1 Packages
library(car)
library(emmeans)
library(multcompView)
2 DATA
database=read.csv("D:/Armazenamento/8 - DATA R/Rumenflow/rumenflow2.csv")
str(database)
'data.frame': 80 obs. of 11 variables:
$ fermentador: int 1 4 3 2 2 1 4 3 3 2 ...
$ periodo : int 1 2 3 4 1 2 3 4 1 2 ...
$ tempo : int 0 0 0 0 0 0 0 0 0 0 ...
$ tratamento : chr "T1" "T1" "T1" "T1" ...
$ efluente : num NA NA NA NA NA NA NA NA NA NA ...
$ gas_total : num NA NA NA NA NA NA NA NA NA NA ...
$ ch4_effic : num NA NA NA NA NA NA NA NA NA NA ...
$ ch4_24h : num NA NA NA NA NA NA NA NA NA NA ...
$ ch4_48h : num NA NA NA NA NA NA NA NA NA NA ...
$ dmo : num NA NA NA NA NA NA NA NA NA NA ...
$ n_nh3 : num 0.0592 0.0349 0.031 0.0428 0.0632 ...
database$fermentador=as.factor(database$fermentador)
database$periodo=as.factor(database$periodo)
database$tempo=as.factor(database$tempo)
database$tratamento=as.factor(database$tratamento)
View(database)
3 Fermentador
names(database)
[1] "fermentador" "periodo" "tempo" "tratamento" "efluente" "gas_total" "ch4_effic"
[8] "ch4_24h" "ch4_48h" "dmo" "n_nh3"
# Variáveis a serem analisadas
variables = c("efluente","gas_total","ch4_effic","ch4_24h","ch4_48h","dmo", "n_nh3")
variables
[1] "efluente" "gas_total" "ch4_effic" "ch4_24h" "ch4_48h" "dmo" "n_nh3"
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ fermentador")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ fermentador")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ fermentador"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ fermentador)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 28166 9389 0.55 0.65
Residuals 60 1023997 17067
16 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 274 32.7 60 209 340
2 289 32.7 60 224 355
3 316 32.7 60 251 381
4 327 32.7 60 262 393
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 14.8750 -107.17765 136.9277 0.9883369
3-1 41.5625 -80.49015 163.6152 0.8049179
4-1 52.9375 -69.11515 174.9902 0.6626877
3-2 26.6875 -95.36515 148.7402 0.9383596
4-2 38.0625 -83.99015 160.1152 0.8428459
4-3 11.3750 -110.67765 133.4277 0.9946960
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 208575 69525 0.484 0.695
Residuals 60 8624536 143742
16 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 1508 94.8 60 1319 1698
2 1476 94.8 60 1286 1666
3 1511 94.8 60 1321 1700
4 1370 94.8 60 1181 1560
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -32.4375 -386.6515 321.7765 0.9949640
3-1 2.1875 -352.0265 356.4015 0.9999984
4-1 -138.0625 -492.2765 216.1515 0.7326237
3-2 34.6250 -319.5890 388.8390 0.9938944
4-2 -105.6250 -459.8390 248.5890 0.8596229
4-3 -140.2500 -494.4640 213.9640 0.7230441
Letras do Tukey:
$fermentador
$fermentador$Letters
3 1 2 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
1 TRUE
2 TRUE
4 TRUE
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 4.06 1.353 1.167 0.34
Residuals 28 32.48 1.160
48 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 5.76 0.381 28 4.98 6.54
2 5.52 0.381 28 4.74 6.30
3 5.38 0.381 28 4.60 6.17
4 4.79 0.381 28 4.01 5.57
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.23750 -1.707803 1.2328028 0.9707978
3-1 -0.37500 -1.845303 1.0953028 0.8976076
4-1 -0.96625 -2.436553 0.5040528 0.2971349
3-2 -0.13750 -1.607803 1.3328028 0.9940257
4-2 -0.72875 -2.199053 0.7415528 0.5380507
4-3 -0.59125 -2.061553 0.8790528 0.6936859
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.00659 0.002196 0.762 0.525
Residuals 28 0.08069 0.002882
48 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 0.146 0.019 28 0.1073 0.185
2 0.131 0.019 28 0.0920 0.170
3 0.124 0.019 28 0.0849 0.163
4 0.106 0.019 28 0.0674 0.145
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.0153125 -0.08859921 0.05797421 0.9400438
3-1 -0.0224250 -0.09571171 0.05086171 0.8371607
4-1 -0.0399250 -0.11321171 0.03336171 0.4581960
3-2 -0.0071125 -0.08039921 0.06617421 0.9933367
4-2 -0.0246125 -0.09789921 0.04867421 0.7960282
4-3 -0.0175000 -0.09078671 0.05578671 0.9139706
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.04519 0.015063 3.04 0.0388 *
Residuals 44 0.21798 0.004954
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 0.314 0.0203 44 0.273 0.355
2 0.301 0.0203 44 0.260 0.342
3 0.294 0.0203 44 0.253 0.335
4 0.234 0.0203 44 0.193 0.275
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.013315833 -0.09003800 0.063406334 0.9666155
3-1 -0.019982500 -0.09670467 0.056739667 0.8982971
4-1 -0.079982500 -0.15670467 -0.003260333 0.0381130
3-2 -0.006666667 -0.08338883 0.070055501 0.9955356
4-2 -0.066666667 -0.14338883 0.010055501 0.1089177
4-3 -0.060000000 -0.13672217 0.016722167 0.1729159
Letras do Tukey:
$fermentador
1 2 3 4
"a" "ab" "ab" "b"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 2776 925.3 0.302 0.824
Residuals 28 85839 3065.7
48 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 560 19.6 28 520 600
2 580 19.6 28 540 620
3 580 19.6 28 540 620
4 584 19.6 28 544 625
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 19.301000 -56.28583 94.88783 0.8972926
3-1 19.838050 -55.74878 95.42488 0.8896671
4-1 24.094575 -51.49225 99.68140 0.8200163
3-2 0.537050 -75.04978 76.12388 0.9999973
4-2 4.793575 -70.79325 80.38040 0.9981096
4-3 4.256525 -71.33030 79.84335 0.9986730
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.0005 0.000173 0.008 0.999
Residuals 44 0.9539 0.021678
32 observations deleted due to missingness
Médias (emmeans):
fermentador emmean SE df lower.CL upper.CL
1 0.128 0.0425 44 0.0423 0.214
2 0.128 0.0425 44 0.0426 0.214
3 0.135 0.0425 44 0.0496 0.221
4 0.134 0.0425 44 0.0483 0.220
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 0.0002483333 -0.1602428 0.1607395 1.0000000
3-1 0.0072995833 -0.1531916 0.1677907 0.9993497
4-1 0.0059537500 -0.1545374 0.1664449 0.9996465
3-2 0.0070512500 -0.1534399 0.1675424 0.9994136
4-2 0.0057054167 -0.1547857 0.1661966 0.9996888
4-3 -0.0013458333 -0.1618370 0.1591453 0.9999959
Letras do Tukey:
$fermentador
$fermentador$Letters
3 4 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
4 TRUE
2 TRUE
1 TRUE
4 Periodo
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ periodo")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ periodo")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ periodo"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ periodo)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 167360 55787 3.783 0.0149 *
Residuals 60 884803 14747
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 359 30.4 60 298 419
2 329 30.4 60 268 389
3 221 30.4 60 161 282
4 298 30.4 60 238 359
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -30.1875 -143.64187 83.266872 0.8954627
3-1 -137.5000 -250.95437 -24.045628 0.0114059
4-1 -60.4375 -173.89187 53.016872 0.4995954
3-2 -107.3125 -220.76687 6.141872 0.0701973
4-2 -30.2500 -143.70437 83.204372 0.8948928
4-3 77.0625 -36.39187 190.516872 0.2858508
Letras do Tukey:
$periodo
1 2 4 3
"a" "ab" "ab" "b"
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 1035268 345089 2.655 0.0565 .
Residuals 60 7797843 129964
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 1512 90.1 60 1331 1692
2 1487 90.1 60 1307 1667
3 1260 90.1 60 1080 1440
4 1606 90.1 60 1426 1787
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -24.6875 -361.4976 312.1226 0.9973962
3-1 -251.6875 -588.4976 85.1226 0.2090126
4-1 94.8125 -241.9976 431.6226 0.8789048
3-2 -227.0000 -563.8101 109.8101 0.2924601
4-2 119.5000 -217.3101 456.3101 0.7848090
4-3 346.5000 9.6899 683.3101 0.0414717
Letras do Tukey:
$periodo
4 1 2 3
"a" "ab" "ab" "b"
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3.15 1.049 0.88 0.463
Residuals 28 33.39 1.193
48 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 5.31 0.386 28 4.52 6.10
2 5.86 0.386 28 5.07 6.65
3 4.99 0.386 28 4.20 5.78
4 5.31 0.386 28 4.52 6.10
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 5.537500e-01 -0.9370642 2.0445642 0.7425920
3-1 -3.175000e-01 -1.8083142 1.1733142 0.9368263
4-1 -8.881784e-16 -1.4908142 1.4908142 1.0000000
3-2 -8.712500e-01 -2.3620642 0.6195642 0.3972977
4-2 -5.537500e-01 -2.0445642 0.9370642 0.7425920
4-3 3.175000e-01 -1.1733142 1.8083142 0.9368263
Letras do Tukey:
$periodo
$periodo$Letters
2 1 4 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
2 TRUE
1 TRUE
4 TRUE
3 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.02021 0.006735 2.811 0.0576 .
Residuals 28 0.06708 0.002396
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 0.1208 0.0173 28 0.0853 0.156
2 0.1200 0.0173 28 0.0846 0.155
3 0.0988 0.0173 28 0.0633 0.134
4 0.1675 0.0173 28 0.1321 0.203
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -0.0007875 -0.067604992 0.06602999 0.9999877
3-1 -0.0220375 -0.088854992 0.04477999 0.8046243
4-1 0.0467125 -0.020104992 0.11352999 0.2473722
3-2 -0.0212500 -0.088067492 0.04556749 0.8210238
4-2 0.0475000 -0.019317492 0.11431749 0.2344835
4-3 0.0687500 0.001932508 0.13556749 0.0419618
Letras do Tukey:
$periodo
4 1 2 3
"a" "ab" "ab" "b"
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.09002 0.030007 7.625 0.000328 ***
Residuals 44 0.17315 0.003935
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 0.277 0.0181 44 0.241 0.314
2 0.291 0.0181 44 0.254 0.327
3 0.227 0.0181 44 0.190 0.263
4 0.348 0.0181 44 0.312 0.385
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 0.01335083 -0.055028017 0.081729684 0.9535168
3-1 -0.05081583 -0.119194684 0.017563017 0.2094358
4-1 0.07085083 0.002471983 0.139229684 0.0397113
3-2 -0.06416667 -0.132545517 0.004212184 0.0729263
4-2 0.05750000 -0.010878850 0.125878850 0.1270633
4-3 0.12166667 0.053287816 0.190045517 0.0001252
Letras do Tukey:
$periodo
4 2 1 3
"a" "ab" "b" "b"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3430 1143 0.376 0.771
Residuals 28 85185 3042
48 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 573 19.5 28 533 613
2 585 19.5 28 545 625
3 586 19.5 28 546 626
4 561 19.5 28 521 601
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 12.7127750 -62.58548 88.01103 0.9668984
3-1 13.1073875 -62.19086 88.40564 0.9639152
4-1 -11.9710875 -87.26934 63.32716 0.9720897
3-2 0.3946125 -74.90364 75.69286 0.9999989
4-2 -24.6838625 -99.98211 50.61439 0.8074500
4-3 -25.0784750 -100.37673 50.21978 0.8000187
Letras do Tukey:
$periodo
$periodo$Letters
3 2 1 4
"a" "a" "a" "a"
$periodo$LetterMatrix
a
3 TRUE
2 TRUE
1 TRUE
4 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.2134 0.07114 4.224 0.0104 *
Residuals 44 0.7410 0.01684
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo emmean SE df lower.CL upper.CL
1 0.2461 0.0375 44 0.1706 0.322
2 0.0869 0.0375 44 0.0114 0.162
3 0.0870 0.0375 44 0.0115 0.162
4 0.1056 0.0375 44 0.0301 0.181
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -1.592404e-01 -0.3006923 -0.0177885533 0.0218530
3-1 -1.591483e-01 -0.3006002 -0.0176964699 0.0219509
4-1 -1.405546e-01 -0.2820064 0.0008972801 0.0520282
3-2 9.208333e-05 -0.1413598 0.1415439467 1.0000000
4-2 1.868583e-02 -0.1227660 0.1601376967 0.9847473
4-3 1.859375e-02 -0.1228581 0.1600456134 0.9849644
Letras do Tukey:
$periodo
1 4 3 2
"a" "ab" "b" "b"
5 Tempo
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ tempo")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ tempo")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ tempo"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ tempo)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 3 349201 116400 9.935 2.07e-05 ***
Residuals 60 702962 11716
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
24 299 27.1 60 244 353
48 190 27.1 60 136 244
72 396 27.1 60 342 450
96 322 27.1 60 268 377
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -108.7500 -209.876247 -7.623753 0.0303408
72-24 97.3125 -3.813747 198.438747 0.0634442
96-24 23.8125 -77.313747 124.938747 0.9245584
72-48 206.0625 104.936253 307.188747 0.0000075
96-48 132.5625 31.436253 233.688747 0.0053235
96-72 -73.5000 -174.626247 27.626247 0.2304342
Letras do Tukey:
$tempo
72 96 24 48
"a" "a" "a" "b"
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 3 3330824 1110275 12.11 2.68e-06 ***
Residuals 60 5502288 91705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
24 1692 75.7 60 1541 1844
48 1367 75.7 60 1216 1519
72 1139 75.7 60 988 1290
96 1667 75.7 60 1515 1818
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -325.0000 -607.92381 -42.07619 0.0181433
72-24 -553.3750 -836.29881 -270.45119 0.0000167
96-24 -25.6875 -308.61131 257.23631 0.9950901
72-48 -228.3750 -511.29881 54.54881 0.1544725
96-48 299.3125 16.38869 582.23631 0.0341548
96-72 527.6875 244.76369 810.61131 0.0000400
Letras do Tukey:
$tempo
24 96 48 72
"a" "a" "b" "b"
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 5.874 5.874 5.746 0.023 *
Residuals 30 30.666 1.022
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
72 4.94 0.253 30 4.42 5.45
96 5.79 0.253 30 5.28 6.31
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
96-72 0.856875 0.1268528 1.586897 0.0229551
Letras do Tukey:
$tempo
96 72
"a" "b"
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 0.03768 0.03768 22.79 4.41e-05 ***
Residuals 30 0.04960 0.00165
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
72 0.0924 0.0102 30 0.0717 0.113
96 0.1611 0.0102 30 0.1403 0.182
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
96-72 0.06863125 0.03927159 0.09799091 4.41e-05
Letras do Tukey:
$tempo
96 72
"a" "b"
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 2 0.0000 0.000001 0 1
Residuals 45 0.2632 0.005848
32 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
48 0.286 0.0191 45 0.247 0.324
72 0.286 0.0191 45 0.247 0.325
96 0.286 0.0191 45 0.247 0.324
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
72-48 0.0003775 -0.06515092 0.06590592 0.9998925
96-48 0.0002325 -0.06529592 0.06576092 0.9999592
96-72 -0.0001450 -0.06567342 0.06538342 0.9999841
Letras do Tukey:
$tempo
$tempo$Letters
72 96 48
"a" "a" "a"
$tempo$LetterMatrix
a
72 TRUE
96 TRUE
48 TRUE
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 42080 42080 27.13 1.29e-05 ***
Residuals 30 46535 1551
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
24 612 9.85 30 592 633
48 540 9.85 30 520 560
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -72.52585 -100.9639 -44.08784 1.29e-05
Letras do Tukey:
$tempo
24 48
"a" "b"
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 2 0.5514 0.27568 30.78 3.77e-09 ***
Residuals 45 0.4030 0.00896
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
tempo emmean SE df lower.CL upper.CL
0 0.0411 0.0237 45 -0.00658 0.0887
72 0.0711 0.0237 45 0.02347 0.1188
96 0.2820 0.0237 45 0.23430 0.3296
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
72-0 0.0300475 -0.05104291 0.1111379 0.6443216
96-0 0.2408847 0.15979428 0.3219751 0.0000000
96-72 0.2108372 0.12974678 0.2919276 0.0000003
Letras do Tukey:
$tempo
96 72 0
"a" "b" "b"
6 Tratamento
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ tratamento")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ tratamento")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ tratamento"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ tratamento)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 4359 1453 0.083 0.969
Residuals 60 1047804 17463
16 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 304 33 60 238 370
T2 289 33 60 223 355
T3 302 33 60 236 369
T4 312 33 60 245 378
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 -15.5625 -139.0258 107.9008 0.9871327
T3-T1 -1.8125 -125.2758 121.6508 0.9999788
T4-T1 7.2500 -116.2133 130.7133 0.9986541
T3-T2 13.7500 -109.7133 137.2133 0.9910406
T4-T2 22.8125 -100.6508 146.2758 0.9614211
T4-T3 9.0625 -114.4008 132.5258 0.9973852
Letras do Tukey:
$tratamento
$tratamento$Letters
T4 T1 T3 T2
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T4 TRUE
T1 TRUE
T3 TRUE
T2 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 157390 52463 0.363 0.78
Residuals 60 8675722 144595
16 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 1407 95.1 60 1216 1597
T2 1500 95.1 60 1309 1690
T3 1529 95.1 60 1338 1719
T4 1430 95.1 60 1240 1621
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 92.9375 -262.3261 448.2011 0.9000324
T3-T1 122.0625 -233.2011 477.3261 0.8006794
T4-T1 23.9375 -331.3261 379.2011 0.9979728
T3-T2 29.1250 -326.1386 384.3886 0.9963703
T4-T2 -69.0000 -424.2636 286.2636 0.9556218
T4-T3 -98.1250 -453.3886 257.1386 0.8847309
Letras do Tukey:
$tratamento
$tratamento$Letters
T3 T2 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T3 TRUE
T2 TRUE
T4 TRUE
T1 TRUE
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 5.676 1.892 1.716 0.186
Residuals 28 30.864 1.102
48 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 5.64 0.371 28 4.88 6.40
T2 5.74 0.371 28 4.98 6.50
T3 5.42 0.371 28 4.66 6.19
T4 4.66 0.371 28 3.90 5.42
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 0.10125 -1.332031 1.5345309 0.9973950
T3-T1 -0.21125 -1.644531 1.2220309 0.9775205
T4-T1 -0.97375 -2.407031 0.4595309 0.2700202
T3-T2 -0.31250 -1.745781 1.1207809 0.9326526
T4-T2 -1.07500 -2.508281 0.3582809 0.1951372
T4-T3 -0.76250 -2.195781 0.6707809 0.4785872
Letras do Tukey:
$tratamento
$tratamento$Letters
T2 T1 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T1 TRUE
T3 TRUE
T4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 0.00599 0.001997 0.688 0.567
Residuals 28 0.08129 0.002903
48 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 0.112 0.0191 28 0.0734 0.151
T2 0.141 0.0191 28 0.1018 0.180
T3 0.140 0.0191 28 0.1010 0.179
T4 0.114 0.0191 28 0.0747 0.153
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 0.0284375 -0.04511958 0.10199458 0.7186936
T3-T1 0.0275750 -0.04598208 0.10113208 0.7372256
T4-T1 0.0013250 -0.07223208 0.07488208 0.9999562
T3-T2 -0.0008625 -0.07441958 0.07269458 0.9999879
T4-T2 -0.0271125 -0.10066958 0.04644458 0.7470164
T4-T3 -0.0262500 -0.09980708 0.04730708 0.7649688
Letras do Tukey:
$tratamento
$tratamento$Letters
T2 T3 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T4 TRUE
T1 TRUE
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 0.09707 0.03236 8.571 0.000136 ***
Residuals 44 0.16610 0.00378
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 0.247 0.0177 44 0.212 0.283
T2 0.336 0.0177 44 0.300 0.372
T3 0.325 0.0177 44 0.289 0.361
T4 0.235 0.0177 44 0.199 0.271
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 0.08835083 0.02137769 0.15532398 0.0053853
T3-T1 0.07751750 0.01054435 0.14449065 0.0175449
T4-T1 -0.01248250 -0.07945565 0.05449065 0.9591849
T3-T2 -0.01083333 -0.07780648 0.05613981 0.9726731
T4-T2 -0.10083333 -0.16780648 -0.03386019 0.0012444
T4-T3 -0.09000000 -0.15697315 -0.02302685 0.0044635
Letras do Tukey:
$tratamento
T2 T3 T1 T4
"a" "a" "b" "b"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 16169 5390 2.083 0.125
Residuals 28 72446 2587
48 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 609 18 28 572 646
T2 581 18 28 545 618
T3 568 18 28 531 604
T4 547 18 28 510 584
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 -27.35585 -96.79607 42.084370 0.7069244
T3-T1 -41.22081 -110.66103 28.219408 0.3837372
T4-T1 -61.86686 -131.30708 7.573358 0.0939294
T3-T2 -13.86496 -83.30518 55.575258 0.9470827
T4-T2 -34.51101 -103.95123 34.929208 0.5358241
T4-T3 -20.64605 -90.08627 48.794170 0.8483683
Letras do Tukey:
$tratamento
$tratamento$Letters
T1 T2 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T1 TRUE
T2 TRUE
T3 TRUE
T4 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tratamento 3 0.0022 0.000719 0.033 0.992
Residuals 44 0.9522 0.021641
32 observations deleted due to missingness
Médias (emmeans):
tratamento emmean SE df lower.CL upper.CL
T1 0.130 0.0425 44 0.0445 0.216
T2 0.138 0.0425 44 0.0521 0.223
T3 0.137 0.0425 44 0.0512 0.222
T4 0.121 0.0425 44 0.0354 0.207
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tratamento
diff lwr upr p adj
T2-T1 0.0075937500 -0.1527596 0.1679471 0.9992663
T3-T1 0.0066620833 -0.1536913 0.1670154 0.9995039
T4-T1 -0.0091691667 -0.1695225 0.1511842 0.9987122
T3-T2 -0.0009316667 -0.1612850 0.1594217 0.9999986
T4-T2 -0.0167629167 -0.1771163 0.1435904 0.9923007
T4-T3 -0.0158312500 -0.1761846 0.1445221 0.9934927
Letras do Tukey:
$tratamento
$tratamento$Letters
T2 T3 T1 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T1 TRUE
T4 TRUE
7 Fermentador x Periodo
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ fermentador * periodo")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * periodo")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
Warning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertos
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ fermentador * periodo"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ fermentador * periodo)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 28166 9389 0.533 0.6615
periodo 3 167360 55787 3.170 0.0326 *
fermentador:periodo 9 11923 1325 0.075 0.9999
Residuals 48 844715 17598
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 342 66.3 48 209.1 476
2 1 318 66.3 48 184.1 451
3 1 373 66.3 48 239.9 507
4 1 402 66.3 48 268.4 535
1 2 282 66.3 48 149.1 416
2 2 332 66.3 48 198.9 466
3 2 336 66.3 48 202.6 469
4 2 364 66.3 48 230.1 497
1 3 192 66.3 48 59.1 326
2 3 228 66.3 48 94.9 362
3 3 232 66.3 48 98.6 365
4 3 232 66.3 48 98.9 366
1 4 280 66.3 48 146.6 413
2 4 279 66.3 48 145.6 412
3 4 322 66.3 48 189.1 456
4 4 312 66.3 48 178.4 445
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 14.8750 -109.94811 139.6981 0.9888188
3-1 41.5625 -83.26061 166.3856 0.8120200
4-1 52.9375 -71.88561 177.7606 0.6738213
3-2 26.6875 -98.13561 151.5106 0.9408072
4-2 38.0625 -86.76061 162.8856 0.8487086
4-3 11.3750 -113.44811 136.1981 0.9949168
$periodo
diff lwr upr p adj
2-1 -30.1875 -155.01061 94.63561 0.9172509
3-1 -137.5000 -262.32311 -12.67689 0.0256337
4-1 -60.4375 -185.26061 64.38561 0.5745558
3-2 -107.3125 -232.13561 17.51061 0.1151264
4-2 -30.2500 -155.07311 94.57311 0.9167904
4-3 77.0625 -47.76061 201.88561 0.3648104
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 -25.00 -363.8834 313.8834 1.0000000
3:1-1:1 30.75 -308.1334 369.6334 1.0000000
4:1-1:1 59.25 -279.6334 398.1334 0.9999991
1:2-1:1 -60.00 -398.8834 278.8834 0.9999989
2:2-1:1 -10.25 -349.1334 328.6334 1.0000000
3:2-1:1 -6.50 -345.3834 332.3834 1.0000000
4:2-1:1 21.00 -317.8834 359.8834 1.0000000
1:3-1:1 -150.00 -488.8834 188.8834 0.9608061
2:3-1:1 -114.25 -453.1334 224.6334 0.9969474
3:3-1:1 -110.50 -449.3834 228.3834 0.9978583
4:3-1:1 -110.25 -449.1334 228.6334 0.9979098
1:4-1:1 -62.50 -401.3834 276.3834 0.9999981
2:4-1:1 -63.50 -402.3834 275.3834 0.9999976
3:4-1:1 -20.00 -358.8834 318.8834 1.0000000
4:4-1:1 -30.75 -369.6334 308.1334 1.0000000
3:1-2:1 55.75 -283.1334 394.6334 0.9999996
4:1-2:1 84.25 -254.6334 423.1334 0.9999076
1:2-2:1 -35.00 -373.8834 303.8834 1.0000000
2:2-2:1 14.75 -324.1334 353.6334 1.0000000
3:2-2:1 18.50 -320.3834 357.3834 1.0000000
4:2-2:1 46.00 -292.8834 384.8834 1.0000000
1:3-2:1 -125.00 -463.8834 213.8834 0.9923939
2:3-2:1 -89.25 -428.1334 249.6334 0.9998134
3:3-2:1 -85.50 -424.3834 253.3834 0.9998892
4:3-2:1 -85.25 -424.1334 253.6334 0.9998932
1:4-2:1 -37.50 -376.3834 301.3834 1.0000000
2:4-2:1 -38.50 -377.3834 300.3834 1.0000000
3:4-2:1 5.00 -333.8834 343.8834 1.0000000
4:4-2:1 -5.75 -344.6334 333.1334 1.0000000
4:1-3:1 28.50 -310.3834 367.3834 1.0000000
1:2-3:1 -90.75 -429.6334 248.1334 0.9997720
2:2-3:1 -41.00 -379.8834 297.8834 1.0000000
3:2-3:1 -37.25 -376.1334 301.6334 1.0000000
4:2-3:1 -9.75 -348.6334 329.1334 1.0000000
1:3-3:1 -180.75 -519.6334 158.1334 0.8505330
2:3-3:1 -145.00 -483.8834 193.8834 0.9704253
3:3-3:1 -141.25 -480.1334 197.6334 0.9763892
4:3-3:1 -141.00 -479.8834 197.8834 0.9767514
1:4-3:1 -93.25 -432.1334 245.6334 0.9996851
2:4-3:1 -94.25 -433.1334 244.6334 0.9996430
3:4-3:1 -50.75 -389.6334 288.1334 0.9999999
4:4-3:1 -61.50 -400.3834 277.3834 0.9999984
1:2-4:1 -119.25 -458.1334 219.6334 0.9952472
2:2-4:1 -69.50 -408.3834 269.3834 0.9999921
3:2-4:1 -65.75 -404.6334 273.1334 0.9999962
4:2-4:1 -38.25 -377.1334 300.6334 1.0000000
1:3-4:1 -209.25 -548.1334 129.6334 0.6723148
2:3-4:1 -173.50 -512.3834 165.3834 0.8849994
3:3-4:1 -169.75 -508.6334 169.1334 0.9007593
4:3-4:1 -169.50 -508.3834 169.3834 0.9017592
1:4-4:1 -121.75 -460.6334 217.1334 0.9941404
2:4-4:1 -122.75 -461.6334 216.1334 0.9936422
3:4-4:1 -79.25 -418.1334 259.6334 0.9999569
4:4-4:1 -90.00 -428.8834 248.8834 0.9997936
2:2-1:2 49.75 -289.1334 388.6334 0.9999999
3:2-1:2 53.50 -285.3834 392.3834 0.9999998
4:2-1:2 81.00 -257.8834 419.8834 0.9999433
1:3-1:2 -90.00 -428.8834 248.8834 0.9997936
2:3-1:2 -54.25 -393.1334 284.6334 0.9999997
3:3-1:2 -50.50 -389.3834 288.3834 0.9999999
4:3-1:2 -50.25 -389.1334 288.6334 0.9999999
1:4-1:2 -2.50 -341.3834 336.3834 1.0000000
2:4-1:2 -3.50 -342.3834 335.3834 1.0000000
3:4-1:2 40.00 -298.8834 378.8834 1.0000000
4:4-1:2 29.25 -309.6334 368.1334 1.0000000
3:2-2:2 3.75 -335.1334 342.6334 1.0000000
4:2-2:2 31.25 -307.6334 370.1334 1.0000000
1:3-2:2 -139.75 -478.6334 199.1334 0.9784992
2:3-2:2 -104.00 -442.8834 234.8834 0.9988971
3:3-2:2 -100.25 -439.1334 238.6334 0.9992709
4:3-2:2 -100.00 -438.8834 238.8834 0.9992913
1:4-2:2 -52.25 -391.1334 286.6334 0.9999998
2:4-2:2 -53.25 -392.1334 285.6334 0.9999998
3:4-2:2 -9.75 -348.6334 329.1334 1.0000000
4:4-2:2 -20.50 -359.3834 318.3834 1.0000000
4:2-3:2 27.50 -311.3834 366.3834 1.0000000
1:3-3:2 -143.50 -482.3834 195.3834 0.9729329
2:3-3:2 -107.75 -446.6334 231.1334 0.9983697
3:3-3:2 -104.00 -442.8834 234.8834 0.9988971
4:3-3:2 -103.75 -442.6334 235.1334 0.9989264
1:4-3:2 -56.00 -394.8834 282.8834 0.9999996
2:4-3:2 -57.00 -395.8834 281.8834 0.9999994
3:4-3:2 -13.50 -352.3834 325.3834 1.0000000
4:4-3:2 -24.25 -363.1334 314.6334 1.0000000
1:3-4:2 -171.00 -509.8834 167.8834 0.8956645
2:3-4:2 -135.25 -474.1334 203.6334 0.9839685
3:3-4:2 -131.50 -470.3834 207.3834 0.9876371
4:3-4:2 -131.25 -470.1334 207.6334 0.9878557
1:4-4:2 -83.50 -422.3834 255.3834 0.9999173
2:4-4:2 -84.50 -423.3834 254.3834 0.9999042
3:4-4:2 -41.00 -379.8834 297.8834 1.0000000
4:4-4:2 -51.75 -390.6334 287.1334 0.9999999
2:3-1:3 35.75 -303.1334 374.6334 1.0000000
3:3-1:3 39.50 -299.3834 378.3834 1.0000000
4:3-1:3 39.75 -299.1334 378.6334 1.0000000
1:4-1:3 87.50 -251.3834 426.3834 0.9998531
2:4-1:3 86.50 -252.3834 425.3834 0.9998723
3:4-1:3 130.00 -208.8834 468.8834 0.9889029
4:4-1:3 119.25 -219.6334 458.1334 0.9952472
3:3-2:3 3.75 -335.1334 342.6334 1.0000000
4:3-2:3 4.00 -334.8834 342.8834 1.0000000
1:4-2:3 51.75 -287.1334 390.6334 0.9999999
2:4-2:3 50.75 -288.1334 389.6334 0.9999999
3:4-2:3 94.25 -244.6334 433.1334 0.9996430
4:4-2:3 83.50 -255.3834 422.3834 0.9999173
4:3-3:3 0.25 -338.6334 339.1334 1.0000000
1:4-3:3 48.00 -290.8834 386.8834 0.9999999
2:4-3:3 47.00 -291.8834 385.8834 1.0000000
3:4-3:3 90.50 -248.3834 429.3834 0.9997794
4:4-3:3 79.75 -259.1334 418.6334 0.9999534
1:4-4:3 47.75 -291.1334 386.6334 1.0000000
2:4-4:3 46.75 -292.1334 385.6334 1.0000000
3:4-4:3 90.25 -248.6334 429.1334 0.9997866
4:4-4:3 79.50 -259.3834 418.3834 0.9999552
2:4-1:4 -1.00 -339.8834 337.8834 1.0000000
3:4-1:4 42.50 -296.3834 381.3834 1.0000000
4:4-1:4 31.75 -307.1334 370.6334 1.0000000
3:4-2:4 43.50 -295.3834 382.3834 1.0000000
4:4-2:4 32.75 -306.1334 371.6334 1.0000000
4:4-3:4 -10.75 -349.6334 328.1334 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$periodo
1 2 4 3
"a" "ab" "ab" "b"
$`fermentador:periodo`
$`fermentador:periodo`$Letters
4:1 3:1 4:2 1:1 3:2 2:2 3:4 2:1 4:4 1:2 1:4 2:4 4:3 3:3 2:3 1:3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
4:1 TRUE
3:1 TRUE
4:2 TRUE
1:1 TRUE
3:2 TRUE
2:2 TRUE
3:4 TRUE
2:1 TRUE
4:4 TRUE
1:2 TRUE
1:4 TRUE
2:4 TRUE
4:3 TRUE
3:3 TRUE
2:3 TRUE
1:3 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 208575 69525 0.456 0.7145
periodo 3 1035268 345089 2.262 0.0932 .
