggplot(data_summary, aes(x=as.numeric(Cycle),y=mean, group=propagacao))+
geom_line(aes(linetype=propagacao))+
geom_point(aes(shape=propagacao), size= 3)+
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), position = position_dodge(0.15), width = 0.2)
p3
p3.1=p3+scale_shape_manual(values = c(1, 15), name= "Propagation",
labels = c("Stem cutting", "Seeds"))+
scale_linetype_manual(values = c("dashed","solid"), name= "Propagation",
labels = c("Stem cutting", "Seeds"))+
scale_x_discrete(name="", limits=c("Cut 1", "Cut 2", "Cut 3"))+
scale_y_continuous(name="Number of branches (n)",breaks=seq(0,46,3))+
coord_cartesian(ylim = c(0,46))
p3.1
p3.2=p3.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
panel.background = element_rect(fill = "white", colour = "black"),
legend.background = element_rect(fill = "transparent",
size=0.5, linetype="solid",colour ="black"),axis.title.x=element_blank(),
legend.position = c(0.2, 0.8),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate("text",size=5,color="black", x=1, y=15, label="***")+
annotate("text",size=5,color="black", x=2, y=22.5,label="***")+
annotate("text",size=5,color="black", x=3, y=42.5, label= "***")
p3.2
library(cowplot)
save_plot("Branches.pdf",p3.2, ncol = 1, nrow = 1)
36- SPAD
#model
mod26 = lm(SPAD~poly(saturacao,degree = 1), data = data2[-10,])
hist(rstandard(mod26))
shapiro.test(resid(mod26))
Shapiro-Wilk normality test
data: resid(mod26)
W = 0.97029, p-value = 0.5677summary(mod26)
Call:
lm(formula = SPAD ~ poly(saturacao, degree = 1), data = data2[-10,
])
Residuals:
Min 1Q Median 3Q Max
-5.0216 -1.3264 -0.1161 0.5884 5.0539
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.5872 0.4524 65.398 <2e-16 ***
poly(saturacao, degree = 1) -4.5696 2.5908 -1.764 0.0891 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.425 on 27 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.1033, Adjusted R-squared: 0.0701
F-statistic: 3.111 on 1 and 27 DF, p-value: 0.08909#Tukey
library(ggplot2)
p1=ggplot(data2[-10,], aes(x=as.numeric(saturacao), y=SPAD))+
geom_point(shape=19, color='black',stat="summary",fun="mean")+
geom_smooth(color='black', method = "gam", formula = y ~ poly(x, 1), se=F)
p1 
p1=p1+scale_x_discrete(name ="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
scale_y_continuous(breaks = seq(27, 33, 1), name= "SPAD index")+
coord_cartesian(ylim = c(27,33))
p1
p1=p1+theme(legend.key=element_blank(),
legend.title=element_blank(),
axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
panel.background = element_rect(fill = "white", colour = "black"),
axis.title.x = element_text(size = 14),
axis.title.y = element_text(size = 14),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate(geom="text", x=2.5, y=32,label=expression(paste("P-value = 0.089; y = - 4.7115x + 29.6062 ", R^2, "= 0.1139")), size=5, color="black")
p1
library(cowplot)
save_plot("SPAD.pdf",p1, ncol = 1, nrow = 1) 
37- Macro e micro accumulation
#MACRO
View(data2)
mod32.1 = aov(ac.N~propagacao*saturacao, data = data2)
summary(mod32.1) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1.8594 1.8594 16.012 0.000601 ***
saturacao 3 0.2346 0.0782 0.673 0.577503
propagacao:saturacao 3 0.2143 0.0714 0.615 0.612537
Residuals 22 2.5547 0.1161
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmedias28=emmeans(mod28,~ propagacao)NOTE: Results may be misleading due to involvement in interactionsmedias28.1=emmeans(mod28,~ saturacao)NOTE: Results may be misleading due to involvement in interactionssummary(medias28) propagacao emmean SE df lower.CL upper.CL
Estaquia 281 11.7 22 257 306
Semente 265 12.6 22 238 291
Results are averaged over the levels of: saturacao
Confidence level used: 0.95 summary(medias28.1) saturacao emmean SE df lower.CL upper.CL
19 293 17.8 22 256 330
39 287 16.5 22 253 321
52 262 16.5 22 228 296
63 250 17.8 22 213 287
Results are averaged over the levels of: propagacao
Confidence level used: 0.95 summary(mod28) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 2501 2501 1.148 0.296
saturacao 3 9698 3233 1.484 0.246
propagacao:saturacao 3 9478 3159 1.450 0.255
Residuals 22 47933 2179
2 observations deleted due to missingnessmod32.2 = aov(ac.P~propagacao*saturacao, data = data2)
summary(mod32.2) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.06755 0.06755 41.517 1.75e-06 ***
saturacao 3 0.01456 0.00485 2.983 0.0533 .
propagacao:saturacao 3 0.00943 0.00314 1.933 0.1538
Residuals 22 0.03580 0.00163
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmod32.3 = aov(ac.K~propagacao*saturacao, data = data2)
summary(mod32.3) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 1.9596 1.9596 19.407 0.000224 ***
saturacao 3 0.6184 0.2061 2.042 0.137375
propagacao:saturacao 3 0.1233 0.0411 0.407 0.749403
Residuals 22 2.2214 0.1010
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmod32.4 = aov(ac.Ca~propagacao*saturacao, data = data2)
summary(mod32.4) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.7943 0.7943 25.086 5.16e-05 ***
saturacao 3 2.7992 0.9331 29.470 6.88e-08 ***
propagacao:saturacao 3 0.0132 0.0044 0.139 0.936
Residuals 22 0.6966 0.0317
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey32.4 = TukeyHSD(mod32.4)
tukey.cld32.4 = multcompLetters4(mod32.4, tukey32.4)
print(tukey.cld32.4)$propagacao
Estaquia Semente
"a" "b"
$saturacao
63 52 39 19
"a" "ab" "b" "c"
$`propagacao:saturacao`
Estaquia:63 Estaquia:52 Semente:63 Estaquia:39 Semente:52 Semente:39 Estaquia:19 Semente:19
"a" "ab" "abc" "abc" "bcd" "cde" "de" "e" mod32.5 = aov(ac.Mg~propagacao*saturacao, data = data2)
summary(mod32.5) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.04373 0.04373 15.385 0.000729 ***
saturacao 3 0.15225 0.05075 17.854 4.22e-06 ***
propagacao:saturacao 3 0.01689 0.00563 1.981 0.146307
Residuals 22 0.06253 0.00284
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey32.5 = TukeyHSD(mod32.5)
tukey.cld32.5 = multcompLetters4(mod32.5, tukey32.5)
print(tukey.cld32.5)$propagacao
Estaquia Semente
"a" "b"
$saturacao
63 52 39 19
"a" "a" "a" "b"
$`propagacao:saturacao`
Estaquia:63 Estaquia:39 Semente:63 Estaquia:52 Semente:52 Semente:39 Estaquia:19 Semente:19
"a" "a" "a" "a" "ab" "bc" "bc" "c" mod32.6 = aov(ac.S~propagacao*saturacao, data = data2)
summary(mod32.6) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.008405 0.008405 32.746 9.35e-06 ***
saturacao 3 0.002148 0.000716 2.790 0.0645 .
propagacao:saturacao 3 0.000346 0.000115 0.449 0.7207
Residuals 22 0.005647 0.000257
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesslibrary(ggplot2)