fermentador:periodo 9 265946 29550 0.194 0.9938
Residuals 48 7323322 152569
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 1549 195 48 1157 1942
2 1 1508 195 48 1115 1901
3 1 1584 195 48 1191 1977
4 1 1406 195 48 1013 1798
1 2 1579 195 48 1187 1972
2 2 1543 195 48 1151 1936
3 2 1536 195 48 1143 1929
4 2 1290 195 48 897 1682
1 3 1389 195 48 996 1781
2 3 1264 195 48 872 1657
3 3 1199 195 48 807 1592
4 3 1188 195 48 795 1580
1 4 1516 195 48 1124 1909
2 4 1588 195 48 1196 1981
3 4 1723 195 48 1330 2116
4 4 1598 195 48 1206 1991
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -32.4375 -399.9686 335.0936 0.9953756
3-1 2.1875 -365.3436 369.7186 0.9999986
4-1 -138.0625 -505.5936 229.4686 0.7502508
3-2 34.6250 -332.9061 402.1561 0.9943926
4-2 -105.6250 -473.1561 261.9061 0.8698176
4-3 -140.2500 -507.7811 227.2811 0.7411645
$periodo
diff lwr upr p adj
2-1 -24.6875 -392.21859 342.8436 0.9979425
3-1 -251.6875 -619.21859 115.8436 0.2755320
4-1 94.8125 -272.71859 462.3436 0.9017234
3-2 -227.0000 -594.53109 140.5311 0.3644373
4-2 119.5000 -248.03109 487.0311 0.8226068
4-3 346.5000 -21.03109 714.0311 0.0711939
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 -41.25 -1039.0634 956.5634 1.0000000
3:1-1:1 34.75 -963.0634 1032.5634 1.0000000
4:1-1:1 -143.75 -1141.5634 854.0634 0.9999999
1:2-1:1 30.00 -967.8134 1027.8134 1.0000000
2:2-1:1 -6.00 -1003.8134 991.8134 1.0000000
3:2-1:1 -13.25 -1011.0634 984.5634 1.0000000
4:2-1:1 -259.75 -1257.5634 738.0634 0.9998378
1:3-1:1 -160.50 -1158.3134 837.3134 0.9999997
2:3-1:1 -285.00 -1282.8134 712.8134 0.9995128
3:3-1:1 -350.00 -1347.8134 647.8134 0.9953996
4:3-1:1 -361.50 -1359.3134 636.3134 0.9936294
1:4-1:1 -33.00 -1030.8134 964.8134 1.0000000
2:4-1:1 39.00 -958.8134 1036.8134 1.0000000
3:4-1:1 173.75 -824.0634 1171.5634 0.9999991
4:4-1:1 49.25 -948.5634 1047.0634 1.0000000
3:1-2:1 76.00 -921.8134 1073.8134 1.0000000
4:1-2:1 -102.50 -1100.3134 895.3134 1.0000000
1:2-2:1 71.25 -926.5634 1069.0634 1.0000000
2:2-2:1 35.25 -962.5634 1033.0634 1.0000000
3:2-2:1 28.00 -969.8134 1025.8134 1.0000000
4:2-2:1 -218.50 -1216.3134 779.3134 0.9999814
1:3-2:1 -119.25 -1117.0634 878.5634 1.0000000
2:3-2:1 -243.75 -1241.5634 754.0634 0.9999256
3:3-2:1 -308.75 -1306.5634 689.0634 0.9987914
4:3-2:1 -320.25 -1318.0634 677.5634 0.9981946
1:4-2:1 8.25 -989.5634 1006.0634 1.0000000
2:4-2:1 80.25 -917.5634 1078.0634 1.0000000
3:4-2:1 215.00 -782.8134 1212.8134 0.9999849
4:4-2:1 90.50 -907.3134 1088.3134 1.0000000
4:1-3:1 -178.50 -1176.3134 819.3134 0.9999987
1:2-3:1 -4.75 -1002.5634 993.0634 1.0000000
2:2-3:1 -40.75 -1038.5634 957.0634 1.0000000
3:2-3:1 -48.00 -1045.8134 949.8134 1.0000000
4:2-3:1 -294.50 -1292.3134 703.3134 0.9992897
1:3-3:1 -195.25 -1193.0634 802.5634 0.9999957
2:3-3:1 -319.75 -1317.5634 678.0634 0.9982249
3:3-3:1 -384.75 -1382.5634 613.0634 0.9883500
4:3-3:1 -396.25 -1394.0634 601.5634 0.9846832
1:4-3:1 -67.75 -1065.5634 930.0634 1.0000000
2:4-3:1 4.25 -993.5634 1002.0634 1.0000000
3:4-3:1 139.00 -858.8134 1136.8134 1.0000000
4:4-3:1 14.50 -983.3134 1012.3134 1.0000000
1:2-4:1 173.75 -824.0634 1171.5634 0.9999991
2:2-4:1 137.75 -860.0634 1135.5634 1.0000000
3:2-4:1 130.50 -867.3134 1128.3134 1.0000000
4:2-4:1 -116.00 -1113.8134 881.8134 1.0000000
1:3-4:1 -16.75 -1014.5634 981.0634 1.0000000
2:3-4:1 -141.25 -1139.0634 856.5634 0.9999999
3:3-4:1 -206.25 -1204.0634 791.5634 0.9999912
4:3-4:1 -217.75 -1215.5634 780.0634 0.9999822
1:4-4:1 110.75 -887.0634 1108.5634 1.0000000
2:4-4:1 182.75 -815.0634 1180.5634 0.9999982
3:4-4:1 317.50 -680.3134 1315.3134 0.9983563
4:4-4:1 193.00 -804.8134 1190.8134 0.9999963
2:2-1:2 -36.00 -1033.8134 961.8134 1.0000000
3:2-1:2 -43.25 -1041.0634 954.5634 1.0000000
4:2-1:2 -289.75 -1287.5634 708.0634 0.9994104
1:3-1:2 -190.50 -1188.3134 807.3134 0.9999969
2:3-1:2 -315.00 -1312.8134 682.8134 0.9984924
3:3-1:2 -380.00 -1377.8134 617.8134 0.9896435
4:3-1:2 -391.50 -1389.3134 606.3134 0.9862942
1:4-1:2 -63.00 -1060.8134 934.8134 1.0000000
2:4-1:2 9.00 -988.8134 1006.8134 1.0000000
3:4-1:2 143.75 -854.0634 1141.5634 0.9999999
4:4-1:2 19.25 -978.5634 1017.0634 1.0000000
3:2-2:2 -7.25 -1005.0634 990.5634 1.0000000
4:2-2:2 -253.75 -1251.5634 744.0634 0.9998780
1:3-2:2 -154.50 -1152.3134 843.3134 0.9999998
2:3-2:2 -279.00 -1276.8134 718.8134 0.9996198
3:3-2:2 -344.00 -1341.8134 653.8134 0.9961477
4:3-2:2 -355.50 -1353.3134 642.3134 0.9946119
1:4-2:2 -27.00 -1024.8134 970.8134 1.0000000
2:4-2:2 45.00 -952.8134 1042.8134 1.0000000
3:4-2:2 179.75 -818.0634 1177.5634 0.9999986
4:4-2:2 55.25 -942.5634 1053.0634 1.0000000
4:2-3:2 -246.50 -1244.3134 751.3134 0.9999146
1:3-3:2 -147.25 -1145.0634 850.5634 0.9999999
2:3-3:2 -271.75 -1269.5634 726.0634 0.9997213
3:3-3:2 -336.75 -1334.5634 661.0634 0.9969138
4:3-3:2 -348.25 -1346.0634 649.5634 0.9956293
1:4-3:2 -19.75 -1017.5634 978.0634 1.0000000
2:4-3:2 52.25 -945.5634 1050.0634 1.0000000
3:4-3:2 187.00 -810.8134 1184.8134 0.9999976
4:4-3:2 62.50 -935.3134 1060.3134 1.0000000
1:3-4:2 99.25 -898.5634 1097.0634 1.0000000
2:3-4:2 -25.25 -1023.0634 972.5634 1.0000000
3:3-4:2 -90.25 -1088.0634 907.5634 1.0000000
4:3-4:2 -101.75 -1099.5634 896.0634 1.0000000
1:4-4:2 226.75 -771.0634 1224.5634 0.9999700
2:4-4:2 298.75 -699.0634 1296.5634 0.9991642
3:4-4:2 433.50 -564.3134 1431.3134 0.9663855
4:4-4:2 309.00 -688.8134 1306.8134 0.9987805
2:3-1:3 -124.50 -1122.3134 873.3134 1.0000000
3:3-1:3 -189.50 -1187.3134 808.3134 0.9999971
4:3-1:3 -201.00 -1198.8134 796.8134 0.9999937
1:4-1:3 127.50 -870.3134 1125.3134 1.0000000
2:4-1:3 199.50 -798.3134 1197.3134 0.9999943
3:4-1:3 334.25 -663.5634 1332.0634 0.9971465
4:4-1:3 209.75 -788.0634 1207.5634 0.9999890
3:3-2:3 -65.00 -1062.8134 932.8134 1.0000000
4:3-2:3 -76.50 -1074.3134 921.3134 1.0000000
1:4-2:3 252.00 -745.8134 1249.8134 0.9998879
2:4-2:3 324.00 -673.8134 1321.8134 0.9979525
3:4-2:3 458.75 -539.0634 1456.5634 0.9469966
4:4-2:3 334.25 -663.5634 1332.0634 0.9971465
4:3-3:3 -11.50 -1009.3134 986.3134 1.0000000
1:4-3:3 317.00 -680.8134 1314.8134 0.9983843
2:4-3:3 389.00 -608.8134 1386.8134 0.9870866
3:4-3:3 523.75 -474.0634 1521.5634 0.8648021
4:4-3:3 399.25 -598.5634 1397.0634 0.9835916
1:4-4:3 328.50 -669.3134 1326.3134 0.9976262
2:4-4:3 400.50 -597.3134 1398.3134 0.9831192
3:4-4:3 535.25 -462.5634 1533.0634 0.8451948
4:4-4:3 410.75 -587.0634 1408.5634 0.9788345
2:4-1:4 72.00 -925.8134 1069.8134 1.0000000
3:4-1:4 206.75 -791.0634 1204.5634 0.9999909
4:4-1:4 82.25 -915.5634 1080.0634 1.0000000
3:4-2:4 134.75 -863.0634 1132.5634 1.0000000
4:4-2:4 10.25 -987.5634 1008.0634 1.0000000
4:4-3:4 -124.50 -1122.3134 873.3134 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
3 1 2 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
1 TRUE
2 TRUE
4 TRUE
$periodo
$periodo$Letters
4 1 2 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
4 TRUE
1 TRUE
2 TRUE
3 TRUE
$`fermentador:periodo`
$`fermentador:periodo`$Letters
3:4 4:4 2:4 3:1 1:2 1:1 2:2 3:2 1:4 2:1 4:1 1:3 4:2 2:3 3:3 4:3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
3:4 TRUE
4:4 TRUE
2:4 TRUE
3:1 TRUE
1:2 TRUE
1:1 TRUE
2:2 TRUE
3:2 TRUE
1:4 TRUE
2:1 TRUE
4:1 TRUE
1:3 TRUE
4:2 TRUE
2:3 TRUE
3:3 TRUE
4:3 TRUE
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 4.060 1.3535 1.385 0.283
periodo 3 3.148 1.0493 1.074 0.388
fermentador:periodo 9 13.700 1.5223 1.558 0.210
Residuals 16 15.631 0.9769
48 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 5.74 0.699 16 4.26 7.22
2 1 5.66 0.699 16 4.18 7.14
3 1 6.04 0.699 16 4.55 7.52
4 1 3.79 0.699 16 2.31 5.27
1 2 6.03 0.699 16 4.55 7.51
2 2 6.34 0.699 16 4.86 7.83
3 2 4.70 0.699 16 3.21 6.18
4 2 6.37 0.699 16 4.89 7.85
1 3 5.53 0.699 16 4.05 7.01
2 3 4.42 0.699 16 2.94 5.91
3 3 4.78 0.699 16 3.29 6.26
4 3 5.22 0.699 16 3.74 6.71
1 4 5.74 0.699 16 4.26 7.22
2 4 5.66 0.699 16 4.18 7.14
3 4 6.04 0.699 16 4.55 7.52
4 4 3.79 0.699 16 2.31 5.27
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.23750 -1.651416 1.1764159 0.9623084
3-1 -0.37500 -1.788916 1.0389159 0.8715960
4-1 -0.96625 -2.380166 0.4476659 0.2453401
3-2 -0.13750 -1.551416 1.2764159 0.9921807
4-2 -0.72875 -2.142666 0.6851659 0.4744922
4-3 -0.59125 -2.005166 0.8226659 0.6377411
$periodo
diff lwr upr p adj
2-1 5.537500e-01 -0.8601659 1.9676659 0.6826183
3-1 -3.175000e-01 -1.7314159 1.0964159 0.9166334
4-1 -8.881784e-16 -1.4139159 1.4139159 1.0000000
3-2 -8.712500e-01 -2.2851659 0.5426659 0.3258970
4-2 -5.537500e-01 -1.9676659 0.8601659 0.6826183
4-3 3.175000e-01 -1.0964159 1.7314159 0.9166334
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 -8.000000e-02 -4.037166 3.877166 1.0000000
3:1-1:1 2.950000e-01 -3.662166 4.252166 1.0000000
4:1-1:1 -1.950000e+00 -5.907166 2.007166 0.8093383
1:2-1:1 2.900000e-01 -3.667166 4.247166 1.0000000
2:2-1:1 6.050000e-01 -3.352166 4.562166 0.9999980
3:2-1:1 -1.045000e+00 -5.002166 2.912166 0.9986029
4:2-1:1 6.300000e-01 -3.327166 4.587166 0.9999966
1:3-1:1 -2.100000e-01 -4.167166 3.747166 1.0000000
2:3-1:1 -1.315000e+00 -5.272166 2.642166 0.9873784
3:3-1:1 -9.650000e-01 -4.922166 2.992166 0.9994041
4:3-1:1 -5.150000e-01 -4.472166 3.442166 0.9999998
1:4-1:1 -4.440892e-15 -3.957166 3.957166 1.0000000
2:4-1:1 -8.000000e-02 -4.037166 3.877166 1.0000000
3:4-1:1 2.950000e-01 -3.662166 4.252166 1.0000000
4:4-1:1 -1.950000e+00 -5.907166 2.007166 0.8093383
3:1-2:1 3.750000e-01 -3.582166 4.332166 1.0000000
4:1-2:1 -1.870000e+00 -5.827166 2.087166 0.8469659
1:2-2:1 3.700000e-01 -3.587166 4.327166 1.0000000
2:2-2:1 6.850000e-01 -3.272166 4.642166 0.9999899
3:2-2:1 -9.650000e-01 -4.922166 2.992166 0.9994041
4:2-2:1 7.100000e-01 -3.247166 4.667166 0.9999841
1:3-2:1 -1.300000e-01 -4.087166 3.827166 1.0000000
2:3-2:1 -1.235000e+00 -5.192166 2.722166 0.9927915
3:3-2:1 -8.850000e-01 -4.842166 3.072166 0.9997744
4:3-2:1 -4.350000e-01 -4.392166 3.522166 1.0000000
1:4-2:1 8.000000e-02 -3.877166 4.037166 1.0000000
2:4-2:1 8.881784e-16 -3.957166 3.957166 1.0000000
3:4-2:1 3.750000e-01 -3.582166 4.332166 1.0000000
4:4-2:1 -1.870000e+00 -5.827166 2.087166 0.8469659
4:1-3:1 -2.245000e+00 -6.202166 1.712166 0.6458700
1:2-3:1 -5.000000e-03 -3.962166 3.952166 1.0000000
2:2-3:1 3.100000e-01 -3.647166 4.267166 1.0000000
3:2-3:1 -1.340000e+00 -5.297166 2.617166 0.9851637
4:2-3:1 3.350000e-01 -3.622166 4.292166 1.0000000
1:3-3:1 -5.050000e-01 -4.462166 3.452166 0.9999998
2:3-3:1 -1.610000e+00 -5.567166 2.347166 0.9390952
3:3-3:1 -1.260000e+00 -5.217166 2.697166 0.9913494
4:3-3:1 -8.100000e-01 -4.767166 3.147166 0.9999201
1:4-3:1 -2.950000e-01 -4.252166 3.662166 1.0000000
2:4-3:1 -3.750000e-01 -4.332166 3.582166 1.0000000
3:4-3:1 -8.881784e-16 -3.957166 3.957166 1.0000000
4:4-3:1 -2.245000e+00 -6.202166 1.712166 0.6458700
1:2-4:1 2.240000e+00 -1.717166 6.197166 0.6488283
2:2-4:1 2.555000e+00 -1.402166 6.512166 0.4646140
3:2-4:1 9.050000e-01 -3.052166 4.862166 0.9997088
4:2-4:1 2.580000e+00 -1.377166 6.537166 0.4507708
1:3-4:1 1.740000e+00 -2.217166 5.697166 0.8991495
2:3-4:1 6.350000e-01 -3.322166 4.592166 0.9999962
3:3-4:1 9.850000e-01 -2.972166 4.942166 0.9992549
4:3-4:1 1.435000e+00 -2.522166 5.392166 0.9739693
1:4-4:1 1.950000e+00 -2.007166 5.907166 0.8093383
2:4-4:1 1.870000e+00 -2.087166 5.827166 0.8469659
3:4-4:1 2.245000e+00 -1.712166 6.202166 0.6458700
4:4-4:1 0.000000e+00 -3.957166 3.957166 1.0000000
2:2-1:2 3.150000e-01 -3.642166 4.272166 1.0000000
3:2-1:2 -1.335000e+00 -5.292166 2.622166 0.9856288
4:2-1:2 3.400000e-01 -3.617166 4.297166 1.0000000
1:3-1:2 -5.000000e-01 -4.457166 3.457166 0.9999998
2:3-1:2 -1.605000e+00 -5.562166 2.352166 0.9403834
3:3-1:2 -1.255000e+00 -5.212166 2.702166 0.9916546
4:3-1:2 -8.050000e-01 -4.762166 3.152166 0.9999258
1:4-1:2 -2.900000e-01 -4.247166 3.667166 1.0000000
2:4-1:2 -3.700000e-01 -4.327166 3.587166 1.0000000
3:4-1:2 5.000000e-03 -3.952166 3.962166 1.0000000
4:4-1:2 -2.240000e+00 -6.197166 1.717166 0.6488283
3:2-2:2 -1.650000e+00 -5.607166 2.307166 0.9281328
4:2-2:2 2.500000e-02 -3.932166 3.982166 1.0000000
1:3-2:2 -8.150000e-01 -4.772166 3.142166 0.9999140
2:3-2:2 -1.920000e+00 -5.877166 2.037166 0.8238969
3:3-2:2 -1.570000e+00 -5.527166 2.387166 0.9488982
4:3-2:2 -1.120000e+00 -5.077166 2.837166 0.9971694
1:4-2:2 -6.050000e-01 -4.562166 3.352166 0.9999980
2:4-2:2 -6.850000e-01 -4.642166 3.272166 0.9999899
3:4-2:2 -3.100000e-01 -4.267166 3.647166 1.0000000
4:4-2:2 -2.555000e+00 -6.512166 1.402166 0.4646140
4:2-3:2 1.675000e+00 -2.282166 5.632166 0.9206831
1:3-3:2 8.350000e-01 -3.122166 4.792166 0.9998853
2:3-3:2 -2.700000e-01 -4.227166 3.687166 1.0000000
3:3-3:2 8.000000e-02 -3.877166 4.037166 1.0000000
4:3-3:2 5.300000e-01 -3.427166 4.487166 0.9999997
1:4-3:2 1.045000e+00 -2.912166 5.002166 0.9986029
2:4-3:2 9.650000e-01 -2.992166 4.922166 0.9994041
3:4-3:2 1.340000e+00 -2.617166 5.297166 0.9851637
4:4-3:2 -9.050000e-01 -4.862166 3.052166 0.9997088
1:3-4:2 -8.400000e-01 -4.797166 3.117166 0.9998770
2:3-4:2 -1.945000e+00 -5.902166 2.012166 0.8118002
3:3-4:2 -1.595000e+00 -5.552166 2.362166 0.9429056
4:3-4:2 -1.145000e+00 -5.102166 2.812166 0.9964816
1:4-4:2 -6.300000e-01 -4.587166 3.327166 0.9999966
2:4-4:2 -7.100000e-01 -4.667166 3.247166 0.9999841
3:4-4:2 -3.350000e-01 -4.292166 3.622166 1.0000000
4:4-4:2 -2.580000e+00 -6.537166 1.377166 0.4507708
2:3-1:3 -1.105000e+00 -5.062166 2.852166 0.9975260
3:3-1:3 -7.550000e-01 -4.712166 3.202166 0.9999659
4:3-1:3 -3.050000e-01 -4.262166 3.652166 1.0000000
1:4-1:3 2.100000e-01 -3.747166 4.167166 1.0000000
2:4-1:3 1.300000e-01 -3.827166 4.087166 1.0000000
3:4-1:3 5.050000e-01 -3.452166 4.462166 0.9999998
4:4-1:3 -1.740000e+00 -5.697166 2.217166 0.8991495
3:3-2:3 3.500000e-01 -3.607166 4.307166 1.0000000
4:3-2:3 8.000000e-01 -3.157166 4.757166 0.9999311
1:4-2:3 1.315000e+00 -2.642166 5.272166 0.9873784
2:4-2:3 1.235000e+00 -2.722166 5.192166 0.9927915
3:4-2:3 1.610000e+00 -2.347166 5.567166 0.9390952
4:4-2:3 -6.350000e-01 -4.592166 3.322166 0.9999962
4:3-3:3 4.500000e-01 -3.507166 4.407166 1.0000000
1:4-3:3 9.650000e-01 -2.992166 4.922166 0.9994041
2:4-3:3 8.850000e-01 -3.072166 4.842166 0.9997744
3:4-3:3 1.260000e+00 -2.697166 5.217166 0.9913494
4:4-3:3 -9.850000e-01 -4.942166 2.972166 0.9992549
1:4-4:3 5.150000e-01 -3.442166 4.472166 0.9999998
2:4-4:3 4.350000e-01 -3.522166 4.392166 1.0000000
3:4-4:3 8.100000e-01 -3.147166 4.767166 0.9999201
4:4-4:3 -1.435000e+00 -5.392166 2.522166 0.9739693
2:4-1:4 -8.000000e-02 -4.037166 3.877166 1.0000000
3:4-1:4 2.950000e-01 -3.662166 4.252166 1.0000000
4:4-1:4 -1.950000e+00 -5.907166 2.007166 0.8093383
3:4-2:4 3.750000e-01 -3.582166 4.332166 1.0000000
4:4-2:4 -1.870000e+00 -5.827166 2.087166 0.8469659
4:4-3:4 -2.245000e+00 -6.202166 1.712166 0.6458700
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$periodo
$periodo$Letters
2 1 4 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
2 TRUE
1 TRUE
4 TRUE
3 TRUE
$`fermentador:periodo`
$`fermentador:periodo`$Letters
4:2 2:2 3:1 3:4 1:2 1:1 1:4 2:1 2:4 1:3 4:3 3:3 3:2 2:3 4:1 4:4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
4:2 TRUE
2:2 TRUE
3:1 TRUE
3:4 TRUE
1:2 TRUE
1:1 TRUE
1:4 TRUE
2:1 TRUE
2:4 TRUE
1:3 TRUE
4:3 TRUE
3:3 TRUE
3:2 TRUE
2:3 TRUE
4:1 TRUE
4:4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.00659 0.002196 0.710 0.560
periodo 3 0.02021 0.006735 2.177 0.131
fermentador:periodo 9 0.01099 0.001221 0.395 0.920
Residuals 16 0.04950 0.003094
48 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 0.120 0.0393 16 0.03632 0.203
2 1 0.128 0.0393 16 0.04507 0.212
3 1 0.150 0.0393 16 0.06662 0.233
4 1 0.085 0.0393 16 0.00162 0.168
1 2 0.145 0.0393 16 0.06162 0.228
2 2 0.145 0.0393 16 0.06162 0.228
3 2 0.100 0.0393 16 0.01662 0.183
4 2 0.090 0.0393 16 0.00662 0.173
1 3 0.130 0.0393 16 0.04662 0.213
2 3 0.080 0.0393 16 -0.00338 0.163
3 3 0.070 0.0393 16 -0.01338 0.153
4 3 0.115 0.0393 16 0.03162 0.198
1 4 0.190 0.0393 16 0.10662 0.273
2 4 0.170 0.0393 16 0.08662 0.253
3 4 0.175 0.0393 16 0.09162 0.258
4 4 0.135 0.0393 16 0.05162 0.218
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.0153125 -0.09488082 0.06425582 0.9450419
3-1 -0.0224250 -0.10199332 0.05714332 0.8505120
4-1 -0.0399250 -0.11949332 0.03964332 0.4966663
3-2 -0.0071125 -0.08668082 0.07245582 0.9938951
4-2 -0.0246125 -0.10418082 0.05495582 0.8126063
4-3 -0.0175000 -0.09706832 0.06206832 0.9211172
$periodo
diff lwr upr p adj
2-1 -0.0007875 -0.08035582 0.07878082 0.9999915
3-1 -0.0220375 -0.10160582 0.05753082 0.8568434
4-1 0.0467125 -0.03285582 0.12628082 0.3656643
3-2 -0.0212500 -0.10081832 0.05831832 0.8693269
4-2 0.0475000 -0.03206832 0.12706832 0.3518334
4-3 0.0687500 -0.01081832 0.14831832 0.1031126
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 8.750000e-03 -0.2139401 0.2314401 1.0000000
3:1-1:1 3.030000e-02 -0.1923901 0.2529901 0.9999996
4:1-1:1 -3.470000e-02 -0.2573901 0.1879901 0.9999974
1:2-1:1 2.530000e-02 -0.1973901 0.2479901 1.0000000
2:2-1:1 2.530000e-02 -0.1973901 0.2479901 1.0000000
3:2-1:1 -1.970000e-02 -0.2423901 0.2029901 1.0000000
4:2-1:1 -2.970000e-02 -0.2523901 0.1929901 0.9999997
1:3-1:1 1.030000e-02 -0.2123901 0.2329901 1.0000000
2:3-1:1 -3.970000e-02 -0.2623901 0.1829901 0.9999854
3:3-1:1 -4.970000e-02 -0.2723901 0.1729901 0.9997797
4:3-1:1 -4.700000e-03 -0.2273901 0.2179901 1.0000000
1:4-1:1 7.030000e-02 -0.1523901 0.2929901 0.9919968
2:4-1:1 5.030000e-02 -0.1723901 0.2729901 0.9997472
3:4-1:1 5.530000e-02 -0.1673901 0.2779901 0.9992737
4:4-1:1 1.530000e-02 -0.2073901 0.2379901 1.0000000
3:1-2:1 2.155000e-02 -0.2011401 0.2442401 1.0000000
4:1-2:1 -4.345000e-02 -0.2661401 0.1792401 0.9999552
1:2-2:1 1.655000e-02 -0.2061401 0.2392401 1.0000000
2:2-2:1 1.655000e-02 -0.2061401 0.2392401 1.0000000
3:2-2:1 -2.845000e-02 -0.2511401 0.1942401 0.9999998
4:2-2:1 -3.845000e-02 -0.2611401 0.1842401 0.9999902
1:3-2:1 1.550000e-03 -0.2211401 0.2242401 1.0000000
2:3-2:1 -4.845000e-02 -0.2711401 0.1742401 0.9998359
3:3-2:1 -5.845000e-02 -0.2811401 0.1642401 0.9986891
4:3-2:1 -1.345000e-02 -0.2361401 0.2092401 1.0000000
1:4-2:1 6.155000e-02 -0.1611401 0.2842401 0.9977686
2:4-2:1 4.155000e-02 -0.1811401 0.2642401 0.9999741
3:4-2:1 4.655000e-02 -0.1761401 0.2692401 0.9998974
4:4-2:1 6.550000e-03 -0.2161401 0.2292401 1.0000000
4:1-3:1 -6.500000e-02 -0.2876901 0.1576901 0.9961694
1:2-3:1 -5.000000e-03 -0.2276901 0.2176901 1.0000000
2:2-3:1 -5.000000e-03 -0.2276901 0.2176901 1.0000000
3:2-3:1 -5.000000e-02 -0.2726901 0.1726901 0.9997640
4:2-3:1 -6.000000e-02 -0.2826901 0.1626901 0.9982798
1:3-3:1 -2.000000e-02 -0.2426901 0.2026901 1.0000000
2:3-3:1 -7.000000e-02 -0.2926901 0.1526901 0.9923025
3:3-3:1 -8.000000e-02 -0.3026901 0.1426901 0.9758418
4:3-3:1 -3.500000e-02 -0.2576901 0.1876901 0.9999971
1:4-3:1 4.000000e-02 -0.1826901 0.2626901 0.9999839
2:4-3:1 2.000000e-02 -0.2026901 0.2426901 1.0000000
3:4-3:1 2.500000e-02 -0.1976901 0.2476901 1.0000000
4:4-3:1 -1.500000e-02 -0.2376901 0.2076901 1.0000000
1:2-4:1 6.000000e-02 -0.1626901 0.2826901 0.9982798
2:2-4:1 6.000000e-02 -0.1626901 0.2826901 0.9982798
3:2-4:1 1.500000e-02 -0.2076901 0.2376901 1.0000000
4:2-4:1 5.000000e-03 -0.2176901 0.2276901 1.0000000
1:3-4:1 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:3-4:1 -5.000000e-03 -0.2276901 0.2176901 1.0000000
3:3-4:1 -1.500000e-02 -0.2376901 0.2076901 1.0000000
4:3-4:1 3.000000e-02 -0.1926901 0.2526901 0.9999996
1:4-4:1 1.050000e-01 -0.1176901 0.3276901 0.8488176
2:4-4:1 8.500000e-02 -0.1376901 0.3076901 0.9614234
3:4-4:1 9.000000e-02 -0.1326901 0.3126901 0.9418336
4:4-4:1 5.000000e-02 -0.1726901 0.2726901 0.9997640
2:2-1:2 -5.551115e-17 -0.2226901 0.2226901 1.0000000
3:2-1:2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
4:2-1:2 -5.500000e-02 -0.2776901 0.1676901 0.9993154
1:3-1:2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
2:3-1:2 -6.500000e-02 -0.2876901 0.1576901 0.9961694
3:3-1:2 -7.500000e-02 -0.2976901 0.1476901 0.9858355
4:3-1:2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
1:4-1:2 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:4-1:2 2.500000e-02 -0.1976901 0.2476901 1.0000000
3:4-1:2 3.000000e-02 -0.1926901 0.2526901 0.9999996
4:4-1:2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
3:2-2:2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
4:2-2:2 -5.500000e-02 -0.2776901 0.1676901 0.9993154
1:3-2:2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
2:3-2:2 -6.500000e-02 -0.2876901 0.1576901 0.9961694
3:3-2:2 -7.500000e-02 -0.2976901 0.1476901 0.9858355
4:3-2:2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
1:4-2:2 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:4-2:2 2.500000e-02 -0.1976901 0.2476901 1.0000000
3:4-2:2 3.000000e-02 -0.1926901 0.2526901 0.9999996
4:4-2:2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
4:2-3:2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
1:3-3:2 3.000000e-02 -0.1926901 0.2526901 0.9999996
2:3-3:2 -2.000000e-02 -0.2426901 0.2026901 1.0000000
3:3-3:2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
4:3-3:2 1.500000e-02 -0.2076901 0.2376901 1.0000000
1:4-3:2 9.000000e-02 -0.1326901 0.3126901 0.9418336
2:4-3:2 7.000000e-02 -0.1526901 0.2926901 0.9923025
3:4-3:2 7.500000e-02 -0.1476901 0.2976901 0.9858355
4:4-3:2 3.500000e-02 -0.1876901 0.2576901 0.9999971
1:3-4:2 4.000000e-02 -0.1826901 0.2626901 0.9999839
2:3-4:2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
3:3-4:2 -2.000000e-02 -0.2426901 0.2026901 1.0000000
4:3-4:2 2.500000e-02 -0.1976901 0.2476901 1.0000000
1:4-4:2 1.000000e-01 -0.1226901 0.3226901 0.8855140
2:4-4:2 8.000000e-02 -0.1426901 0.3026901 0.9758418
3:4-4:2 8.500000e-02 -0.1376901 0.3076901 0.9614234
4:4-4:2 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:3-1:3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
3:3-1:3 -6.000000e-02 -0.2826901 0.1626901 0.9982798
4:3-1:3 -1.500000e-02 -0.2376901 0.2076901 1.0000000
1:4-1:3 6.000000e-02 -0.1626901 0.2826901 0.9982798
2:4-1:3 4.000000e-02 -0.1826901 0.2626901 0.9999839
3:4-1:3 4.500000e-02 -0.1776901 0.2676901 0.9999315
4:4-1:3 5.000000e-03 -0.2176901 0.2276901 1.0000000
3:3-2:3 -1.000000e-02 -0.2326901 0.2126901 1.0000000
4:3-2:3 3.500000e-02 -0.1876901 0.2576901 0.9999971
1:4-2:3 1.100000e-01 -0.1126901 0.3326901 0.8070205
2:4-2:3 9.000000e-02 -0.1326901 0.3126901 0.9418336
3:4-2:3 9.500000e-02 -0.1276901 0.3176901 0.9165838
4:4-2:3 5.500000e-02 -0.1676901 0.2776901 0.9993154
4:3-3:3 4.500000e-02 -0.1776901 0.2676901 0.9999315
1:4-3:3 1.200000e-01 -0.1026901 0.3426901 0.7115193
2:4-3:3 1.000000e-01 -0.1226901 0.3226901 0.8855140
3:4-3:3 1.050000e-01 -0.1176901 0.3276901 0.8488176
4:4-3:3 6.500000e-02 -0.1576901 0.2876901 0.9961694
1:4-4:3 7.500000e-02 -0.1476901 0.2976901 0.9858355
2:4-4:3 5.500000e-02 -0.1676901 0.2776901 0.9993154
3:4-4:3 6.000000e-02 -0.1626901 0.2826901 0.9982798
4:4-4:3 2.000000e-02 -0.2026901 0.2426901 1.0000000
2:4-1:4 -2.000000e-02 -0.2426901 0.2026901 1.0000000
3:4-1:4 -1.500000e-02 -0.2376901 0.2076901 1.0000000
4:4-1:4 -5.500000e-02 -0.2776901 0.1676901 0.9993154
3:4-2:4 5.000000e-03 -0.2176901 0.2276901 1.0000000
4:4-2:4 -3.500000e-02 -0.2576901 0.1876901 0.9999971
4:4-3:4 -4.000000e-02 -0.2626901 0.1826901 0.9999839
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$periodo
$periodo$Letters
4 1 2 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
4 TRUE
1 TRUE
2 TRUE
3 TRUE
$`fermentador:periodo`
$`fermentador:periodo`$Letters
1:4 3:4 2:4 3:1 1:2 2:2 4:4 1:3 2:1 1:1 4:3 3:2 4:2 4:1 2:3 3:3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
1:4 TRUE
3:4 TRUE
2:4 TRUE
3:1 TRUE
1:2 TRUE
2:2 TRUE
4:4 TRUE
1:3 TRUE
2:1 TRUE
1:1 TRUE
4:3 TRUE
3:2 TRUE
4:2 TRUE
4:1 TRUE
2:3 TRUE
3:3 TRUE
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.04519 0.015063 96.40 4.14e-16 ***
periodo 3 0.09002 0.030007 192.05 < 2e-16 ***
fermentador:periodo 9 0.12296 0.013662 87.44 < 2e-16 ***
Residuals 32 0.00500 0.000156
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 0.277 0.00722 32 0.262 0.291
2 1 0.300 0.00722 32 0.285 0.315
3 1 0.350 0.00722 32 0.335 0.365
4 1 0.183 0.00722 32 0.169 0.198
1 2 0.357 0.00722 32 0.342 0.371
2 2 0.367 0.00722 32 0.352 0.381
3 2 0.230 0.00722 32 0.215 0.245
4 2 0.210 0.00722 32 0.195 0.225
1 3 0.293 0.00722 32 0.279 0.308
2 3 0.197 0.00722 32 0.182 0.211
3 3 0.163 0.00722 32 0.149 0.178
4 3 0.253 0.00722 32 0.239 0.268
1 4 0.330 0.00722 32 0.315 0.345
2 4 0.340 0.00722 32 0.325 0.355
3 4 0.433 0.00722 32 0.419 0.448
4 4 0.290 0.00722 32 0.275 0.305
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.013315833 -0.02714185 0.0005101829 0.0624366
3-1 -0.019982500 -0.03380852 -0.0061564838 0.0023853
4-1 -0.079982500 -0.09380852 -0.0661564838 0.0000000
3-2 -0.006666667 -0.02049268 0.0071593495 0.5656958
4-2 -0.066666667 -0.08049268 -0.0528406505 0.0000000
4-3 -0.060000000 -0.07382602 -0.0461739838 0.0000000
$periodo
diff lwr upr p adj
2-1 0.01335083 -0.0004751829 0.02717685 0.0615041
3-1 -0.05081583 -0.0646418495 -0.03698982 0.0000000
4-1 0.07085083 0.0570248171 0.08467685 0.0000000
3-2 -0.06416667 -0.0779926829 -0.05034065 0.0000000
4-2 0.05750000 0.0436739838 0.07132602 0.0000000
4-3 0.12166667 0.1078406505 0.13549268 0.0000000
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 0.023403333 -0.014441776 0.061248443 0.6308596
3:1-1:1 0.073403333 0.035558224 0.111248443 0.0000038
4:1-1:1 -0.093263333 -0.131108443 -0.055418224 0.0000000
1:2-1:1 0.080070000 0.042224891 0.117915109 0.0000006
2:2-1:1 0.090070000 0.052224891 0.127915109 0.0000000
3:2-1:1 -0.046596667 -0.084441776 -0.008751557 0.0056910
4:2-1:1 -0.066596667 -0.104441776 -0.028751557 0.0000243
1:3-1:1 0.016736667 -0.021108443 0.054581776 0.9477916
2:3-1:1 -0.079930000 -0.117775109 -0.042084891 0.0000007
3:3-1:1 -0.113263333 -0.151108443 -0.075418224 0.0000000
4:3-1:1 -0.023263333 -0.061108443 0.014581776 0.6398228
1:4-1:1 0.053403333 0.015558224 0.091248443 0.0009179
2:4-1:1 0.063403333 0.025558224 0.101248443 0.0000586
3:4-1:1 0.156736667 0.118891557 0.194581776 0.0000000
4:4-1:1 0.013403333 -0.024441776 0.051248443 0.9922893
3:1-2:1 0.050000000 0.012154891 0.087845109 0.0023062
4:1-2:1 -0.116666667 -0.154511776 -0.078821557 0.0000000
1:2-2:1 0.056666667 0.018821557 0.094511776 0.0003755
2:2-2:1 0.066666667 0.028821557 0.104511776 0.0000239
3:2-2:1 -0.070000000 -0.107845109 -0.032154891 0.0000096
4:2-2:1 -0.090000000 -0.127845109 -0.052154891 0.0000000
1:3-2:1 -0.006666667 -0.044511776 0.031178443 0.9999979
2:3-2:1 -0.103333333 -0.141178443 -0.065488224 0.0000000
3:3-2:1 -0.136666667 -0.174511776 -0.098821557 0.0000000
4:3-2:1 -0.046666667 -0.084511776 -0.008821557 0.0055876
1:4-2:1 0.030000000 -0.007845109 0.067845109 0.2503711
2:4-2:1 0.040000000 0.002154891 0.077845109 0.0300852
3:4-2:1 0.133333333 0.095488224 0.171178443 0.0000000
4:4-2:1 -0.010000000 -0.047845109 0.027845109 0.9996615
4:1-3:1 -0.166666667 -0.204511776 -0.128821557 0.0000000
1:2-3:1 0.006666667 -0.031178443 0.044511776 0.9999979
2:2-3:1 0.016666667 -0.021178443 0.054511776 0.9494264
3:2-3:1 -0.120000000 -0.157845109 -0.082154891 0.0000000
4:2-3:1 -0.140000000 -0.177845109 -0.102154891 0.0000000
1:3-3:1 -0.056666667 -0.094511776 -0.018821557 0.0003755
2:3-3:1 -0.153333333 -0.191178443 -0.115488224 0.0000000
3:3-3:1 -0.186666667 -0.224511776 -0.148821557 0.0000000
4:3-3:1 -0.096666667 -0.134511776 -0.058821557 0.0000000
1:4-3:1 -0.020000000 -0.057845109 0.017845109 0.8289662
2:4-3:1 -0.010000000 -0.047845109 0.027845109 0.9996615
3:4-3:1 0.083333333 0.045488224 0.121178443 0.0000003
4:4-3:1 -0.060000000 -0.097845109 -0.022154891 0.0001499
1:2-4:1 0.173333333 0.135488224 0.211178443 0.0000000
2:2-4:1 0.183333333 0.145488224 0.221178443 0.0000000
3:2-4:1 0.046666667 0.008821557 0.084511776 0.0055876
4:2-4:1 0.026666667 -0.011178443 0.064511776 0.4243390
1:3-4:1 0.110000000 0.072154891 0.147845109 0.0000000
2:3-4:1 0.013333333 -0.024511776 0.051178443 0.9926678
3:3-4:1 -0.020000000 -0.057845109 0.017845109 0.8289662
4:3-4:1 0.070000000 0.032154891 0.107845109 0.0000096
1:4-4:1 0.146666667 0.108821557 0.184511776 0.0000000
2:4-4:1 0.156666667 0.118821557 0.194511776 0.0000000
3:4-4:1 0.250000000 0.212154891 0.287845109 0.0000000
4:4-4:1 0.106666667 0.068821557 0.144511776 0.0000000
2:2-1:2 0.010000000 -0.027845109 0.047845109 0.9996615
3:2-1:2 -0.126666667 -0.164511776 -0.088821557 0.0000000
4:2-1:2 -0.146666667 -0.184511776 -0.108821557 0.0000000
1:3-1:2 -0.063333333 -0.101178443 -0.025488224 0.0000598
2:3-1:2 -0.160000000 -0.197845109 -0.122154891 0.0000000
3:3-1:2 -0.193333333 -0.231178443 -0.155488224 0.0000000
4:3-1:2 -0.103333333 -0.141178443 -0.065488224 0.0000000
1:4-1:2 -0.026666667 -0.064511776 0.011178443 0.4243390
2:4-1:2 -0.016666667 -0.054511776 0.021178443 0.9494264
3:4-1:2 0.076666667 0.038821557 0.114511776 0.0000016
4:4-1:2 -0.066666667 -0.104511776 -0.028821557 0.0000239
3:2-2:2 -0.136666667 -0.174511776 -0.098821557 0.0000000
4:2-2:2 -0.156666667 -0.194511776 -0.118821557 0.0000000
1:3-2:2 -0.073333333 -0.111178443 -0.035488224 0.0000039
2:3-2:2 -0.170000000 -0.207845109 -0.132154891 0.0000000
3:3-2:2 -0.203333333 -0.241178443 -0.165488224 0.0000000
4:3-2:2 -0.113333333 -0.151178443 -0.075488224 0.0000000
1:4-2:2 -0.036666667 -0.074511776 0.001178443 0.0653618
2:4-2:2 -0.026666667 -0.064511776 0.011178443 0.4243390
3:4-2:2 0.066666667 0.028821557 0.104511776 0.0000239
4:4-2:2 -0.076666667 -0.114511776 -0.038821557 0.0000016
4:2-3:2 -0.020000000 -0.057845109 0.017845109 0.8289662
1:3-3:2 0.063333333 0.025488224 0.101178443 0.0000598
2:3-3:2 -0.033333333 -0.071178443 0.004511776 0.1332726
3:3-3:2 -0.066666667 -0.104511776 -0.028821557 0.0000239
4:3-3:2 0.023333333 -0.014511776 0.061178443 0.6353449
1:4-3:2 0.100000000 0.062154891 0.137845109 0.0000000
2:4-3:2 0.110000000 0.072154891 0.147845109 0.0000000
3:4-3:2 0.203333333 0.165488224 0.241178443 0.0000000
4:4-3:2 0.060000000 0.022154891 0.097845109 0.0001499
1:3-4:2 0.083333333 0.045488224 0.121178443 0.0000003
2:3-4:2 -0.013333333 -0.051178443 0.024511776 0.9926678
3:3-4:2 -0.046666667 -0.084511776 -0.008821557 0.0055876
4:3-4:2 0.043333333 0.005488224 0.081178443 0.0131979
1:4-4:2 0.120000000 0.082154891 0.157845109 0.0000000
2:4-4:2 0.130000000 0.092154891 0.167845109 0.0000000
3:4-4:2 0.223333333 0.185488224 0.261178443 0.0000000
4:4-4:2 0.080000000 0.042154891 0.117845109 0.0000007
2:3-1:3 -0.096666667 -0.134511776 -0.058821557 0.0000000
3:3-1:3 -0.130000000 -0.167845109 -0.092154891 0.0000000
4:3-1:3 -0.040000000 -0.077845109 -0.002154891 0.0300852
1:4-1:3 0.036666667 -0.001178443 0.074511776 0.0653618
2:4-1:3 0.046666667 0.008821557 0.084511776 0.0055876
3:4-1:3 0.140000000 0.102154891 0.177845109 0.0000000
4:4-1:3 -0.003333333 -0.041178443 0.034511776 1.0000000
3:3-2:3 -0.033333333 -0.071178443 0.004511776 0.1332726
4:3-2:3 0.056666667 0.018821557 0.094511776 0.0003755
1:4-2:3 0.133333333 0.095488224 0.171178443 0.0000000
2:4-2:3 0.143333333 0.105488224 0.181178443 0.0000000
3:4-2:3 0.236666667 0.198821557 0.274511776 0.0000000
4:4-2:3 0.093333333 0.055488224 0.131178443 0.0000000
4:3-3:3 0.090000000 0.052154891 0.127845109 0.0000000
1:4-3:3 0.166666667 0.128821557 0.204511776 0.0000000
2:4-3:3 0.176666667 0.138821557 0.214511776 0.0000000
3:4-3:3 0.270000000 0.232154891 0.307845109 0.0000000
4:4-3:3 0.126666667 0.088821557 0.164511776 0.0000000
1:4-4:3 0.076666667 0.038821557 0.114511776 0.0000016
2:4-4:3 0.086666667 0.048821557 0.124511776 0.0000001
3:4-4:3 0.180000000 0.142154891 0.217845109 0.0000000
4:4-4:3 0.036666667 -0.001178443 0.074511776 0.0653618
2:4-1:4 0.010000000 -0.027845109 0.047845109 0.9996615
3:4-1:4 0.103333333 0.065488224 0.141178443 0.0000000
4:4-1:4 -0.040000000 -0.077845109 -0.002154891 0.0300852
3:4-2:4 0.093333333 0.055488224 0.131178443 0.0000000
4:4-2:4 -0.050000000 -0.087845109 -0.012154891 0.0023062
4:4-3:4 -0.143333333 -0.181178443 -0.105488224 0.0000000
Letras do Tukey:
$fermentador
1 2 3 4
"a" "ab" "b" "c"
$periodo
4 2 1 3
"a" "b" "b" "c"
$`fermentador:periodo`
3:4 2:2 1:2 3:1 2:4 1:4 2:1 1:3 4:4 1:1 4:3 3:2 4:2 2:3 4:1 3:3
"a" "b" "b" "b" "b" "bc" "cd" "cd" "de" "de" "ef" "fg" "gh" "ghi" "hi" "i"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 2776 925 0.236 0.870
periodo 3 3430 1143 0.291 0.831
fermentador:periodo 9 19600 2178 0.555 0.814
Residuals 16 62809 3926
48 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 599 44.3 16 506 693
2 1 591 44.3 16 497 685
3 1 552 44.3 16 458 646
4 1 549 44.3 16 455 642
1 2 571 44.3 16 477 665
2 2 578 44.3 16 484 672
3 2 562 44.3 16 468 656
4 2 630 44.3 16 536 724
1 3 564 44.3 16 470 658
2 3 570 44.3 16 476 664
3 3 627 44.3 16 533 720
4 3 582 44.3 16 488 676
1 4 507 44.3 16 413 601
2 4 579 44.3 16 485 673
3 4 581 44.3 16 487 675
4 4 577 44.3 16 483 670
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 19.301000 -70.32643 108.92843 0.9254430
3-1 19.838050 -69.78938 109.46548 0.9197694
4-1 24.094575 -65.53286 113.72201 0.8671400
3-2 0.537050 -89.09038 90.16448 0.9999981
4-2 4.793575 -84.83386 94.42101 0.9986679
4-3 4.256525 -85.37091 93.88396 0.9990653
$periodo
diff lwr upr p adj
2-1 12.7127750 -76.91466 102.34021 0.9766460
3-1 13.1073875 -76.52004 102.73482 0.9745182
4-1 -11.9710875 -101.59852 77.65634 0.9803409
3-2 0.3946125 -89.23282 90.02204 0.9999992
4-2 -24.6838625 -114.31129 64.94357 0.8588430
4-3 -25.0784750 -114.70591 64.54896 0.8531598
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 -8.08520 -258.9280 242.7576 1.0000000
3:1-1:1 -47.83165 -298.6745 203.0112 0.9999662
4:1-1:1 -50.92575 -301.7686 199.9171 0.9999276
1:2-1:1 -28.18835 -279.0312 222.6545 1.0000000
2:2-1:1 -21.28085 -272.1237 229.5620 1.0000000
3:2-1:1 -37.49350 -288.3363 213.3493 0.9999985
4:2-1:1 30.97120 -219.8716 281.8140 0.9999999
1:3-1:1 -35.46415 -286.3070 215.3787 0.9999993
2:3-1:1 -29.02355 -279.8664 221.8193 1.0000000
3:3-1:1 27.11235 -223.7305 277.9552 1.0000000
4:3-1:1 -17.03770 -267.8805 233.8051 1.0000000
1:4-1:1 -92.57465 -343.4175 158.2682 0.9701347
2:4-1:1 -20.63355 -271.4764 230.2093 1.0000000
3:4-1:1 -18.66215 -269.5050 232.1807 1.0000000
4:4-1:1 -22.85660 -273.6994 227.9862 1.0000000
3:1-2:1 -39.74645 -290.5893 211.0964 0.9999968
4:1-2:1 -42.84055 -293.6834 208.0023 0.9999915
1:2-2:1 -20.10315 -270.9460 230.7397 1.0000000
2:2-2:1 -13.19565 -264.0385 237.6472 1.0000000
3:2-2:1 -29.40830 -280.2511 221.4345 0.9999999
4:2-2:1 39.05640 -211.7864 289.8992 0.9999974
1:3-2:1 -27.37895 -278.2218 223.4639 1.0000000
2:3-2:1 -20.93835 -271.7812 229.9045 1.0000000
3:3-2:1 35.19755 -215.6453 286.0404 0.9999994
4:3-2:1 -8.95250 -259.7953 241.8903 1.0000000
1:4-2:1 -84.48945 -335.3323 166.3534 0.9858243
2:4-2:1 -12.54835 -263.3912 238.2945 1.0000000
3:4-2:1 -10.57695 -261.4198 240.2659 1.0000000
4:4-2:1 -14.77140 -265.6142 236.0714 1.0000000
4:1-3:1 -3.09410 -253.9369 247.7487 1.0000000
1:2-3:1 19.64330 -231.1995 270.4861 1.0000000
2:2-3:1 26.55080 -224.2920 277.3936 1.0000000
3:2-3:1 10.33815 -240.5047 261.1810 1.0000000
4:2-3:1 78.80285 -172.0400 329.6457 0.9923439
1:3-3:1 12.36750 -238.4753 263.2103 1.0000000
2:3-3:1 18.80810 -232.0347 269.6509 1.0000000
3:3-3:1 74.94400 -175.8988 325.7868 0.9952071
4:3-3:1 30.79395 -220.0489 281.6368 0.9999999
1:4-3:1 -44.74300 -295.5858 206.0998 0.9999853
2:4-3:1 27.19810 -223.6447 278.0409 1.0000000
3:4-3:1 29.16950 -221.6733 280.0123 1.0000000
4:4-3:1 24.97505 -225.8678 275.8179 1.0000000
1:2-4:1 22.73740 -228.1054 273.5802 1.0000000
2:2-4:1 29.64490 -221.1979 280.4877 0.9999999
3:2-4:1 13.43225 -237.4106 264.2751 1.0000000
4:2-4:1 81.89695 -168.9459 332.7398 0.9891845
1:3-4:1 15.46160 -235.3812 266.3044 1.0000000
2:3-4:1 21.90220 -228.9406 272.7450 1.0000000
3:3-4:1 78.03810 -172.8047 328.8809 0.9929987
4:3-4:1 33.88805 -216.9548 284.7309 0.9999996
1:4-4:1 -41.64890 -292.4917 209.1939 0.9999941
2:4-4:1 30.29220 -220.5506 281.1350 0.9999999
3:4-4:1 32.26360 -218.5792 283.1064 0.9999998
4:4-4:1 28.06915 -222.7737 278.9120 1.0000000
2:2-1:2 6.90750 -243.9353 257.7503 1.0000000
3:2-1:2 -9.30515 -260.1480 241.5377 1.0000000
4:2-1:2 59.15955 -191.6833 310.0024 0.9995883
1:3-1:2 -7.27580 -258.1186 243.5670 1.0000000
2:3-1:2 -0.83520 -251.6780 250.0076 1.0000000
3:3-1:2 55.30070 -195.5421 306.1435 0.9998087
4:3-1:2 11.15065 -239.6922 261.9935 1.0000000
1:4-1:2 -64.38630 -315.2291 186.4565 0.9989641
2:4-1:2 7.55480 -243.2880 258.3976 1.0000000
3:4-1:2 9.52620 -241.3166 260.3690 1.0000000
4:4-1:2 5.33175 -245.5111 256.1746 1.0000000
3:2-2:2 -16.21265 -267.0555 234.6302 1.0000000
4:2-2:2 52.25205 -198.5908 303.0949 0.9999016
1:3-2:2 -14.18330 -265.0261 236.6595 1.0000000
2:3-2:2 -7.74270 -258.5855 243.1001 1.0000000
3:3-2:2 48.39320 -202.4496 299.2360 0.9999610
4:3-2:2 4.24315 -246.5997 255.0860 1.0000000
1:4-2:2 -71.29380 -322.1366 179.5490 0.9970495
2:4-2:2 0.64730 -250.1955 251.4901 1.0000000
3:4-2:2 2.61870 -248.2241 253.4615 1.0000000
4:4-2:2 -1.57575 -252.4186 249.2671 1.0000000
4:2-3:2 68.46470 -182.3781 319.3075 0.9980362
1:3-3:2 2.02935 -248.8135 252.8722 1.0000000
2:3-3:2 8.46995 -242.3729 259.3128 1.0000000
3:3-3:2 64.60585 -186.2370 315.4487 0.9989260
4:3-3:2 20.45580 -230.3870 271.2986 1.0000000
1:4-3:2 -55.08115 -305.9240 195.7617 0.9998173
2:4-3:2 16.85995 -233.9829 267.7028 1.0000000
3:4-3:2 18.83135 -232.0115 269.6742 1.0000000
4:4-3:2 14.63690 -236.2059 265.4797 1.0000000
1:3-4:2 -66.43535 -317.2782 184.4075 0.9985598
2:3-4:2 -59.99475 -310.8376 190.8481 0.9995189
3:3-4:2 -3.85885 -254.7017 246.9840 1.0000000
4:3-4:2 -48.00890 -298.8517 202.8339 0.9999646
1:4-4:2 -123.54585 -374.3887 127.2970 0.8098341
2:4-4:2 -51.60475 -302.4476 199.2381 0.9999151
3:4-4:2 -49.63335 -300.4762 201.2095 0.9999469
4:4-4:2 -53.82780 -304.6706 197.0150 0.9998603
2:3-1:3 6.44060 -244.4022 257.2834 1.0000000
3:3-1:3 62.57650 -188.2663 313.4193 0.9992369
4:3-1:3 18.42645 -232.4164 269.2693 1.0000000
1:4-1:3 -57.11050 -307.9533 193.7323 0.9997233
2:4-1:3 14.83060 -236.0122 265.6734 1.0000000
3:4-1:3 16.80200 -234.0408 267.6448 1.0000000
4:4-1:3 12.60755 -238.2353 263.4504 1.0000000
3:3-2:3 56.13590 -194.7069 306.9787 0.9997727
4:3-2:3 11.98585 -238.8570 262.8287 1.0000000
1:4-2:3 -63.55110 -314.3939 187.2917 0.9990988
2:4-2:3 8.39000 -242.4528 259.2328 1.0000000
3:4-2:3 10.36140 -240.4814 261.2042 1.0000000
4:4-2:3 6.16695 -244.6759 257.0098 1.0000000
4:3-3:3 -44.15005 -294.9929 206.6928 0.9999876
1:4-3:3 -119.68700 -370.5298 131.1558 0.8387851
2:4-3:3 -47.74590 -298.5887 203.0969 0.9999669
3:4-3:3 -45.77450 -296.6173 205.0683 0.9999804
4:4-3:3 -49.96895 -300.8118 200.8739 0.9999424
1:4-4:3 -75.53695 -326.3798 175.3059 0.9948346
2:4-4:3 -3.59585 -254.4387 247.2470 1.0000000
3:4-4:3 -1.62445 -252.4673 249.2184 1.0000000
4:4-4:3 -5.81890 -256.6617 245.0239 1.0000000
2:4-1:4 71.94110 -178.9017 322.7839 0.9967742
3:4-1:4 73.91250 -176.9303 324.7553 0.9958030
4:4-1:4 69.71805 -181.1248 320.5609 0.9976398
3:4-2:4 1.97140 -248.8714 252.8142 1.0000000
4:4-2:4 -2.22305 -253.0659 248.6198 1.0000000
4:4-3:4 -4.19445 -255.0373 246.6484 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$periodo
$periodo$Letters
3 2 1 4
"a" "a" "a" "a"
$periodo$LetterMatrix
a
3 TRUE
2 TRUE
1 TRUE
4 TRUE
$`fermentador:periodo`
$`fermentador:periodo`$Letters
4:2 3:3 1:1 2:1 4:3 3:4 2:4 2:2 4:4 1:2 2:3 1:3 3:2 3:1 4:1 1:4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
4:2 TRUE
3:3 TRUE
1:1 TRUE
2:1 TRUE
4:3 TRUE
3:4 TRUE
2:4 TRUE
2:2 TRUE
4:4 TRUE
1:2 TRUE
2:3 TRUE
1:3 TRUE
3:2 TRUE
3:1 TRUE
4:1 TRUE
1:4 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.0005 0.00017 0.008 0.999
periodo 3 0.2134 0.07114 3.087 0.041 *
fermentador:periodo 9 0.0031 0.00035 0.015 1.000
Residuals 32 0.7373 0.02304
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador periodo emmean SE df lower.CL upper.CL
1 1 0.2415 0.0876 32 0.0630 0.420
2 1 0.2428 0.0876 32 0.0643 0.421
3 1 0.2608 0.0876 32 0.0823 0.439
4 1 0.2394 0.0876 32 0.0609 0.418
1 2 0.0975 0.0876 32 -0.0810 0.276
2 2 0.0830 0.0876 32 -0.0955 0.262
3 2 0.0801 0.0876 32 -0.0984 0.259
4 2 0.0869 0.0876 32 -0.0916 0.265
1 3 0.0885 0.0876 32 -0.0900 0.267
2 3 0.0798 0.0876 32 -0.0988 0.258
3 3 0.0847 0.0876 32 -0.0938 0.263
4 3 0.0949 0.0876 32 -0.0836 0.273
1 4 0.0845 0.0876 32 -0.0940 0.263
2 4 0.1074 0.0876 32 -0.0711 0.286
3 4 0.1156 0.0876 32 -0.0629 0.294
4 4 0.1147 0.0876 32 -0.0638 0.293
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 0.0002483333 -0.1676472 0.1681439 1.0000000
3-1 0.0072995833 -0.1605960 0.1751951 0.9994027
4-1 0.0059537500 -0.1619418 0.1738493 0.9996753
3-2 0.0070512500 -0.1608443 0.1749468 0.9994614
4-2 0.0057054167 -0.1621901 0.1736010 0.9997142
4-3 -0.0013458333 -0.1692414 0.1665497 0.9999962
$periodo
diff lwr upr p adj
2-1 -1.592404e-01 -0.3271360 0.008655137 0.0680796
3-1 -1.591483e-01 -0.3270439 0.008747220 0.0682991
4-1 -1.405546e-01 -0.3084501 0.027340970 0.1270168
3-2 9.208333e-05 -0.1678035 0.167987637 1.0000000
4-2 1.868583e-02 -0.1492097 0.186581387 0.9902907
4-3 1.859375e-02 -0.1493018 0.186489304 0.9904301
$`fermentador:periodo`
diff lwr upr p adj
2:1-1:1 0.001333333 -0.4582369 0.4609036 1.0000000
3:1-1:1 0.019338333 -0.4402319 0.4789086 1.0000000
4:1-1:1 -0.002081667 -0.4616519 0.4574886 1.0000000
1:2-1:1 -0.143975000 -0.6035452 0.3155952 0.9977586
2:2-1:1 -0.158425000 -0.6179952 0.3011452 0.9940667
3:2-1:1 -0.161371667 -0.6209419 0.2981986 0.9929008
4:2-1:1 -0.154600000 -0.6141702 0.3049702 0.9953426
1:3-1:1 -0.152928333 -0.6124986 0.3066419 0.9958244
2:3-1:1 -0.161711667 -0.6212819 0.2978586 0.9927551
3:3-1:1 -0.156781667 -0.6163519 0.3027886 0.9946459
4:3-1:1 -0.146581667 -0.6061519 0.3129886 0.9972954
1:4-1:1 -0.156951667 -0.6165219 0.3026186 0.9945883
2:4-1:1 -0.134058333 -0.5936286 0.3255119 0.9989616
3:4-1:1 -0.125841667 -0.5854119 0.3337286 0.9994895
4:4-1:1 -0.126776667 -0.5863469 0.3327936 0.9994445
3:1-2:1 0.018005000 -0.4415652 0.4775752 1.0000000
4:1-2:1 -0.003415000 -0.4629852 0.4561552 1.0000000
1:2-2:1 -0.145308333 -0.6048786 0.3142619 0.9975308
2:2-2:1 -0.159758333 -0.6193286 0.2998119 0.9935601
3:2-2:1 -0.162705000 -0.6222752 0.2968652 0.9923155
4:2-2:1 -0.155933333 -0.6155036 0.3036369 0.9949263
1:3-2:1 -0.154261667 -0.6138319 0.3053086 0.9954436
2:3-2:1 -0.163045000 -0.6226152 0.2965252 0.9921602
3:3-2:1 -0.158115000 -0.6176852 0.3014552 0.9941797
4:3-2:1 -0.147915000 -0.6074852 0.3116552 0.9970291
1:4-2:1 -0.158285000 -0.6178552 0.3012852 0.9941180
2:4-2:1 -0.135391667 -0.5949619 0.3241786 0.9988423
3:4-2:1 -0.127175000 -0.5867452 0.3323952 0.9994244
4:4-2:1 -0.128110000 -0.5876802 0.3314602 0.9993745
4:1-3:1 -0.021420000 -0.4809902 0.4381502 1.0000000
1:2-3:1 -0.163313333 -0.6228836 0.2962569 0.9920358
2:2-3:1 -0.177763333 -0.6373336 0.2818069 0.9826215
3:2-3:1 -0.180710000 -0.6402802 0.2788602 0.9799294
4:2-3:1 -0.173938333 -0.6335086 0.2856319 0.9856898
1:3-3:1 -0.172266667 -0.6318369 0.2873036 0.9868897
2:3-3:1 -0.181050000 -0.6406202 0.2785202 0.9795993
3:3-3:1 -0.176120000 -0.6356902 0.2834502 0.9839965
4:3-3:1 -0.165920000 -0.6254902 0.2936502 0.9907434
1:4-3:1 -0.176290000 -0.6358602 0.2832802 0.9838583
2:4-3:1 -0.153396667 -0.6129669 0.3061736 0.9956937
3:4-3:1 -0.145180000 -0.6047502 0.3143902 0.9975535
4:4-3:1 -0.146115000 -0.6056852 0.3134552 0.9973837
1:2-4:1 -0.141893333 -0.6014636 0.3176769 0.9980788
2:2-4:1 -0.156343333 -0.6159136 0.3032269 0.9947923
3:2-4:1 -0.159290000 -0.6188602 0.3002802 0.9937419
4:2-4:1 -0.152518333 -0.6120886 0.3070519 0.9959360
1:3-4:1 -0.150846667 -0.6104169 0.3087236 0.9963661
2:3-4:1 -0.159630000 -0.6192002 0.2999402 0.9936104
3:3-4:1 -0.154700000 -0.6142702 0.3048702 0.9953124
4:3-4:1 -0.144500000 -0.6040702 0.3150702 0.9976711
1:4-4:1 -0.154870000 -0.6144402 0.3047002 0.9952606
2:4-4:1 -0.131976667 -0.5915469 0.3275936 0.9991269
3:4-4:1 -0.123760000 -0.5833302 0.3358102 0.9995784
4:4-4:1 -0.124695000 -0.5842652 0.3348752 0.9995403
2:2-1:2 -0.014450000 -0.4740202 0.4451202 1.0000000
3:2-1:2 -0.017396667 -0.4769669 0.4421736 1.0000000
4:2-1:2 -0.010625000 -0.4701952 0.4489452 1.0000000
1:3-1:2 -0.008953333 -0.4685236 0.4506169 1.0000000
2:3-1:2 -0.017736667 -0.4773069 0.4418336 1.0000000
3:3-1:2 -0.012806667 -0.4723769 0.4467636 1.0000000
4:3-1:2 -0.002606667 -0.4621769 0.4569636 1.0000000
1:4-1:2 -0.012976667 -0.4725469 0.4465936 1.0000000
2:4-1:2 0.009916667 -0.4496536 0.4694869 1.0000000
3:4-1:2 0.018133333 -0.4414369 0.4777036 1.0000000
4:4-1:2 0.017198333 -0.4423719 0.4767686 1.0000000
3:2-2:2 -0.002946667 -0.4625169 0.4566236 1.0000000
4:2-2:2 0.003825000 -0.4557452 0.4633952 1.0000000
1:3-2:2 0.005496667 -0.4540736 0.4650669 1.0000000
2:3-2:2 -0.003286667 -0.4628569 0.4562836 1.0000000
3:3-2:2 0.001643333 -0.4579269 0.4612136 1.0000000
4:3-2:2 0.011843333 -0.4477269 0.4714136 1.0000000
1:4-2:2 0.001473333 -0.4580969 0.4610436 1.0000000
2:4-2:2 0.024366667 -0.4352036 0.4839369 1.0000000
3:4-2:2 0.032583333 -0.4269869 0.4921536 1.0000000
4:4-2:2 0.031648333 -0.4279219 0.4912186 1.0000000
4:2-3:2 0.006771667 -0.4527986 0.4663419 1.0000000
1:3-3:2 0.008443333 -0.4511269 0.4680136 1.0000000
2:3-3:2 -0.000340000 -0.4599102 0.4592302 1.0000000
3:3-3:2 0.004590000 -0.4549802 0.4641602 1.0000000
4:3-3:2 0.014790000 -0.4447802 0.4743602 1.0000000
1:4-3:2 0.004420000 -0.4551502 0.4639902 1.0000000
2:4-3:2 0.027313333 -0.4322569 0.4868836 1.0000000
3:4-3:2 0.035530000 -0.4240402 0.4951002 1.0000000
4:4-3:2 0.034595000 -0.4249752 0.4941652 1.0000000
1:3-4:2 0.001671667 -0.4578986 0.4612419 1.0000000
2:3-4:2 -0.007111667 -0.4666819 0.4524586 1.0000000
3:3-4:2 -0.002181667 -0.4617519 0.4573886 1.0000000
4:3-4:2 0.008018333 -0.4515519 0.4675886 1.0000000
1:4-4:2 -0.002351667 -0.4619219 0.4572186 1.0000000
2:4-4:2 0.020541667 -0.4390286 0.4801119 1.0000000
3:4-4:2 0.028758333 -0.4308119 0.4883286 1.0000000
4:4-4:2 0.027823333 -0.4317469 0.4873936 1.0000000
2:3-1:3 -0.008783333 -0.4683536 0.4507869 1.0000000
3:3-1:3 -0.003853333 -0.4634236 0.4557169 1.0000000
4:3-1:3 0.006346667 -0.4532236 0.4659169 1.0000000
1:4-1:3 -0.004023333 -0.4635936 0.4555469 1.0000000
2:4-1:3 0.018870000 -0.4407002 0.4784402 1.0000000
3:4-1:3 0.027086667 -0.4324836 0.4866569 1.0000000
4:4-1:3 0.026151667 -0.4334186 0.4857219 1.0000000
3:3-2:3 0.004930000 -0.4546402 0.4645002 1.0000000
4:3-2:3 0.015130000 -0.4444402 0.4747002 1.0000000
1:4-2:3 0.004760000 -0.4548102 0.4643302 1.0000000
2:4-2:3 0.027653333 -0.4319169 0.4872236 1.0000000
3:4-2:3 0.035870000 -0.4237002 0.4954402 1.0000000
4:4-2:3 0.034935000 -0.4246352 0.4945052 1.0000000
4:3-3:3 0.010200000 -0.4493702 0.4697702 1.0000000
1:4-3:3 -0.000170000 -0.4597402 0.4594002 1.0000000
2:4-3:3 0.022723333 -0.4368469 0.4822936 1.0000000
3:4-3:3 0.030940000 -0.4286302 0.4905102 1.0000000
4:4-3:3 0.030005000 -0.4295652 0.4895752 1.0000000
1:4-4:3 -0.010370000 -0.4699402 0.4492002 1.0000000
2:4-4:3 0.012523333 -0.4470469 0.4720936 1.0000000
3:4-4:3 0.020740000 -0.4388302 0.4803102 1.0000000
4:4-4:3 0.019805000 -0.4397652 0.4793752 1.0000000
2:4-1:4 0.022893333 -0.4366769 0.4824636 1.0000000
3:4-1:4 0.031110000 -0.4284602 0.4906802 1.0000000
4:4-1:4 0.030175000 -0.4293952 0.4897452 1.0000000
3:4-2:4 0.008216667 -0.4513536 0.4677869 1.0000000
4:4-2:4 0.007281667 -0.4522886 0.4668519 1.0000000
4:4-3:4 -0.000935000 -0.4605052 0.4586352 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
3 4 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
4 TRUE
2 TRUE
1 TRUE
$periodo
$periodo$Letters
1 4 3 2
"a" "a" "a" "a"
$periodo$LetterMatrix
a
1 TRUE
4 TRUE
3 TRUE
2 TRUE
$`fermentador:periodo`
$`fermentador:periodo`$Letters
3:1 2:1 1:1 4:1 3:4 4:4 2:4 1:2 4:3 1:3 4:2 3:3 1:4 2:2 3:2 2:3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:periodo`$LetterMatrix
a
3:1 TRUE
2:1 TRUE
1:1 TRUE
4:1 TRUE
3:4 TRUE
4:4 TRUE
2:4 TRUE
1:2 TRUE
4:3 TRUE
1:3 TRUE
4:2 TRUE
3:3 TRUE
1:4 TRUE
2:2 TRUE
3:2 TRUE
2:3 TRUE
8 Fermentador x Tempo
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ fermentador * tempo")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * tempo")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ fermentador * tempo"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ fermentador * tempo)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 28166 9389 0.734 0.537
tempo 3 349201 116400 9.096 7.11e-05 ***
fermentador:tempo 9 60521 6725 0.525 0.849
Residuals 48 614276 12797
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 24 271 56.6 48 157.5 385
2 24 276 56.6 48 162.0 389
3 24 290 56.6 48 176.3 404
4 24 358 56.6 48 243.8 471
1 48 210 56.6 48 96.3 324
2 48 174 56.6 48 60.8 288
3 48 192 56.6 48 78.8 306
4 48 182 56.6 48 68.8 296
1 72 395 56.6 48 281.3 509
2 72 395 56.6 48 281.3 509
3 72 399 56.6 48 285.0 512
4 72 395 56.6 48 281.3 509
1 96 221 56.6 48 107.5 335
2 96 312 56.6 48 198.0 425
3 96 382 56.6 48 268.8 496
4 96 374 56.6 48 260.5 488
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 14.8750 -91.56911 121.3191 0.9822353
3-1 41.5625 -64.88161 148.0066 0.7274556
4-1 52.9375 -53.50661 159.3816 0.5527514
3-2 26.6875 -79.75661 133.1316 0.9088821
4-2 38.0625 -68.38161 144.5066 0.7771733
4-3 11.3750 -95.06911 117.8191 0.9918742
$tempo
diff lwr upr p adj
48-24 -108.7500 -215.194107 -2.305893 0.0435440
72-24 97.3125 -9.131607 203.756607 0.0843646
96-24 23.8125 -82.631607 130.256607 0.9329878
72-48 206.0625 99.618393 312.506607 0.0000279
96-48 132.5625 26.118393 239.006607 0.0091909
96-72 -73.5000 -179.944107 32.944107 0.2686746
$`fermentador:tempo`
diff lwr upr p adj
2:24-1:24 4.500000e+00 -284.48605 293.4861 1.0000000
3:24-1:24 1.875000e+01 -270.23605 307.7361 1.0000000
4:24-1:24 8.625000e+01 -202.73605 375.2361 0.9991937
1:48-1:24 -6.125000e+01 -350.23605 227.7361 0.9999878
2:48-1:24 -9.675000e+01 -385.73605 192.2361 0.9971636
3:48-1:24 -7.875000e+01 -367.73605 210.2361 0.9997194
4:48-1:24 -8.875000e+01 -377.73605 200.2361 0.9988884
1:72-1:24 1.237500e+02 -165.23605 412.7361 0.9702225
2:72-1:24 1.237500e+02 -165.23605 412.7361 0.9702225
3:72-1:24 1.275000e+02 -161.48605 416.4861 0.9618288
4:72-1:24 1.237500e+02 -165.23605 412.7361 0.9702225
1:96-1:24 -5.000000e+01 -338.98605 238.9861 0.9999992
2:96-1:24 4.050000e+01 -248.48605 329.4861 1.0000000
3:96-1:24 1.112500e+02 -177.73605 400.2361 0.9885272
4:96-1:24 1.030000e+02 -185.98605 391.9861 0.9945906
3:24-2:24 1.425000e+01 -274.73605 303.2361 1.0000000
4:24-2:24 8.175000e+01 -207.23605 370.7361 0.9995644
1:48-2:24 -6.575000e+01 -354.73605 223.2361 0.9999696
2:48-2:24 -1.012500e+02 -390.23605 187.7361 0.9954534
3:48-2:24 -8.325000e+01 -372.23605 205.7361 0.9994622
4:48-2:24 -9.325000e+01 -382.23605 195.7361 0.9980865
1:72-2:24 1.192500e+02 -169.73605 408.2361 0.9783767
2:72-2:24 1.192500e+02 -169.73605 408.2361 0.9783767
3:72-2:24 1.230000e+02 -165.98605 411.9861 0.9717207
4:72-2:24 1.192500e+02 -169.73605 408.2361 0.9783767
1:96-2:24 -5.450000e+01 -343.48605 234.4861 0.9999974
2:96-2:24 3.600000e+01 -252.98605 324.9861 1.0000000
3:96-2:24 1.067500e+02 -182.23605 395.7361 0.9922849
4:96-2:24 9.850000e+01 -190.48605 387.4861 0.9965772
4:24-3:24 6.750000e+01 -221.48605 356.4861 0.9999576
1:48-3:24 -8.000000e+01 -368.98605 208.9861 0.9996620
2:48-3:24 -1.155000e+02 -404.48605 173.4861 0.9837593
3:48-3:24 -9.750000e+01 -386.48605 191.4861 0.9969236
4:48-3:24 -1.075000e+02 -396.48605 181.4861 0.9917396
1:72-3:24 1.050000e+02 -183.98605 393.9861 0.9934442
2:72-3:24 1.050000e+02 -183.98605 393.9861 0.9934442
3:72-3:24 1.087500e+02 -180.23605 397.7361 0.9907619
4:72-3:24 1.050000e+02 -183.98605 393.9861 0.9934442
1:96-3:24 -6.875000e+01 -357.73605 220.2361 0.9999466
2:96-3:24 2.175000e+01 -267.23605 310.7361 1.0000000
3:96-3:24 9.250000e+01 -196.48605 381.4861 0.9982468
4:96-3:24 8.425000e+01 -204.73605 373.2361 0.9993829
1:48-4:24 -1.475000e+02 -436.48605 141.4861 0.8873226
2:48-4:24 -1.830000e+02 -471.98605 105.9861 0.6338385
3:48-4:24 -1.650000e+02 -453.98605 123.9861 0.7781268
4:48-4:24 -1.750000e+02 -463.98605 113.9861 0.7006700
1:72-4:24 3.750000e+01 -251.48605 326.4861 1.0000000
2:72-4:24 3.750000e+01 -251.48605 326.4861 1.0000000
3:72-4:24 4.125000e+01 -247.73605 330.2361 0.9999999
4:72-4:24 3.750000e+01 -251.48605 326.4861 1.0000000
1:96-4:24 -1.362500e+02 -425.23605 152.7361 0.9357699
2:96-4:24 -4.575000e+01 -334.73605 243.2361 0.9999998
3:96-4:24 2.500000e+01 -263.98605 313.9861 1.0000000
4:96-4:24 1.675000e+01 -272.23605 305.7361 1.0000000
2:48-1:48 -3.550000e+01 -324.48605 253.4861 1.0000000
3:48-1:48 -1.750000e+01 -306.48605 271.4861 1.0000000
4:48-1:48 -2.750000e+01 -316.48605 261.4861 1.0000000
1:72-1:48 1.850000e+02 -103.98605 473.9861 0.6167395
2:72-1:48 1.850000e+02 -103.98605 473.9861 0.6167395
3:72-1:48 1.887500e+02 -100.23605 477.7361 0.5844734
4:72-1:48 1.850000e+02 -103.98605 473.9861 0.6167395
1:96-1:48 1.125000e+01 -277.73605 300.2361 1.0000000
2:96-1:48 1.017500e+02 -187.23605 390.7361 0.9952194
3:96-1:48 1.725000e+02 -116.48605 461.4861 0.7208037
4:96-1:48 1.642500e+02 -124.73605 453.2361 0.7835630
3:48-2:48 1.800000e+01 -270.98605 306.9861 1.0000000