p37=ggplot(ac.Macro, aes(x=as.numeric(saturacao),y=value, color=variables))+
geom_line(stat="summary",fun="mean")+
geom_point(stat="summary",fun="mean")
p37
p37.1=p37+scale_colour_manual("Composition:",values=c("ac.N" ="green4","ac.P"="gray40","ac.K"="red4",
"ac.Ca"="darkblue","ac.Mg"="cyan2","ac.S"="gold"),
labels = c("ac.N" ="N - P value = 0.578","ac.P" ="P - P value = 0.053","ac.K" ="K - P value = 0.137","ac.Ca" ="Ca - P value = <0.001","ac.Mg" ="Mg - P value = <0.001","ac.S" ="S - P value = 0.065"))+scale_x_discrete(name="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
scale_y_continuous(name="Macronutrients accumulation (g/plant)",breaks=seq(0,2.25,0.25))+
coord_cartesian(ylim = c(0,2.25))
p37.1
p37.2=p37.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
panel.background = element_rect(fill = "white", colour = "black"),
legend.background = element_rect(fill = "transparent",size=0.5,linetype="solid",colour ="black"),
legend.key = element_rect(fill = "white", colour = "black"),
legend.justification = "top",
axis.title.x = element_text(size = 11),
axis.title.y = element_text(size = 11),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate("text", size=3, x=1, y=0.65,label="c")+
annotate("text",size=3, x=2, y=1.1,label= "b")+
annotate("text",size=3, x=3, y=1.3,label= "ab")+
annotate("text",size=3, x=4, y=1.5,label= "a")+
annotate("text", size=3, x=1, y=0.30,label="b")+
annotate("text",size=3, x=2, y=0.37,label= "a")+
annotate("text",size=3, x=3, y=0.41, label= "a")+
annotate("text",size=3, x=4, y=0.44,label= "a")
p37.2
#MICRO
mod32.7 = aov(ac.B~propagacao*saturacao, data = data2)
summary(mod32.7) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 73.27 73.27 23.870 6.95e-05 ***
saturacao 3 26.69 8.90 2.898 0.0579 .
propagacao:saturacao 3 10.18 3.39 1.106 0.3679
Residuals 22 67.53 3.07
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmod32.8 = aov(ac.Cu~propagacao*saturacao, data = data2)
summary(mod32.8) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 0.3036 0.30361 24.447 6.03e-05 ***
saturacao 3 0.1613 0.05377 4.330 0.0153 *
propagacao:saturacao 3 0.0895 0.02983 2.402 0.0949 .
Residuals 22 0.2732 0.01242
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey32.8 = TukeyHSD(mod32.8)
tukey.cld32.8 = multcompLetters4(mod32.8, tukey32.8)
print(tukey.cld32.8)$propagacao
Estaquia Semente
"a" "b"
$saturacao
39 52 19 63
"a" "ab" "b" "b"
$`propagacao:saturacao`
Estaquia:39 Estaquia:52 Estaquia:19 Estaquia:63 Semente:52 Semente:39 Semente:63 Semente:19
"a" "ab" "abc" "bc" "bc" "bc" "bc" "c" mod32.9 = aov(ac.Fe~propagacao*saturacao, data = data2)
summary(mod32.9) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 450.0 450.0 19.047 0.000248 ***
saturacao 3 124.5 41.5 1.757 0.184818
propagacao:saturacao 3 56.0 18.7 0.791 0.512123
Residuals 22 519.8 23.6
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnessmod32.10 = aov(ac.Mn~propagacao*saturacao, data = data2)
summary(mod32.10) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 46.1 46.08 1.546 0.227
saturacao 3 51.9 17.28 0.580 0.634
propagacao:saturacao 3 29.9 9.98 0.335 0.800
Residuals 22 655.5 29.80
2 observations deleted due to missingnessmod32.11 = aov(ac.Zn~propagacao*saturacao, data = data2)
summary(mod32.11) Df Sum Sq Mean Sq F value Pr(>F)
propagacao 1 8.12 8.12 0.968 0.3358
saturacao 3 100.52 33.51 3.998 0.0205 *
propagacao:saturacao 3 6.30 2.10 0.250 0.8602
Residuals 22 184.38 8.38
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
2 observations deleted due to missingnesstukey32.11 = TukeyHSD(mod32.11)
tukey.cld32.11 = multcompLetters4(mod32.11, tukey32.11)
print(tukey.cld32.11)$propagacao
$propagacao$Letters
Estaquia Semente
"a" "a"
$propagacao$LetterMatrix
a
Estaquia TRUE
Semente TRUE
$saturacao
39 19 52 63
"a" "a" "ab" "b"
$`propagacao:saturacao`
$`propagacao:saturacao`$Letters
Estaquia:19 Estaquia:39 Semente:39 Semente:19 Estaquia:52 Semente:52 Semente:63 Estaquia:63
"a" "a" "a" "a" "a" "a" "a" "a"
$`propagacao:saturacao`$LetterMatrix
a
Estaquia:19 TRUE
Estaquia:39 TRUE
Semente:39 TRUE
Semente:19 TRUE
Estaquia:52 TRUE
Semente:52 TRUE
Semente:63 TRUE
Estaquia:63 TRUEp38=ggplot(ac.Micro, aes(x=as.numeric(saturacao),y=value, color=variables))+
geom_line(stat="summary",fun="mean")+
geom_point(stat="summary",fun="mean")
p38
p38.1=p38+scale_colour_manual("Composition:",values=c("ac.B" ="darkblue","ac.Cu"="tan4","ac.Fe"="grey0", "ac.Mn"="maroon4","ac.Zn"="cyan4"),labels = c("ac.B"="B - P value = 0.058","ac.Cu"="Cu - P value = 0.015","ac.Fe" ="Fe - P value = 0.185","ac.Mn"="Mn - P value = 0.634","ac.Zn" ="Zn - P value = 0.021"))+
scale_x_discrete(name="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
scale_y_continuous(name="Micronutrients accumulation (mg/plant)",breaks=seq(0,25,5))+
coord_cartesian(ylim = c(0,25))
p38.1
p38.2=p38.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
panel.background = element_rect(fill = "white", colour = "black"),
legend.background = element_rect(fill = "transparent",size=0.5,linetype="solid",colour ="black"),
legend.key = element_rect(fill = "white", colour = "black"),
legend.justification = "top",
axis.title.x = element_text(size = 11),
axis.title.y = element_text(size = 11),
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12))+
annotate("text", size=3, x=1, y=13.5,label="a")+
annotate("text",size=3, x=2, y=13.5,label= "a")+
annotate("text",size=3, x=3, y=11, label= "ab")+
annotate("text",size=3, x=4, y=9,label= "b")+
annotate("text", size=3, x=1, y=1.3,label="b")+
annotate("text",size=3, x=2, y=1.35,label= "a")+
annotate("text",size=3, x=3, y=1.4, label= "ab")+
annotate("text",size=3, x=4, y=1.3,label= "b")
p38.2
#Save plots
library(cowplot)
side.by.side <- plot_grid(p37.2,p38.2,
labels = c("A", "B"),
ncol = 1, nrow =2, align = "V")Warning: Removed 12 rows containing non-finite outside the scale range (`stat_summary()`).Warning: Removed 12 rows containing non-finite outside the scale range (`stat_summary()`).Warning: Removed 10 rows containing non-finite outside the scale range (`stat_summary()`).Warning: Removed 10 rows containing non-finite outside the scale range (`stat_summary()`).side.by.side
save_plot("acumulativo.pdf", side.by.side, ncol = 1, nrow = 2)
https://stackoverflow.com/questions/63301270/how-to-draw-both-positive-mirror-bar-graph-in-r
---
title: "Unveiling the aluminum tolerance by Tithonia diversifolia grown in acid soil: insights from morphological, anatomical, and nutritional analysis"
author: "Vagner S. Ovani"
date: "05/04/2022"
output:
  html_notebook:
    toc: TRUE
    toc_depth: 2
    theme: united
---