4:48-2:48 8.000000e+00 -280.98605 296.9861 1.0000000
1:72-2:48 2.205000e+02 -68.48605 509.4861 0.3274972
2:72-2:48 2.205000e+02 -68.48605 509.4861 0.3274972
3:72-2:48 2.242500e+02 -64.73605 513.2361 0.3016210
4:72-2:48 2.205000e+02 -68.48605 509.4861 0.3274972
1:96-2:48 4.675000e+01 -242.23605 335.7361 0.9999997
2:96-2:48 1.372500e+02 -151.73605 426.2361 0.9321666
3:96-2:48 2.080000e+02 -80.98605 496.9861 0.4221282
4:96-2:48 1.997500e+02 -89.23605 488.7361 0.4901825
4:48-3:48 -1.000000e+01 -298.98605 278.9861 1.0000000
1:72-3:48 2.025000e+02 -86.48605 491.4861 0.4671243
2:72-3:48 2.025000e+02 -86.48605 491.4861 0.4671243
3:72-3:48 2.062500e+02 -82.73605 495.2361 0.4362615
4:72-3:48 2.025000e+02 -86.48605 491.4861 0.4671243
1:96-3:48 2.875000e+01 -260.23605 317.7361 1.0000000
2:96-3:48 1.192500e+02 -169.73605 408.2361 0.9783767
3:96-3:48 1.900000e+02 -98.98605 478.9861 0.5736899
4:96-3:48 1.817500e+02 -107.23605 470.7361 0.6444660
1:72-4:48 2.125000e+02 -76.48605 501.4861 0.3866998
2:72-4:48 2.125000e+02 -76.48605 501.4861 0.3866998
3:72-4:48 2.162500e+02 -72.73605 505.2361 0.3583029
4:72-4:48 2.125000e+02 -76.48605 501.4861 0.3866998
1:96-4:48 3.875000e+01 -250.23605 327.7361 1.0000000
2:96-4:48 1.292500e+02 -159.73605 418.2361 0.9573693
3:96-4:48 2.000000e+02 -88.98605 488.9861 0.4880735
4:96-4:48 1.917500e+02 -97.23605 480.7361 0.5585945
2:72-1:72 -5.684342e-14 -288.98605 288.9861 1.0000000
3:72-1:72 3.750000e+00 -285.23605 292.7361 1.0000000
4:72-1:72 -1.136868e-13 -288.98605 288.9861 1.0000000
1:96-1:72 -1.737500e+02 -462.73605 115.2361 0.7107918
2:96-1:72 -8.325000e+01 -372.23605 205.7361 0.9994622
3:96-1:72 -1.250000e+01 -301.48605 276.4861 1.0000000
4:96-1:72 -2.075000e+01 -309.73605 268.2361 1.0000000
3:72-2:72 3.750000e+00 -285.23605 292.7361 1.0000000
4:72-2:72 -5.684342e-14 -288.98605 288.9861 1.0000000
1:96-2:72 -1.737500e+02 -462.73605 115.2361 0.7107918
2:96-2:72 -8.325000e+01 -372.23605 205.7361 0.9994622
3:96-2:72 -1.250000e+01 -301.48605 276.4861 1.0000000
4:96-2:72 -2.075000e+01 -309.73605 268.2361 1.0000000
4:72-3:72 -3.750000e+00 -292.73605 285.2361 1.0000000
1:96-3:72 -1.775000e+02 -466.48605 111.4861 0.6801293
2:96-3:72 -8.700000e+01 -375.98605 201.9861 0.9991108
3:96-3:72 -1.625000e+01 -305.23605 272.7361 1.0000000
4:96-3:72 -2.450000e+01 -313.48605 264.4861 1.0000000
1:96-4:72 -1.737500e+02 -462.73605 115.2361 0.7107918
2:96-4:72 -8.325000e+01 -372.23605 205.7361 0.9994622
3:96-4:72 -1.250000e+01 -301.48605 276.4861 1.0000000
4:96-4:72 -2.075000e+01 -309.73605 268.2361 1.0000000
2:96-1:96 9.050000e+01 -198.48605 379.4861 0.9986198
3:96-1:96 1.612500e+02 -127.73605 450.2361 0.8046969
4:96-1:96 1.530000e+02 -135.98605 441.9861 0.8572556
3:96-2:96 7.075000e+01 -218.23605 359.7361 0.9999236
4:96-2:96 6.250000e+01 -226.48605 351.4861 0.9999841
4:96-3:96 -8.250000e+00 -297.23605 280.7361 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$tempo
72 96 24 48
"a" "a" "a" "b"
$`fermentador:tempo`
$`fermentador:tempo`$Letters
3:72 1:72 2:72 4:72 3:96 4:96 4:24 2:96 3:24 2:24 1:24 1:96 1:48 3:48 4:48 2:48
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tempo`$LetterMatrix
a
3:72 TRUE
1:72 TRUE
2:72 TRUE
4:72 TRUE
3:96 TRUE
4:96 TRUE
4:24 TRUE
2:96 TRUE
3:24 TRUE
2:24 TRUE
1:24 TRUE
1:96 TRUE
1:48 TRUE
3:48 TRUE
4:48 TRUE
2:48 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 208575 69525 0.691 0.562
tempo 3 3330824 1110275 11.038 1.25e-05 ***
fermentador:tempo 9 465419 51713 0.514 0.857
Residuals 48 4828294 100589
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 24 1782 159 48 1464 2101
2 24 1788 159 48 1469 2107
3 24 1784 159 48 1465 2103
4 24 1414 159 48 1096 1733
1 48 1368 159 48 1049 1687
2 48 1258 159 48 940 1577
3 48 1446 159 48 1127 1765
4 48 1396 159 48 1077 1715
1 72 1224 159 48 905 1543
2 72 1222 159 48 903 1541
3 72 1144 159 48 825 1463
4 72 966 159 48 647 1285
1 96 1659 159 48 1340 1978
2 96 1635 159 48 1316 1954
3 96 1668 159 48 1349 1987
4 96 1705 159 48 1386 2024
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -32.4375 -330.8636 265.9886 0.9914586
3-1 2.1875 -296.2386 300.6136 0.9999973
4-1 -138.0625 -436.4886 160.3636 0.6103741
3-2 34.6250 -263.8011 333.0511 0.9896591
4-2 -105.6250 -404.0511 192.8011 0.7824702
4-3 -140.2500 -438.6761 158.1761 0.5981948
$tempo
diff lwr upr p adj
48-24 -325.0000 -623.4261449 -26.57386 0.0279074
72-24 -553.3750 -851.8011449 -254.94886 0.0000581
96-24 -25.6875 -324.1136449 272.73864 0.9957055
72-48 -228.3750 -526.8011449 70.05114 0.1890690
96-48 299.3125 0.8863551 597.73864 0.0490681
96-72 527.6875 229.2613551 826.11364 0.0001250
$`fermentador:tempo`
diff lwr upr p adj
2:24-1:24 5.50 -804.69979 815.69979 1.0000000
3:24-1:24 1.75 -808.44979 811.94979 1.0000000
4:24-1:24 -368.00 -1178.19979 442.19979 0.9517970
1:48-1:24 -414.25 -1224.44979 395.94979 0.8860118
2:48-1:24 -524.00 -1334.19979 286.19979 0.6003899
3:48-1:24 -336.25 -1146.44979 473.94979 0.9772602
4:48-1:24 -386.25 -1196.44979 423.94979 0.9302415
1:72-1:24 -558.75 -1368.94979 251.44979 0.4940039
2:72-1:24 -560.25 -1370.44979 249.94979 0.4894844
3:72-1:24 -638.50 -1448.69979 171.69979 0.2786722
4:72-1:24 -816.75 -1626.94979 -6.55021 0.0463407
1:96-1:24 -123.50 -933.69979 686.69979 0.9999999
2:96-1:24 -147.50 -957.69979 662.69979 0.9999984
3:96-1:24 -114.75 -924.94979 695.44979 0.9999999
4:96-1:24 -77.75 -887.94979 732.44979 1.0000000
3:24-2:24 -3.75 -813.94979 806.44979 1.0000000
4:24-2:24 -373.50 -1183.69979 436.69979 0.9458771
1:48-2:24 -419.75 -1229.94979 390.44979 0.8756938
2:48-2:24 -529.50 -1339.69979 280.69979 0.5834844
3:48-2:24 -341.75 -1151.94979 468.44979 0.9738201
4:48-2:24 -391.75 -1201.94979 418.44979 0.9226426
1:72-2:24 -564.25 -1374.44979 245.94979 0.4774864
2:72-2:24 -565.75 -1375.94979 244.44979 0.4730089
3:72-2:24 -644.00 -1454.19979 166.19979 0.2662871
4:72-2:24 -822.25 -1632.44979 -12.05021 0.0434543
1:96-2:24 -129.00 -939.19979 681.19979 0.9999997
2:96-2:24 -153.00 -963.19979 657.19979 0.9999973
3:96-2:24 -120.25 -930.44979 689.94979 0.9999999
4:96-2:24 -83.25 -893.44979 726.94979 1.0000000
4:24-3:24 -369.75 -1179.94979 440.44979 0.9499658
1:48-3:24 -416.00 -1226.19979 394.19979 0.8827866
2:48-3:24 -525.75 -1335.94979 284.44979 0.5950149
3:48-3:24 -338.00 -1148.19979 472.19979 0.9762054
4:48-3:24 -388.00 -1198.19979 422.19979 0.9278805
1:72-3:24 -560.50 -1370.69979 249.69979 0.4887321
2:72-3:24 -562.00 -1372.19979 248.19979 0.4842252
3:72-3:24 -640.25 -1450.44979 169.94979 0.2746920
4:72-3:24 -818.50 -1628.69979 -8.30021 0.0454043
1:96-3:24 -125.25 -935.44979 684.94979 0.9999998
2:96-3:24 -149.25 -959.44979 660.94979 0.9999981
3:96-3:24 -116.50 -926.69979 693.69979 0.9999999
4:96-3:24 -79.50 -889.69979 730.69979 1.0000000
1:48-4:24 -46.25 -856.44979 763.94979 1.0000000
2:48-4:24 -156.00 -966.19979 654.19979 0.9999966
3:48-4:24 31.75 -778.44979 841.94979 1.0000000
4:48-4:24 -18.25 -828.44979 791.94979 1.0000000
1:72-4:24 -190.75 -1000.94979 619.44979 0.9999531
2:72-4:24 -192.25 -1002.44979 617.94979 0.9999483
3:72-4:24 -270.50 -1080.69979 539.69979 0.9972451
4:72-4:24 -448.75 -1258.94979 361.44979 0.8127807
1:96-4:24 244.50 -565.69979 1054.69979 0.9990864
2:96-4:24 220.50 -589.69979 1030.69979 0.9997236
3:96-4:24 253.25 -556.94979 1063.44979 0.9986480
4:96-4:24 290.25 -519.94979 1100.44979 0.9943054
2:48-1:48 -109.75 -919.94979 700.44979 1.0000000
3:48-1:48 78.00 -732.19979 888.19979 1.0000000
4:48-1:48 28.00 -782.19979 838.19979 1.0000000
1:72-1:48 -144.50 -954.69979 665.69979 0.9999988
2:72-1:48 -146.00 -956.19979 664.19979 0.9999986
3:72-1:48 -224.25 -1034.44979 585.94979 0.9996626
4:72-1:48 -402.50 -1212.69979 407.69979 0.9062606
1:96-1:48 290.75 -519.44979 1100.94979 0.9942063
2:96-1:48 266.75 -543.44979 1076.94979 0.9976247
3:96-1:48 299.50 -510.69979 1109.69979 0.9922302
4:96-1:48 336.50 -473.69979 1146.69979 0.9771118
3:48-2:48 187.75 -622.44979 997.94979 0.9999616
4:48-2:48 137.75 -672.44979 947.94979 0.9999994
1:72-2:48 -34.75 -844.94979 775.44979 1.0000000
2:72-2:48 -36.25 -846.44979 773.94979 1.0000000
3:72-2:48 -114.50 -924.69979 695.69979 1.0000000
4:72-2:48 -292.75 -1102.94979 517.44979 0.9937957
1:96-2:48 400.50 -409.69979 1210.69979 0.9094630
2:96-2:48 376.50 -433.69979 1186.69979 0.9424420
3:96-2:48 409.25 -400.94979 1219.44979 0.8949280
4:96-2:48 446.25 -363.94979 1256.44979 0.8187405
4:48-3:48 -50.00 -860.19979 760.19979 1.0000000
1:72-3:48 -222.50 -1032.69979 587.69979 0.9996924
2:72-3:48 -224.00 -1034.19979 586.19979 0.9996670
3:72-3:48 -302.25 -1112.44979 507.94979 0.9915068
4:72-3:48 -480.50 -1290.69979 329.69979 0.7296183
1:96-3:48 212.75 -597.44979 1022.94979 0.9998199
2:96-3:48 188.75 -621.44979 998.94979 0.9999589
3:96-3:48 221.50 -588.69979 1031.69979 0.9997084
4:96-3:48 258.50 -551.69979 1068.69979 0.9983071
1:72-4:48 -172.50 -982.69979 637.69979 0.9999870
2:72-4:48 -174.00 -984.19979 636.19979 0.9999855
3:72-4:48 -252.25 -1062.44979 557.94979 0.9987058
4:72-4:48 -430.50 -1240.69979 379.69979 0.8540062
1:96-4:48 262.75 -547.44979 1072.94979 0.9979800
2:96-4:48 238.75 -571.44979 1048.94979 0.9993024
3:96-4:48 271.50 -538.69979 1081.69979 0.9971357
4:96-4:48 308.50 -501.69979 1118.69979 0.9896596
2:72-1:72 -1.50 -811.69979 808.69979 1.0000000
3:72-1:72 -79.75 -889.94979 730.44979 1.0000000
4:72-1:72 -258.00 -1068.19979 552.19979 0.9983424
1:96-1:72 435.25 -374.94979 1245.44979 0.8437968
2:96-1:72 411.25 -398.94979 1221.44979 0.8914147
3:96-1:72 444.00 -366.19979 1254.19979 0.8240220
4:96-1:72 481.00 -329.19979 1291.19979 0.7282123
3:72-2:72 -78.25 -888.44979 731.94979 1.0000000
4:72-2:72 -256.50 -1066.69979 553.69979 0.9984447
1:96-2:72 436.75 -373.44979 1246.94979 0.8404949
2:96-2:72 412.75 -397.44979 1222.94979 0.8887332
3:96-2:72 445.50 -364.69979 1255.69979 0.8205097
4:96-2:72 482.50 -327.69979 1292.69979 0.7239792
4:72-3:72 -178.25 -988.44979 631.94979 0.9999802
1:96-3:72 515.00 -295.19979 1325.19979 0.6279279
2:96-3:72 491.00 -319.19979 1301.19979 0.6995926
3:96-3:72 523.75 -286.44979 1333.94979 0.6011574
4:96-3:72 560.75 -249.44979 1370.94979 0.4879802
1:96-4:72 693.25 -116.94979 1503.44979 0.1718093
2:96-4:72 669.25 -140.94979 1479.44979 0.2141628
3:96-4:72 702.00 -108.19979 1512.19979 0.1580609
4:96-4:72 739.00 -71.19979 1549.19979 0.1092074
2:96-1:96 -24.00 -834.19979 786.19979 1.0000000
3:96-1:96 8.75 -801.44979 818.94979 1.0000000
4:96-1:96 45.75 -764.44979 855.94979 1.0000000
3:96-2:96 32.75 -777.44979 842.94979 1.0000000
4:96-2:96 69.75 -740.44979 879.94979 1.0000000
4:96-3:96 37.00 -773.19979 847.19979 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
3 1 2 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
1 TRUE
2 TRUE
4 TRUE
$tempo
24 96 48 72
"a" "a" "b" "b"
$`fermentador:tempo`
2:24 3:24 1:24 4:96 3:96 1:96 2:96 3:48 4:24 4:48 1:48 2:48 1:72 2:72 3:72 4:72
"a" "a" "a" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "ab" "b"
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 4.060 1.353 1.512 0.2369
tempo 1 5.874 5.874 6.561 0.0171 *
fermentador:tempo 3 5.118 1.706 1.906 0.1556
Residuals 24 21.487 0.895
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 72 5.47 0.473 24 4.50 6.45
2 72 4.94 0.473 24 3.96 5.92
3 72 5.51 0.473 24 4.53 6.48
4 72 3.83 0.473 24 2.85 4.80
1 96 6.04 0.473 24 5.07 7.02
2 96 6.11 0.473 24 5.13 7.08
3 96 5.26 0.473 24 4.29 6.24
4 96 5.76 0.473 24 4.79 6.74
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.23750 -1.542603 1.0676026 0.9578105
3-1 -0.37500 -1.680103 0.9301026 0.8570666
4-1 -0.96625 -2.271353 0.3388526 0.2008910
3-2 -0.13750 -1.442603 1.1676026 0.9912267
4-2 -0.72875 -2.033853 0.5763526 0.4302327
4-3 -0.59125 -1.896353 0.7138526 0.6024459
$tempo
diff lwr upr p adj
96-72 0.856875 0.1664319 1.547318 0.0171319
$`fermentador:tempo`
diff lwr upr p adj
2:72-1:72 -0.5350 -2.750891247 1.6808912 0.9915147
3:72-1:72 0.0325 -2.183391247 2.2483912 1.0000000
4:72-1:72 -1.6500 -3.865891247 0.5658912 0.2564991
1:96-1:72 0.5700 -1.645891247 2.7858912 0.9877168
2:96-1:72 0.6300 -1.585891247 2.8458912 0.9783728
3:96-1:72 -0.2125 -2.428391247 2.0033912 0.9999786
4:96-1:72 0.2875 -1.928391247 2.5033912 0.9998357
3:72-2:72 0.5675 -1.648391247 2.7833912 0.9880246
4:72-2:72 -1.1150 -3.330891247 1.1008912 0.7072398
1:96-2:72 1.1050 -1.110891247 3.3208912 0.7160504
2:96-2:72 1.1650 -1.050891247 3.3808912 0.6622104
3:96-2:72 0.3225 -1.893391247 2.5383912 0.9996484
4:96-2:72 0.8225 -1.393391247 3.0383912 0.9149297
4:72-3:72 -1.6825 -3.898391247 0.5333912 0.2365052
1:96-3:72 0.5375 -1.678391247 2.7533912 0.9912781
2:96-3:72 0.5975 -1.618391247 2.8133912 0.9839204
3:96-3:72 -0.2450 -2.460891247 1.9708912 0.9999438
4:96-3:72 0.2550 -1.960891247 2.4708912 0.9999264
1:96-4:72 2.2200 0.004108753 4.4358912 0.0493415
2:96-4:72 2.2800 0.064108753 4.4958912 0.0405873
3:96-4:72 1.4375 -0.778391247 3.6533912 0.4146562
4:96-4:72 1.9375 -0.278391247 4.1533912 0.1178690
2:96-1:96 0.0600 -2.155891247 2.2758912 1.0000000
3:96-1:96 -0.7825 -2.998391247 1.4333912 0.9329802
4:96-1:96 -0.2825 -2.498391247 1.9333912 0.9998538
3:96-2:96 -0.8425 -3.058391247 1.3733912 0.9048796
4:96-2:96 -0.3425 -2.558391247 1.8733912 0.9994785
4:96-3:96 0.5000 -1.715891247 2.7158912 0.9943335
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$tempo
96 72
"a" "b"
$`fermentador:tempo`
2:96 1:96 4:96 3:72 1:72 3:96 2:72 4:72
"a" "a" "ab" "ab" "ab" "ab" "ab" "b"
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.00659 0.00220 1.373 0.275
tempo 1 0.03768 0.03768 23.561 6.02e-05 ***
fermentador:tempo 3 0.00463 0.00154 0.965 0.425
Residuals 24 0.03838 0.00160
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 72 0.1173 0.02 24 0.0760 0.1585
2 72 0.0975 0.02 24 0.0562 0.1388
3 72 0.1025 0.02 24 0.0612 0.1438
4 72 0.0525 0.02 24 0.0112 0.0938
1 96 0.1751 0.02 24 0.1338 0.2163
2 96 0.1642 0.02 24 0.1230 0.2055
3 96 0.1450 0.02 24 0.1037 0.1863
4 96 0.1600 0.02 24 0.1187 0.2013
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.0153125 -0.07047274 0.03984774 0.8689827
3-1 -0.0224250 -0.07758524 0.03273524 0.6801404
4-1 -0.0399250 -0.09508524 0.01523524 0.2171202
3-2 -0.0071125 -0.06227274 0.04804774 0.9841957
4-2 -0.0246125 -0.07977274 0.03054774 0.6139378
4-3 -0.0175000 -0.07266024 0.03766024 0.8175559
$tempo
diff lwr upr p adj
96-72 0.06863125 0.03944964 0.09781286 6.02e-05
$`fermentador:tempo`
diff lwr upr p adj
2:72-1:72 -0.019775 -0.113429769 0.07387977 0.9962175
3:72-1:72 -0.014775 -0.108429769 0.07887977 0.9994041
4:72-1:72 -0.064775 -0.158429769 0.02887977 0.3382617
1:96-1:72 0.057800 -0.035854769 0.15145477 0.4755851
2:96-1:72 0.046950 -0.046704769 0.14060477 0.7109082
3:96-1:72 0.027725 -0.065929769 0.12137977 0.9730160
4:96-1:72 0.042725 -0.050929769 0.13637977 0.7943989
3:72-2:72 0.005000 -0.088654769 0.09865477 0.9999996
4:72-2:72 -0.045000 -0.138654769 0.04865477 0.7507001
1:96-2:72 0.077575 -0.016079769 0.17122977 0.1576119
2:96-2:72 0.066725 -0.026929769 0.16037977 0.3043799
3:96-2:72 0.047500 -0.046154769 0.14115477 0.6993728
4:96-2:72 0.062500 -0.031154769 0.15615477 0.3804876
4:72-3:72 -0.050000 -0.143654769 0.04365477 0.6456829
1:96-3:72 0.072575 -0.021079769 0.16622977 0.2164645
2:96-3:72 0.061725 -0.031929769 0.15537977 0.3954902
3:96-3:72 0.042500 -0.051154769 0.13615477 0.7985334
4:96-3:72 0.057500 -0.036154769 0.15115477 0.4819458
1:96-4:72 0.122575 0.028920231 0.21622977 0.0047165
2:96-4:72 0.111725 0.018070231 0.20537977 0.0117652
3:96-4:72 0.092500 -0.001154769 0.18615477 0.0545868
4:96-4:72 0.107500 0.013845231 0.20115477 0.0166713
2:96-1:96 -0.010850 -0.104504769 0.08280477 0.9999230
3:96-1:96 -0.030075 -0.123729769 0.06357977 0.9584493
4:96-1:96 -0.015075 -0.108729769 0.07857977 0.9993210
3:96-2:96 -0.019225 -0.112879769 0.07442977 0.9968213
4:96-2:96 -0.004225 -0.097879769 0.08942977 0.9999999
4:96-3:96 0.015000 -0.078654769 0.10865477 0.9993426
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$tempo
96 72
"a" "b"
$`fermentador:tempo`
1:96 2:96 4:96 3:96 1:72 3:72 2:72 4:72
"a" "a" "a" "ab" "ab" "ab" "ab" "b"
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.04519 0.015063 2.492 0.0757 .
tempo 2 0.00000 0.000001 0.000 0.9999
fermentador:tempo 6 0.00036 0.000061 0.010 1.0000
Residuals 36 0.21762 0.006045
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 48 0.310 0.0389 36 0.231 0.389
2 48 0.305 0.0389 36 0.226 0.384
3 48 0.295 0.0389 36 0.216 0.374
4 48 0.233 0.0389 36 0.154 0.311
1 72 0.319 0.0389 36 0.240 0.398
2 72 0.297 0.0389 36 0.219 0.376
3 72 0.295 0.0389 36 0.216 0.374
4 72 0.233 0.0389 36 0.154 0.311
1 96 0.313 0.0389 36 0.235 0.392
2 96 0.300 0.0389 36 0.221 0.379
3 96 0.292 0.0389 36 0.214 0.371
4 96 0.237 0.0389 36 0.159 0.316
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.013315833 -0.09880132 0.07216966 0.9747832
3-1 -0.019982500 -0.10546799 0.06550299 0.9218566
4-1 -0.079982500 -0.16546799 0.00550299 0.0736837
3-2 -0.006666667 -0.09215216 0.07881882 0.9966635
4-2 -0.066666667 -0.15215216 0.01881882 0.1723756
4-3 -0.060000000 -0.14548549 0.02548549 0.2501038
$tempo
diff lwr upr p adj
72-48 0.0003775 -0.06681241 0.06756741 0.9998960
96-48 0.0002325 -0.06695741 0.06742241 0.9999606
96-72 -0.0001450 -0.06733491 0.06704491 0.9999847
$`fermentador:tempo`
diff lwr upr p adj
2:48-1:48 -5.002500e-03 -0.1968895 0.1868845 1.0000000
3:48-1:48 -1.500250e-02 -0.2068895 0.1768845 1.0000000
4:48-1:48 -7.750250e-02 -0.2693895 0.1143845 0.9539361
1:72-1:48 9.010000e-03 -0.1828770 0.2008970 1.0000000
2:72-1:48 -1.250250e-02 -0.2043895 0.1793845 1.0000000
3:72-1:48 -1.500250e-02 -0.2068895 0.1768845 1.0000000
4:72-1:48 -7.750250e-02 -0.2693895 0.1143845 0.9539361
1:96-1:48 3.430000e-03 -0.1884570 0.1953170 1.0000000
2:96-1:48 -1.000250e-02 -0.2018895 0.1818845 1.0000000
3:96-1:48 -1.750250e-02 -0.2093895 0.1743845 1.0000000
4:96-1:48 -7.250250e-02 -0.2643895 0.1193845 0.9709852
3:48-2:48 -1.000000e-02 -0.2018870 0.1818870 1.0000000
4:48-2:48 -7.250000e-02 -0.2643870 0.1193870 0.9709923
1:72-2:48 1.401250e-02 -0.1778745 0.2058995 1.0000000
2:72-2:48 -7.500000e-03 -0.1993870 0.1843870 1.0000000
3:72-2:48 -1.000000e-02 -0.2018870 0.1818870 1.0000000
4:72-2:48 -7.250000e-02 -0.2643870 0.1193870 0.9709923
1:96-2:48 8.432500e-03 -0.1834545 0.2003195 1.0000000
2:96-2:48 -5.000000e-03 -0.1968870 0.1868870 1.0000000
3:96-2:48 -1.250000e-02 -0.2043870 0.1793870 1.0000000
4:96-2:48 -6.750000e-02 -0.2593870 0.1243870 0.9828468
4:48-3:48 -6.250000e-02 -0.2543870 0.1293870 0.9905653
1:72-3:48 2.401250e-02 -0.1678745 0.2158995 0.9999990
2:72-3:48 2.500000e-03 -0.1893870 0.1943870 1.0000000
3:72-3:48 0.000000e+00 -0.1918870 0.1918870 1.0000000
4:72-3:48 -6.250000e-02 -0.2543870 0.1293870 0.9905653
1:96-3:48 1.843250e-02 -0.1734545 0.2103195 0.9999999
2:96-3:48 5.000000e-03 -0.1868870 0.1968870 1.0000000
3:96-3:48 -2.500000e-03 -0.1943870 0.1893870 1.0000000
4:96-3:48 -5.750000e-02 -0.2493870 0.1343870 0.9952274
1:72-4:48 8.651250e-02 -0.1053745 0.2783995 0.9074607
2:72-4:48 6.500000e-02 -0.1268870 0.2568870 0.9871545
3:72-4:48 6.250000e-02 -0.1293870 0.2543870 0.9905653
4:72-4:48 -5.551115e-17 -0.1918870 0.1918870 1.0000000
1:96-4:48 8.093250e-02 -0.1109545 0.2728195 0.9387675
2:96-4:48 6.750000e-02 -0.1243870 0.2593870 0.9828468
3:96-4:48 6.000000e-02 -0.1318870 0.2518870 0.9932137
4:96-4:48 5.000000e-03 -0.1868870 0.1968870 1.0000000
2:72-1:72 -2.151250e-02 -0.2133995 0.1703745 0.9999997
3:72-1:72 -2.401250e-02 -0.2158995 0.1678745 0.9999990
4:72-1:72 -8.651250e-02 -0.2783995 0.1053745 0.9074607
1:96-1:72 -5.580000e-03 -0.1974670 0.1863070 1.0000000
2:96-1:72 -1.901250e-02 -0.2108995 0.1728745 0.9999999
3:96-1:72 -2.651250e-02 -0.2183995 0.1653745 0.9999971
4:96-1:72 -8.151250e-02 -0.2733995 0.1103745 0.9359031
3:72-2:72 -2.500000e-03 -0.1943870 0.1893870 1.0000000
4:72-2:72 -6.500000e-02 -0.2568870 0.1268870 0.9871545
1:96-2:72 1.593250e-02 -0.1759545 0.2078195 1.0000000
2:96-2:72 2.500000e-03 -0.1893870 0.1943870 1.0000000
3:96-2:72 -5.000000e-03 -0.1968870 0.1868870 1.0000000
4:96-2:72 -6.000000e-02 -0.2518870 0.1318870 0.9932137
4:72-3:72 -6.250000e-02 -0.2543870 0.1293870 0.9905653
1:96-3:72 1.843250e-02 -0.1734545 0.2103195 0.9999999
2:96-3:72 5.000000e-03 -0.1868870 0.1968870 1.0000000
3:96-3:72 -2.500000e-03 -0.1943870 0.1893870 1.0000000
4:96-3:72 -5.750000e-02 -0.2493870 0.1343870 0.9952274
1:96-4:72 8.093250e-02 -0.1109545 0.2728195 0.9387675
2:96-4:72 6.750000e-02 -0.1243870 0.2593870 0.9828468
3:96-4:72 6.000000e-02 -0.1318870 0.2518870 0.9932137
4:96-4:72 5.000000e-03 -0.1868870 0.1968870 1.0000000
2:96-1:96 -1.343250e-02 -0.2053195 0.1784545 1.0000000
3:96-1:96 -2.093250e-02 -0.2128195 0.1709545 0.9999998
4:96-1:96 -7.593250e-02 -0.2678195 0.1159545 0.9599073
3:96-2:96 -7.500000e-03 -0.1993870 0.1843870 1.0000000
4:96-2:96 -6.250000e-02 -0.2543870 0.1293870 0.9905653
4:96-3:96 -5.500000e-02 -0.2468870 0.1368870 0.9967245
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$tempo
$tempo$Letters
72 96 48
"a" "a" "a"
$tempo$LetterMatrix
a
72 TRUE
96 TRUE
48 TRUE
$`fermentador:tempo`
$`fermentador:tempo`$Letters
1:72 1:96 1:48 2:48 2:96 2:72 3:48 3:72 3:96 4:96 4:48 4:72
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tempo`$LetterMatrix
a
1:72 TRUE
1:96 TRUE
1:48 TRUE
2:48 TRUE
2:96 TRUE
2:72 TRUE
3:48 TRUE
3:72 TRUE
3:96 TRUE
4:96 TRUE
4:48 TRUE
4:72 TRUE
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 2776 925 0.552 0.652
tempo 1 42080 42080 25.113 4.04e-05 ***
fermentador:tempo 3 3544 1181 0.705 0.558
Residuals 24 40216 1676
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 24 584 20.5 24 541 626
2 24 617 20.5 24 574 659
3 24 633 20.5 24 590 675
4 24 617 20.5 24 575 659
1 48 537 20.5 24 495 579
2 48 543 20.5 24 501 585
3 48 528 20.5 24 486 570
4 48 552 20.5 24 510 594
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 19.301000 -37.16049 75.76249 0.7822083
3-1 19.838050 -36.62344 76.29954 0.7679323
4-1 24.094575 -32.36691 80.55606 0.6465844
3-2 0.537050 -55.92444 56.99854 0.9999933
4-2 4.793575 -51.66791 61.25506 0.9953518
4-3 4.256525 -52.20496 60.71801 0.9967297
$tempo
diff lwr upr p adj
48-24 -72.52585 -102.3959 -42.65583 4.04e-05
$`fermentador:tempo`
diff lwr upr p adj
2:24-1:24 32.923900 -62.94022 128.7880217 0.9415536
3:24-1:24 48.964425 -46.89970 144.8285467 0.6922694
4:24-1:24 33.420850 -62.44327 129.2849717 0.9370616
1:48-1:24 -46.488075 -142.35220 49.3760467 0.7423585
2:48-1:24 -40.809975 -136.67410 55.0541467 0.8439938
3:48-1:24 -55.776400 -151.64052 40.0877217 0.5470144
4:48-1:24 -31.719775 -127.58390 64.1443467 0.9515419
3:24-2:24 16.040525 -79.82360 111.9046467 0.9991275
4:24-2:24 0.496950 -95.36717 96.3610717 1.0000000
1:48-2:24 -79.411975 -175.27610 16.4521467 0.1575417
2:48-2:24 -73.733875 -169.59800 22.1302467 0.2237106
3:48-2:24 -88.700300 -184.56442 7.1638217 0.0841450
4:48-2:24 -64.643675 -160.50780 31.2204467 0.3680676
4:24-3:24 -15.543575 -111.40770 80.3205467 0.9992881
1:48-3:24 -95.452500 -191.31662 0.4116217 0.0515559
2:48-3:24 -89.774400 -185.63852 6.0897217 0.0779707
3:48-3:24 -104.740825 -200.60495 -8.8767033 0.0253528
4:48-3:24 -80.684200 -176.54832 15.1799217 0.1451067
1:48-4:24 -79.908925 -175.77305 15.9551967 0.1525853
2:48-4:24 -74.230825 -170.09495 21.6332967 0.2171932
3:48-4:24 -89.197250 -185.06137 6.6668717 0.0812368
4:48-4:24 -65.140625 -161.00475 30.7234967 0.3589912
2:48-1:48 5.678100 -90.18602 101.5422217 0.9999992
3:48-1:48 -9.288325 -105.15245 86.5757967 0.9999770
4:48-1:48 14.768300 -81.09582 110.6324217 0.9994896
3:48-2:48 -14.966425 -110.83055 80.8976967 0.9994433
4:48-2:48 9.090200 -86.77392 104.9543217 0.9999802
4:48-3:48 24.056625 -71.80750 119.9207467 0.9893582
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$tempo
24 48
"a" "b"
$`fermentador:tempo`
3:24 4:24 2:24 1:24 4:48 2:48 1:48 3:48
"a" "ab" "ab" "ab" "ab" "ab" "ab" "b"
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.0005 0.00017 0.016 0.997
tempo 2 0.5514 0.27568 24.721 1.75e-07 ***
fermentador:tempo 6 0.0010 0.00017 0.015 1.000
Residuals 36 0.4015 0.01115
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador tempo emmean SE df lower.CL upper.CL
1 0 0.0386 0.0528 36 -0.0685 0.146
2 0 0.0417 0.0528 36 -0.0653 0.149
3 0 0.0418 0.0528 36 -0.0653 0.149
4 0 0.0421 0.0528 36 -0.0650 0.149
1 72 0.0692 0.0528 36 -0.0379 0.176
2 72 0.0706 0.0528 36 -0.0365 0.178
3 72 0.0791 0.0528 36 -0.0280 0.186
4 72 0.0656 0.0528 36 -0.0415 0.173
1 96 0.2762 0.0528 36 0.1691 0.383
2 96 0.2724 0.0528 36 0.1654 0.380
3 96 0.2850 0.0528 36 0.1779 0.392
4 96 0.2942 0.0528 36 0.1871 0.401
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 0.0002483333 -0.1158605 0.1163571 0.9999999
3-1 0.0072995833 -0.1088092 0.1234084 0.9982410
4-1 0.0059537500 -0.1101550 0.1220625 0.9990419
3-2 0.0070512500 -0.1090575 0.1231600 0.9984133
4-2 0.0057054167 -0.1104034 0.1218142 0.9991563
4-3 -0.0013458333 -0.1174546 0.1147630 0.9999889
$tempo
diff lwr upr p adj
72-0 0.0300475 -0.06121171 0.1213067 0.7025028
96-0 0.2408847 0.14962547 0.3321439 0.0000005
96-72 0.2108372 0.11957797 0.3020964 0.0000061
$`fermentador:tempo`
diff lwr upr p adj
2:0-1:0 0.00312500 -0.257501255 0.2637513 1.0000000
3:0-1:0 0.00317750 -0.257448755 0.2638038 1.0000000
4:0-1:0 0.00349625 -0.257130005 0.2641225 1.0000000
1:72-1:0 0.03059000 -0.230036255 0.2912163 0.9999995
2:72-1:0 0.03195000 -0.228676255 0.2925763 0.9999992
3:72-1:0 0.04047125 -0.220155005 0.3010975 0.9999905
4:72-1:0 0.02697750 -0.233648755 0.2876038 0.9999999
1:96-1:0 0.23756500 -0.023061255 0.4981913 0.1020790
2:96-1:0 0.23382500 -0.026801255 0.4944513 0.1138646
3:96-1:0 0.24640500 -0.014221255 0.5070313 0.0782548
4:96-1:0 0.25554250 -0.005083755 0.5161688 0.0588432
3:0-2:0 0.00005250 -0.260573755 0.2606788 1.0000000
4:0-2:0 0.00037125 -0.260255005 0.2609975 1.0000000
1:72-2:0 0.02746500 -0.233161255 0.2880913 0.9999998
2:72-2:0 0.02882500 -0.231801255 0.2894513 0.9999997
3:72-2:0 0.03734625 -0.223280005 0.2979725 0.9999958
4:72-2:0 0.02385250 -0.236773755 0.2844788 1.0000000
1:96-2:0 0.23444000 -0.026186255 0.4950663 0.1118521
2:96-2:0 0.23070000 -0.029926255 0.4913263 0.1245596
3:96-2:0 0.24328000 -0.017346255 0.5039063 0.0860644
4:96-2:0 0.25241750 -0.008208755 0.5130438 0.0649429
4:0-3:0 0.00031875 -0.260307505 0.2609450 1.0000000
1:72-3:0 0.02741250 -0.233213755 0.2880388 0.9999998
2:72-3:0 0.02877250 -0.231853755 0.2893988 0.9999997
3:72-3:0 0.03729375 -0.223332505 0.2979200 0.9999959
4:72-3:0 0.02380000 -0.236826255 0.2844263 1.0000000
1:96-3:0 0.23438750 -0.026238755 0.4950138 0.1120228
2:96-3:0 0.23064750 -0.029978755 0.4912738 0.1247461
3:96-3:0 0.24322750 -0.017398755 0.5038538 0.0862012
4:96-3:0 0.25236500 -0.008261255 0.5129913 0.0650500
1:72-4:0 0.02709375 -0.233532505 0.2877200 0.9999999
2:72-4:0 0.02845375 -0.232172505 0.2890800 0.9999998
3:72-4:0 0.03697500 -0.223651255 0.2976013 0.9999962
4:72-4:0 0.02348125 -0.237145005 0.2841075 1.0000000
1:96-4:0 0.23406875 -0.026557505 0.4946950 0.1130634
2:96-4:0 0.23032875 -0.030297505 0.4909550 0.1258833
3:96-4:0 0.24290875 -0.017717505 0.5035350 0.0870353
4:96-4:0 0.25204625 -0.008580005 0.5126725 0.0657033
2:72-1:72 0.00136000 -0.259266255 0.2619863 1.0000000
3:72-1:72 0.00988125 -0.250745005 0.2705075 1.0000000
4:72-1:72 -0.00361250 -0.264238755 0.2570138 1.0000000
1:96-1:72 0.20697500 -0.053651255 0.4676013 0.2343455
2:96-1:72 0.20323500 -0.057391255 0.4638613 0.2566491
3:96-1:72 0.21581500 -0.044811255 0.4764413 0.1871827
4:96-1:72 0.22495250 -0.035673755 0.4855788 0.1463700
3:72-2:72 0.00852125 -0.252105005 0.2691475 1.0000000
4:72-2:72 -0.00497250 -0.265598755 0.2556538 1.0000000
1:96-2:72 0.20561500 -0.055011255 0.4662413 0.2422935
2:96-2:72 0.20187500 -0.058751255 0.4625013 0.2651069
3:96-2:72 0.21445500 -0.046171255 0.4750813 0.1939360
4:96-2:72 0.22359250 -0.037033755 0.4842188 0.1519549
4:72-3:72 -0.01349375 -0.274120005 0.2471325 1.0000000
1:96-3:72 0.19709375 -0.063532505 0.4577200 0.2962969
2:96-3:72 0.19335375 -0.067272505 0.4539800 0.3222401
3:96-3:72 0.20593375 -0.054692505 0.4665600 0.2404140
4:96-3:72 0.21507125 -0.045555005 0.4756975 0.1908536
1:96-4:72 0.21058750 -0.050038755 0.4712138 0.2141342
2:96-4:72 0.20684750 -0.053778755 0.4674738 0.2350827
3:96-4:72 0.21942750 -0.041198755 0.4800538 0.1701114
4:96-4:72 0.22856500 -0.032061255 0.4891913 0.1323300
2:96-1:96 -0.00374000 -0.264366255 0.2568863 1.0000000
3:96-1:96 0.00884000 -0.251786255 0.2694663 1.0000000
4:96-1:96 0.01797750 -0.242648755 0.2786038 1.0000000
3:96-2:96 0.01258000 -0.248046255 0.2732063 1.0000000
4:96-2:96 0.02171750 -0.238908755 0.2823438 1.0000000
4:96-3:96 0.00913750 -0.251488755 0.2697638 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
3 4 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
4 TRUE
2 TRUE
1 TRUE
$tempo
96 72 0
"a" "b" "b"
$`fermentador:tempo`
$`fermentador:tempo`$Letters
4:96 3:96 1:96 2:96 3:72 2:72 1:72 4:72 4:0 3:0 2:0 1:0
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tempo`$LetterMatrix
a
4:96 TRUE
3:96 TRUE
1:96 TRUE
2:96 TRUE
3:72 TRUE
2:72 TRUE
1:72 TRUE
4:72 TRUE
4:0 TRUE
3:0 TRUE
2:0 TRUE
1:0 TRUE
9 Periodo x Tempo
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ periodo * tempo")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ periodo * tempo")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ periodo * tempo"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ periodo * tempo)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 167360 55787 18.07 5.53e-08 ***
tempo 3 349201 116400 37.69 1.14e-12 ***
periodo:tempo 9 387377 43042 13.94 1.10e-10 ***
Residuals 48 148225 3088
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 24 475.0 27.8 48 419.13 531
2 24 352.5 27.8 48 296.63 408
3 24 288.8 27.8 48 232.88 345
4 24 78.2 27.8 48 22.38 134
1 48 230.0 27.8 48 174.13 286
2 48 181.2 27.8 48 125.38 237
3 48 49.5 27.8 48 -6.37 105
4 48 298.8 27.8 48 242.88 355
1 72 407.5 27.8 48 351.63 463
2 72 417.5 27.8 48 361.63 473
3 72 296.2 27.8 48 240.38 352
4 72 462.5 27.8 48 406.63 518
1 96 322.5 27.8 48 266.63 378
2 96 363.0 27.8 48 307.13 419
3 96 250.5 27.8 48 194.63 306
4 96 353.8 27.8 48 297.88 410
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -30.1875 -82.47543 22.100435 0.4242093
3-1 -137.5000 -189.78793 -85.212065 0.0000000
4-1 -60.4375 -112.72543 -8.149565 0.0175826
3-2 -107.3125 -159.60043 -55.024565 0.0000096
4-2 -30.2500 -82.53793 22.037935 0.4223772
4-3 77.0625 24.77457 129.350435 0.0015398
$tempo
diff lwr upr p adj
48-24 -108.7500 -161.03793 -56.46207 0.0000075
72-24 97.3125 45.02457 149.60043 0.0000547
96-24 23.8125 -28.47543 76.10043 0.6223591
72-48 206.0625 153.77457 258.35043 0.0000000
96-48 132.5625 80.27457 184.85043 0.0000001
96-72 -73.5000 -125.78793 -21.21207 0.0026700
$`periodo:tempo`
diff lwr upr p adj
2:24-1:24 -122.50 -264.4569782 19.456978 0.1624394
3:24-1:24 -186.25 -328.2069782 -44.293022 0.0018389
4:24-1:24 -396.75 -538.7069782 -254.793022 0.0000000
1:48-1:24 -245.00 -386.9569782 -103.043022 0.0000120
2:48-1:24 -293.75 -435.7069782 -151.793022 0.0000002
3:48-1:24 -425.50 -567.4569782 -283.543022 0.0000000
4:48-1:24 -176.25 -318.2069782 -34.293022 0.0040928
1:72-1:24 -67.50 -209.4569782 74.456978 0.9315711
2:72-1:24 -57.50 -199.4569782 84.456978 0.9816721
3:72-1:24 -178.75 -320.7069782 -36.793022 0.0033587
4:72-1:24 -12.50 -154.4569782 129.456978 1.0000000
1:96-1:24 -152.50 -294.4569782 -10.543022 0.0243091
2:96-1:24 -112.00 -253.9569782 29.956978 0.2770200
3:96-1:24 -224.50 -366.4569782 -82.543022 0.0000725
4:96-1:24 -121.25 -263.2069782 20.706978 0.1738171
3:24-2:24 -63.75 -205.7069782 78.206978 0.9559535
4:24-2:24 -274.25 -416.2069782 -132.293022 0.0000009
1:48-2:24 -122.50 -264.4569782 19.456978 0.1624394
2:48-2:24 -171.25 -313.2069782 -29.293022 0.0060463
3:48-2:24 -303.00 -444.9569782 -161.043022 0.0000001
4:48-2:24 -53.75 -195.7069782 88.206978 0.9902002
1:72-2:24 55.00 -86.9569782 196.956978 0.9878145
2:72-2:24 65.00 -76.9569782 206.956978 0.9486512
3:72-2:24 -56.25 -198.2069782 85.706978 0.9849887
4:72-2:24 110.00 -31.9569782 251.956978 0.3037777
1:96-2:24 -30.00 -171.9569782 111.956978 0.9999882
2:96-2:24 10.50 -131.4569782 152.456978 1.0000000
3:96-2:24 -102.00 -243.9569782 39.956978 0.4249847
4:96-2:24 1.25 -140.7069782 143.206978 1.0000000
4:24-3:24 -210.50 -352.4569782 -68.543022 0.0002428
1:48-3:24 -58.75 -200.7069782 83.206978 0.9778133
2:48-3:24 -107.50 -249.4569782 34.456978 0.3393409
3:48-3:24 -239.25 -381.2069782 -97.293022 0.0000199
4:48-3:24 10.00 -131.9569782 151.956978 1.0000000
1:72-3:24 118.75 -23.2069782 260.706978 0.1983719
2:72-3:24 128.75 -13.2069782 270.706978 0.1140139
3:72-3:24 7.50 -134.4569782 149.456978 1.0000000
4:72-3:24 173.75 31.7930218 315.706978 0.0049789
1:96-3:24 33.75 -108.2069782 175.706978 0.9999470
2:96-3:24 74.25 -67.7069782 216.206978 0.8678139
3:96-3:24 -38.25 -180.2069782 103.706978 0.9997546
4:96-3:24 65.00 -76.9569782 206.956978 0.9486512
1:48-4:24 151.75 9.7930218 293.706978 0.0256303
2:48-4:24 103.00 -38.9569782 244.956978 0.4087394
3:48-4:24 -28.75 -170.7069782 113.206978 0.9999933
4:48-4:24 220.50 78.5430218 362.456978 0.0001027
1:72-4:24 329.25 187.2930218 471.206978 0.0000000
2:72-4:24 339.25 197.2930218 481.206978 0.0000000
3:72-4:24 218.00 76.0430218 359.956978 0.0001275
4:72-4:24 384.25 242.2930218 526.206978 0.0000000
1:96-4:24 244.25 102.2930218 386.206978 0.0000128
2:96-4:24 284.75 142.7930218 426.706978 0.0000004
3:96-4:24 172.25 30.2930218 314.206978 0.0055956
4:96-4:24 275.50 133.5430218 417.456978 0.0000008
2:48-1:48 -48.75 -190.7069782 93.206978 0.9962998
3:48-1:48 -180.50 -322.4569782 -38.543022 0.0029218
4:48-1:48 68.75 -73.2069782 210.706978 0.9217371
1:72-1:48 177.50 35.5430218 319.456978 0.0037084
2:72-1:48 187.50 45.5430218 329.456978 0.0016611
3:72-1:48 66.25 -75.7069782 208.206978 0.9405341
4:72-1:48 232.50 90.5430218 374.456978 0.0000361
1:96-1:48 92.50 -49.4569782 234.456978 0.5883106
2:96-1:48 133.00 -8.9569782 274.956978 0.0883877
3:96-1:48 20.50 -121.4569782 162.456978 0.9999999
4:96-1:48 123.75 -18.2069782 265.706978 0.1516457
3:48-2:48 -131.75 -273.7069782 10.206978 0.0953659
4:48-2:48 117.50 -24.4569782 259.456978 0.2115675
1:72-2:48 226.25 84.2930218 368.206978 0.0000623
2:72-2:48 236.25 94.2930218 378.206978 0.0000259
3:72-2:48 115.00 -26.9569782 256.956978 0.2398239
4:72-2:48 281.25 139.2930218 423.206978 0.0000005
1:96-2:48 141.25 -0.7069782 283.206978 0.0523796
2:96-2:48 181.75 39.7930218 323.706978 0.0026438
3:96-2:48 69.25 -72.7069782 211.206978 0.9175552
4:96-2:48 172.50 30.5430218 314.456978 0.0054880
4:48-3:48 249.25 107.2930218 391.206978 0.0000082
1:72-3:48 358.00 216.0430218 499.956978 0.0000000
2:72-3:48 368.00 226.0430218 509.956978 0.0000000
3:72-3:48 246.75 104.7930218 388.706978 0.0000103
4:72-3:48 413.00 271.0430218 554.956978 0.0000000
1:96-3:48 273.00 131.0430218 414.956978 0.0000010
2:96-3:48 313.50 171.5430218 455.456978 0.0000000
3:96-3:48 201.00 59.0430218 342.956978 0.0005431
4:96-3:48 304.25 162.2930218 446.206978 0.0000001
1:72-4:48 108.75 -33.2069782 250.706978 0.3212730
2:72-4:48 118.75 -23.2069782 260.706978 0.1983719
3:72-4:48 -2.50 -144.4569782 139.456978 1.0000000
4:72-4:48 163.75 21.7930218 305.706978 0.0107046
1:96-4:48 23.75 -118.2069782 165.706978 0.9999995
2:96-4:48 64.25 -77.7069782 206.206978 0.9531281
3:96-4:48 -48.25 -190.2069782 93.706978 0.9966759
4:96-4:48 55.00 -86.9569782 196.956978 0.9878145
2:72-1:72 10.00 -131.9569782 151.956978 1.0000000
3:72-1:72 -111.25 -253.2069782 30.706978 0.2868732
4:72-1:72 55.00 -86.9569782 196.956978 0.9878145
1:96-1:72 -85.00 -226.9569782 56.956978 0.7165163
2:96-1:72 -44.50 -186.4569782 97.456978 0.9986046
3:96-1:72 -157.00 -298.9569782 -15.043022 0.0176109
4:96-1:72 -53.75 -195.7069782 88.206978 0.9902002
3:72-2:72 -121.25 -263.2069782 20.706978 0.1738171
4:72-2:72 45.00 -96.9569782 186.956978 0.9984226
1:96-2:72 -95.00 -236.9569782 46.956978 0.5444298
2:96-2:72 -54.50 -196.4569782 87.456978 0.9888188
3:96-2:72 -167.00 -308.9569782 -25.043022 0.0083757
4:96-2:72 -63.75 -205.7069782 78.206978 0.9559535
4:72-3:72 166.25 24.2930218 308.206978 0.0088663
1:96-3:72 26.25 -115.7069782 168.206978 0.9999980
2:96-3:72 66.75 -75.2069782 208.706978 0.9370520
3:96-3:72 -45.75 -187.7069782 96.206978 0.9981119
4:96-3:72 57.50 -84.4569782 199.456978 0.9816721
1:96-4:72 -140.00 -281.9569782 1.956978 0.0568323
2:96-4:72 -99.50 -241.4569782 42.456978 0.4666656
3:96-4:72 -212.00 -353.9569782 -70.043022 0.0002135
4:96-4:72 -108.75 -250.7069782 33.206978 0.3212730
2:96-1:96 40.50 -101.4569782 182.456978 0.9995192
3:96-1:96 -72.00 -213.9569782 69.956978 0.8919815
4:96-1:96 31.25 -110.7069782 173.206978 0.9999801
3:96-2:96 -112.50 -254.4569782 29.456978 0.2705733
4:96-2:96 -9.25 -151.2069782 132.706978 1.0000000
4:96-3:96 103.25 -38.7069782 245.206978 0.4047207
Letras do Tukey:
$periodo
1 2 4 3
"a" "ab" "b" "c"
$tempo
72 96 24 48
"a" "b" "b" "c"
$`periodo:tempo`
1:24 4:72 2:72 1:72 2:96 4:96 2:24 1:96 4:48 3:72 3:24 3:96 1:48 2:48 4:24
"a" "ab" "abc" "abc" "abcd" "abcd" "abcd" "bcde" "cde" "cde" "cde" "de" "de" "ef" "f"
3:48
"f"
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 1035268 345089 5.890 0.00166 **
tempo 3 3330824 1110275 18.951 3.02e-08 ***
periodo:tempo 9 1654805 183867 3.138 0.00473 **
Residuals 48 2812214 58588
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 24 1878 121 48 1635 2121
2 24 1960 121 48 1717 2203
3 24 1436 121 48 1193 1679
4 24 1495 121 48 1252 1739
1 48 1516 121 48 1273 1760
2 48 1378 121 48 1135 1621
3 48 1208 121 48 965 1451
4 48 1367 121 48 1123 1610
1 72 1115 121 48 872 1359
2 72 1024 121 48 781 1268
3 72 949 121 48 705 1192
4 72 1468 121 48 1224 1711
1 96 1537 121 48 1294 1780
2 96 1586 121 48 1342 1829
3 96 1447 121 48 1204 1691
4 96 2096 121 48 1853 2340
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -24.6875 -252.4406 203.0655643 0.9915274
3-1 -251.6875 -479.4406 -23.9344357 0.0250230
4-1 94.8125 -132.9406 322.5655643 0.6864580
3-2 -227.0000 -454.7531 0.7530643 0.0510555
4-2 119.5000 -108.2531 347.2530643 0.5077448
4-3 346.5000 118.7469 574.2530643 0.0010404
$tempo
diff lwr upr p adj
48-24 -325.0000 -552.75306 -97.2469357 0.0022511
72-24 -553.3750 -781.12806 -325.6219357 0.0000003
96-24 -25.6875 -253.44056 202.0655643 0.9904815
72-48 -228.3750 -456.12806 -0.6219357 0.0491427
96-48 299.3125 71.55944 527.0655643 0.0054650
96-72 527.6875 299.93444 755.4405643 0.0000008
$`periodo:tempo`
diff lwr upr p adj
2:24-1:24 82.00 -536.32882 700.32882 1.0000000
3:24-1:24 -442.00 -1060.32882 176.32882 0.4336156
4:24-1:24 -382.75 -1001.07882 235.57882 0.6686036
1:48-1:24 -361.50 -979.82882 256.82882 0.7485450
2:48-1:24 -500.00 -1118.32882 118.32882 0.2423010
3:48-1:24 -670.00 -1288.32882 -51.67118 0.0221349
4:48-1:24 -511.25 -1129.57882 107.07882 0.2129358
1:72-1:24 -762.75 -1381.07882 -144.42118 0.0044752
2:72-1:24 -853.75 -1472.07882 -235.42118 0.0008246
3:72-1:24 -929.25 -1547.57882 -310.92118 0.0001904
4:72-1:24 -410.50 -1028.82882 207.82882 0.5576990
1:96-1:24 -341.00 -959.32882 277.32882 0.8174037
2:96-1:24 -292.25 -910.57882 326.07882 0.9345676
3:96-1:24 -430.75 -1049.07882 187.57882 0.4769957
4:96-1:24 218.50 -399.82882 836.82882 0.9950401
3:24-2:24 -524.00 -1142.32882 94.32882 0.1828546
4:24-2:24 -464.75 -1083.07882 153.57882 0.3512234
1:48-2:24 -443.50 -1061.82882 174.82882 0.4279452
2:48-2:24 -582.00 -1200.32882 36.32882 0.0851079
3:48-2:24 -752.00 -1370.32882 -133.67118 0.0054263
4:48-2:24 -593.25 -1211.57882 25.07882 0.0724715
1:72-2:24 -844.75 -1463.07882 -226.42118 0.0009788
2:72-2:24 -935.75 -1554.07882 -317.42118 0.0001675
3:72-2:24 -1011.25 -1629.57882 -392.92118 0.0000371
4:72-2:24 -492.50 -1110.82882 125.82882 0.2633569
1:96-2:24 -423.00 -1041.32882 195.32882 0.5075938
2:96-2:24 -374.25 -992.57882 244.07882 0.7013873
3:96-2:24 -512.75 -1131.07882 105.57882 0.2092215
4:96-2:24 136.50 -481.82882 754.82882 0.9999793
4:24-3:24 59.25 -559.07882 677.57882 1.0000000
1:48-3:24 80.50 -537.82882 698.82882 1.0000000
2:48-3:24 -58.00 -676.32882 560.32882 1.0000000
3:48-3:24 -228.00 -846.32882 390.32882 0.9924186
4:48-3:24 -69.25 -687.57882 549.07882 1.0000000
1:72-3:24 -320.75 -939.07882 297.57882 0.8746704
2:72-3:24 -411.75 -1030.07882 206.57882 0.5526638
3:72-3:24 -487.25 -1105.57882 131.07882 0.2787946
4:72-3:24 31.50 -586.82882 649.82882 1.0000000
1:96-3:24 101.00 -517.32882 719.32882 0.9999996
2:96-3:24 149.75 -468.57882 768.07882 0.9999332
3:96-3:24 11.25 -607.07882 629.57882 1.0000000
4:96-3:24 660.50 42.17118 1278.82882 0.0258319
1:48-4:24 21.25 -597.07882 639.57882 1.0000000
2:48-4:24 -117.25 -735.57882 501.07882 0.9999972
3:48-4:24 -287.25 -905.57882 331.07882 0.9425760
4:48-4:24 -128.50 -746.82882 489.82882 0.9999906
1:72-4:24 -380.00 -998.32882 238.32882 0.6793068
2:72-4:24 -471.00 -1089.32882 147.32882 0.3301213
3:72-4:24 -546.50 -1164.82882 71.82882 0.1377507
4:72-4:24 -27.75 -646.07882 590.57882 1.0000000
1:96-4:24 41.75 -576.57882 660.07882 1.0000000
2:96-4:24 90.50 -527.82882 708.82882 0.9999999
3:96-4:24 -48.00 -666.32882 570.32882 1.0000000
4:96-4:24 601.25 -17.07882 1219.57882 0.0645007
2:48-1:48 -138.50 -756.82882 479.82882 0.9999751
3:48-1:48 -308.50 -926.82882 309.82882 0.9034344
4:48-1:48 -149.75 -768.07882 468.57882 0.9999332
1:72-1:48 -401.25 -1019.57882 217.07882 0.5949837
2:72-1:48 -492.25 -1110.57882 126.07882 0.2640790
3:72-1:48 -567.75 -1186.07882 50.57882 0.1037555
4:72-1:48 -49.00 -667.32882 569.32882 1.0000000
1:96-1:48 20.50 -597.82882 638.82882 1.0000000
2:96-1:48 69.25 -549.07882 687.57882 1.0000000
3:96-1:48 -69.25 -687.57882 549.07882 1.0000000
4:96-1:48 580.00 -38.32882 1198.32882 0.0875405
3:48-2:48 -170.00 -788.32882 448.32882 0.9996883
4:48-2:48 -11.25 -629.57882 607.07882 1.0000000
1:72-2:48 -262.75 -881.07882 355.57882 0.9721095
2:72-2:48 -353.75 -972.07882 264.57882 0.7757102
3:72-2:48 -429.25 -1047.57882 189.07882 0.4828791
4:72-2:48 89.50 -528.82882 707.82882 0.9999999
1:96-2:48 159.00 -459.32882 777.32882 0.9998603
2:96-2:48 207.75 -410.57882 826.07882 0.9970551
3:96-2:48 69.25 -549.07882 687.57882 1.0000000
4:96-2:48 718.50 100.17118 1336.82882 0.0097782
4:48-3:48 158.75 -459.57882 777.07882 0.9998629
1:72-3:48 -92.75 -711.07882 525.57882 0.9999999
2:72-3:48 -183.75 -802.07882 434.57882 0.9992322
3:72-3:48 -259.25 -877.57882 359.07882 0.9751475
4:72-3:48 259.50 -358.82882 877.82882 0.9749393
1:96-3:48 329.00 -289.32882 947.32882 0.8527574
2:96-3:48 377.75 -240.57882 996.07882 0.6879981
3:96-3:48 239.25 -379.07882 857.57882 0.9879645
4:96-3:48 888.50 270.17118 1506.82882 0.0004224
1:72-4:48 -251.50 -869.82882 366.82882 0.9809755
2:72-4:48 -342.50 -960.82882 275.82882 0.8127093
3:72-4:48 -418.00 -1036.32882 200.32882 0.5275547
4:72-4:48 100.75 -517.57882 719.07882 0.9999996
1:96-4:48 170.25 -448.07882 788.57882 0.9996828
2:96-4:48 219.00 -399.32882 837.32882 0.9949239
3:96-4:48 80.50 -537.82882 698.82882 1.0000000
4:96-4:48 729.75 111.42118 1348.07882 0.0080402
2:72-1:72 -91.00 -709.32882 527.32882 0.9999999
3:72-1:72 -166.50 -784.82882 451.82882 0.9997565
4:72-1:72 352.25 -266.07882 970.57882 0.7808176
1:96-1:72 421.75 -196.57882 1040.07882 0.5125704
2:96-1:72 470.50 -147.82882 1088.82882 0.3317827
3:96-1:72 332.00 -286.32882 950.32882 0.8442965
4:96-1:72 981.25 362.92118 1599.57882 0.0000678
3:72-2:72 -75.50 -693.82882 542.82882 1.0000000
4:72-2:72 443.25 -175.07882 1061.57882 0.4288882
1:96-2:72 512.75 -105.57882 1131.07882 0.2092215
2:96-2:72 561.50 -56.82882 1179.82882 0.1129478
3:96-2:72 423.00 -195.32882 1041.32882 0.5075938
4:96-2:72 1072.25 453.92118 1690.57882 0.0000108
4:72-3:72 518.75 -99.57882 1137.07882 0.1948333
1:96-3:72 588.25 -30.07882 1206.57882 0.0778735
2:96-3:72 637.00 18.67118 1255.32882 0.0375219
3:96-3:72 498.50 -119.82882 1116.82882 0.2464177
4:96-3:72 1147.75 529.42118 1766.07882 0.0000023
1:96-4:72 69.50 -548.82882 687.82882 1.0000000
2:96-4:72 118.25 -500.07882 736.57882 0.9999969
3:96-4:72 -20.25 -638.57882 598.07882 1.0000000
4:96-4:72 629.00 10.67118 1247.32882 0.0424790
2:96-1:96 48.75 -569.57882 667.07882 1.0000000
3:96-1:96 -89.75 -708.07882 528.57882 0.9999999
4:96-1:96 559.50 -58.82882 1177.82882 0.1160271
3:96-2:96 -138.50 -756.82882 479.82882 0.9999751
4:96-2:96 510.75 -107.57882 1129.07882 0.2141844
4:96-3:96 649.25 30.92118 1267.57882 0.0309360
Letras do Tukey:
$periodo
4 1 2 3
"a" "a" "ab" "b"
$tempo
24 96 48 72
"a" "a" "b" "c"
$`periodo:tempo`
4:96 2:24 1:24 2:96 1:96 1:48 4:24 4:72 3:96 3:24 2:48 4:48 3:48 1:72 2:72
"a" "ab" "ab" "abc" "abcd" "abcd" "abcd" "bcd" "bcd" "bcd" "bcd" "bcd" "cd" "cd" "cd"
3:72
"d"
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3.148 1.049 1.026 0.3985
tempo 1 5.874 5.874 5.745 0.0247 *
periodo:tempo 3 2.979 0.993 0.971 0.4225
Residuals 24 24.539 1.022
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 72 4.58 0.506 24 3.53 5.62
2 72 5.79 0.506 24 4.74 6.83
3 72 4.81 0.506 24 3.77 5.85
4 72 4.58 0.506 24 3.53 5.62
1 96 6.04 0.506 24 4.99 7.08
2 96 5.93 0.506 24 4.89 6.98
3 96 5.17 0.506 24 4.12 6.21
4 96 6.04 0.506 24 4.99 7.08
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 5.537500e-01 -0.8409448 1.9484448 0.6957319
3-1 -3.175000e-01 -1.7121948 1.0771948 0.9220323
4-1 -8.881784e-16 -1.3946948 1.3946948 1.0000000
3-2 -8.712500e-01 -2.2659448 0.5234448 0.3340933
4-2 -5.537500e-01 -1.9484448 0.8409448 0.6957319
4-3 3.175000e-01 -1.0771948 1.7121948 0.9220323
$tempo
diff lwr upr p adj
96-72 0.856875 0.1190347 1.594715 0.0246787
$`periodo:tempo`
diff lwr upr p adj
2:72-1:72 1.212500e+00 -1.1555068 3.580507 0.6897549
3:72-1:72 2.350000e-01 -2.1330068 2.603007 0.9999730
4:72-1:72 -8.881784e-16 -2.3680068 2.368007 1.0000000
1:96-1:72 1.462500e+00 -0.9055068 3.830507 0.4746982
2:96-1:72 1.357500e+00 -1.0105068 3.725507 0.5646171
3:96-1:72 5.925000e-01 -1.7755068 2.960507 0.9895385
4:96-1:72 1.462500e+00 -0.9055068 3.830507 0.4746982
3:72-2:72 -9.775000e-01 -3.3455068 1.390507 0.8629596
4:72-2:72 -1.212500e+00 -3.5805068 1.155507 0.6897549
1:96-2:72 2.500000e-01 -2.1180068 2.618007 0.9999589
2:96-2:72 1.450000e-01 -2.2230068 2.513007 0.9999990
3:96-2:72 -6.200000e-01 -2.9880068 1.748007 0.9864018
4:96-2:72 2.500000e-01 -2.1180068 2.618007 0.9999589
4:72-3:72 -2.350000e-01 -2.6030068 2.133007 0.9999730
1:96-3:72 1.227500e+00 -1.1405068 3.595507 0.6770929
2:96-3:72 1.122500e+00 -1.2455068 3.490507 0.7626386
3:96-3:72 3.575000e-01 -2.0105068 2.725507 0.9995528
4:96-3:72 1.227500e+00 -1.1405068 3.595507 0.6770929
1:96-4:72 1.462500e+00 -0.9055068 3.830507 0.4746982
2:96-4:72 1.357500e+00 -1.0105068 3.725507 0.5646171
3:96-4:72 5.925000e-01 -1.7755068 2.960507 0.9895385
4:96-4:72 1.462500e+00 -0.9055068 3.830507 0.4746982
2:96-1:96 -1.050000e-01 -2.4730068 2.263007 0.9999999
3:96-1:96 -8.700000e-01 -3.2380068 1.498007 0.9189404
4:96-1:96 0.000000e+00 -2.3680068 2.368007 1.0000000
3:96-2:96 -7.650000e-01 -3.1330068 1.603007 0.9571402
4:96-2:96 1.050000e-01 -2.2630068 2.473007 0.9999999
4:96-3:96 8.700000e-01 -1.4980068 3.238007 0.9189404
Letras do Tukey:
$periodo
$periodo$Letters
2 1 4 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
2 TRUE
1 TRUE
4 TRUE
3 TRUE
$tempo
96 72
"a" "b"
$`periodo:tempo`
$`periodo:tempo`$Letters
1:96 4:96 2:96 2:72 3:96 3:72 1:72 4:72
"a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tempo`$LetterMatrix
a
1:96 TRUE
4:96 TRUE
2:96 TRUE
2:72 TRUE
3:96 TRUE
3:72 TRUE
1:72 TRUE
4:72 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.02021 0.00674 6.344 0.00254 **
tempo 1 0.03768 0.03768 35.493 3.78e-06 ***
periodo:tempo 3 0.00392 0.00131 1.229 0.32077
Residuals 24 0.02548 0.00106
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 72 0.0873 0.0163 24 0.0537 0.121
2 72 0.0925 0.0163 24 0.0589 0.126
3 72 0.0750 0.0163 24 0.0414 0.109
4 72 0.1150 0.0163 24 0.0814 0.149
1 96 0.1543 0.0163 24 0.1207 0.188
2 96 0.1475 0.0163 24 0.1139 0.181
3 96 0.1225 0.0163 24 0.0889 0.156
4 96 0.2200 0.0163 24 0.1864 0.254
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -0.0007875 -0.045729553 0.04415455 0.9999582
3-1 -0.0220375 -0.066979553 0.02290455 0.5398594
4-1 0.0467125 0.001770447 0.09165455 0.0395553
3-2 -0.0212500 -0.066192053 0.02369205 0.5691518
4-2 0.0475000 0.002557947 0.09244205 0.0355841
4-3 0.0687500 0.023807947 0.11369205 0.0016087
$tempo
diff lwr upr p adj
96-72 0.06863125 0.0448554 0.0924071 3.8e-06
$`periodo:tempo`
diff lwr upr p adj
2:72-1:72 0.005225 -0.071080649 0.08153065 0.9999979
3:72-1:72 -0.012275 -0.088580649 0.06403065 0.9993236
4:72-1:72 0.027725 -0.048580649 0.10403065 0.9231051
1:96-1:72 0.067025 -0.009280649 0.14333065 0.1148476
2:96-1:72 0.060225 -0.016080649 0.13653065 0.1992207
3:96-1:72 0.035225 -0.041080649 0.11153065 0.7849167
4:96-1:72 0.132725 0.056419351 0.20903065 0.0001453
3:72-2:72 -0.017500 -0.093805649 0.05880565 0.9937514
4:72-2:72 0.022500 -0.053805649 0.09880565 0.9735884
1:96-2:72 0.061800 -0.014505649 0.13810565 0.1762724
2:96-2:72 0.055000 -0.021305649 0.13130565 0.2914548
3:96-2:72 0.030000 -0.046305649 0.10630565 0.8892702
4:96-2:72 0.127500 0.051194351 0.20380565 0.0002524
4:72-3:72 0.040000 -0.036305649 0.11630565 0.6653273
1:96-3:72 0.079300 0.002994351 0.15560565 0.0376482
2:96-3:72 0.072500 -0.003805649 0.14880565 0.0709920
3:96-3:72 0.047500 -0.028805649 0.12380565 0.4650535
4:96-3:72 0.145000 0.068694351 0.22130565 0.0000402
1:96-4:72 0.039300 -0.037005649 0.11560565 0.6837724
2:96-4:72 0.032500 -0.043805649 0.10880565 0.8436715
3:96-4:72 0.007500 -0.068805649 0.08380565 0.9999747
4:96-4:72 0.105000 0.028694351 0.18130565 0.0027504
2:96-1:96 -0.006800 -0.083105649 0.06950565 0.9999870
3:96-1:96 -0.031800 -0.108105649 0.04450565 0.8572936
4:96-1:96 0.065700 -0.010605649 0.14200565 0.1284210
3:96-2:96 -0.025000 -0.101305649 0.05130565 0.9539260
4:96-2:96 0.072500 -0.003805649 0.14880565 0.0709920
4:96-3:96 0.097500 0.021194351 0.17380565 0.0060386
Letras do Tukey:
$periodo
4 1 2 3
"a" "b" "b" "b"
$tempo
96 72
"a" "b"
$`periodo:tempo`
4:96 1:96 2:96 3:96 4:72 2:72 1:72 3:72
"a" "ab" "abc" "bc" "bc" "bc" "bc" "c"
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.09002 0.030007 6.314 0.00149 **
tempo 2 0.00000 0.000001 0.000 0.99988
periodo:tempo 6 0.00206 0.000343 0.072 0.99836
Residuals 36 0.17109 0.004753
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 48 0.278 0.0345 36 0.208 0.347
2 48 0.300 0.0345 36 0.230 0.370
3 48 0.230 0.0345 36 0.160 0.300
4 48 0.335 0.0345 36 0.265 0.405
1 72 0.277 0.0345 36 0.207 0.346
2 72 0.287 0.0345 36 0.218 0.357
3 72 0.220 0.0345 36 0.150 0.290
4 72 0.360 0.0345 36 0.290 0.430
1 96 0.278 0.0345 36 0.209 0.348
2 96 0.285 0.0345 36 0.215 0.355
3 96 0.230 0.0345 36 0.160 0.300
4 96 0.350 0.0345 36 0.280 0.420
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 0.01335083 -0.062447924 0.08914959 0.9642445
3-1 -0.05081583 -0.126614591 0.02498292 0.2874134
4-1 0.07085083 -0.004947924 0.14664959 0.0740754
3-2 -0.06416667 -0.139965424 0.01163209 0.1217800
4-2 0.05750000 -0.018298758 0.13329876 0.1915173
4-3 0.12166667 0.045867909 0.19746542 0.0006464
$tempo
diff lwr upr p adj
72-48 0.0003775 -0.05919883 0.05995383 0.9998678
96-48 0.0002325 -0.05934383 0.05980883 0.9999498
96-72 -0.0001450 -0.05972133 0.05943133 0.9999805
$`periodo:tempo`
diff lwr upr p adj
2:48-1:48 2.249750e-02 -0.14764592 0.19264092 0.9999982
3:48-1:48 -4.750250e-02 -0.21764592 0.12264092 0.9973886
4:48-1:48 5.749750e-02 -0.11264592 0.22764092 0.9873900
1:72-1:48 -9.900000e-04 -0.17113342 0.16915342 1.0000000
2:72-1:48 9.997500e-03 -0.16014592 0.18014092 1.0000000
3:72-1:48 -5.750250e-02 -0.22764592 0.11264092 0.9873815
4:72-1:48 8.249750e-02 -0.08764592 0.25264092 0.8600468
1:96-1:48 9.300000e-04 -0.16921342 0.17107342 1.0000000
2:96-1:48 7.497500e-03 -0.16264592 0.17764092 1.0000000
3:96-1:48 -4.750250e-02 -0.21764592 0.12264092 0.9973886
4:96-1:48 7.249750e-02 -0.09764592 0.24264092 0.9346419
3:48-2:48 -7.000000e-02 -0.24014342 0.10014342 0.9479170
4:48-2:48 3.500000e-02 -0.13514342 0.20514342 0.9998432
1:72-2:48 -2.348750e-02 -0.19363092 0.14665592 0.9999972
2:72-2:48 -1.250000e-02 -0.18264342 0.15764342 1.0000000
3:72-2:48 -8.000000e-02 -0.25014342 0.09014342 0.8819250
4:72-2:48 6.000000e-02 -0.11014342 0.23014342 0.9825223
1:96-2:48 -2.156750e-02 -0.19171092 0.14857592 0.9999988
2:96-2:48 -1.500000e-02 -0.18514342 0.15514342 1.0000000
3:96-2:48 -7.000000e-02 -0.24014342 0.10014342 0.9479170
4:96-2:48 5.000000e-02 -0.12014342 0.22014342 0.9959495
4:48-3:48 1.050000e-01 -0.06514342 0.27514342 0.5903862
1:72-3:48 4.651250e-02 -0.12363092 0.21665592 0.9978265
2:72-3:48 5.750000e-02 -0.11264342 0.22764342 0.9873857
3:72-3:48 -1.000000e-02 -0.18014342 0.16014342 1.0000000
4:72-3:48 1.300000e-01 -0.04014342 0.30014342 0.2827095
1:96-3:48 4.843250e-02 -0.12171092 0.21857592 0.9969127
2:96-3:48 5.500000e-02 -0.11514342 0.22514342 0.9911188