***



# **DATA**

```{r}
data2=read.csv("D:\\Armazenamento\\DATA R\\Ensaio 2\\data2.csv")
data2$propagacao=as.factor(data2$propagacao)
data2$saturacao=as.factor(data2$saturacao)
str(data2)
data2
biomass=read.csv("D:\\Armazenamento\\DATA R\\Ensaio 2\\Assay2_biomass.csv")
biomass$propagacao=as.factor(biomass$propagacao)
biomass$Cycle=as.factor(biomass$Cycle)
biomass$Perfilhos=as.numeric(biomass$Perfilhos)
str(biomass)
biomass
```

# **SOIL PROPERTIES**

### _**1 - V.final**_

```{r}
#model
mod1 = aov(V.final~propagacao*saturacao, data = data2)
hist(rstandard(mod1))
shapiro.test(resid(mod1))

#Tukey
library(emmeans)
medias1=emmeans(mod1,~ propagacao)
medias1.1=emmeans(mod1,~ saturacao)
summary(medias1)
summary(medias1.1)
summary(mod1)

library(multcompView)
citation("multcompView")

tukey1 = TukeyHSD(mod1)
print(tukey1)
tukey.cld1 = multcompLetters4(mod1, tukey1)
print(tukey.cld1)
```

### _**2 - m.final**_

```{r}
#model
mod2 = aov(m.final~propagacao*saturacao, data = data2)
hist(rstandard(mod2))
shapiro.test(resid(mod2))

#Tukey
medias2=emmeans(mod2,~ propagacao)
medias2.1=emmeans(mod2,~ saturacao)
summary(medias2)
summary(medias2.1)
summary(mod2)

tukey2 = TukeyHSD(mod2)
print(tukey2)
tukey.cld2 = multcompLetters4(mod2, tukey2)
print(tukey.cld2)
```

### _**3 - pH.final**_

```{r}
#model
mod3 = aov(pH.final~propagacao*saturacao, data = data2)
hist(rstandard(mod3))
shapiro.test(resid(mod3))

#Tukey
medias3=emmeans(mod3,~ propagacao)
medias3.1=emmeans(mod3,~ saturacao)
summary(medias3)
summary(medias3.1)
summary(mod3)

tukey3 = TukeyHSD(mod3)
print(tukey3)
tukey.cld3 = multcompLetters4(mod3, tukey3)
print(tukey.cld3)
```

### _**4 - Al.final**_

```{r}
#model
mod4 = aov(Al.final~propagacao*saturacao, data = data2)
hist(rstandard(mod4))
shapiro.test(resid(mod4))
mod4.1 = aov(Al.final^0.8~propagacao*saturacao, data = data2)
hist(rstandard(mod4.1))
shapiro.test(resid(mod4.1))

#Tukey
medias4=emmeans(mod4,~ propagacao)
medias4.1=emmeans(mod4,~ saturacao)
summary(medias4)
summary(medias4.1)
summary(mod4.1)

tukey4 = TukeyHSD(mod4.1)
print(tukey4)
tukey.cld4 = multcompLetters4(mod4.1, tukey4)
print(tukey.cld4)
```
### _**5 - H.Al.final**_

```{r}
#model
mod5 = aov(H.Al.final~propagacao*saturacao, data = data2)
hist(rstandard(mod5))
shapiro.test(resid(mod5))

#Tukey
medias5=emmeans(mod5,~ propagacao)
medias5.1=emmeans(mod5,~ saturacao)
summary(medias5)
summary(medias5.1)
summary(mod5)

tukey5 = TukeyHSD(mod5)
print(tukey5)
tukey.cld5 = multcompLetters4(mod5, tukey5)
print(tukey.cld5)
```
### _**6 - sb.final**_

```{r}
#model
mod6 = aov(sb.final~propagacao*saturacao, data = data2)
hist(rstandard(mod6))
shapiro.test(resid(mod6))

#Tukey
medias6=emmeans(mod6,~ propagacao)
medias6.1=emmeans(mod6,~ saturacao)
summary(medias6)
summary(medias6.1)
summary(mod6)

tukey6 = TukeyHSD(mod6)
print(tukey6)
tukey.cld6 = multcompLetters4(mod6, tukey6)
print(tukey.cld6)
```

### _**7 - Ca.final**_

```{r}
#model
mod7 = aov(Ca.final~propagacao*saturacao, data = data2)
hist(rstandard(mod7))
shapiro.test(resid(mod7))

#Tukey
medias7=emmeans(mod7,~ propagacao)
medias7.1=emmeans(mod7,~ saturacao)
summary(medias7)
summary(medias7.1)
summary(mod7)

tukey7 = TukeyHSD(mod7)
print(tukey7)
tukey.cld7 = multcompLetters4(mod7, tukey7)
print(tukey.cld7)
```

### _**8 - Mg.final**_

```{r}
#model
mod8 = aov(Mg.final~propagacao*saturacao, data = data2)
hist(rstandard(mod8))
shapiro.test(resid(mod8))

#Tukey
medias8=emmeans(mod8,~ propagacao)
medias8.1=emmeans(mod8,~ saturacao)
summary(medias8)
summary(medias8.1)
summary(mod8)

tukey8 = TukeyHSD(mod8)
print(tukey8)
tukey.cld8 = multcompLetters4(mod8, tukey8)
print(tukey.cld8)
```

# **BIOMASSA PRODUCTION**

### _**9 - MF.mean**_

```{r}
#model
mod9 = aov(MF.mean~propagacao*saturacao, data = data2)
hist(rstandard(mod9))
shapiro.test(resid(mod9))
summary(mod9)

#means and sd
library(dplyr)
data_summary9 = group_by(data2, saturacao) %>%
  summarise(sd = sd(MF.mean, na.rm = TRUE),
    biomass.mean = mean(MF.mean, na.rm = TRUE))%>%
 arrange(desc(biomass.mean))
print(data_summary9)

data_summary9.1 = group_by(data2, propagacao) %>%
  summarise(sd = sd(MF.mean, na.rm = TRUE),
    biomass.mean = mean(MF.mean, na.rm = TRUE))%>%
 arrange(desc(biomass.mean))
print(data_summary9.1)

library(multcompView)
#tukey
tukey9 = TukeyHSD(mod9)
print(tukey9)
tukey.cld9 = multcompLetters4(mod9, tukey9)
print(tukey.cld9)

cld9 = as.data.frame.list(tukey.cld9$`saturacao`)
data_summary9$Tukey = cld9$Letters
print(data_summary9)
library(ggplot2)
#plot
plt1=ggplot(data_summary9, aes(x=as.factor(saturacao), y=biomass.mean, fill=saturacao))+
  geom_col(position = "dodge")+
  geom_errorbar(aes(ymin=biomass.mean-sd, ymax=biomass.mean+sd), width=.2, position=position_dodge(.9))+ 
  geom_text(aes(label=Tukey), position = position_dodge(0.9), size = 4, 
            vjust=-0.9, hjust=-0.9, colour = "Black")+
  scale_x_discrete(name="BCS levels (%)")+
  scale_y_continuous(breaks = seq(0, 45, 5), name= "Shoot biomass production (g/pot)")+
  coord_cartesian(ylim = c(0,45))+scale_fill_manual(values = c("grey80", "grey60", "grey40","grey20"))
plt1
plt1=plt1+theme(legend.key=element_blank(), 
            axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
            panel.background = element_rect(fill = 'transparent',size = 0.5, colour = "black",linetype = "solid"),
            legend.position = "none",
            axis.title.x = element_text(size = 13),
            axis.title.y = element_text(size = 13),
            axis.text.x = element_text(size = 12),
            axis.text.y = element_text(size = 12))+
            annotate("text", size=5, x=3, y=4, color= "white",label="-0.82%")+
            annotate("text", size=5, x=2, y=4, color= "white",label="-3.93%")+
            annotate("text", size=5, x=1, y=4, color= "white",label="-31.0%*")+
            annotate("text", size=4, x=4, y=45,label="P-value")+
            annotate("text",size=3, x=4, y=43,label= "PM = <.001")+
            annotate("text",size=3, x=4, y=41,  label= "BCS = <.001")+
            annotate("text",size=3, x=4, y=39,label= "P*B = 0.070")
plt1
```

### _**10- Roots biomass**_

```{r}
#model
mod10 = aov(Raizes~propagacao*saturacao, data = data2)
hist(rstandard(mod10))
shapiro.test(resid(mod10))
summary(mod10)

#means and sd
data_summary10 = group_by(data2, saturacao) %>%
  summarise(sd = sd(Raizes, na.rm = TRUE),
    Root = mean(Raizes, na.rm = TRUE))%>%
 arrange(desc(Root))
print(data_summary10)

data_summary10.1 = group_by(data2, propagacao) %>%
  summarise(sd = sd(Raizes, na.rm = TRUE),
    Root = mean(Raizes, na.rm = TRUE))%>%
 arrange(desc(Root))
print(data_summary10.1)

#tukey
tukey10 = TukeyHSD(mod10)
print(tukey10)
tukey.cld10 = multcompLetters4(mod10, tukey10)
print(tukey.cld10)

cld10 = as.data.frame.list(tukey.cld10$`saturacao`)
data_summary10$Tukey = cld10$Letters
print(data_summary10)

#plot
plt2=ggplot(data_summary10, aes(x=as.factor(saturacao), y=Root, fill=saturacao))+
  geom_col(position = "dodge")+
  geom_errorbar(aes(ymin=Root-sd, ymax=Root+sd), width=.2, position=position_dodge(.9))+ 
  geom_text(aes(label=Tukey), position = position_dodge(0.9), size = 4, 
            vjust=-0.9, hjust=-0.9, colour = "Black")+
  scale_x_discrete(name="BCS levels (%)")+
  scale_y_continuous(breaks = seq(0, 45, 5), name= "Root biomass production (g/pot)")+
  coord_cartesian(ylim = c(0,45))+scale_fill_manual(values = c("grey80", "grey60", "grey40","grey20"))
plt2
plt2=plt2+theme(legend.key=element_blank(), 
            axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
            panel.background = element_rect(fill = 'transparent',size = 0.5, colour = "black",linetype = "solid"),
            legend.position = "none", 
            axis.title.x = element_text(size = 13),
            axis.title.y = element_text(size = 13),
            axis.text.x = element_text(size = 12),
            axis.text.y = element_text(size = 12))+
            annotate("text", size=5, x=3, y=4, color= "white",label="-0.65%")+
            annotate("text", size=5, x=2, y=4, color= "white",label="-21.7%")+
            annotate("text", size=5, x=1, y=4, color= "white",label="-44.9%*")+
            annotate("text", size=4, x=4, y=45,label="P-value")+
            annotate("text",size=3, x=4, y=43,label= "PM = 0.014")+
            annotate("text",size=3, x=4, y=41,  label= "BCS = 0.026")+
            annotate("text",size=3, x=4, y=39,label= "P*B = 0.323")
plt2

library(cowplot)
side.by.side = plot_grid(plt1,plt2,labels = c("A", "B"), ncol = 1, nrow = 2, align = "v")
save_plot("shoot and root.pdf",side.by.side, ncol = 1, nrow = 2) 
```

### _**11- Branches**_

```{r}
#Outlier
boxplot(data2$Perfilhos)
#model
mod11 = aov(Perfilhos~propagacao*saturacao, data = data2)
hist(rstandard(mod11))
shapiro.test(rstandard(mod11))
#Tukey
medias11=emmeans(mod11,~ propagacao)
medias11.1=emmeans(mod11,~ saturacao)
summary(medias11)
summary(medias11.1)
summary(mod11)

tukey11 = TukeyHSD(mod11)
print(tukey11)
tukey.cld11 = multcompLetters4(mod11, tukey11)
print(tukey.cld11)
```

### _**12- Leaves**_

```{r}
#Outlier
boxplot(data2$Folha)
#model
mod12 = aov(Folha~propagacao*saturacao, data = data2)
hist(rstandard(mod12))
shapiro.test(rstandard(mod12))
#Tukey
medias12=emmeans(mod12,~ propagacao)
medias12.1=emmeans(mod12,~ saturacao)
summary(medias12)
summary(medias12.1)
summary(mod12)
```
### _**13- Stems**_

```{r}
#Outlier
boxplot(data2$Caule)
#model
mod13 = aov(Caule~propagacao*saturacao, data = data2)
hist(rstandard(mod13))
shapiro.test(rstandard(mod13))
#Tukey
medias13=emmeans(mod13,~ propagacao)
medias13.1=emmeans(mod13,~ saturacao)
summary(medias13)
summary(medias13.1)
summary(mod13)

tukey13 = TukeyHSD(mod13)
print(tukey13)
tukey.cld13 = multcompLetters4(mod13, tukey13)
print(tukey.cld13)
```

### _**14- Dead material**_

```{r}
#Outlier
boxplot(data2$Senescente)
#model
mod14 = aov(Senescente^0.8~propagacao*saturacao, data = data2)
mod14.1 = aov(Senescente~propagacao*saturacao, data = data2)
hist(rstandard(mod14))
shapiro.test(rstandard(mod14))
#Tukey
medias14=emmeans(mod14.1,~ propagacao)
medias14.1=emmeans(mod14.1,~ saturacao)
summary(medias14)
summary(medias14.1)
summary(mod14)

tukey14 = TukeyHSD(mod14)
print(tukey14)
tukey.cld14 = multcompLetters4(mod14, tukey14)
print(tukey.cld14)
```

# **BROMATOLOGICAL ANALYSIS**

### _**15- Dried matter**_

```{r}
#Outlier
boxplot(data2$DM105)
#model
mod15 = aov(DM105~propagacao*saturacao, data = data2)
hist(rstandard(mod15))
shapiro.test(rstandard(mod15))
#Tukey
medias15=emmeans(mod15,~ propagacao)
medias15.1=emmeans(mod15,~ saturacao)
summary(medias15)
summary(medias15.1)
summary(mod15)

tukey15 = TukeyHSD(mod15)
print(tukey15)
tukey.cld15 = multcompLetters4(mod15, tukey15)
print(tukey.cld15)
```