3:96-3:48 2.775558e-17 -0.17014342 0.17014342 1.0000000
4:96-3:48 1.200000e-01 -0.05014342 0.29014342 0.3941017
1:72-4:48 -5.848750e-02 -0.22863092 0.11165592 0.9856107
2:72-4:48 -4.750000e-02 -0.21764342 0.12264342 0.9973898
3:72-4:48 -1.150000e-01 -0.28514342 0.05514342 0.4568797
4:72-4:48 2.500000e-02 -0.14514342 0.19514342 0.9999946
1:96-4:48 -5.656750e-02 -0.22671092 0.11357592 0.9888999
2:96-4:48 -5.000000e-02 -0.22014342 0.12014342 0.9959495
3:96-4:48 -1.050000e-01 -0.27514342 0.06514342 0.5903862
4:96-4:48 1.500000e-02 -0.15514342 0.18514342 1.0000000
2:72-1:72 1.098750e-02 -0.15915592 0.18113092 1.0000000
3:72-1:72 -5.651250e-02 -0.22665592 0.11363092 0.9889845
4:72-1:72 8.348750e-02 -0.08665592 0.25363092 0.8507939
1:96-1:72 1.920000e-03 -0.16822342 0.17206342 1.0000000
2:96-1:72 8.487500e-03 -0.16165592 0.17863092 1.0000000
3:96-1:72 -4.651250e-02 -0.21665592 0.12363092 0.9978265
4:96-1:72 7.348750e-02 -0.09665592 0.24363092 0.9288024
3:72-2:72 -6.750000e-02 -0.23764342 0.10264342 0.9592023
4:72-2:72 7.250000e-02 -0.09764342 0.24264342 0.9346275
1:96-2:72 -9.067500e-03 -0.17921092 0.16107592 1.0000000
2:96-2:72 -2.500000e-03 -0.17264342 0.16764342 1.0000000
3:96-2:72 -5.750000e-02 -0.22764342 0.11264342 0.9873857
4:96-2:72 6.250000e-02 -0.10764342 0.23264342 0.9763316
4:72-3:72 1.400000e-01 -0.03014342 0.31014342 0.1939488
1:96-3:72 5.843250e-02 -0.11171092 0.22857592 0.9857144
2:96-3:72 6.500000e-02 -0.10514342 0.23514342 0.9686194
3:96-3:72 1.000000e-02 -0.16014342 0.18014342 1.0000000
4:96-3:72 1.300000e-01 -0.04014342 0.30014342 0.2827095
1:96-4:72 -8.156750e-02 -0.25171092 0.08857592 0.8684413
2:96-4:72 -7.500000e-02 -0.24514342 0.09514342 0.9192325
3:96-4:72 -1.300000e-01 -0.30014342 0.04014342 0.2827095
4:96-4:72 -1.000000e-02 -0.18014342 0.16014342 1.0000000
2:96-1:96 6.567500e-03 -0.16357592 0.17671092 1.0000000
3:96-1:96 -4.843250e-02 -0.21857592 0.12171092 0.9969127
4:96-1:96 7.156750e-02 -0.09857592 0.24171092 0.9398261
3:96-2:96 -5.500000e-02 -0.22514342 0.11514342 0.9911188
4:96-2:96 6.500000e-02 -0.10514342 0.23514342 0.9686194
4:96-3:96 1.200000e-01 -0.05014342 0.29014342 0.3941017
Letras do Tukey:
$periodo
4 2 1 3
"a" "ab" "ab" "b"
$tempo
$tempo$Letters
72 96 48
"a" "a" "a"
$tempo$LetterMatrix
a
72 TRUE
96 TRUE
48 TRUE
$`periodo:tempo`
$`periodo:tempo`$Letters
4:72 4:96 4:48 2:48 2:72 2:96 1:96 1:48 1:72 3:96 3:48 3:72
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tempo`$LetterMatrix
a
4:72 TRUE
4:96 TRUE
4:48 TRUE
2:48 TRUE
2:72 TRUE
2:96 TRUE
1:96 TRUE
1:48 TRUE
1:72 TRUE
3:96 TRUE
3:48 TRUE
3:72 TRUE
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3430 1143 0.790 0.511
tempo 1 42080 42080 29.089 1.54e-05 ***
periodo:tempo 3 8387 2796 1.932 0.151
Residuals 24 34718 1447
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 24 612 19 24 572 651
2 24 637 19 24 598 676
3 24 631 19 24 592 671
4 24 570 19 24 531 609
1 48 534 19 24 495 573
2 48 534 19 24 495 573
3 48 540 19 24 501 580
4 48 551 19 24 512 591
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 12.7127750 -39.74799 65.17354 0.9079080
3-1 13.1073875 -39.35337 65.56815 0.9001880
4-1 -11.9710875 -64.43185 40.48967 0.9215325
3-2 0.3946125 -52.06615 52.85537 0.9999967
4-2 -24.6838625 -77.14462 27.77690 0.5730300
4-3 -25.0784750 -77.53924 27.38229 0.5604158
$tempo
diff lwr upr p adj
48-24 -72.52585 -100.2794 -44.77235 1.54e-05
$`periodo:tempo`
diff lwr upr p adj
2:24-1:24 25.202400 -63.86902 114.273819 0.9789409
3:24-1:24 19.638200 -69.43322 108.709619 0.9950745
4:24-1:24 -41.599025 -130.67044 47.472394 0.7753196
1:48-1:24 -77.829600 -166.90102 11.241819 0.1183095
2:48-1:24 -77.606450 -166.67787 11.464969 0.1202374
3:48-1:24 -71.253025 -160.32444 17.818394 0.1870591
4:48-1:24 -60.172750 -149.24417 28.898669 0.3659024
3:24-2:24 -5.564200 -94.63562 83.507219 0.9999989
4:24-2:24 -66.801425 -155.87284 22.269994 0.2490656
1:48-2:24 -103.032000 -192.10342 -13.960581 0.0155695
2:48-2:24 -102.808850 -191.88027 -13.737431 0.0158723
3:48-2:24 -96.455425 -185.52684 -7.384006 0.0272621
4:48-2:24 -85.375150 -174.44657 3.696269 0.0669905
4:24-3:24 -61.237225 -150.30864 27.834194 0.3452398
1:48-3:24 -97.467800 -186.53922 -8.396381 0.0250371
2:48-3:24 -97.244650 -186.31607 -8.173231 0.0255123
3:48-3:24 -90.891225 -179.96264 -1.819806 0.0431682
4:48-3:24 -79.810950 -168.88237 9.260469 0.1023128
1:48-4:24 -36.230575 -125.30199 52.840844 0.8713777
2:48-4:24 -36.007425 -125.07884 53.063994 0.8747863
3:48-4:24 -29.654000 -118.72542 59.417419 0.9500038
4:48-4:24 -18.573725 -107.64514 70.497694 0.9964974
2:48-1:48 0.223150 -88.84827 89.294569 1.0000000
3:48-1:48 6.576575 -82.49484 95.647994 0.9999964
4:48-1:48 17.656850 -71.41457 106.728269 0.9974410
3:48-2:48 6.353425 -82.71799 95.424844 0.9999972
4:48-2:48 17.433700 -71.63772 106.505119 0.9976365
4:48-3:48 11.080275 -77.99114 100.151694 0.9998759
Letras do Tukey:
$periodo
$periodo$Letters
3 2 1 4
"a" "a" "a" "a"
$periodo$LetterMatrix
a
3 TRUE
2 TRUE
1 TRUE
4 TRUE
$tempo
24 48
"a" "b"
$`periodo:tempo`
2:24 3:24 1:24 4:24 4:48 3:48 2:48 1:48
"a" "a" "ab" "ab" "ab" "b" "b" "b"
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.2134 0.07114 319.0 <2e-16 ***
tempo 2 0.5514 0.27568 1236.3 <2e-16 ***
periodo:tempo 6 0.1816 0.03026 135.7 <2e-16 ***
Residuals 36 0.0080 0.00022
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo tempo emmean SE df lower.CL upper.CL
1 0 0.0614 0.00747 36 0.0463 0.0766
2 0 0.0336 0.00747 36 0.0184 0.0487
3 0 0.0267 0.00747 36 0.0116 0.0419
4 0 0.0425 0.00747 36 0.0274 0.0577
1 72 0.1319 0.00747 36 0.1168 0.1471
2 72 0.0497 0.00747 36 0.0346 0.0649
3 72 0.0453 0.00747 36 0.0302 0.0605
4 72 0.0575 0.00747 36 0.0423 0.0726
1 96 0.5450 0.00747 36 0.5298 0.5601
2 96 0.1773 0.00747 36 0.1622 0.1925
3 96 0.1888 0.00747 36 0.1737 0.2040
4 96 0.2167 0.00747 36 0.2015 0.2318
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -1.592404e-01 -0.175658860 -0.14282197 0.0000000
3-1 -1.591483e-01 -0.175566777 -0.14272989 0.0000000
4-1 -1.405546e-01 -0.156973027 -0.12413614 0.0000000
3-2 9.208333e-05 -0.016326360 0.01651053 0.9999987
4-2 1.868583e-02 0.002267390 0.03510428 0.0204749
4-3 1.859375e-02 0.002175306 0.03501219 0.0212621
$tempo
diff lwr upr p adj
72-0 0.0300475 0.01714293 0.04295207 5.3e-06
96-0 0.2408847 0.22798011 0.25378926 0.0e+00
96-72 0.2108372 0.19793261 0.22374176 0.0e+00
$`periodo:tempo`
diff lwr upr p adj
2:0-1:0 -0.02785875 -0.064712787 0.008995287 0.2968434
3:0-1:0 -0.03470125 -0.071555287 0.002152787 0.0806918
4:0-1:0 -0.01889125 -0.055745287 0.017962787 0.8131493
1:72-1:0 0.07050750 0.033653463 0.107361537 0.0000052
2:72-1:0 -0.01170875 -0.048562787 0.025145287 0.9922748
3:72-1:0 -0.01610750 -0.052961537 0.020746537 0.9233577
4:72-1:0 -0.00395250 -0.040806537 0.032901537 0.9999998
1:96-1:0 0.48354375 0.446689713 0.520397787 0.0000000
2:96-1:0 0.11589750 0.079043463 0.152751537 0.0000000
3:96-1:0 0.12741500 0.090560963 0.164269037 0.0000000
4:96-1:0 0.15523125 0.118377213 0.192085287 0.0000000
3:0-2:0 -0.00684250 -0.043696537 0.030011537 0.9999425
4:0-2:0 0.00896750 -0.027886537 0.045821537 0.9992356
1:72-2:0 0.09836625 0.061512213 0.135220287 0.0000000
2:72-2:0 0.01615000 -0.020704037 0.053004037 0.9221016
3:72-2:0 0.01175125 -0.025102787 0.048605287 0.9920451
4:72-2:0 0.02390625 -0.012947787 0.060760287 0.5179259
1:96-2:0 0.51140250 0.474548463 0.548256537 0.0000000
2:96-2:0 0.14375625 0.106902213 0.180610287 0.0000000
3:96-2:0 0.15527375 0.118419713 0.192127787 0.0000000
4:96-2:0 0.18309000 0.146235963 0.219944037 0.0000000
4:0-3:0 0.01581000 -0.021044037 0.052664037 0.9317799
1:72-3:0 0.10520875 0.068354713 0.142062787 0.0000000
2:72-3:0 0.02299250 -0.013861537 0.059846537 0.5748242
3:72-3:0 0.01859375 -0.018260287 0.055447787 0.8275209
4:72-3:0 0.03074875 -0.006105287 0.067602787 0.1792987
1:96-3:0 0.51824500 0.481390963 0.555099037 0.0000000
2:96-3:0 0.15059875 0.113744713 0.187452787 0.0000000
3:96-3:0 0.16211625 0.125262213 0.198970287 0.0000000
4:96-3:0 0.18993250 0.153078463 0.226786537 0.0000000
1:72-4:0 0.08939875 0.052544713 0.126252787 0.0000000
2:72-4:0 0.00718250 -0.029671537 0.044036537 0.9999073
3:72-4:0 0.00278375 -0.034070287 0.039637787 1.0000000
4:72-4:0 0.01493875 -0.021915287 0.051792787 0.9528138
1:96-4:0 0.50243500 0.465580963 0.539289037 0.0000000
2:96-4:0 0.13478875 0.097934713 0.171642787 0.0000000
3:96-4:0 0.14630625 0.109452213 0.183160287 0.0000000
4:96-4:0 0.17412250 0.137268463 0.210976537 0.0000000
2:72-1:72 -0.08221625 -0.119070287 -0.045362213 0.0000002
3:72-1:72 -0.08661500 -0.123469037 -0.049760963 0.0000001
4:72-1:72 -0.07446000 -0.111314037 -0.037605963 0.0000017
1:96-1:72 0.41303625 0.376182213 0.449890287 0.0000000
2:96-1:72 0.04539000 0.008535963 0.082244037 0.0060296
3:96-1:72 0.05690750 0.020053463 0.093761537 0.0002535
4:96-1:72 0.08472375 0.047869713 0.121577787 0.0000001
3:72-2:72 -0.00439875 -0.041252787 0.032455287 0.9999994
4:72-2:72 0.00775625 -0.029097787 0.044610287 0.9998046
1:96-2:72 0.49525250 0.458398463 0.532106537 0.0000000
2:96-2:72 0.12760625 0.090752213 0.164460287 0.0000000
3:96-2:72 0.13912375 0.102269713 0.175977787 0.0000000
4:96-2:72 0.16694000 0.130085963 0.203794037 0.0000000
4:72-3:72 0.01215500 -0.024699037 0.049009037 0.9895799
1:96-3:72 0.49965125 0.462797213 0.536505287 0.0000000
2:96-3:72 0.13200500 0.095150963 0.168859037 0.0000000
3:96-3:72 0.14352250 0.106668463 0.180376537 0.0000000
4:96-3:72 0.17133875 0.134484713 0.208192787 0.0000000
1:96-4:72 0.48749625 0.450642213 0.524350287 0.0000000
2:96-4:72 0.11985000 0.082995963 0.156704037 0.0000000
3:96-4:72 0.13136750 0.094513463 0.168221537 0.0000000
4:96-4:72 0.15918375 0.122329713 0.196037787 0.0000000
2:96-1:96 -0.36764625 -0.404500287 -0.330792213 0.0000000
3:96-1:96 -0.35612875 -0.392982787 -0.319274713 0.0000000
4:96-1:96 -0.32831250 -0.365166537 -0.291458463 0.0000000
3:96-2:96 0.01151750 -0.025336537 0.048371537 0.9932433
4:96-2:96 0.03933375 0.002479713 0.076187787 0.0279194
4:96-3:96 0.02781625 -0.009037787 0.064670287 0.2988811
Letras do Tukey:
$periodo
1 4 3 2
"a" "b" "c" "c"
$tempo
96 72 0
"a" "b" "c"
$`periodo:tempo`
1:96 4:96 3:96 2:96 1:72 1:0 4:72 2:72 3:72 4:0 2:0 3:0
"a" "b" "bc" "c" "d" "e" "e" "e" "e" "e" "e" "e"
10 Fermentador x Tratamento
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ fermentador * tratamento")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * tratamento")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
Warning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertos
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ fermentador * tratamento"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ fermentador * tratamento)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 28166 9389 0.533 0.662
tratamento 3 4359 1453 0.083 0.969
fermentador:tratamento 9 174924 19436 1.104 0.378
Residuals 48 844715 17598
16 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 342 66.3 48 209.1 476
2 T1 279 66.3 48 145.6 412
3 T1 232 66.3 48 98.6 365
4 T1 364 66.3 48 230.1 497
1 T2 282 66.3 48 149.1 416
2 T2 318 66.3 48 184.1 451
3 T2 322 66.3 48 189.1 456
4 T2 232 66.3 48 98.9 366
1 T3 192 66.3 48 59.1 326
2 T3 332 66.3 48 198.9 466
3 T3 373 66.3 48 239.9 507
4 T3 312 66.3 48 178.4 445
1 T4 280 66.3 48 146.6 413
2 T4 228 66.3 48 94.9 362
3 T4 336 66.3 48 202.6 469
4 T4 402 66.3 48 268.4 535
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 14.8750 -109.94811 139.6981 0.9888188
3-1 41.5625 -83.26061 166.3856 0.8120200
4-1 52.9375 -71.88561 177.7606 0.6738213
3-2 26.6875 -98.13561 151.5106 0.9408072
4-2 38.0625 -86.76061 162.8856 0.8487086
4-3 11.3750 -113.44811 136.1981 0.9949168
$tratamento
diff lwr upr p adj
T2-T1 -15.5625 -140.3856 109.2606 0.9872437
T3-T1 -1.8125 -126.6356 123.0106 0.9999790
T4-T1 7.2500 -117.5731 132.0731 0.9986655
T3-T2 13.7500 -111.0731 138.5731 0.9911175
T4-T2 22.8125 -102.0106 147.6356 0.9617602
T4-T3 9.0625 -115.7606 133.8856 0.9974075
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 -63.50 -402.3834 275.3834 0.9999976
3:T1-1:T1 -110.50 -449.3834 228.3834 0.9978583
4:T1-1:T1 21.00 -317.8834 359.8834 1.0000000
1:T2-1:T1 -60.00 -398.8834 278.8834 0.9999989
2:T2-1:T1 -25.00 -363.8834 313.8834 1.0000000
3:T2-1:T1 -20.00 -358.8834 318.8834 1.0000000
4:T2-1:T1 -110.25 -449.1334 228.6334 0.9979098
1:T3-1:T1 -150.00 -488.8834 188.8834 0.9608061
2:T3-1:T1 -10.25 -349.1334 328.6334 1.0000000
3:T3-1:T1 30.75 -308.1334 369.6334 1.0000000
4:T3-1:T1 -30.75 -369.6334 308.1334 1.0000000
1:T4-1:T1 -62.50 -401.3834 276.3834 0.9999981
2:T4-1:T1 -114.25 -453.1334 224.6334 0.9969474
3:T4-1:T1 -6.50 -345.3834 332.3834 1.0000000
4:T4-1:T1 59.25 -279.6334 398.1334 0.9999991
3:T1-2:T1 -47.00 -385.8834 291.8834 1.0000000
4:T1-2:T1 84.50 -254.3834 423.3834 0.9999042
1:T2-2:T1 3.50 -335.3834 342.3834 1.0000000
2:T2-2:T1 38.50 -300.3834 377.3834 1.0000000
3:T2-2:T1 43.50 -295.3834 382.3834 1.0000000
4:T2-2:T1 -46.75 -385.6334 292.1334 1.0000000
1:T3-2:T1 -86.50 -425.3834 252.3834 0.9998723
2:T3-2:T1 53.25 -285.6334 392.1334 0.9999998
3:T3-2:T1 94.25 -244.6334 433.1334 0.9996430
4:T3-2:T1 32.75 -306.1334 371.6334 1.0000000
1:T4-2:T1 1.00 -337.8834 339.8834 1.0000000
2:T4-2:T1 -50.75 -389.6334 288.1334 0.9999999
3:T4-2:T1 57.00 -281.8834 395.8834 0.9999994
4:T4-2:T1 122.75 -216.1334 461.6334 0.9936422
4:T1-3:T1 131.50 -207.3834 470.3834 0.9876371
1:T2-3:T1 50.50 -288.3834 389.3834 0.9999999
2:T2-3:T1 85.50 -253.3834 424.3834 0.9998892
3:T2-3:T1 90.50 -248.3834 429.3834 0.9997794
4:T2-3:T1 0.25 -338.6334 339.1334 1.0000000
1:T3-3:T1 -39.50 -378.3834 299.3834 1.0000000
2:T3-3:T1 100.25 -238.6334 439.1334 0.9992709
3:T3-3:T1 141.25 -197.6334 480.1334 0.9763892
4:T3-3:T1 79.75 -259.1334 418.6334 0.9999534
1:T4-3:T1 48.00 -290.8834 386.8834 0.9999999
2:T4-3:T1 -3.75 -342.6334 335.1334 1.0000000
3:T4-3:T1 104.00 -234.8834 442.8834 0.9988971
4:T4-3:T1 169.75 -169.1334 508.6334 0.9007593
1:T2-4:T1 -81.00 -419.8834 257.8834 0.9999433
2:T2-4:T1 -46.00 -384.8834 292.8834 1.0000000
3:T2-4:T1 -41.00 -379.8834 297.8834 1.0000000
4:T2-4:T1 -131.25 -470.1334 207.6334 0.9878557
1:T3-4:T1 -171.00 -509.8834 167.8834 0.8956645
2:T3-4:T1 -31.25 -370.1334 307.6334 1.0000000
3:T3-4:T1 9.75 -329.1334 348.6334 1.0000000
4:T3-4:T1 -51.75 -390.6334 287.1334 0.9999999
1:T4-4:T1 -83.50 -422.3834 255.3834 0.9999173
2:T4-4:T1 -135.25 -474.1334 203.6334 0.9839685
3:T4-4:T1 -27.50 -366.3834 311.3834 1.0000000
4:T4-4:T1 38.25 -300.6334 377.1334 1.0000000
2:T2-1:T2 35.00 -303.8834 373.8834 1.0000000
3:T2-1:T2 40.00 -298.8834 378.8834 1.0000000
4:T2-1:T2 -50.25 -389.1334 288.6334 0.9999999
1:T3-1:T2 -90.00 -428.8834 248.8834 0.9997936
2:T3-1:T2 49.75 -289.1334 388.6334 0.9999999
3:T3-1:T2 90.75 -248.1334 429.6334 0.9997720
4:T3-1:T2 29.25 -309.6334 368.1334 1.0000000
1:T4-1:T2 -2.50 -341.3834 336.3834 1.0000000
2:T4-1:T2 -54.25 -393.1334 284.6334 0.9999997
3:T4-1:T2 53.50 -285.3834 392.3834 0.9999998
4:T4-1:T2 119.25 -219.6334 458.1334 0.9952472
3:T2-2:T2 5.00 -333.8834 343.8834 1.0000000
4:T2-2:T2 -85.25 -424.1334 253.6334 0.9998932
1:T3-2:T2 -125.00 -463.8834 213.8834 0.9923939
2:T3-2:T2 14.75 -324.1334 353.6334 1.0000000
3:T3-2:T2 55.75 -283.1334 394.6334 0.9999996
4:T3-2:T2 -5.75 -344.6334 333.1334 1.0000000
1:T4-2:T2 -37.50 -376.3834 301.3834 1.0000000
2:T4-2:T2 -89.25 -428.1334 249.6334 0.9998134
3:T4-2:T2 18.50 -320.3834 357.3834 1.0000000
4:T4-2:T2 84.25 -254.6334 423.1334 0.9999076
4:T2-3:T2 -90.25 -429.1334 248.6334 0.9997866
1:T3-3:T2 -130.00 -468.8834 208.8834 0.9889029
2:T3-3:T2 9.75 -329.1334 348.6334 1.0000000
3:T3-3:T2 50.75 -288.1334 389.6334 0.9999999
4:T3-3:T2 -10.75 -349.6334 328.1334 1.0000000
1:T4-3:T2 -42.50 -381.3834 296.3834 1.0000000
2:T4-3:T2 -94.25 -433.1334 244.6334 0.9996430
3:T4-3:T2 13.50 -325.3834 352.3834 1.0000000
4:T4-3:T2 79.25 -259.6334 418.1334 0.9999569
1:T3-4:T2 -39.75 -378.6334 299.1334 1.0000000
2:T3-4:T2 100.00 -238.8834 438.8834 0.9992913
3:T3-4:T2 141.00 -197.8834 479.8834 0.9767514
4:T3-4:T2 79.50 -259.3834 418.3834 0.9999552
1:T4-4:T2 47.75 -291.1334 386.6334 1.0000000
2:T4-4:T2 -4.00 -342.8834 334.8834 1.0000000
3:T4-4:T2 103.75 -235.1334 442.6334 0.9989264
4:T4-4:T2 169.50 -169.3834 508.3834 0.9017592
2:T3-1:T3 139.75 -199.1334 478.6334 0.9784992
3:T3-1:T3 180.75 -158.1334 519.6334 0.8505330
4:T3-1:T3 119.25 -219.6334 458.1334 0.9952472
1:T4-1:T3 87.50 -251.3834 426.3834 0.9998531
2:T4-1:T3 35.75 -303.1334 374.6334 1.0000000
3:T4-1:T3 143.50 -195.3834 482.3834 0.9729329
4:T4-1:T3 209.25 -129.6334 548.1334 0.6723148
3:T3-2:T3 41.00 -297.8834 379.8834 1.0000000
4:T3-2:T3 -20.50 -359.3834 318.3834 1.0000000
1:T4-2:T3 -52.25 -391.1334 286.6334 0.9999998
2:T4-2:T3 -104.00 -442.8834 234.8834 0.9988971
3:T4-2:T3 3.75 -335.1334 342.6334 1.0000000
4:T4-2:T3 69.50 -269.3834 408.3834 0.9999921
4:T3-3:T3 -61.50 -400.3834 277.3834 0.9999984
1:T4-3:T3 -93.25 -432.1334 245.6334 0.9996851
2:T4-3:T3 -145.00 -483.8834 193.8834 0.9704253
3:T4-3:T3 -37.25 -376.1334 301.6334 1.0000000
4:T4-3:T3 28.50 -310.3834 367.3834 1.0000000
1:T4-4:T3 -31.75 -370.6334 307.1334 1.0000000
2:T4-4:T3 -83.50 -422.3834 255.3834 0.9999173
3:T4-4:T3 24.25 -314.6334 363.1334 1.0000000
4:T4-4:T3 90.00 -248.8834 428.8834 0.9997936
2:T4-1:T4 -51.75 -390.6334 287.1334 0.9999999
3:T4-1:T4 56.00 -282.8834 394.8834 0.9999996
4:T4-1:T4 121.75 -217.1334 460.6334 0.9941404
3:T4-2:T4 107.75 -231.1334 446.6334 0.9983697
4:T4-2:T4 173.50 -165.3834 512.3834 0.8849994
4:T4-3:T4 65.75 -273.1334 404.6334 0.9999962
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$tratamento
$tratamento$Letters
T4 T1 T3 T2
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T4 TRUE
T1 TRUE
T3 TRUE
T2 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
4:T4 3:T3 4:T1 1:T1 3:T4 2:T3 3:T2 2:T2 4:T3 1:T2 1:T4 2:T1 4:T2 3:T1 2:T4 1:T3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
4:T4 TRUE
3:T3 TRUE
4:T1 TRUE
1:T1 TRUE
3:T4 TRUE
2:T3 TRUE
3:T2 TRUE
2:T2 TRUE
4:T3 TRUE
1:T2 TRUE
1:T4 TRUE
2:T1 TRUE
4:T2 TRUE
3:T1 TRUE
2:T4 TRUE
1:T3 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 208575 69525 0.456 0.714
tratamento 3 157390 52463 0.344 0.794
fermentador:tratamento 9 1143825 127092 0.833 0.589
Residuals 48 7323322 152569
16 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 1549 195 48 1157 1942
2 T1 1588 195 48 1196 1981
3 T1 1199 195 48 807 1592
4 T1 1290 195 48 897 1682
1 T2 1579 195 48 1187 1972
2 T2 1508 195 48 1115 1901
3 T2 1723 195 48 1330 2116
4 T2 1188 195 48 795 1580
1 T3 1389 195 48 996 1781
2 T3 1543 195 48 1151 1936
3 T3 1584 195 48 1191 1977
4 T3 1598 195 48 1206 1991
1 T4 1516 195 48 1124 1909
2 T4 1264 195 48 872 1657
3 T4 1536 195 48 1143 1929
4 T4 1406 195 48 1013 1798
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -32.4375 -399.9686 335.0936 0.9953756
3-1 2.1875 -365.3436 369.7186 0.9999986
4-1 -138.0625 -505.5936 229.4686 0.7502508
3-2 34.6250 -332.9061 402.1561 0.9943926
4-2 -105.6250 -473.1561 261.9061 0.8698176
4-3 -140.2500 -507.7811 227.2811 0.7411645
$tratamento
diff lwr upr p adj
T2-T1 92.9375 -274.5936 460.4686 0.9067886
T3-T1 122.0625 -245.4686 489.5936 0.8131906
T4-T1 23.9375 -343.5936 391.4686 0.9981229
T3-T2 29.1250 -338.4061 396.6561 0.9966384
T4-T2 -69.0000 -436.5311 298.5311 0.9587578
T4-T3 -98.1250 -465.6561 269.4061 0.8924319
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 39.00 -958.8134 1036.8134 1.0000000
3:T1-1:T1 -350.00 -1347.8134 647.8134 0.9953996
4:T1-1:T1 -259.75 -1257.5634 738.0634 0.9998378
1:T2-1:T1 30.00 -967.8134 1027.8134 1.0000000
2:T2-1:T1 -41.25 -1039.0634 956.5634 1.0000000
3:T2-1:T1 173.75 -824.0634 1171.5634 0.9999991
4:T2-1:T1 -361.50 -1359.3134 636.3134 0.9936294
1:T3-1:T1 -160.50 -1158.3134 837.3134 0.9999997
2:T3-1:T1 -6.00 -1003.8134 991.8134 1.0000000
3:T3-1:T1 34.75 -963.0634 1032.5634 1.0000000
4:T3-1:T1 49.25 -948.5634 1047.0634 1.0000000
1:T4-1:T1 -33.00 -1030.8134 964.8134 1.0000000
2:T4-1:T1 -285.00 -1282.8134 712.8134 0.9995128
3:T4-1:T1 -13.25 -1011.0634 984.5634 1.0000000
4:T4-1:T1 -143.75 -1141.5634 854.0634 0.9999999
3:T1-2:T1 -389.00 -1386.8134 608.8134 0.9870866
4:T1-2:T1 -298.75 -1296.5634 699.0634 0.9991642
1:T2-2:T1 -9.00 -1006.8134 988.8134 1.0000000
2:T2-2:T1 -80.25 -1078.0634 917.5634 1.0000000
3:T2-2:T1 134.75 -863.0634 1132.5634 1.0000000
4:T2-2:T1 -400.50 -1398.3134 597.3134 0.9831192
1:T3-2:T1 -199.50 -1197.3134 798.3134 0.9999943
2:T3-2:T1 -45.00 -1042.8134 952.8134 1.0000000
3:T3-2:T1 -4.25 -1002.0634 993.5634 1.0000000
4:T3-2:T1 10.25 -987.5634 1008.0634 1.0000000
1:T4-2:T1 -72.00 -1069.8134 925.8134 1.0000000
2:T4-2:T1 -324.00 -1321.8134 673.8134 0.9979525
3:T4-2:T1 -52.25 -1050.0634 945.5634 1.0000000
4:T4-2:T1 -182.75 -1180.5634 815.0634 0.9999982
4:T1-3:T1 90.25 -907.5634 1088.0634 1.0000000
1:T2-3:T1 380.00 -617.8134 1377.8134 0.9896435
2:T2-3:T1 308.75 -689.0634 1306.5634 0.9987914
3:T2-3:T1 523.75 -474.0634 1521.5634 0.8648021
4:T2-3:T1 -11.50 -1009.3134 986.3134 1.0000000
1:T3-3:T1 189.50 -808.3134 1187.3134 0.9999971
2:T3-3:T1 344.00 -653.8134 1341.8134 0.9961477
3:T3-3:T1 384.75 -613.0634 1382.5634 0.9883500
4:T3-3:T1 399.25 -598.5634 1397.0634 0.9835916
1:T4-3:T1 317.00 -680.8134 1314.8134 0.9983843
2:T4-3:T1 65.00 -932.8134 1062.8134 1.0000000
3:T4-3:T1 336.75 -661.0634 1334.5634 0.9969138
4:T4-3:T1 206.25 -791.5634 1204.0634 0.9999912
1:T2-4:T1 289.75 -708.0634 1287.5634 0.9994104
2:T2-4:T1 218.50 -779.3134 1216.3134 0.9999814
3:T2-4:T1 433.50 -564.3134 1431.3134 0.9663855
4:T2-4:T1 -101.75 -1099.5634 896.0634 1.0000000
1:T3-4:T1 99.25 -898.5634 1097.0634 1.0000000
2:T3-4:T1 253.75 -744.0634 1251.5634 0.9998780
3:T3-4:T1 294.50 -703.3134 1292.3134 0.9992897
4:T3-4:T1 309.00 -688.8134 1306.8134 0.9987805
1:T4-4:T1 226.75 -771.0634 1224.5634 0.9999700
2:T4-4:T1 -25.25 -1023.0634 972.5634 1.0000000
3:T4-4:T1 246.50 -751.3134 1244.3134 0.9999146
4:T4-4:T1 116.00 -881.8134 1113.8134 1.0000000
2:T2-1:T2 -71.25 -1069.0634 926.5634 1.0000000
3:T2-1:T2 143.75 -854.0634 1141.5634 0.9999999
4:T2-1:T2 -391.50 -1389.3134 606.3134 0.9862942
1:T3-1:T2 -190.50 -1188.3134 807.3134 0.9999969
2:T3-1:T2 -36.00 -1033.8134 961.8134 1.0000000
3:T3-1:T2 4.75 -993.0634 1002.5634 1.0000000
4:T3-1:T2 19.25 -978.5634 1017.0634 1.0000000
1:T4-1:T2 -63.00 -1060.8134 934.8134 1.0000000
2:T4-1:T2 -315.00 -1312.8134 682.8134 0.9984924
3:T4-1:T2 -43.25 -1041.0634 954.5634 1.0000000
4:T4-1:T2 -173.75 -1171.5634 824.0634 0.9999991
3:T2-2:T2 215.00 -782.8134 1212.8134 0.9999849
4:T2-2:T2 -320.25 -1318.0634 677.5634 0.9981946
1:T3-2:T2 -119.25 -1117.0634 878.5634 1.0000000
2:T3-2:T2 35.25 -962.5634 1033.0634 1.0000000
3:T3-2:T2 76.00 -921.8134 1073.8134 1.0000000
4:T3-2:T2 90.50 -907.3134 1088.3134 1.0000000
1:T4-2:T2 8.25 -989.5634 1006.0634 1.0000000
2:T4-2:T2 -243.75 -1241.5634 754.0634 0.9999256
3:T4-2:T2 28.00 -969.8134 1025.8134 1.0000000
4:T4-2:T2 -102.50 -1100.3134 895.3134 1.0000000
4:T2-3:T2 -535.25 -1533.0634 462.5634 0.8451948
1:T3-3:T2 -334.25 -1332.0634 663.5634 0.9971465
2:T3-3:T2 -179.75 -1177.5634 818.0634 0.9999986
3:T3-3:T2 -139.00 -1136.8134 858.8134 1.0000000
4:T3-3:T2 -124.50 -1122.3134 873.3134 1.0000000
1:T4-3:T2 -206.75 -1204.5634 791.0634 0.9999909
2:T4-3:T2 -458.75 -1456.5634 539.0634 0.9469966
3:T4-3:T2 -187.00 -1184.8134 810.8134 0.9999976
4:T4-3:T2 -317.50 -1315.3134 680.3134 0.9983563
1:T3-4:T2 201.00 -796.8134 1198.8134 0.9999937
2:T3-4:T2 355.50 -642.3134 1353.3134 0.9946119
3:T3-4:T2 396.25 -601.5634 1394.0634 0.9846832
4:T3-4:T2 410.75 -587.0634 1408.5634 0.9788345
1:T4-4:T2 328.50 -669.3134 1326.3134 0.9976262
2:T4-4:T2 76.50 -921.3134 1074.3134 1.0000000
3:T4-4:T2 348.25 -649.5634 1346.0634 0.9956293
4:T4-4:T2 217.75 -780.0634 1215.5634 0.9999822
2:T3-1:T3 154.50 -843.3134 1152.3134 0.9999998
3:T3-1:T3 195.25 -802.5634 1193.0634 0.9999957
4:T3-1:T3 209.75 -788.0634 1207.5634 0.9999890
1:T4-1:T3 127.50 -870.3134 1125.3134 1.0000000
2:T4-1:T3 -124.50 -1122.3134 873.3134 1.0000000
3:T4-1:T3 147.25 -850.5634 1145.0634 0.9999999
4:T4-1:T3 16.75 -981.0634 1014.5634 1.0000000
3:T3-2:T3 40.75 -957.0634 1038.5634 1.0000000
4:T3-2:T3 55.25 -942.5634 1053.0634 1.0000000
1:T4-2:T3 -27.00 -1024.8134 970.8134 1.0000000
2:T4-2:T3 -279.00 -1276.8134 718.8134 0.9996198
3:T4-2:T3 -7.25 -1005.0634 990.5634 1.0000000
4:T4-2:T3 -137.75 -1135.5634 860.0634 1.0000000
4:T3-3:T3 14.50 -983.3134 1012.3134 1.0000000
1:T4-3:T3 -67.75 -1065.5634 930.0634 1.0000000
2:T4-3:T3 -319.75 -1317.5634 678.0634 0.9982249
3:T4-3:T3 -48.00 -1045.8134 949.8134 1.0000000
4:T4-3:T3 -178.50 -1176.3134 819.3134 0.9999987
1:T4-4:T3 -82.25 -1080.0634 915.5634 1.0000000
2:T4-4:T3 -334.25 -1332.0634 663.5634 0.9971465
3:T4-4:T3 -62.50 -1060.3134 935.3134 1.0000000
4:T4-4:T3 -193.00 -1190.8134 804.8134 0.9999963
2:T4-1:T4 -252.00 -1249.8134 745.8134 0.9998879
3:T4-1:T4 19.75 -978.0634 1017.5634 1.0000000
4:T4-1:T4 -110.75 -1108.5634 887.0634 1.0000000
3:T4-2:T4 271.75 -726.0634 1269.5634 0.9997213
4:T4-2:T4 141.25 -856.5634 1139.0634 0.9999999
4:T4-3:T4 -130.50 -1128.3134 867.3134 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
3 1 2 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
1 TRUE
2 TRUE
4 TRUE
$tratamento
$tratamento$Letters
T3 T2 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T3 TRUE
T2 TRUE
T4 TRUE
T1 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
3:T2 4:T3 2:T1 3:T3 1:T2 1:T1 2:T3 3:T4 1:T4 2:T2 4:T4 1:T3 4:T1 2:T4 3:T1 4:T2
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
3:T2 TRUE
4:T3 TRUE
2:T1 TRUE
3:T3 TRUE
1:T2 TRUE
1:T1 TRUE
2:T3 TRUE
3:T4 TRUE
1:T4 TRUE
2:T2 TRUE
4:T4 TRUE
1:T3 TRUE
4:T1 TRUE
2:T4 TRUE
3:T1 TRUE
4:T2 TRUE
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 4.060 1.3535 1.385 0.283
tratamento 3 5.676 1.8918 1.937 0.164
fermentador:tratamento 9 11.173 1.2414 1.271 0.324
Residuals 16 15.631 0.9769
48 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 5.74 0.699 16 4.26 7.22
2 T1 5.66 0.699 16 4.18 7.14
3 T1 4.78 0.699 16 3.29 6.26
4 T1 6.37 0.699 16 4.89 7.85
1 T2 6.03 0.699 16 4.55 7.51
2 T2 5.66 0.699 16 4.18 7.14
3 T2 6.04 0.699 16 4.55 7.52
4 T2 5.22 0.699 16 3.74 6.71
1 T3 5.53 0.699 16 4.05 7.01
2 T3 6.34 0.699 16 4.86 7.83
3 T3 6.04 0.699 16 4.55 7.52
4 T3 3.79 0.699 16 2.31 5.27
1 T4 5.74 0.699 16 4.26 7.22
2 T4 4.42 0.699 16 2.94 5.91
3 T4 4.70 0.699 16 3.21 6.18
4 T4 3.79 0.699 16 2.31 5.27
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.23750 -1.651416 1.1764159 0.9623084