### _**16- Ash**_

```{r}
#Outlier
boxplot(data2$MM)
#model
mod16 = aov(MM~propagacao*saturacao, data = data2)
mod16.1 = aov(MM^0.9~propagacao*saturacao, data = data2)
hist(rstandard(mod16.1))
shapiro.test(rstandard(mod16.1))
#Tukey
medias16=emmeans(mod16,~ propagacao)
medias16.1=emmeans(mod16,~ saturacao)
summary(medias16)
summary(medias16.1)
summary(mod16.1)

tukey16 = TukeyHSD(mod16.1)
print(tukey16)
tukey.cld16 = multcompLetters4(mod16.1, tukey16)
print(tukey.cld16)
```

### _**17- Crude protein**_

```{r}
#Outlier
boxplot(data2$PB)
#model
mod17 = aov(PB~propagacao*saturacao, data = data2)
hist(rstandard(mod17))
shapiro.test(rstandard(mod17))
#Tukey
medias17=emmeans(mod17,~ propagacao)
medias17.1=emmeans(mod17,~ saturacao)
summary(medias17)
summary(medias17.1)
summary(mod17)

tukey17 = TukeyHSD(mod17)
print(tukey17)
tukey.cld17 = multcompLetters4(mod17, tukey17)
print(tukey.cld17)
```
### _**18- Ether extract**_

```{r}
#Outlier
boxplot(data2$EE)

#model
mod18 = aov(EE~propagacao*saturacao, data = data2)
hist(rstandard(mod18))
shapiro.test(rstandard(mod18))

#Tukey
medias18=emmeans(mod18,~ propagacao)
medias18.1=emmeans(mod18,~ saturacao)
summary(medias18)
summary(medias18.1)
summary(mod18)

tukey18 = TukeyHSD(mod18)
print(tukey18)
tukey.cld18 = multcompLetters4(mod18, tukey18)
print(tukey.cld18)
```

### _**12- aNDFom**_

```{r}
#Outlier
boxplot(data2$aFDNom)

#model
mod18 = aov(aFDNom~propagacao*saturacao, data = data2)
hist(rstandard(mod18))
shapiro.test(rstandard(mod18))

#Tukey 
medias18=emmeans(mod18,~ propagacao)
medias18.1=emmeans(mod18,~ saturacao)
summary(medias18)
summary(medias18.1)
summary(mod18)

tukey18 = TukeyHSD(mod18)
print(tukey18)
tukey.cld18 = multcompLetters4(mod18, tukey18)
print(tukey.cld18)
```

### _**19- ADFom**_

```{r}
#Outlier
boxplot(data2$aFDAom)

#model
mod19 = aov(aFDAom~propagacao*saturacao, data = data2)
hist(rstandard(mod19))
shapiro.test(rstandard(mod19))

#Tukey
medias19=emmeans(mod19,~ propagacao)
medias19.1=emmeans(mod19,~ saturacao)
summary(medias19)
summary(medias19.1)
summary(mod19)

tukey19 = TukeyHSD(mod19)
print(tukey19)
tukey.cld19 = multcompLetters4(mod19, tukey19)
print(tukey.cld19)
```

### _**20- aLDAom**_

```{r}
#Outlier
boxplot(data2$aLDAom)

#model
mod20 = aov(aLDAom~propagacao*saturacao, data = data2)
hist(rstandard(mod20))
shapiro.test(rstandard(mod20))

#Tukey 
medias20=emmeans(mod20,~ propagacao)
medias20.1=emmeans(mod20,~ saturacao)
summary(medias20)
summary(medias20.1)
summary(mod20)

tukey20 = TukeyHSD(mod20)
print(tukey20)
tukey.cld20 = multcompLetters4(mod20, tukey20)
print(tukey.cld20)
```
# **CHEMICAL ANALYSIS**

### _**21- Fosforo**_

```{r}
#Outlier
boxplot(data2$Fósforo)
#model
mod21 = aov(Fósforo~propagacao*saturacao, data = data2)
hist(rstandard(mod21))
shapiro.test(rstandard(mod21))

#Tukey
medias21=emmeans(mod21,~ propagacao)
medias21.1=emmeans(mod21,~ saturacao)
summary(medias21)
summary(medias21.1)
summary(mod21)

tukey21 = TukeyHSD(mod21)
print(tukey21)
tukey.cld21 = multcompLetters4(mod21, tukey21)
print(tukey.cld21)
```

### _**22- Potassio**_

```{r}
#Outlier
boxplot(data2$Potássio)
#model
mod22 = aov(Potássio~propagacao*saturacao, data = data2)
hist(rstandard(mod22))
shapiro.test(rstandard(mod22))

#Tukey
medias22=emmeans(mod22,~ propagacao)
medias22.1=emmeans(mod22,~ saturacao)
summary(medias22)
summary(medias22.1)
summary(mod22)

tukey22 = TukeyHSD(mod22)
print(tukey22)
tukey.cld22 = multcompLetters4(mod22, tukey22)
print(tukey.cld22)
```
### _**23- Calcio**_

```{r}
#Outlier
boxplot(data2$Cálcio)

#model
mod23 = aov(Cálcio~propagacao*saturacao, data = data2)
hist(rstandard(mod23))
shapiro.test(rstandard(mod23))

#Tukey 
medias23=emmeans(mod23,~ propagacao)
medias23.1=emmeans(mod23,~ saturacao)
summary(medias23)
summary(medias23.1)
summary(mod23)

tukey23 = TukeyHSD(mod23)
print(tukey23)
tukey.cld23 = multcompLetters4(mod23, tukey23)
print(tukey.cld23)
```

### _**24- Magnesio**_

```{r}
#Outlier
boxplot(data2$Magnésio)
#model
mod24 = aov(Magnésio~propagacao*saturacao, data = data2)
hist(rstandard(mod24))
shapiro.test(rstandard(mod24))

#Tukey
medias24=emmeans(mod24,~ propagacao)
medias24.1=emmeans(mod24,~ saturacao)
summary(medias24)
summary(medias24.1)
summary(mod24)

tukey24 = TukeyHSD(mod24)
print(tukey24)
tukey.cld24 = multcompLetters4(mod24, tukey24)
print(tukey.cld24)
```

### _**25- Enxofre**_

```{r}
#Outlier
boxplot(data2$Enxofre)
#model
mod25 = aov(Enxofre~propagacao*saturacao, data = data2)
hist(rstandard(mod25))
shapiro.test(rstandard(mod25))

#Tukey
medias25=emmeans(mod25,~ propagacao)
medias25.1=emmeans(mod25,~ saturacao)
summary(medias25)
summary(medias25.1)
summary(mod25)

tukey25 = TukeyHSD(mod25)
print(tukey25)
tukey.cld25 = multcompLetters4(mod25, tukey25)
print(tukey.cld25)
```

### _**26- Boro**_

```{r}
#Outlier
boxplot(data2$Boro)
#model
mod26 = aov(Boro~propagacao*saturacao, data = data2)
hist(rstandard(mod26))
shapiro.test(rstandard(mod26))

#Tukey 
medias26=emmeans(mod26,~ propagacao)
medias26.1=emmeans(mod26,~ saturacao)
summary(medias26)
summary(medias26.1)
summary(mod26)

tukey26 = TukeyHSD(mod26)
print(tukey26)
tukey.cld26 = multcompLetters4(mod26, tukey26)
print(tukey.cld26)
```
### _**27- Cobre**_

```{r}
#Outlier
boxplot(data2$Cobre)

#model
mod27 = aov(Cobre~propagacao*saturacao, data = data2)
mod27.1 = aov(Cobre^0.5~propagacao*saturacao, data = data2)
hist(rstandard(mod27))
shapiro.test(rstandard(mod27.1))

#Tukey
medias27=emmeans(mod27,~ propagacao)
medias27.1=emmeans(mod27,~ saturacao)
summary(medias27)
summary(medias27.1)
summary(mod27.1)

tukey27 = TukeyHSD(mod27.1)
print(tukey27)
tukey.cld27 = multcompLetters4(mod27.1, tukey27)
print(tukey.cld27)
```
### _**28- Ferro**_

```{r}
#Outlier
boxplot(data2$Ferro)

#model
mod28 = aov(Ferro~propagacao*saturacao, data = data2)
hist(rstandard(mod28))
shapiro.test(rstandard(mod28))

#Tukey 
medias28=emmeans(mod28,~ propagacao)
medias28.1=emmeans(mod28,~ saturacao)
summary(medias28)
summary(medias28.1)
summary(mod28)

tukey28 = TukeyHSD(mod28)
print(tukey28)
tukey.cld28 = multcompLetters4(mod28, tukey28)
print(tukey.cld28)
```

### _**29- Manganês**_

```{r}
#Outlier
boxplot(data2$Manganês)

#model
mod29 = aov(Manganês~propagacao*saturacao, data = data2)
hist(rstandard(mod29))
shapiro.test(rstandard(mod29))

#Tukey 
medias29=emmeans(mod29,~ propagacao)
medias29.1=emmeans(mod29,~ saturacao)
summary(medias29)
summary(medias29.1)
summary(mod29)

tukey29 = TukeyHSD(mod29)
print(tukey29)
tukey.cld29 = multcompLetters4(mod29, tukey29)
print(tukey.cld29)
```

### _**30- Zinco**_

```{r}
#Outlier
boxplot(data2$Zinco)

#model
mod30 = aov(Zinco~propagacao*saturacao, data = data2)
hist(rstandard(mod30))
shapiro.test(rstandard(mod30))

#Tukey
medias30=emmeans(mod30,~ propagacao)
medias30.1=emmeans(mod30,~ saturacao)
summary(medias30)
summary(medias30.1)
summary(mod30)

tukey30 = TukeyHSD(mod30)
print(tukey30)
tukey.cld30 = multcompLetters4(mod30, tukey30)
print(tukey.cld30)
```

### _**31- Nitrogênio**_

```{r}
#Outlier
boxplot(data2$Nitrogênio)

#model
mod31 = aov(Nitrogênio~propagacao*saturacao, data = data2)
hist(rstandard(mod31))
shapiro.test(rstandard(mod31))

#Tukey
medias31=emmeans(mod31,~ propagacao)
medias31.1=emmeans(mod31,~ saturacao)
summary(medias31)
summary(medias31.1)
summary(mod31)

tukey31 = TukeyHSD(mod31)
print(tukey31)
tukey.cld31 = multcompLetters4(mod31, tukey31)
print(tukey.cld31)
```

### _**31.1- Aluminio**_

```{r}
#Outlier
boxplot(data2$Al.1)

#model
mod31.1 = aov(Al.1~propagacao*saturacao, data = data2)
hist(rstandard(mod31.1))
shapiro.test(rstandard(mod31.1))

#Tukey
medias31.1=emmeans(mod31.1,~ propagacao)
medias31.1.1=emmeans(mod31.1,~ saturacao)
summary(medias31.1)
summary(medias31.1.1)
summary(mod31.1)

tukey31.1 = TukeyHSD(mod31.1)
print(tukey31.1)
tukey.cld31.1 = multcompLetters4(mod31.1, tukey31.1)
print(tukey.cld31.1)
```

### _**32- Nutrient accumulation**_

### _**DATA**_

```{r}
library(tidyr)

citation("tidyr")

data2$ac.N=data2$MF.accumulation*data2$Nitrogênio/1000
data2$ac.P=data2$MF.accumulation*data2$Fósforo/1000
data2$ac.K=data2$MF.accumulation*data2$Potássio/1000
data2$ac.Ca=data2$MF.accumulation*data2$Cálcio/1000
data2$ac.Mg=data2$MF.accumulation*data2$Magnésio/1000
data2$ac.S=data2$MF.accumulation*data2$Enxofre/1000
data2$ac.B=data2$MF.accumulation*data2$Boro/1000
data2$ac.Cu=data2$MF.accumulation*data2$Cobre/1000
data2$ac.Fe=data2$MF.accumulation*data2$Ferro/1000
data2$ac.Mn=data2$MF.accumulation*data2$Manganês/1000
data2$ac.Zn=data2$MF.accumulation*data2$Zinco/1000
data2$ac.Al=data2$MF.accumulation*data2$Al.1/1000

ac.Macro=gather(data2[,-c(5:56)],variables,value,
                   ac.N,ac.P,ac.K,ac.Ca,ac.Mg,ac.S)
View(ac.Macro)
ac.Micro=gather(data2[,-c(5:56)],variables,value,
                   ac.B,ac.Cu,ac.Fe,ac.Mn,ac.Zn)
View(ac.Micro)
```

### _**MACRO - Anova**_

```{r}
#MACRO
mod32.1 = aov(ac.N~propagacao*saturacao, data = data2)
summary(mod32.1)
medias32.1=emmeans(mod32.1,~ propagacao)
medias32.2=emmeans(mod32.1,~ saturacao)
summary(medias32.1)
summary(medias32.2)

mod33.1 = aov(ac.P~propagacao*saturacao, data = data2)
summary(mod33.1)
medias33.1=emmeans(mod33.1,~ propagacao)
medias33.2=emmeans(mod33.1,~ saturacao)
summary(medias33.1)
summary(medias33.2)

mod34.1 = aov(ac.K~propagacao*saturacao, data = data2)
summary(mod34.1)
medias34.1=emmeans(mod34.1,~ propagacao)
medias34.2=emmeans(mod34.1,~ saturacao)
summary(medias34.1)
summary(medias34.2)

mod35.1 = aov(ac.Ca~propagacao*saturacao, data = data2)
summary(mod35.1)
medias35.1=emmeans(mod35.1,~ propagacao)
medias35.2=emmeans(mod35.1,~ saturacao)
summary(medias35.1)
summary(medias35.2)
tukey35.1 = TukeyHSD(mod35.1)
tukey.cld35.1 = multcompLetters4(mod35.1, tukey35.1)
print(tukey.cld35.1)

mod36.1 = aov(ac.Mg~propagacao*saturacao, data = data2)
summary(mod36.1)
medias36.1=emmeans(mod36.1,~ propagacao)
medias36.2=emmeans(mod36.1,~ saturacao)
summary(medias36.1)
summary(medias36.2)
tukey36.1 = TukeyHSD(mod36.1)
tukey.cld36.1 = multcompLetters4(mod36.1, tukey36.1)
print(tukey.cld36.1)

mod37.1 = aov(ac.S~propagacao*saturacao, data = data2)
summary(mod37.1)
medias37.1=emmeans(mod37.1,~ propagacao)
medias37.2=emmeans(mod37.1,~ saturacao)
summary(medias37.1)
summary(medias37.2)
```