3-1 -0.37500 -1.788916 1.0389159 0.8715960
4-1 -0.96625 -2.380166 0.4476659 0.2453401
3-2 -0.13750 -1.551416 1.2764159 0.9921807
4-2 -0.72875 -2.142666 0.6851659 0.4744922
4-3 -0.59125 -2.005166 0.8226659 0.6377411
$tratamento
diff lwr upr p adj
T2-T1 0.10125 -1.312666 1.5151659 0.9968287
T3-T1 -0.21125 -1.625166 1.2026659 0.9729178
T4-T1 -0.97375 -2.387666 0.4401659 0.2396590
T3-T2 -0.31250 -1.726416 1.1014159 0.9200796
T4-T2 -1.07500 -2.488916 0.3389159 0.1724193
T4-T3 -0.76250 -2.176416 0.6514159 0.4366937
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
3:T1-1:T1 -9.650000e-01 -4.922166 2.992166 0.9994041
4:T1-1:T1 6.300000e-01 -3.327166 4.587166 0.9999966
1:T2-1:T1 2.900000e-01 -3.667166 4.247166 1.0000000
2:T2-1:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
3:T2-1:T1 2.950000e-01 -3.662166 4.252166 1.0000000
4:T2-1:T1 -5.150000e-01 -4.472166 3.442166 0.9999998
1:T3-1:T1 -2.100000e-01 -4.167166 3.747166 1.0000000
2:T3-1:T1 6.050000e-01 -3.352166 4.562166 0.9999980
3:T3-1:T1 2.950000e-01 -3.662166 4.252166 1.0000000
4:T3-1:T1 -1.950000e+00 -5.907166 2.007166 0.8093383
1:T4-1:T1 -3.552714e-15 -3.957166 3.957166 1.0000000
2:T4-1:T1 -1.315000e+00 -5.272166 2.642166 0.9873784
3:T4-1:T1 -1.045000e+00 -5.002166 2.912166 0.9986029
4:T4-1:T1 -1.950000e+00 -5.907166 2.007166 0.8093383
3:T1-2:T1 -8.850000e-01 -4.842166 3.072166 0.9997744
4:T1-2:T1 7.100000e-01 -3.247166 4.667166 0.9999841
1:T2-2:T1 3.700000e-01 -3.587166 4.327166 1.0000000
2:T2-2:T1 1.776357e-15 -3.957166 3.957166 1.0000000
3:T2-2:T1 3.750000e-01 -3.582166 4.332166 1.0000000
4:T2-2:T1 -4.350000e-01 -4.392166 3.522166 1.0000000
1:T3-2:T1 -1.300000e-01 -4.087166 3.827166 1.0000000
2:T3-2:T1 6.850000e-01 -3.272166 4.642166 0.9999899
3:T3-2:T1 3.750000e-01 -3.582166 4.332166 1.0000000
4:T3-2:T1 -1.870000e+00 -5.827166 2.087166 0.8469659
1:T4-2:T1 8.000000e-02 -3.877166 4.037166 1.0000000
2:T4-2:T1 -1.235000e+00 -5.192166 2.722166 0.9927915
3:T4-2:T1 -9.650000e-01 -4.922166 2.992166 0.9994041
4:T4-2:T1 -1.870000e+00 -5.827166 2.087166 0.8469659
4:T1-3:T1 1.595000e+00 -2.362166 5.552166 0.9429056
1:T2-3:T1 1.255000e+00 -2.702166 5.212166 0.9916546
2:T2-3:T1 8.850000e-01 -3.072166 4.842166 0.9997744
3:T2-3:T1 1.260000e+00 -2.697166 5.217166 0.9913494
4:T2-3:T1 4.500000e-01 -3.507166 4.407166 1.0000000
1:T3-3:T1 7.550000e-01 -3.202166 4.712166 0.9999659
2:T3-3:T1 1.570000e+00 -2.387166 5.527166 0.9488982
3:T3-3:T1 1.260000e+00 -2.697166 5.217166 0.9913494
4:T3-3:T1 -9.850000e-01 -4.942166 2.972166 0.9992549
1:T4-3:T1 9.650000e-01 -2.992166 4.922166 0.9994041
2:T4-3:T1 -3.500000e-01 -4.307166 3.607166 1.0000000
3:T4-3:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
4:T4-3:T1 -9.850000e-01 -4.942166 2.972166 0.9992549
1:T2-4:T1 -3.400000e-01 -4.297166 3.617166 1.0000000
2:T2-4:T1 -7.100000e-01 -4.667166 3.247166 0.9999841
3:T2-4:T1 -3.350000e-01 -4.292166 3.622166 1.0000000
4:T2-4:T1 -1.145000e+00 -5.102166 2.812166 0.9964816
1:T3-4:T1 -8.400000e-01 -4.797166 3.117166 0.9998770
2:T3-4:T1 -2.500000e-02 -3.982166 3.932166 1.0000000
3:T3-4:T1 -3.350000e-01 -4.292166 3.622166 1.0000000
4:T3-4:T1 -2.580000e+00 -6.537166 1.377166 0.4507708
1:T4-4:T1 -6.300000e-01 -4.587166 3.327166 0.9999966
2:T4-4:T1 -1.945000e+00 -5.902166 2.012166 0.8118002
3:T4-4:T1 -1.675000e+00 -5.632166 2.282166 0.9206831
4:T4-4:T1 -2.580000e+00 -6.537166 1.377166 0.4507708
2:T2-1:T2 -3.700000e-01 -4.327166 3.587166 1.0000000
3:T2-1:T2 5.000000e-03 -3.952166 3.962166 1.0000000
4:T2-1:T2 -8.050000e-01 -4.762166 3.152166 0.9999258
1:T3-1:T2 -5.000000e-01 -4.457166 3.457166 0.9999998
2:T3-1:T2 3.150000e-01 -3.642166 4.272166 1.0000000
3:T3-1:T2 5.000000e-03 -3.952166 3.962166 1.0000000
4:T3-1:T2 -2.240000e+00 -6.197166 1.717166 0.6488283
1:T4-1:T2 -2.900000e-01 -4.247166 3.667166 1.0000000
2:T4-1:T2 -1.605000e+00 -5.562166 2.352166 0.9403834
3:T4-1:T2 -1.335000e+00 -5.292166 2.622166 0.9856288
4:T4-1:T2 -2.240000e+00 -6.197166 1.717166 0.6488283
3:T2-2:T2 3.750000e-01 -3.582166 4.332166 1.0000000
4:T2-2:T2 -4.350000e-01 -4.392166 3.522166 1.0000000
1:T3-2:T2 -1.300000e-01 -4.087166 3.827166 1.0000000
2:T3-2:T2 6.850000e-01 -3.272166 4.642166 0.9999899
3:T3-2:T2 3.750000e-01 -3.582166 4.332166 1.0000000
4:T3-2:T2 -1.870000e+00 -5.827166 2.087166 0.8469659
1:T4-2:T2 8.000000e-02 -3.877166 4.037166 1.0000000
2:T4-2:T2 -1.235000e+00 -5.192166 2.722166 0.9927915
3:T4-2:T2 -9.650000e-01 -4.922166 2.992166 0.9994041
4:T4-2:T2 -1.870000e+00 -5.827166 2.087166 0.8469659
4:T2-3:T2 -8.100000e-01 -4.767166 3.147166 0.9999201
1:T3-3:T2 -5.050000e-01 -4.462166 3.452166 0.9999998
2:T3-3:T2 3.100000e-01 -3.647166 4.267166 1.0000000
3:T3-3:T2 0.000000e+00 -3.957166 3.957166 1.0000000
4:T3-3:T2 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T4-3:T2 -2.950000e-01 -4.252166 3.662166 1.0000000
2:T4-3:T2 -1.610000e+00 -5.567166 2.347166 0.9390952
3:T4-3:T2 -1.340000e+00 -5.297166 2.617166 0.9851637
4:T4-3:T2 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T3-4:T2 3.050000e-01 -3.652166 4.262166 1.0000000
2:T3-4:T2 1.120000e+00 -2.837166 5.077166 0.9971694
3:T3-4:T2 8.100000e-01 -3.147166 4.767166 0.9999201
4:T3-4:T2 -1.435000e+00 -5.392166 2.522166 0.9739693
1:T4-4:T2 5.150000e-01 -3.442166 4.472166 0.9999998
2:T4-4:T2 -8.000000e-01 -4.757166 3.157166 0.9999311
3:T4-4:T2 -5.300000e-01 -4.487166 3.427166 0.9999997
4:T4-4:T2 -1.435000e+00 -5.392166 2.522166 0.9739693
2:T3-1:T3 8.150000e-01 -3.142166 4.772166 0.9999140
3:T3-1:T3 5.050000e-01 -3.452166 4.462166 0.9999998
4:T3-1:T3 -1.740000e+00 -5.697166 2.217166 0.8991495
1:T4-1:T3 2.100000e-01 -3.747166 4.167166 1.0000000
2:T4-1:T3 -1.105000e+00 -5.062166 2.852166 0.9975260
3:T4-1:T3 -8.350000e-01 -4.792166 3.122166 0.9998853
4:T4-1:T3 -1.740000e+00 -5.697166 2.217166 0.8991495
3:T3-2:T3 -3.100000e-01 -4.267166 3.647166 1.0000000
4:T3-2:T3 -2.555000e+00 -6.512166 1.402166 0.4646140
1:T4-2:T3 -6.050000e-01 -4.562166 3.352166 0.9999980
2:T4-2:T3 -1.920000e+00 -5.877166 2.037166 0.8238969
3:T4-2:T3 -1.650000e+00 -5.607166 2.307166 0.9281328
4:T4-2:T3 -2.555000e+00 -6.512166 1.402166 0.4646140
4:T3-3:T3 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T4-3:T3 -2.950000e-01 -4.252166 3.662166 1.0000000
2:T4-3:T3 -1.610000e+00 -5.567166 2.347166 0.9390952
3:T4-3:T3 -1.340000e+00 -5.297166 2.617166 0.9851637
4:T4-3:T3 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T4-4:T3 1.950000e+00 -2.007166 5.907166 0.8093383
2:T4-4:T3 6.350000e-01 -3.322166 4.592166 0.9999962
3:T4-4:T3 9.050000e-01 -3.052166 4.862166 0.9997088
4:T4-4:T3 -1.332268e-15 -3.957166 3.957166 1.0000000
2:T4-1:T4 -1.315000e+00 -5.272166 2.642166 0.9873784
3:T4-1:T4 -1.045000e+00 -5.002166 2.912166 0.9986029
4:T4-1:T4 -1.950000e+00 -5.907166 2.007166 0.8093383
3:T4-2:T4 2.700000e-01 -3.687166 4.227166 1.0000000
4:T4-2:T4 -6.350000e-01 -4.592166 3.322166 0.9999962
4:T4-3:T4 -9.050000e-01 -4.862166 3.052166 0.9997088
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$tratamento
$tratamento$Letters
T2 T1 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T1 TRUE
T3 TRUE
T4 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
4:T1 2:T3 3:T2 3:T3 1:T2 1:T1 1:T4 2:T1 2:T2 1:T3 4:T2 3:T1 3:T4 2:T4 4:T3 4:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
4:T1 TRUE
2:T3 TRUE
3:T2 TRUE
3:T3 TRUE
1:T2 TRUE
1:T1 TRUE
1:T4 TRUE
2:T1 TRUE
2:T2 TRUE
1:T3 TRUE
4:T2 TRUE
3:T1 TRUE
3:T4 TRUE
2:T4 TRUE
4:T3 TRUE
4:T4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.00659 0.002196 0.710 0.560
tratamento 3 0.00599 0.001997 0.646 0.597
fermentador:tratamento 9 0.02520 0.002800 0.905 0.544
Residuals 16 0.04950 0.003094
48 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 0.120 0.0393 16 0.03632 0.203
2 T1 0.170 0.0393 16 0.08662 0.253
3 T1 0.070 0.0393 16 -0.01338 0.153
4 T1 0.090 0.0393 16 0.00662 0.173
1 T2 0.145 0.0393 16 0.06162 0.228
2 T2 0.128 0.0393 16 0.04507 0.212
3 T2 0.175 0.0393 16 0.09162 0.258
4 T2 0.115 0.0393 16 0.03162 0.198
1 T3 0.130 0.0393 16 0.04662 0.213
2 T3 0.145 0.0393 16 0.06162 0.228
3 T3 0.150 0.0393 16 0.06662 0.233
4 T3 0.135 0.0393 16 0.05162 0.218
1 T4 0.190 0.0393 16 0.10662 0.273
2 T4 0.080 0.0393 16 -0.00338 0.163
3 T4 0.100 0.0393 16 0.01662 0.183
4 T4 0.085 0.0393 16 0.00162 0.168
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.0153125 -0.09488082 0.06425582 0.9450419
3-1 -0.0224250 -0.10199332 0.05714332 0.8505120
4-1 -0.0399250 -0.11949332 0.03964332 0.4966663
3-2 -0.0071125 -0.08668082 0.07245582 0.9938951
4-2 -0.0246125 -0.10418082 0.05495582 0.8126063
4-3 -0.0175000 -0.09706832 0.06206832 0.9211172
$tratamento
diff lwr upr p adj
T2-T1 0.0284375 -0.05113082 0.10800582 0.7389940
T3-T1 0.0275750 -0.05199332 0.10714332 0.7562630
T4-T1 0.0013250 -0.07824332 0.08089332 0.9999594
T3-T2 -0.0008625 -0.08043082 0.07870582 0.9999888
T4-T2 -0.0271125 -0.10668082 0.05245582 0.7653800
T4-T3 -0.0262500 -0.10581832 0.05331832 0.7820860
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 5.030000e-02 -0.1723901 0.2729901 0.9997472
3:T1-1:T1 -4.970000e-02 -0.2723901 0.1729901 0.9997797
4:T1-1:T1 -2.970000e-02 -0.2523901 0.1929901 0.9999997
1:T2-1:T1 2.530000e-02 -0.1973901 0.2479901 1.0000000
2:T2-1:T1 8.750000e-03 -0.2139401 0.2314401 1.0000000
3:T2-1:T1 5.530000e-02 -0.1673901 0.2779901 0.9992737
4:T2-1:T1 -4.700000e-03 -0.2273901 0.2179901 1.0000000
1:T3-1:T1 1.030000e-02 -0.2123901 0.2329901 1.0000000
2:T3-1:T1 2.530000e-02 -0.1973901 0.2479901 1.0000000
3:T3-1:T1 3.030000e-02 -0.1923901 0.2529901 0.9999996
4:T3-1:T1 1.530000e-02 -0.2073901 0.2379901 1.0000000
1:T4-1:T1 7.030000e-02 -0.1523901 0.2929901 0.9919968
2:T4-1:T1 -3.970000e-02 -0.2623901 0.1829901 0.9999854
3:T4-1:T1 -1.970000e-02 -0.2423901 0.2029901 1.0000000
4:T4-1:T1 -3.470000e-02 -0.2573901 0.1879901 0.9999974
3:T1-2:T1 -1.000000e-01 -0.3226901 0.1226901 0.8855140
4:T1-2:T1 -8.000000e-02 -0.3026901 0.1426901 0.9758418
1:T2-2:T1 -2.500000e-02 -0.2476901 0.1976901 1.0000000
2:T2-2:T1 -4.155000e-02 -0.2642401 0.1811401 0.9999741
3:T2-2:T1 5.000000e-03 -0.2176901 0.2276901 1.0000000
4:T2-2:T1 -5.500000e-02 -0.2776901 0.1676901 0.9993154
1:T3-2:T1 -4.000000e-02 -0.2626901 0.1826901 0.9999839
2:T3-2:T1 -2.500000e-02 -0.2476901 0.1976901 1.0000000
3:T3-2:T1 -2.000000e-02 -0.2426901 0.2026901 1.0000000
4:T3-2:T1 -3.500000e-02 -0.2576901 0.1876901 0.9999971
1:T4-2:T1 2.000000e-02 -0.2026901 0.2426901 1.0000000
2:T4-2:T1 -9.000000e-02 -0.3126901 0.1326901 0.9418336
3:T4-2:T1 -7.000000e-02 -0.2926901 0.1526901 0.9923025
4:T4-2:T1 -8.500000e-02 -0.3076901 0.1376901 0.9614234
4:T1-3:T1 2.000000e-02 -0.2026901 0.2426901 1.0000000
1:T2-3:T1 7.500000e-02 -0.1476901 0.2976901 0.9858355
2:T2-3:T1 5.845000e-02 -0.1642401 0.2811401 0.9986891
3:T2-3:T1 1.050000e-01 -0.1176901 0.3276901 0.8488176
4:T2-3:T1 4.500000e-02 -0.1776901 0.2676901 0.9999315
1:T3-3:T1 6.000000e-02 -0.1626901 0.2826901 0.9982798
2:T3-3:T1 7.500000e-02 -0.1476901 0.2976901 0.9858355
3:T3-3:T1 8.000000e-02 -0.1426901 0.3026901 0.9758418
4:T3-3:T1 6.500000e-02 -0.1576901 0.2876901 0.9961694
1:T4-3:T1 1.200000e-01 -0.1026901 0.3426901 0.7115193
2:T4-3:T1 1.000000e-02 -0.2126901 0.2326901 1.0000000
3:T4-3:T1 3.000000e-02 -0.1926901 0.2526901 0.9999996
4:T4-3:T1 1.500000e-02 -0.2076901 0.2376901 1.0000000
1:T2-4:T1 5.500000e-02 -0.1676901 0.2776901 0.9993154
2:T2-4:T1 3.845000e-02 -0.1842401 0.2611401 0.9999902
3:T2-4:T1 8.500000e-02 -0.1376901 0.3076901 0.9614234
4:T2-4:T1 2.500000e-02 -0.1976901 0.2476901 1.0000000
1:T3-4:T1 4.000000e-02 -0.1826901 0.2626901 0.9999839
2:T3-4:T1 5.500000e-02 -0.1676901 0.2776901 0.9993154
3:T3-4:T1 6.000000e-02 -0.1626901 0.2826901 0.9982798
4:T3-4:T1 4.500000e-02 -0.1776901 0.2676901 0.9999315
1:T4-4:T1 1.000000e-01 -0.1226901 0.3226901 0.8855140
2:T4-4:T1 -1.000000e-02 -0.2326901 0.2126901 1.0000000
3:T4-4:T1 1.000000e-02 -0.2126901 0.2326901 1.0000000
4:T4-4:T1 -5.000000e-03 -0.2276901 0.2176901 1.0000000
2:T2-1:T2 -1.655000e-02 -0.2392401 0.2061401 1.0000000
3:T2-1:T2 3.000000e-02 -0.1926901 0.2526901 0.9999996
4:T2-1:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
1:T3-1:T2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
2:T3-1:T2 -2.775558e-17 -0.2226901 0.2226901 1.0000000
3:T3-1:T2 5.000000e-03 -0.2176901 0.2276901 1.0000000
4:T3-1:T2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
1:T4-1:T2 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:T4-1:T2 -6.500000e-02 -0.2876901 0.1576901 0.9961694
3:T4-1:T2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
4:T4-1:T2 -6.000000e-02 -0.2826901 0.1626901 0.9982798
3:T2-2:T2 4.655000e-02 -0.1761401 0.2692401 0.9998974
4:T2-2:T2 -1.345000e-02 -0.2361401 0.2092401 1.0000000
1:T3-2:T2 1.550000e-03 -0.2211401 0.2242401 1.0000000
2:T3-2:T2 1.655000e-02 -0.2061401 0.2392401 1.0000000
3:T3-2:T2 2.155000e-02 -0.2011401 0.2442401 1.0000000
4:T3-2:T2 6.550000e-03 -0.2161401 0.2292401 1.0000000
1:T4-2:T2 6.155000e-02 -0.1611401 0.2842401 0.9977686
2:T4-2:T2 -4.845000e-02 -0.2711401 0.1742401 0.9998359
3:T4-2:T2 -2.845000e-02 -0.2511401 0.1942401 0.9999998
4:T4-2:T2 -4.345000e-02 -0.2661401 0.1792401 0.9999552
4:T2-3:T2 -6.000000e-02 -0.2826901 0.1626901 0.9982798
1:T3-3:T2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
2:T3-3:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
3:T3-3:T2 -2.500000e-02 -0.2476901 0.1976901 1.0000000
4:T3-3:T2 -4.000000e-02 -0.2626901 0.1826901 0.9999839
1:T4-3:T2 1.500000e-02 -0.2076901 0.2376901 1.0000000
2:T4-3:T2 -9.500000e-02 -0.3176901 0.1276901 0.9165838
3:T4-3:T2 -7.500000e-02 -0.2976901 0.1476901 0.9858355
4:T4-3:T2 -9.000000e-02 -0.3126901 0.1326901 0.9418336
1:T3-4:T2 1.500000e-02 -0.2076901 0.2376901 1.0000000
2:T3-4:T2 3.000000e-02 -0.1926901 0.2526901 0.9999996
3:T3-4:T2 3.500000e-02 -0.1876901 0.2576901 0.9999971
4:T3-4:T2 2.000000e-02 -0.2026901 0.2426901 1.0000000
1:T4-4:T2 7.500000e-02 -0.1476901 0.2976901 0.9858355
2:T4-4:T2 -3.500000e-02 -0.2576901 0.1876901 0.9999971
3:T4-4:T2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
4:T4-4:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
2:T3-1:T3 1.500000e-02 -0.2076901 0.2376901 1.0000000
3:T3-1:T3 2.000000e-02 -0.2026901 0.2426901 1.0000000
4:T3-1:T3 5.000000e-03 -0.2176901 0.2276901 1.0000000
1:T4-1:T3 6.000000e-02 -0.1626901 0.2826901 0.9982798
2:T4-1:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
3:T4-1:T3 -3.000000e-02 -0.2526901 0.1926901 0.9999996
4:T4-1:T3 -4.500000e-02 -0.2676901 0.1776901 0.9999315
3:T3-2:T3 5.000000e-03 -0.2176901 0.2276901 1.0000000
4:T3-2:T3 -1.000000e-02 -0.2326901 0.2126901 1.0000000
1:T4-2:T3 4.500000e-02 -0.1776901 0.2676901 0.9999315
2:T4-2:T3 -6.500000e-02 -0.2876901 0.1576901 0.9961694
3:T4-2:T3 -4.500000e-02 -0.2676901 0.1776901 0.9999315
4:T4-2:T3 -6.000000e-02 -0.2826901 0.1626901 0.9982798
4:T3-3:T3 -1.500000e-02 -0.2376901 0.2076901 1.0000000
1:T4-3:T3 4.000000e-02 -0.1826901 0.2626901 0.9999839
2:T4-3:T3 -7.000000e-02 -0.2926901 0.1526901 0.9923025
3:T4-3:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
4:T4-3:T3 -6.500000e-02 -0.2876901 0.1576901 0.9961694
1:T4-4:T3 5.500000e-02 -0.1676901 0.2776901 0.9993154
2:T4-4:T3 -5.500000e-02 -0.2776901 0.1676901 0.9993154
3:T4-4:T3 -3.500000e-02 -0.2576901 0.1876901 0.9999971
4:T4-4:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
2:T4-1:T4 -1.100000e-01 -0.3326901 0.1126901 0.8070205
3:T4-1:T4 -9.000000e-02 -0.3126901 0.1326901 0.9418336
4:T4-1:T4 -1.050000e-01 -0.3276901 0.1176901 0.8488176
3:T4-2:T4 2.000000e-02 -0.2026901 0.2426901 1.0000000
4:T4-2:T4 5.000000e-03 -0.2176901 0.2276901 1.0000000
4:T4-3:T4 -1.500000e-02 -0.2376901 0.2076901 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
1 2 3 4
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
1 TRUE
2 TRUE
3 TRUE
4 TRUE
$tratamento
$tratamento$Letters
T2 T3 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T4 TRUE
T1 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
1:T4 3:T2 2:T1 3:T3 1:T2 2:T3 4:T3 1:T3 2:T2 1:T1 4:T2 3:T4 4:T1 4:T4 2:T4 3:T1
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
1:T4 TRUE
3:T2 TRUE
2:T1 TRUE
3:T3 TRUE
1:T2 TRUE
2:T3 TRUE
4:T3 TRUE
1:T3 TRUE
2:T2 TRUE
1:T1 TRUE
4:T2 TRUE
3:T4 TRUE
4:T1 TRUE
4:T4 TRUE
2:T4 TRUE
3:T1 TRUE
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.04519 0.01506 96.40 4.14e-16 ***
tratamento 3 0.09707 0.03236 207.08 < 2e-16 ***
fermentador:tratamento 9 0.11592 0.01288 82.43 < 2e-16 ***
Residuals 32 0.00500 0.00016
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 0.277 0.00722 32 0.262 0.291
2 T1 0.340 0.00722 32 0.325 0.355
3 T1 0.163 0.00722 32 0.149 0.178
4 T1 0.210 0.00722 32 0.195 0.225
1 T2 0.357 0.00722 32 0.342 0.371
2 T2 0.300 0.00722 32 0.285 0.315
3 T2 0.433 0.00722 32 0.419 0.448
4 T2 0.253 0.00722 32 0.239 0.268
1 T3 0.293 0.00722 32 0.279 0.308
2 T3 0.367 0.00722 32 0.352 0.381
3 T3 0.350 0.00722 32 0.335 0.365
4 T3 0.290 0.00722 32 0.275 0.305
1 T4 0.330 0.00722 32 0.315 0.345
2 T4 0.197 0.00722 32 0.182 0.211
3 T4 0.230 0.00722 32 0.215 0.245
4 T4 0.183 0.00722 32 0.169 0.198
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 -0.013315833 -0.02714185 0.0005101829 0.0624366
3-1 -0.019982500 -0.03380852 -0.0061564838 0.0023853
4-1 -0.079982500 -0.09380852 -0.0661564838 0.0000000
3-2 -0.006666667 -0.02049268 0.0071593495 0.5656958
4-2 -0.066666667 -0.08049268 -0.0528406505 0.0000000
4-3 -0.060000000 -0.07382602 -0.0461739838 0.0000000
$tratamento
diff lwr upr p adj
T2-T1 0.08835083 0.07452482 0.102176850 0.0000000
T3-T1 0.07751750 0.06369148 0.091343516 0.0000000
T4-T1 -0.01248250 -0.02630852 0.001343516 0.0885647
T3-T2 -0.01083333 -0.02465935 0.002992683 0.1675959
T4-T2 -0.10083333 -0.11465935 -0.087007317 0.0000000
T4-T3 -0.09000000 -0.10382602 -0.076173984 0.0000000
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 0.063403333 0.025558224 0.101248443 0.0000586
3:T1-1:T1 -0.113263333 -0.151108443 -0.075418224 0.0000000
4:T1-1:T1 -0.066596667 -0.104441776 -0.028751557 0.0000243
1:T2-1:T1 0.080070000 0.042224891 0.117915109 0.0000006
2:T2-1:T1 0.023403333 -0.014441776 0.061248443 0.6308596
3:T2-1:T1 0.156736667 0.118891557 0.194581776 0.0000000
4:T2-1:T1 -0.023263333 -0.061108443 0.014581776 0.6398228
1:T3-1:T1 0.016736667 -0.021108443 0.054581776 0.9477916
2:T3-1:T1 0.090070000 0.052224891 0.127915109 0.0000000
3:T3-1:T1 0.073403333 0.035558224 0.111248443 0.0000038
4:T3-1:T1 0.013403333 -0.024441776 0.051248443 0.9922893
1:T4-1:T1 0.053403333 0.015558224 0.091248443 0.0009179
2:T4-1:T1 -0.079930000 -0.117775109 -0.042084891 0.0000007
3:T4-1:T1 -0.046596667 -0.084441776 -0.008751557 0.0056910
4:T4-1:T1 -0.093263333 -0.131108443 -0.055418224 0.0000000
3:T1-2:T1 -0.176666667 -0.214511776 -0.138821557 0.0000000
4:T1-2:T1 -0.130000000 -0.167845109 -0.092154891 0.0000000
1:T2-2:T1 0.016666667 -0.021178443 0.054511776 0.9494264
2:T2-2:T1 -0.040000000 -0.077845109 -0.002154891 0.0300852
3:T2-2:T1 0.093333333 0.055488224 0.131178443 0.0000000
4:T2-2:T1 -0.086666667 -0.124511776 -0.048821557 0.0000001
1:T3-2:T1 -0.046666667 -0.084511776 -0.008821557 0.0055876
2:T3-2:T1 0.026666667 -0.011178443 0.064511776 0.4243390
3:T3-2:T1 0.010000000 -0.027845109 0.047845109 0.9996615
4:T3-2:T1 -0.050000000 -0.087845109 -0.012154891 0.0023062
1:T4-2:T1 -0.010000000 -0.047845109 0.027845109 0.9996615
2:T4-2:T1 -0.143333333 -0.181178443 -0.105488224 0.0000000
3:T4-2:T1 -0.110000000 -0.147845109 -0.072154891 0.0000000
4:T4-2:T1 -0.156666667 -0.194511776 -0.118821557 0.0000000
4:T1-3:T1 0.046666667 0.008821557 0.084511776 0.0055876
1:T2-3:T1 0.193333333 0.155488224 0.231178443 0.0000000
2:T2-3:T1 0.136666667 0.098821557 0.174511776 0.0000000
3:T2-3:T1 0.270000000 0.232154891 0.307845109 0.0000000
4:T2-3:T1 0.090000000 0.052154891 0.127845109 0.0000000
1:T3-3:T1 0.130000000 0.092154891 0.167845109 0.0000000
2:T3-3:T1 0.203333333 0.165488224 0.241178443 0.0000000
3:T3-3:T1 0.186666667 0.148821557 0.224511776 0.0000000
4:T3-3:T1 0.126666667 0.088821557 0.164511776 0.0000000
1:T4-3:T1 0.166666667 0.128821557 0.204511776 0.0000000
2:T4-3:T1 0.033333333 -0.004511776 0.071178443 0.1332726
3:T4-3:T1 0.066666667 0.028821557 0.104511776 0.0000239
4:T4-3:T1 0.020000000 -0.017845109 0.057845109 0.8289662
1:T2-4:T1 0.146666667 0.108821557 0.184511776 0.0000000
2:T2-4:T1 0.090000000 0.052154891 0.127845109 0.0000000
3:T2-4:T1 0.223333333 0.185488224 0.261178443 0.0000000
4:T2-4:T1 0.043333333 0.005488224 0.081178443 0.0131979
1:T3-4:T1 0.083333333 0.045488224 0.121178443 0.0000003
2:T3-4:T1 0.156666667 0.118821557 0.194511776 0.0000000
3:T3-4:T1 0.140000000 0.102154891 0.177845109 0.0000000
4:T3-4:T1 0.080000000 0.042154891 0.117845109 0.0000007
1:T4-4:T1 0.120000000 0.082154891 0.157845109 0.0000000
2:T4-4:T1 -0.013333333 -0.051178443 0.024511776 0.9926678
3:T4-4:T1 0.020000000 -0.017845109 0.057845109 0.8289662
4:T4-4:T1 -0.026666667 -0.064511776 0.011178443 0.4243390
2:T2-1:T2 -0.056666667 -0.094511776 -0.018821557 0.0003755
3:T2-1:T2 0.076666667 0.038821557 0.114511776 0.0000016
4:T2-1:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
1:T3-1:T2 -0.063333333 -0.101178443 -0.025488224 0.0000598
2:T3-1:T2 0.010000000 -0.027845109 0.047845109 0.9996615
3:T3-1:T2 -0.006666667 -0.044511776 0.031178443 0.9999979
4:T3-1:T2 -0.066666667 -0.104511776 -0.028821557 0.0000239
1:T4-1:T2 -0.026666667 -0.064511776 0.011178443 0.4243390
2:T4-1:T2 -0.160000000 -0.197845109 -0.122154891 0.0000000
3:T4-1:T2 -0.126666667 -0.164511776 -0.088821557 0.0000000
4:T4-1:T2 -0.173333333 -0.211178443 -0.135488224 0.0000000
3:T2-2:T2 0.133333333 0.095488224 0.171178443 0.0000000
4:T2-2:T2 -0.046666667 -0.084511776 -0.008821557 0.0055876
1:T3-2:T2 -0.006666667 -0.044511776 0.031178443 0.9999979
2:T3-2:T2 0.066666667 0.028821557 0.104511776 0.0000239
3:T3-2:T2 0.050000000 0.012154891 0.087845109 0.0023062
4:T3-2:T2 -0.010000000 -0.047845109 0.027845109 0.9996615
1:T4-2:T2 0.030000000 -0.007845109 0.067845109 0.2503711
2:T4-2:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
3:T4-2:T2 -0.070000000 -0.107845109 -0.032154891 0.0000096
4:T4-2:T2 -0.116666667 -0.154511776 -0.078821557 0.0000000
4:T2-3:T2 -0.180000000 -0.217845109 -0.142154891 0.0000000
1:T3-3:T2 -0.140000000 -0.177845109 -0.102154891 0.0000000
2:T3-3:T2 -0.066666667 -0.104511776 -0.028821557 0.0000239
3:T3-3:T2 -0.083333333 -0.121178443 -0.045488224 0.0000003
4:T3-3:T2 -0.143333333 -0.181178443 -0.105488224 0.0000000
1:T4-3:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
2:T4-3:T2 -0.236666667 -0.274511776 -0.198821557 0.0000000
3:T4-3:T2 -0.203333333 -0.241178443 -0.165488224 0.0000000
4:T4-3:T2 -0.250000000 -0.287845109 -0.212154891 0.0000000
1:T3-4:T2 0.040000000 0.002154891 0.077845109 0.0300852
2:T3-4:T2 0.113333333 0.075488224 0.151178443 0.0000000
3:T3-4:T2 0.096666667 0.058821557 0.134511776 0.0000000
4:T3-4:T2 0.036666667 -0.001178443 0.074511776 0.0653618
1:T4-4:T2 0.076666667 0.038821557 0.114511776 0.0000016
2:T4-4:T2 -0.056666667 -0.094511776 -0.018821557 0.0003755
3:T4-4:T2 -0.023333333 -0.061178443 0.014511776 0.6353449
4:T4-4:T2 -0.070000000 -0.107845109 -0.032154891 0.0000096
2:T3-1:T3 0.073333333 0.035488224 0.111178443 0.0000039
3:T3-1:T3 0.056666667 0.018821557 0.094511776 0.0003755
4:T3-1:T3 -0.003333333 -0.041178443 0.034511776 1.0000000
1:T4-1:T3 0.036666667 -0.001178443 0.074511776 0.0653618
2:T4-1:T3 -0.096666667 -0.134511776 -0.058821557 0.0000000
3:T4-1:T3 -0.063333333 -0.101178443 -0.025488224 0.0000598
4:T4-1:T3 -0.110000000 -0.147845109 -0.072154891 0.0000000
3:T3-2:T3 -0.016666667 -0.054511776 0.021178443 0.9494264
4:T3-2:T3 -0.076666667 -0.114511776 -0.038821557 0.0000016
1:T4-2:T3 -0.036666667 -0.074511776 0.001178443 0.0653618
2:T4-2:T3 -0.170000000 -0.207845109 -0.132154891 0.0000000
3:T4-2:T3 -0.136666667 -0.174511776 -0.098821557 0.0000000
4:T4-2:T3 -0.183333333 -0.221178443 -0.145488224 0.0000000
4:T3-3:T3 -0.060000000 -0.097845109 -0.022154891 0.0001499
1:T4-3:T3 -0.020000000 -0.057845109 0.017845109 0.8289662
2:T4-3:T3 -0.153333333 -0.191178443 -0.115488224 0.0000000
3:T4-3:T3 -0.120000000 -0.157845109 -0.082154891 0.0000000
4:T4-3:T3 -0.166666667 -0.204511776 -0.128821557 0.0000000
1:T4-4:T3 0.040000000 0.002154891 0.077845109 0.0300852
2:T4-4:T3 -0.093333333 -0.131178443 -0.055488224 0.0000000
3:T4-4:T3 -0.060000000 -0.097845109 -0.022154891 0.0001499
4:T4-4:T3 -0.106666667 -0.144511776 -0.068821557 0.0000000
2:T4-1:T4 -0.133333333 -0.171178443 -0.095488224 0.0000000
3:T4-1:T4 -0.100000000 -0.137845109 -0.062154891 0.0000000
4:T4-1:T4 -0.146666667 -0.184511776 -0.108821557 0.0000000
3:T4-2:T4 0.033333333 -0.004511776 0.071178443 0.1332726
4:T4-2:T4 -0.013333333 -0.051178443 0.024511776 0.9926678
4:T4-3:T4 -0.046666667 -0.084511776 -0.008821557 0.0055876
Letras do Tukey:
$fermentador
1 2 3 4
"a" "ab" "b" "c"
$tratamento
T2 T3 T1 T4
"a" "a" "b" "b"
$`fermentador:tratamento`
3:T2 2:T3 1:T2 3:T3 2:T1 1:T4 2:T2 1:T3 4:T3 1:T1 4:T2 3:T4 4:T1 2:T4 4:T4 3:T1
"a" "b" "b" "b" "b" "bc" "cd" "cd" "de" "de" "ef" "fg" "gh" "ghi" "hi" "i"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 2776 925 0.236 0.870
tratamento 3 16169 5390 1.373 0.287
fermentador:tratamento 9 6861 762 0.194 0.991
Residuals 16 62809 3926
48 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 599 44.3 16 506 693
2 T1 579 44.3 16 485 673
3 T1 627 44.3 16 533 720
4 T1 630 44.3 16 536 724
1 T2 571 44.3 16 477 665
2 T2 591 44.3 16 497 685
3 T2 581 44.3 16 487 675
4 T2 582 44.3 16 488 676
1 T3 564 44.3 16 470 658
2 T3 578 44.3 16 484 672
3 T3 552 44.3 16 458 646
4 T3 577 44.3 16 483 670
1 T4 507 44.3 16 413 601
2 T4 570 44.3 16 476 664
3 T4 562 44.3 16 468 656
4 T4 549 44.3 16 455 642
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 19.301000 -70.32643 108.92843 0.9254430
3-1 19.838050 -69.78938 109.46548 0.9197694
4-1 24.094575 -65.53286 113.72201 0.8671400
3-2 0.537050 -89.09038 90.16448 0.9999981
4-2 4.793575 -84.83386 94.42101 0.9986679
4-3 4.256525 -85.37091 93.88396 0.9990653
$tratamento
diff lwr upr p adj
T2-T1 -27.35585 -116.9833 62.27158 0.8184909
T3-T1 -41.22081 -130.8482 48.40662 0.5666118
T4-T1 -61.86686 -151.4943 27.76057 0.2379893
T3-T2 -13.86496 -103.4924 75.76247 0.9701102
T4-T2 -34.51101 -124.1384 55.11642 0.6936451
T4-T3 -20.64605 -110.2735 68.98138 0.9108132
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 -20.63355 -271.4764 230.2093 1.0000000