### _**MICRO - Anova**_

```{r}
mod38.1 = aov(ac.B~propagacao*saturacao, data = data2)
summary(mod38.1)
medias38.1=emmeans(mod38.1,~ propagacao)
medias38.2=emmeans(mod38.1,~ saturacao)
summary(medias38.1)
summary(medias38.2)


mod39.1 = aov(ac.Cu~propagacao*saturacao, data = data2)
summary(mod39.1)
medias39.1=emmeans(mod39.1,~ propagacao)
medias39.2=emmeans(mod39.1,~ saturacao)
summary(medias39.1)
summary(medias39.2)
tukey39.1 = TukeyHSD(mod39.1)
tukey.cld39.1 = multcompLetters4(mod39.1, tukey39.1)
print(tukey.cld39.1)

mod40.1 = aov(ac.Fe~propagacao*saturacao, data = data2)
summary(mod40.1)
medias40.1=emmeans(mod40.1,~ propagacao)
medias40.2=emmeans(mod40.1,~ saturacao)
summary(medias40.1)
summary(medias40.2)


mod41.1 = aov(ac.Mn~propagacao*saturacao, data = data2)
summary(mod41.1)
medias41.1=emmeans(mod41.1,~ propagacao)
medias41.2=emmeans(mod41.1,~ saturacao)
summary(medias41.1)
summary(medias41.2)

mod42.1 = aov(ac.Zn~propagacao*saturacao, data = data2)
summary(mod42.1)
medias42.1=emmeans(mod42.1,~ propagacao)
medias42.2=emmeans(mod42.1,~ saturacao)
summary(medias42.1)
summary(medias42.2)

tukey42.1 = TukeyHSD(mod42.1)
tukey.cld42.1 = multcompLetters4(mod42.1, tukey42.1)
print(tukey.cld42.1)

mod43.1 = aov(ac.Al~propagacao*saturacao, data = data2)
summary(mod43.1)
medias43.1=emmeans(mod43.1,~ propagacao)
medias43.2=emmeans(mod43.1,~ saturacao)
summary(medias43.1)
summary(medias43.2)

tukey43.1 = TukeyHSD(mod43.1)
tukey.cld43.1 = multcompLetters4(mod43.1, tukey43.1)
print(tukey.cld43.1)
```


## **PLOTS**

### _**33- V%, m% and pH**_

```{r}
library(ggplot2)
p1=ggplot(data2, aes(x=as.numeric(saturacao)))+
  geom_line(aes(y=V.,color="V (%)"), stat="summary",fun="mean", size=1, linetype = 1)+
  geom_line(aes(y=m.,color="m (%)"), stat="summary",fun="mean", size=1, linetype = 7)+
  geom_line(aes(y=pH*8,color="pH"), stat="summary",fun="mean", size=1, linetype = 1)+
  geom_line(aes(y=Al*8,color="Al+3"), stat="summary",fun="mean", size=1, linetype = 1)
p1 
p1=p1+scale_x_discrete(name ="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
  scale_y_continuous(breaks = seq(0, 70, 5), name= "V (%); m (%)", sec.axis = sec_axis(~./8, name="pH; Al+3 (mmolc dm3)",    breaks = seq(0, 9, 0.5)))+
  scale_colour_manual(" ", values=c("gold4", "firebrick3","steelblue","blue4"))+
  coord_cartesian(ylim = c(0,70))
p1
p1=p1+theme(legend.key=element_blank(),legend.title=element_blank(),legend.box="h", axis.line = element_line(colour = "black", size = 1, linetype = "solid"),panel.background = element_rect(fill = "transparent"),legend.background = element_rect(fill = "transparent", size=0.5, linetype="solid",colour ="black"), legend.position = c(0.1, 0.80))
p1
library(cowplot)
save_plot("V.pdf",p1, ncol = 1, nrow = 1) 
```

### _**35- Branches evolution**_

```{r}
cut1=biomass[-c(33:96),]

cut2=biomass[-c(1:32,65:96),]
cut3=biomass[-c(1:64,69,71,72),]

mod35.1 = aov(Perfilhos^0.8~propagacao*saturacao, data = cut1)
hist(rstandard(mod35.1))
shapiro.test(rstandard(mod35.1))
summary(mod35.1)
mod35.2 = aov(Perfilhos~propagacao*saturacao, data = cut2)
hist(rstandard(mod35.2))
shapiro.test(rstandard(mod35.2))
summary(mod35.2)
mod35.3 = aov(Perfilhos~propagacao*saturacao, data = cut3)
hist(rstandard(mod35.3))
shapiro.test(rstandard(mod35.3))
summary(mod35.3)

library(dplyr)
data_summary = group_by(biomass[-c(69,71,72),], propagacao, Cycle) %>%
  summarise(mean=mean(Perfilhos), 
            sd=sd(Perfilhos))
print(data_summary)



#Plot
library(ggplot2)

p3=ggplot(data_summary, aes(x=as.numeric(Cycle),y=mean, group=propagacao))+ 
  geom_line(aes(linetype=propagacao))+
  geom_point(aes(shape=propagacao), size= 3)+
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), position = position_dodge(0.15), width = 0.2)
p3
p3.1=p3+scale_shape_manual(values = c(1, 15), name= "Propagation",
                           labels = c("Stem cutting", "Seeds"))+
  scale_linetype_manual(values = c("dashed","solid"), name= "Propagation",
                           labels = c("Stem cutting", "Seeds"))+
  scale_x_discrete(name="", limits=c("Cut 1", "Cut 2", "Cut 3"))+
  scale_y_continuous(name="Number of branches (n)",breaks=seq(0,46,3))+
  coord_cartesian(ylim = c(0,46))
p3.1
p3.2=p3.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
  panel.background = element_rect(fill = "white", colour = "black"),
  legend.background = element_rect(fill = "transparent",
  size=0.5, linetype="solid",colour ="black"),axis.title.x=element_blank(),
  legend.position = c(0.2, 0.8),
            axis.title.y = element_text(size = 14),
            axis.text.x = element_text(size = 12),
            axis.text.y = element_text(size = 12))+
  annotate("text",size=5,color="black", x=1, y=15, label="***")+
  annotate("text",size=5,color="black", x=2, y=22.5,label="***")+
  annotate("text",size=5,color="black", x=3, y=42.5, label= "***")
p3.2 
library(cowplot)
save_plot("Branches.pdf",p3.2, ncol = 1, nrow = 1)
```

### _**36- SPAD**_

```{r}
#model
mod26 = lm(SPAD~poly(saturacao,degree = 1), data = data2[-10,])
hist(rstandard(mod26))
shapiro.test(resid(mod26))
summary(mod26)

#Tukey

library(ggplot2)
p1=ggplot(data2[-10,], aes(x=as.numeric(saturacao), y=SPAD))+
  geom_point(shape=19, color='black',stat="summary",fun="mean")+ 
  geom_smooth(color='black', method = "gam", formula = y ~ poly(x, 1), se=F)
p1 
p1=p1+scale_x_discrete(name ="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
  scale_y_continuous(breaks = seq(27, 33, 1), name= "SPAD index")+
  coord_cartesian(ylim = c(27,33))
p1
p1=p1+theme(legend.key=element_blank(),
            legend.title=element_blank(),
            axis.line = element_line(colour = "black",size = 0.5, linetype = "solid"),
            panel.background = element_rect(fill = "white", colour = "black"),
            axis.title.x = element_text(size = 14),
            axis.title.y = element_text(size = 14),
            axis.text.x = element_text(size = 12),
            axis.text.y = element_text(size = 12))+
  annotate(geom="text", x=2.5, y=32,label=expression(paste("P-value = 0.089;   y = - 4.7115x + 29.6062  ", R^2, "= 0.1139")), size=5, color="black")
p1
library(cowplot)
save_plot("SPAD.pdf",p1, ncol = 1, nrow = 1) 
```

### _**37- Macro e micro accumulation**_

```{r}
#MACRO
View(data2)
mod32.1 = aov(ac.N~propagacao*saturacao, data = data2)
summary(mod32.1)
medias28=emmeans(mod28,~ propagacao)
medias28.1=emmeans(mod28,~ saturacao)
summary(medias28)
summary(medias28.1)
summary(mod28)


mod32.2 = aov(ac.P~propagacao*saturacao, data = data2)
summary(mod32.2)
mod32.3 = aov(ac.K~propagacao*saturacao, data = data2)
summary(mod32.3)
mod32.4 = aov(ac.Ca~propagacao*saturacao, data = data2)
summary(mod32.4)
tukey32.4 = TukeyHSD(mod32.4)
tukey.cld32.4 = multcompLetters4(mod32.4, tukey32.4)
print(tukey.cld32.4)
mod32.5 = aov(ac.Mg~propagacao*saturacao, data = data2)
summary(mod32.5)
tukey32.5 = TukeyHSD(mod32.5)
tukey.cld32.5 = multcompLetters4(mod32.5, tukey32.5)
print(tukey.cld32.5)

mod32.6 = aov(ac.S~propagacao*saturacao, data = data2)
summary(mod32.6)

library(ggplot2)

p37=ggplot(ac.Macro, aes(x=as.numeric(saturacao),y=value, color=variables))+ 
  geom_line(stat="summary",fun="mean")+
  geom_point(stat="summary",fun="mean")
p37
p37.1=p37+scale_colour_manual("Composition:",values=c("ac.N" ="green4","ac.P"="gray40","ac.K"="red4",
  "ac.Ca"="darkblue","ac.Mg"="cyan2","ac.S"="gold"), 
  labels = c("ac.N" ="N -  P value = 0.578","ac.P" ="P -  P value = 0.053","ac.K" ="K -   P value = 0.137","ac.Ca" ="Ca - P value = <0.001","ac.Mg" ="Mg - P value = <0.001","ac.S" ="S -  P value = 0.065"))+scale_x_discrete(name="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
  scale_y_continuous(name="Macronutrients accumulation (g/plant)",breaks=seq(0,2.25,0.25))+
  coord_cartesian(ylim = c(0,2.25))
p37.1
p37.2=p37.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
                  panel.background = element_rect(fill = "white", colour = "black"),
                  legend.background = element_rect(fill = "transparent",size=0.5,linetype="solid",colour ="black"),
                  legend.key = element_rect(fill = "white", colour = "black"),
                  legend.justification = "top",
                  axis.title.x = element_text(size = 11),
                  axis.title.y = element_text(size = 11),
                  axis.text.x = element_text(size = 12),
                  axis.text.y = element_text(size = 12))+
   annotate("text", size=3, x=1, y=0.65,label="c")+
   annotate("text",size=3, x=2, y=1.1,label= "b")+
   annotate("text",size=3, x=3, y=1.3,label= "ab")+
   annotate("text",size=3, x=4, y=1.5,label= "a")+
   annotate("text", size=3, x=1, y=0.30,label="b")+
   annotate("text",size=3, x=2, y=0.37,label= "a")+
   annotate("text",size=3, x=3, y=0.41, label= "a")+
   annotate("text",size=3, x=4, y=0.44,label= "a")
p37.2
#MICRO
mod32.7 = aov(ac.B~propagacao*saturacao, data = data2)
summary(mod32.7)
mod32.8 = aov(ac.Cu~propagacao*saturacao, data = data2)
summary(mod32.8)
tukey32.8 = TukeyHSD(mod32.8)
tukey.cld32.8 = multcompLetters4(mod32.8, tukey32.8)
print(tukey.cld32.8)
mod32.9 = aov(ac.Fe~propagacao*saturacao, data = data2)
summary(mod32.9)
mod32.10 = aov(ac.Mn~propagacao*saturacao, data = data2)
summary(mod32.10)
mod32.11 = aov(ac.Zn~propagacao*saturacao, data = data2)
summary(mod32.11)
tukey32.11 = TukeyHSD(mod32.11)
tukey.cld32.11 = multcompLetters4(mod32.11, tukey32.11)
print(tukey.cld32.11)


p38=ggplot(ac.Micro, aes(x=as.numeric(saturacao),y=value, color=variables))+ 
  geom_line(stat="summary",fun="mean")+
  geom_point(stat="summary",fun="mean")
p38
p38.1=p38+scale_colour_manual("Composition:",values=c("ac.B" ="darkblue","ac.Cu"="tan4","ac.Fe"="grey0",  "ac.Mn"="maroon4","ac.Zn"="cyan4"),labels = c("ac.B"="B - P value = 0.058","ac.Cu"="Cu - P value = 0.015","ac.Fe" ="Fe - P value = 0.185","ac.Mn"="Mn - P value = 0.634","ac.Zn" ="Zn - P value = 0.021"))+
  scale_x_discrete(name="Base saturation levels (%)",limits=c("19", "39", "52","63"))+
  scale_y_continuous(name="Micronutrients accumulation (mg/plant)",breaks=seq(0,25,5))+
  coord_cartesian(ylim = c(0,25))
p38.1
p38.2=p38.1+theme(axis.line = element_line(colour = "black", size = 0.7, linetype = "solid"),
                  panel.background = element_rect(fill = "white", colour = "black"),
                  legend.background = element_rect(fill = "transparent",size=0.5,linetype="solid",colour ="black"),
                  legend.key = element_rect(fill = "white", colour = "black"),
                  legend.justification = "top",
                  axis.title.x = element_text(size = 11),
                  axis.title.y = element_text(size = 11),
                  axis.text.x = element_text(size = 12),
                  axis.text.y = element_text(size = 12))+
  annotate("text", size=3, x=1, y=13.5,label="a")+
  annotate("text",size=3, x=2, y=13.5,label= "a")+
  annotate("text",size=3, x=3, y=11,  label= "ab")+
  annotate("text",size=3, x=4, y=9,label= "b")+
  annotate("text", size=3, x=1, y=1.3,label="b")+
  annotate("text",size=3, x=2, y=1.35,label= "a")+
  annotate("text",size=3, x=3, y=1.4,  label= "ab")+
  annotate("text",size=3, x=4, y=1.3,label= "b")
p38.2

#Save plots
library(cowplot)
side.by.side <- plot_grid(p37.2,p38.2, 
                          labels = c("A", "B"),
                          ncol = 1, nrow =2, align = "V")
side.by.side
save_plot("acumulativo.pdf", side.by.side, ncol = 1, nrow = 2)
```
```{r}
citation()
```



https://stackoverflow.com/questions/63301270/how-to-draw-both-positive-mirror-bar-graph-in-r