3:T1-1:T1 27.11235 -223.7305 277.9552 1.0000000
4:T1-1:T1 30.97120 -219.8716 281.8140 0.9999999
1:T2-1:T1 -28.18835 -279.0312 222.6545 1.0000000
2:T2-1:T1 -8.08520 -258.9280 242.7576 1.0000000
3:T2-1:T1 -18.66215 -269.5050 232.1807 1.0000000
4:T2-1:T1 -17.03770 -267.8805 233.8051 1.0000000
1:T3-1:T1 -35.46415 -286.3070 215.3787 0.9999993
2:T3-1:T1 -21.28085 -272.1237 229.5620 1.0000000
3:T3-1:T1 -47.83165 -298.6745 203.0112 0.9999662
4:T3-1:T1 -22.85660 -273.6994 227.9862 1.0000000
1:T4-1:T1 -92.57465 -343.4175 158.2682 0.9701347
2:T4-1:T1 -29.02355 -279.8664 221.8193 1.0000000
3:T4-1:T1 -37.49350 -288.3363 213.3493 0.9999985
4:T4-1:T1 -50.92575 -301.7686 199.9171 0.9999276
3:T1-2:T1 47.74590 -203.0969 298.5887 0.9999669
4:T1-2:T1 51.60475 -199.2381 302.4476 0.9999151
1:T2-2:T1 -7.55480 -258.3976 243.2880 1.0000000
2:T2-2:T1 12.54835 -238.2945 263.3912 1.0000000
3:T2-2:T1 1.97140 -248.8714 252.8142 1.0000000
4:T2-2:T1 3.59585 -247.2470 254.4387 1.0000000
1:T3-2:T1 -14.83060 -265.6734 236.0122 1.0000000
2:T3-2:T1 -0.64730 -251.4901 250.1955 1.0000000
3:T3-2:T1 -27.19810 -278.0409 223.6447 1.0000000
4:T3-2:T1 -2.22305 -253.0659 248.6198 1.0000000
1:T4-2:T1 -71.94110 -322.7839 178.9017 0.9967742
2:T4-2:T1 -8.39000 -259.2328 242.4528 1.0000000
3:T4-2:T1 -16.85995 -267.7028 233.9829 1.0000000
4:T4-2:T1 -30.29220 -281.1350 220.5506 0.9999999
4:T1-3:T1 3.85885 -246.9840 254.7017 1.0000000
1:T2-3:T1 -55.30070 -306.1435 195.5421 0.9998087
2:T2-3:T1 -35.19755 -286.0404 215.6453 0.9999994
3:T2-3:T1 -45.77450 -296.6173 205.0683 0.9999804
4:T2-3:T1 -44.15005 -294.9929 206.6928 0.9999876
1:T3-3:T1 -62.57650 -313.4193 188.2663 0.9992369
2:T3-3:T1 -48.39320 -299.2360 202.4496 0.9999610
3:T3-3:T1 -74.94400 -325.7868 175.8988 0.9952071
4:T3-3:T1 -49.96895 -300.8118 200.8739 0.9999424
1:T4-3:T1 -119.68700 -370.5298 131.1558 0.8387851
2:T4-3:T1 -56.13590 -306.9787 194.7069 0.9997727
3:T4-3:T1 -64.60585 -315.4487 186.2370 0.9989260
4:T4-3:T1 -78.03810 -328.8809 172.8047 0.9929987
1:T2-4:T1 -59.15955 -310.0024 191.6833 0.9995883
2:T2-4:T1 -39.05640 -289.8992 211.7864 0.9999974
3:T2-4:T1 -49.63335 -300.4762 201.2095 0.9999469
4:T2-4:T1 -48.00890 -298.8517 202.8339 0.9999646
1:T3-4:T1 -66.43535 -317.2782 184.4075 0.9985598
2:T3-4:T1 -52.25205 -303.0949 198.5908 0.9999016
3:T3-4:T1 -78.80285 -329.6457 172.0400 0.9923439
4:T3-4:T1 -53.82780 -304.6706 197.0150 0.9998603
1:T4-4:T1 -123.54585 -374.3887 127.2970 0.8098341
2:T4-4:T1 -59.99475 -310.8376 190.8481 0.9995189
3:T4-4:T1 -68.46470 -319.3075 182.3781 0.9980362
4:T4-4:T1 -81.89695 -332.7398 168.9459 0.9891845
2:T2-1:T2 20.10315 -230.7397 270.9460 1.0000000
3:T2-1:T2 9.52620 -241.3166 260.3690 1.0000000
4:T2-1:T2 11.15065 -239.6922 261.9935 1.0000000
1:T3-1:T2 -7.27580 -258.1186 243.5670 1.0000000
2:T3-1:T2 6.90750 -243.9353 257.7503 1.0000000
3:T3-1:T2 -19.64330 -270.4861 231.1995 1.0000000
4:T3-1:T2 5.33175 -245.5111 256.1746 1.0000000
1:T4-1:T2 -64.38630 -315.2291 186.4565 0.9989641
2:T4-1:T2 -0.83520 -251.6780 250.0076 1.0000000
3:T4-1:T2 -9.30515 -260.1480 241.5377 1.0000000
4:T4-1:T2 -22.73740 -273.5802 228.1054 1.0000000
3:T2-2:T2 -10.57695 -261.4198 240.2659 1.0000000
4:T2-2:T2 -8.95250 -259.7953 241.8903 1.0000000
1:T3-2:T2 -27.37895 -278.2218 223.4639 1.0000000
2:T3-2:T2 -13.19565 -264.0385 237.6472 1.0000000
3:T3-2:T2 -39.74645 -290.5893 211.0964 0.9999968
4:T3-2:T2 -14.77140 -265.6142 236.0714 1.0000000
1:T4-2:T2 -84.48945 -335.3323 166.3534 0.9858243
2:T4-2:T2 -20.93835 -271.7812 229.9045 1.0000000
3:T4-2:T2 -29.40830 -280.2511 221.4345 0.9999999
4:T4-2:T2 -42.84055 -293.6834 208.0023 0.9999915
4:T2-3:T2 1.62445 -249.2184 252.4673 1.0000000
1:T3-3:T2 -16.80200 -267.6448 234.0408 1.0000000
2:T3-3:T2 -2.61870 -253.4615 248.2241 1.0000000
3:T3-3:T2 -29.16950 -280.0123 221.6733 1.0000000
4:T3-3:T2 -4.19445 -255.0373 246.6484 1.0000000
1:T4-3:T2 -73.91250 -324.7553 176.9303 0.9958030
2:T4-3:T2 -10.36140 -261.2042 240.4814 1.0000000
3:T4-3:T2 -18.83135 -269.6742 232.0115 1.0000000
4:T4-3:T2 -32.26360 -283.1064 218.5792 0.9999998
1:T3-4:T2 -18.42645 -269.2693 232.4164 1.0000000
2:T3-4:T2 -4.24315 -255.0860 246.5997 1.0000000
3:T3-4:T2 -30.79395 -281.6368 220.0489 0.9999999
4:T3-4:T2 -5.81890 -256.6617 245.0239 1.0000000
1:T4-4:T2 -75.53695 -326.3798 175.3059 0.9948346
2:T4-4:T2 -11.98585 -262.8287 238.8570 1.0000000
3:T4-4:T2 -20.45580 -271.2986 230.3870 1.0000000
4:T4-4:T2 -33.88805 -284.7309 216.9548 0.9999996
2:T3-1:T3 14.18330 -236.6595 265.0261 1.0000000
3:T3-1:T3 -12.36750 -263.2103 238.4753 1.0000000
4:T3-1:T3 12.60755 -238.2353 263.4504 1.0000000
1:T4-1:T3 -57.11050 -307.9533 193.7323 0.9997233
2:T4-1:T3 6.44060 -244.4022 257.2834 1.0000000
3:T4-1:T3 -2.02935 -252.8722 248.8135 1.0000000
4:T4-1:T3 -15.46160 -266.3044 235.3812 1.0000000
3:T3-2:T3 -26.55080 -277.3936 224.2920 1.0000000
4:T3-2:T3 -1.57575 -252.4186 249.2671 1.0000000
1:T4-2:T3 -71.29380 -322.1366 179.5490 0.9970495
2:T4-2:T3 -7.74270 -258.5855 243.1001 1.0000000
3:T4-2:T3 -16.21265 -267.0555 234.6302 1.0000000
4:T4-2:T3 -29.64490 -280.4877 221.1979 0.9999999
4:T3-3:T3 24.97505 -225.8678 275.8179 1.0000000
1:T4-3:T3 -44.74300 -295.5858 206.0998 0.9999853
2:T4-3:T3 18.80810 -232.0347 269.6509 1.0000000
3:T4-3:T3 10.33815 -240.5047 261.1810 1.0000000
4:T4-3:T3 -3.09410 -253.9369 247.7487 1.0000000
1:T4-4:T3 -69.71805 -320.5609 181.1248 0.9976398
2:T4-4:T3 -6.16695 -257.0098 244.6759 1.0000000
3:T4-4:T3 -14.63690 -265.4797 236.2059 1.0000000
4:T4-4:T3 -28.06915 -278.9120 222.7737 1.0000000
2:T4-1:T4 63.55110 -187.2917 314.3939 0.9990988
3:T4-1:T4 55.08115 -195.7617 305.9240 0.9998173
4:T4-1:T4 41.64890 -209.1939 292.4917 0.9999941
3:T4-2:T4 -8.46995 -259.3128 242.3729 1.0000000
4:T4-2:T4 -21.90220 -272.7450 228.9406 1.0000000
4:T4-3:T4 -13.43225 -264.2751 237.4106 1.0000000
Letras do Tukey:
$fermentador
$fermentador$Letters
4 3 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
4 TRUE
3 TRUE
2 TRUE
1 TRUE
$tratamento
$tratamento$Letters
T1 T2 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T1 TRUE
T2 TRUE
T3 TRUE
T4 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
4:T1 3:T1 1:T1 2:T2 4:T2 3:T2 2:T1 2:T3 4:T3 1:T2 2:T4 1:T3 3:T4 3:T3 4:T4 1:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
4:T1 TRUE
3:T1 TRUE
1:T1 TRUE
2:T2 TRUE
4:T2 TRUE
3:T2 TRUE
2:T1 TRUE
2:T3 TRUE
4:T3 TRUE
1:T2 TRUE
2:T4 TRUE
1:T3 TRUE
3:T4 TRUE
3:T3 TRUE
4:T4 TRUE
1:T4 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
fermentador 3 0.0005 0.000173 0.008 0.999
tratamento 3 0.0022 0.000719 0.031 0.992
fermentador:tratamento 9 0.2144 0.023822 1.034 0.436
Residuals 32 0.7373 0.023041
32 observations deleted due to missingness
Médias (emmeans):
fermentador tratamento emmean SE df lower.CL upper.CL
1 T1 0.2415 0.0876 32 0.0630 0.420
2 T1 0.1074 0.0876 32 -0.0711 0.286
3 T1 0.0847 0.0876 32 -0.0938 0.263
4 T1 0.0869 0.0876 32 -0.0916 0.265
1 T2 0.0975 0.0876 32 -0.0810 0.276
2 T2 0.2428 0.0876 32 0.0643 0.421
3 T2 0.1156 0.0876 32 -0.0629 0.294
4 T2 0.0949 0.0876 32 -0.0836 0.273
1 T3 0.0885 0.0876 32 -0.0900 0.267
2 T3 0.0830 0.0876 32 -0.0955 0.262
3 T3 0.2608 0.0876 32 0.0823 0.439
4 T3 0.1147 0.0876 32 -0.0638 0.293
1 T4 0.0845 0.0876 32 -0.0940 0.263
2 T4 0.0798 0.0876 32 -0.0988 0.258
3 T4 0.0801 0.0876 32 -0.0984 0.259
4 T4 0.2394 0.0876 32 0.0609 0.418
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$fermentador
diff lwr upr p adj
2-1 0.0002483333 -0.1676472 0.1681439 1.0000000
3-1 0.0072995833 -0.1605960 0.1751951 0.9994027
4-1 0.0059537500 -0.1619418 0.1738493 0.9996753
3-2 0.0070512500 -0.1608443 0.1749468 0.9994614
4-2 0.0057054167 -0.1621901 0.1736010 0.9997142
4-3 -0.0013458333 -0.1692414 0.1665497 0.9999962
$tratamento
diff lwr upr p adj
T2-T1 0.0075937500 -0.1603018 0.1754893 0.9993279
T3-T1 0.0066620833 -0.1612335 0.1745576 0.9995455
T4-T1 -0.0091691667 -0.1770647 0.1587264 0.9988201
T3-T2 -0.0009316667 -0.1688272 0.1669639 0.9999988
T4-T2 -0.0167629167 -0.1846585 0.1511326 0.9929396
T4-T3 -0.0158312500 -0.1837268 0.1520643 0.9940334
$`fermentador:tratamento`
diff lwr upr p adj
2:T1-1:T1 -0.134058333 -0.5936286 0.3255119 0.9989616
3:T1-1:T1 -0.156781667 -0.6163519 0.3027886 0.9946459
4:T1-1:T1 -0.154600000 -0.6141702 0.3049702 0.9953426
1:T2-1:T1 -0.143975000 -0.6035452 0.3155952 0.9977586
2:T2-1:T1 0.001333333 -0.4582369 0.4609036 1.0000000
3:T2-1:T1 -0.125841667 -0.5854119 0.3337286 0.9994895
4:T2-1:T1 -0.146581667 -0.6061519 0.3129886 0.9972954
1:T3-1:T1 -0.152928333 -0.6124986 0.3066419 0.9958244
2:T3-1:T1 -0.158425000 -0.6179952 0.3011452 0.9940667
3:T3-1:T1 0.019338333 -0.4402319 0.4789086 1.0000000
4:T3-1:T1 -0.126776667 -0.5863469 0.3327936 0.9994445
1:T4-1:T1 -0.156951667 -0.6165219 0.3026186 0.9945883
2:T4-1:T1 -0.161711667 -0.6212819 0.2978586 0.9927551
3:T4-1:T1 -0.161371667 -0.6209419 0.2981986 0.9929008
4:T4-1:T1 -0.002081667 -0.4616519 0.4574886 1.0000000
3:T1-2:T1 -0.022723333 -0.4822936 0.4368469 1.0000000
4:T1-2:T1 -0.020541667 -0.4801119 0.4390286 1.0000000
1:T2-2:T1 -0.009916667 -0.4694869 0.4496536 1.0000000
2:T2-2:T1 0.135391667 -0.3241786 0.5949619 0.9988423
3:T2-2:T1 0.008216667 -0.4513536 0.4677869 1.0000000
4:T2-2:T1 -0.012523333 -0.4720936 0.4470469 1.0000000
1:T3-2:T1 -0.018870000 -0.4784402 0.4407002 1.0000000
2:T3-2:T1 -0.024366667 -0.4839369 0.4352036 1.0000000
3:T3-2:T1 0.153396667 -0.3061736 0.6129669 0.9956937
4:T3-2:T1 0.007281667 -0.4522886 0.4668519 1.0000000
1:T4-2:T1 -0.022893333 -0.4824636 0.4366769 1.0000000
2:T4-2:T1 -0.027653333 -0.4872236 0.4319169 1.0000000
3:T4-2:T1 -0.027313333 -0.4868836 0.4322569 1.0000000
4:T4-2:T1 0.131976667 -0.3275936 0.5915469 0.9991269
4:T1-3:T1 0.002181667 -0.4573886 0.4617519 1.0000000
1:T2-3:T1 0.012806667 -0.4467636 0.4723769 1.0000000
2:T2-3:T1 0.158115000 -0.3014552 0.6176852 0.9941797
3:T2-3:T1 0.030940000 -0.4286302 0.4905102 1.0000000
4:T2-3:T1 0.010200000 -0.4493702 0.4697702 1.0000000
1:T3-3:T1 0.003853333 -0.4557169 0.4634236 1.0000000
2:T3-3:T1 -0.001643333 -0.4612136 0.4579269 1.0000000
3:T3-3:T1 0.176120000 -0.2834502 0.6356902 0.9839965
4:T3-3:T1 0.030005000 -0.4295652 0.4895752 1.0000000
1:T4-3:T1 -0.000170000 -0.4597402 0.4594002 1.0000000
2:T4-3:T1 -0.004930000 -0.4645002 0.4546402 1.0000000
3:T4-3:T1 -0.004590000 -0.4641602 0.4549802 1.0000000
4:T4-3:T1 0.154700000 -0.3048702 0.6142702 0.9953124
1:T2-4:T1 0.010625000 -0.4489452 0.4701952 1.0000000
2:T2-4:T1 0.155933333 -0.3036369 0.6155036 0.9949263
3:T2-4:T1 0.028758333 -0.4308119 0.4883286 1.0000000
4:T2-4:T1 0.008018333 -0.4515519 0.4675886 1.0000000
1:T3-4:T1 0.001671667 -0.4578986 0.4612419 1.0000000
2:T3-4:T1 -0.003825000 -0.4633952 0.4557452 1.0000000
3:T3-4:T1 0.173938333 -0.2856319 0.6335086 0.9856898
4:T3-4:T1 0.027823333 -0.4317469 0.4873936 1.0000000
1:T4-4:T1 -0.002351667 -0.4619219 0.4572186 1.0000000
2:T4-4:T1 -0.007111667 -0.4666819 0.4524586 1.0000000
3:T4-4:T1 -0.006771667 -0.4663419 0.4527986 1.0000000
4:T4-4:T1 0.152518333 -0.3070519 0.6120886 0.9959360
2:T2-1:T2 0.145308333 -0.3142619 0.6048786 0.9975308
3:T2-1:T2 0.018133333 -0.4414369 0.4777036 1.0000000
4:T2-1:T2 -0.002606667 -0.4621769 0.4569636 1.0000000
1:T3-1:T2 -0.008953333 -0.4685236 0.4506169 1.0000000
2:T3-1:T2 -0.014450000 -0.4740202 0.4451202 1.0000000
3:T3-1:T2 0.163313333 -0.2962569 0.6228836 0.9920358
4:T3-1:T2 0.017198333 -0.4423719 0.4767686 1.0000000
1:T4-1:T2 -0.012976667 -0.4725469 0.4465936 1.0000000
2:T4-1:T2 -0.017736667 -0.4773069 0.4418336 1.0000000
3:T4-1:T2 -0.017396667 -0.4769669 0.4421736 1.0000000
4:T4-1:T2 0.141893333 -0.3176769 0.6014636 0.9980788
3:T2-2:T2 -0.127175000 -0.5867452 0.3323952 0.9994244
4:T2-2:T2 -0.147915000 -0.6074852 0.3116552 0.9970291
1:T3-2:T2 -0.154261667 -0.6138319 0.3053086 0.9954436
2:T3-2:T2 -0.159758333 -0.6193286 0.2998119 0.9935601
3:T3-2:T2 0.018005000 -0.4415652 0.4775752 1.0000000
4:T3-2:T2 -0.128110000 -0.5876802 0.3314602 0.9993745
1:T4-2:T2 -0.158285000 -0.6178552 0.3012852 0.9941180
2:T4-2:T2 -0.163045000 -0.6226152 0.2965252 0.9921602
3:T4-2:T2 -0.162705000 -0.6222752 0.2968652 0.9923155
4:T4-2:T2 -0.003415000 -0.4629852 0.4561552 1.0000000
4:T2-3:T2 -0.020740000 -0.4803102 0.4388302 1.0000000
1:T3-3:T2 -0.027086667 -0.4866569 0.4324836 1.0000000
2:T3-3:T2 -0.032583333 -0.4921536 0.4269869 1.0000000
3:T3-3:T2 0.145180000 -0.3143902 0.6047502 0.9975535
4:T3-3:T2 -0.000935000 -0.4605052 0.4586352 1.0000000
1:T4-3:T2 -0.031110000 -0.4906802 0.4284602 1.0000000
2:T4-3:T2 -0.035870000 -0.4954402 0.4237002 1.0000000
3:T4-3:T2 -0.035530000 -0.4951002 0.4240402 1.0000000
4:T4-3:T2 0.123760000 -0.3358102 0.5833302 0.9995784
1:T3-4:T2 -0.006346667 -0.4659169 0.4532236 1.0000000
2:T3-4:T2 -0.011843333 -0.4714136 0.4477269 1.0000000
3:T3-4:T2 0.165920000 -0.2936502 0.6254902 0.9907434
4:T3-4:T2 0.019805000 -0.4397652 0.4793752 1.0000000
1:T4-4:T2 -0.010370000 -0.4699402 0.4492002 1.0000000
2:T4-4:T2 -0.015130000 -0.4747002 0.4444402 1.0000000
3:T4-4:T2 -0.014790000 -0.4743602 0.4447802 1.0000000
4:T4-4:T2 0.144500000 -0.3150702 0.6040702 0.9976711
2:T3-1:T3 -0.005496667 -0.4650669 0.4540736 1.0000000
3:T3-1:T3 0.172266667 -0.2873036 0.6318369 0.9868897
4:T3-1:T3 0.026151667 -0.4334186 0.4857219 1.0000000
1:T4-1:T3 -0.004023333 -0.4635936 0.4555469 1.0000000
2:T4-1:T3 -0.008783333 -0.4683536 0.4507869 1.0000000
3:T4-1:T3 -0.008443333 -0.4680136 0.4511269 1.0000000
4:T4-1:T3 0.150846667 -0.3087236 0.6104169 0.9963661
3:T3-2:T3 0.177763333 -0.2818069 0.6373336 0.9826215
4:T3-2:T3 0.031648333 -0.4279219 0.4912186 1.0000000
1:T4-2:T3 0.001473333 -0.4580969 0.4610436 1.0000000
2:T4-2:T3 -0.003286667 -0.4628569 0.4562836 1.0000000
3:T4-2:T3 -0.002946667 -0.4625169 0.4566236 1.0000000
4:T4-2:T3 0.156343333 -0.3032269 0.6159136 0.9947923
4:T3-3:T3 -0.146115000 -0.6056852 0.3134552 0.9973837
1:T4-3:T3 -0.176290000 -0.6358602 0.2832802 0.9838583
2:T4-3:T3 -0.181050000 -0.6406202 0.2785202 0.9795993
3:T4-3:T3 -0.180710000 -0.6402802 0.2788602 0.9799294
4:T4-3:T3 -0.021420000 -0.4809902 0.4381502 1.0000000
1:T4-4:T3 -0.030175000 -0.4897452 0.4293952 1.0000000
2:T4-4:T3 -0.034935000 -0.4945052 0.4246352 1.0000000
3:T4-4:T3 -0.034595000 -0.4941652 0.4249752 1.0000000
4:T4-4:T3 0.124695000 -0.3348752 0.5842652 0.9995403
2:T4-1:T4 -0.004760000 -0.4643302 0.4548102 1.0000000
3:T4-1:T4 -0.004420000 -0.4639902 0.4551502 1.0000000
4:T4-1:T4 0.154870000 -0.3047002 0.6144402 0.9952606
3:T4-2:T4 0.000340000 -0.4592302 0.4599102 1.0000000
4:T4-2:T4 0.159630000 -0.2999402 0.6192002 0.9936104
4:T4-3:T4 0.159290000 -0.3002802 0.6188602 0.9937419
Letras do Tukey:
$fermentador
$fermentador$Letters
3 4 2 1
"a" "a" "a" "a"
$fermentador$LetterMatrix
a
3 TRUE
4 TRUE
2 TRUE
1 TRUE
$tratamento
$tratamento$Letters
T2 T3 T1 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T1 TRUE
T4 TRUE
$`fermentador:tratamento`
$`fermentador:tratamento`$Letters
3:T3 2:T2 1:T1 4:T4 3:T2 4:T3 2:T1 1:T2 4:T2 1:T3 4:T1 3:T1 1:T4 2:T3 3:T4 2:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`fermentador:tratamento`$LetterMatrix
a
3:T3 TRUE
2:T2 TRUE
1:T1 TRUE
4:T4 TRUE
3:T2 TRUE
4:T3 TRUE
2:T1 TRUE
1:T2 TRUE
4:T2 TRUE
1:T3 TRUE
4:T1 TRUE
3:T1 TRUE
1:T4 TRUE
2:T3 TRUE
3:T4 TRUE
2:T4 TRUE
11 Periodo x Tratamento
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ periodo * tratamento")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ periodo * tratamento")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
Warning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertosWarning: testes-F ANOVA sobre um ajuste essencialmente perfeito, são incertos
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ periodo * tratamento"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ periodo * tratamento)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 167360 55787 3.170 0.0326 *
tratamento 3 4359 1453 0.083 0.9692
periodo:tratamento 9 35730 3970 0.226 0.9892
Residuals 48 844714 17598
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 342 66.3 48 209.1 476
2 T1 364 66.3 48 230.1 497
3 T1 232 66.3 48 98.6 365
4 T1 279 66.3 48 145.6 412
1 T2 318 66.3 48 184.1 451
2 T2 282 66.3 48 149.1 416
3 T2 232 66.3 48 98.9 366
4 T2 322 66.3 48 189.1 456
1 T3 373 66.3 48 239.9 507
2 T3 332 66.3 48 198.9 466
3 T3 192 66.3 48 59.1 326
4 T3 312 66.3 48 178.4 445
1 T4 402 66.3 48 268.4 535
2 T4 336 66.3 48 202.6 469
3 T4 228 66.3 48 94.9 362
4 T4 280 66.3 48 146.6 413
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -30.1875 -155.01061 94.63561 0.9172509
3-1 -137.5000 -262.32311 -12.67689 0.0256337
4-1 -60.4375 -185.26061 64.38561 0.5745558
3-2 -107.3125 -232.13561 17.51061 0.1151264
4-2 -30.2500 -155.07311 94.57311 0.9167904
4-3 77.0625 -47.76061 201.88561 0.3648104
$tratamento
diff lwr upr p adj
T2-T1 -15.5625 -140.3856 109.2606 0.9872437
T3-T1 -1.8125 -126.6356 123.0106 0.9999790
T4-T1 7.2500 -117.5731 132.0731 0.9986655
T3-T2 13.7500 -111.0731 138.5731 0.9911175
T4-T2 22.8125 -102.0106 147.6356 0.9617602
T4-T3 9.0625 -115.7606 133.8856 0.9974075
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 21.00 -317.8834 359.8834 1.0000000
3:T1-1:T1 -110.50 -449.3834 228.3834 0.9978583
4:T1-1:T1 -63.50 -402.3834 275.3834 0.9999976
1:T2-1:T1 -25.00 -363.8834 313.8834 1.0000000
2:T2-1:T1 -60.00 -398.8834 278.8834 0.9999989
3:T2-1:T1 -110.25 -449.1334 228.6334 0.9979098
4:T2-1:T1 -20.00 -358.8834 318.8834 1.0000000
1:T3-1:T1 30.75 -308.1334 369.6334 1.0000000
2:T3-1:T1 -10.25 -349.1334 328.6334 1.0000000
3:T3-1:T1 -150.00 -488.8834 188.8834 0.9608061
4:T3-1:T1 -30.75 -369.6334 308.1334 1.0000000
1:T4-1:T1 59.25 -279.6334 398.1334 0.9999991
2:T4-1:T1 -6.50 -345.3834 332.3834 1.0000000
3:T4-1:T1 -114.25 -453.1334 224.6334 0.9969474
4:T4-1:T1 -62.50 -401.3834 276.3834 0.9999981
3:T1-2:T1 -131.50 -470.3834 207.3834 0.9876371
4:T1-2:T1 -84.50 -423.3834 254.3834 0.9999042
1:T2-2:T1 -46.00 -384.8834 292.8834 1.0000000
2:T2-2:T1 -81.00 -419.8834 257.8834 0.9999433
3:T2-2:T1 -131.25 -470.1334 207.6334 0.9878557
4:T2-2:T1 -41.00 -379.8834 297.8834 1.0000000
1:T3-2:T1 9.75 -329.1334 348.6334 1.0000000
2:T3-2:T1 -31.25 -370.1334 307.6334 1.0000000
3:T3-2:T1 -171.00 -509.8834 167.8834 0.8956645
4:T3-2:T1 -51.75 -390.6334 287.1334 0.9999999
1:T4-2:T1 38.25 -300.6334 377.1334 1.0000000
2:T4-2:T1 -27.50 -366.3834 311.3834 1.0000000
3:T4-2:T1 -135.25 -474.1334 203.6334 0.9839685
4:T4-2:T1 -83.50 -422.3834 255.3834 0.9999173
4:T1-3:T1 47.00 -291.8834 385.8834 1.0000000
1:T2-3:T1 85.50 -253.3834 424.3834 0.9998892
2:T2-3:T1 50.50 -288.3834 389.3834 0.9999999
3:T2-3:T1 0.25 -338.6334 339.1334 1.0000000
4:T2-3:T1 90.50 -248.3834 429.3834 0.9997794
1:T3-3:T1 141.25 -197.6334 480.1334 0.9763892
2:T3-3:T1 100.25 -238.6334 439.1334 0.9992709
3:T3-3:T1 -39.50 -378.3834 299.3834 1.0000000
4:T3-3:T1 79.75 -259.1334 418.6334 0.9999534
1:T4-3:T1 169.75 -169.1334 508.6334 0.9007593
2:T4-3:T1 104.00 -234.8834 442.8834 0.9988971
3:T4-3:T1 -3.75 -342.6334 335.1334 1.0000000
4:T4-3:T1 48.00 -290.8834 386.8834 0.9999999
1:T2-4:T1 38.50 -300.3834 377.3834 1.0000000
2:T2-4:T1 3.50 -335.3834 342.3834 1.0000000
3:T2-4:T1 -46.75 -385.6334 292.1334 1.0000000
4:T2-4:T1 43.50 -295.3834 382.3834 1.0000000
1:T3-4:T1 94.25 -244.6334 433.1334 0.9996430
2:T3-4:T1 53.25 -285.6334 392.1334 0.9999998
3:T3-4:T1 -86.50 -425.3834 252.3834 0.9998723
4:T3-4:T1 32.75 -306.1334 371.6334 1.0000000
1:T4-4:T1 122.75 -216.1334 461.6334 0.9936422
2:T4-4:T1 57.00 -281.8834 395.8834 0.9999994
3:T4-4:T1 -50.75 -389.6334 288.1334 0.9999999
4:T4-4:T1 1.00 -337.8834 339.8834 1.0000000
2:T2-1:T2 -35.00 -373.8834 303.8834 1.0000000
3:T2-1:T2 -85.25 -424.1334 253.6334 0.9998932
4:T2-1:T2 5.00 -333.8834 343.8834 1.0000000
1:T3-1:T2 55.75 -283.1334 394.6334 0.9999996
2:T3-1:T2 14.75 -324.1334 353.6334 1.0000000
3:T3-1:T2 -125.00 -463.8834 213.8834 0.9923939
4:T3-1:T2 -5.75 -344.6334 333.1334 1.0000000
1:T4-1:T2 84.25 -254.6334 423.1334 0.9999076
2:T4-1:T2 18.50 -320.3834 357.3834 1.0000000
3:T4-1:T2 -89.25 -428.1334 249.6334 0.9998134
4:T4-1:T2 -37.50 -376.3834 301.3834 1.0000000
3:T2-2:T2 -50.25 -389.1334 288.6334 0.9999999
4:T2-2:T2 40.00 -298.8834 378.8834 1.0000000
1:T3-2:T2 90.75 -248.1334 429.6334 0.9997720
2:T3-2:T2 49.75 -289.1334 388.6334 0.9999999
3:T3-2:T2 -90.00 -428.8834 248.8834 0.9997936
4:T3-2:T2 29.25 -309.6334 368.1334 1.0000000
1:T4-2:T2 119.25 -219.6334 458.1334 0.9952472
2:T4-2:T2 53.50 -285.3834 392.3834 0.9999998
3:T4-2:T2 -54.25 -393.1334 284.6334 0.9999997
4:T4-2:T2 -2.50 -341.3834 336.3834 1.0000000
4:T2-3:T2 90.25 -248.6334 429.1334 0.9997866
1:T3-3:T2 141.00 -197.8834 479.8834 0.9767514
2:T3-3:T2 100.00 -238.8834 438.8834 0.9992913
3:T3-3:T2 -39.75 -378.6334 299.1334 1.0000000
4:T3-3:T2 79.50 -259.3834 418.3834 0.9999552
1:T4-3:T2 169.50 -169.3834 508.3834 0.9017592
2:T4-3:T2 103.75 -235.1334 442.6334 0.9989264
3:T4-3:T2 -4.00 -342.8834 334.8834 1.0000000
4:T4-3:T2 47.75 -291.1334 386.6334 1.0000000
1:T3-4:T2 50.75 -288.1334 389.6334 0.9999999
2:T3-4:T2 9.75 -329.1334 348.6334 1.0000000
3:T3-4:T2 -130.00 -468.8834 208.8834 0.9889029
4:T3-4:T2 -10.75 -349.6334 328.1334 1.0000000
1:T4-4:T2 79.25 -259.6334 418.1334 0.9999569
2:T4-4:T2 13.50 -325.3834 352.3834 1.0000000
3:T4-4:T2 -94.25 -433.1334 244.6334 0.9996430
4:T4-4:T2 -42.50 -381.3834 296.3834 1.0000000
2:T3-1:T3 -41.00 -379.8834 297.8834 1.0000000
3:T3-1:T3 -180.75 -519.6334 158.1334 0.8505330
4:T3-1:T3 -61.50 -400.3834 277.3834 0.9999984
1:T4-1:T3 28.50 -310.3834 367.3834 1.0000000
2:T4-1:T3 -37.25 -376.1334 301.6334 1.0000000
3:T4-1:T3 -145.00 -483.8834 193.8834 0.9704253
4:T4-1:T3 -93.25 -432.1334 245.6334 0.9996851
3:T3-2:T3 -139.75 -478.6334 199.1334 0.9784992
4:T3-2:T3 -20.50 -359.3834 318.3834 1.0000000
1:T4-2:T3 69.50 -269.3834 408.3834 0.9999921
2:T4-2:T3 3.75 -335.1334 342.6334 1.0000000
3:T4-2:T3 -104.00 -442.8834 234.8834 0.9988971
4:T4-2:T3 -52.25 -391.1334 286.6334 0.9999998
4:T3-3:T3 119.25 -219.6334 458.1334 0.9952472
1:T4-3:T3 209.25 -129.6334 548.1334 0.6723148
2:T4-3:T3 143.50 -195.3834 482.3834 0.9729329
3:T4-3:T3 35.75 -303.1334 374.6334 1.0000000
4:T4-3:T3 87.50 -251.3834 426.3834 0.9998531
1:T4-4:T3 90.00 -248.8834 428.8834 0.9997936
2:T4-4:T3 24.25 -314.6334 363.1334 1.0000000
3:T4-4:T3 -83.50 -422.3834 255.3834 0.9999173
4:T4-4:T3 -31.75 -370.6334 307.1334 1.0000000
2:T4-1:T4 -65.75 -404.6334 273.1334 0.9999962
3:T4-1:T4 -173.50 -512.3834 165.3834 0.8849994
4:T4-1:T4 -121.75 -460.6334 217.1334 0.9941404
3:T4-2:T4 -107.75 -446.6334 231.1334 0.9983697
4:T4-2:T4 -56.00 -394.8834 282.8834 0.9999996
4:T4-3:T4 51.75 -287.1334 390.6334 0.9999999
Letras do Tukey:
$periodo
1 2 4 3
"a" "ab" "ab" "b"
$tratamento
$tratamento$Letters
T4 T1 T3 T2
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T4 TRUE
T1 TRUE
T3 TRUE
T2 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
1:T4 1:T3 2:T1 1:T1 2:T4 2:T3 4:T2 1:T2 4:T3 2:T2 4:T4 4:T1 3:T2 3:T1 3:T4 3:T3
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
1:T4 TRUE
1:T3 TRUE
2:T1 TRUE
1:T1 TRUE
2:T4 TRUE
2:T3 TRUE
4:T2 TRUE
1:T2 TRUE
4:T3 TRUE
2:T2 TRUE
4:T4 TRUE
4:T1 TRUE
3:T2 TRUE
3:T1 TRUE
3:T4 TRUE
3:T3 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 1035268 345089 2.262 0.0932 .
tratamento 3 157390 52463 0.344 0.7937
periodo:tratamento 9 317132 35237 0.231 0.9883
Residuals 48 7323322 152569
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 1549 195 48 1157 1942
2 T1 1290 195 48 897 1682
3 T1 1199 195 48 807 1592
4 T1 1588 195 48 1196 1981
1 T2 1508 195 48 1115 1901
2 T2 1579 195 48 1187 1972
3 T2 1188 195 48 795 1580
4 T2 1723 195 48 1330 2116
1 T3 1584 195 48 1191 1977
2 T3 1543 195 48 1151 1936
3 T3 1389 195 48 996 1781
4 T3 1598 195 48 1206 1991
1 T4 1406 195 48 1013 1798
2 T4 1536 195 48 1143 1929
3 T4 1264 195 48 872 1657
4 T4 1516 195 48 1124 1909
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -24.6875 -392.21859 342.8436 0.9979425
3-1 -251.6875 -619.21859 115.8436 0.2755320
4-1 94.8125 -272.71859 462.3436 0.9017234
3-2 -227.0000 -594.53109 140.5311 0.3644373
4-2 119.5000 -248.03109 487.0311 0.8226068
4-3 346.5000 -21.03109 714.0311 0.0711939
$tratamento
diff lwr upr p adj
T2-T1 92.9375 -274.5936 460.4686 0.9067886
T3-T1 122.0625 -245.4686 489.5936 0.8131906
T4-T1 23.9375 -343.5936 391.4686 0.9981229
T3-T2 29.1250 -338.4061 396.6561 0.9966384
T4-T2 -69.0000 -436.5311 298.5311 0.9587578
T4-T3 -98.1250 -465.6561 269.4061 0.8924319
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 -259.75 -1257.5634 738.0634 0.9998378
3:T1-1:T1 -350.00 -1347.8134 647.8134 0.9953996
4:T1-1:T1 39.00 -958.8134 1036.8134 1.0000000
1:T2-1:T1 -41.25 -1039.0634 956.5634 1.0000000
2:T2-1:T1 30.00 -967.8134 1027.8134 1.0000000
3:T2-1:T1 -361.50 -1359.3134 636.3134 0.9936294
4:T2-1:T1 173.75 -824.0634 1171.5634 0.9999991
1:T3-1:T1 34.75 -963.0634 1032.5634 1.0000000
2:T3-1:T1 -6.00 -1003.8134 991.8134 1.0000000
3:T3-1:T1 -160.50 -1158.3134 837.3134 0.9999997
4:T3-1:T1 49.25 -948.5634 1047.0634 1.0000000
1:T4-1:T1 -143.75 -1141.5634 854.0634 0.9999999
2:T4-1:T1 -13.25 -1011.0634 984.5634 1.0000000
3:T4-1:T1 -285.00 -1282.8134 712.8134 0.9995128
4:T4-1:T1 -33.00 -1030.8134 964.8134 1.0000000
3:T1-2:T1 -90.25 -1088.0634 907.5634 1.0000000
4:T1-2:T1 298.75 -699.0634 1296.5634 0.9991642
1:T2-2:T1 218.50 -779.3134 1216.3134 0.9999814
2:T2-2:T1 289.75 -708.0634 1287.5634 0.9994104
3:T2-2:T1 -101.75 -1099.5634 896.0634 1.0000000
4:T2-2:T1 433.50 -564.3134 1431.3134 0.9663855
1:T3-2:T1 294.50 -703.3134 1292.3134 0.9992897
2:T3-2:T1 253.75 -744.0634 1251.5634 0.9998780
3:T3-2:T1 99.25 -898.5634 1097.0634 1.0000000
4:T3-2:T1 309.00 -688.8134 1306.8134 0.9987805
1:T4-2:T1 116.00 -881.8134 1113.8134 1.0000000
2:T4-2:T1 246.50 -751.3134 1244.3134 0.9999146
3:T4-2:T1 -25.25 -1023.0634 972.5634 1.0000000
4:T4-2:T1 226.75 -771.0634 1224.5634 0.9999700
4:T1-3:T1 389.00 -608.8134 1386.8134 0.9870866
1:T2-3:T1 308.75 -689.0634 1306.5634 0.9987914
2:T2-3:T1 380.00 -617.8134 1377.8134 0.9896435
3:T2-3:T1 -11.50 -1009.3134 986.3134 1.0000000
4:T2-3:T1 523.75 -474.0634 1521.5634 0.8648021
1:T3-3:T1 384.75 -613.0634 1382.5634 0.9883500
2:T3-3:T1 344.00 -653.8134 1341.8134 0.9961477
3:T3-3:T1 189.50 -808.3134 1187.3134 0.9999971
4:T3-3:T1 399.25 -598.5634 1397.0634 0.9835916
1:T4-3:T1 206.25 -791.5634 1204.0634 0.9999912
2:T4-3:T1 336.75 -661.0634 1334.5634 0.9969138
3:T4-3:T1 65.00 -932.8134 1062.8134 1.0000000
4:T4-3:T1 317.00 -680.8134 1314.8134 0.9983843
1:T2-4:T1 -80.25 -1078.0634 917.5634 1.0000000
2:T2-4:T1 -9.00 -1006.8134 988.8134 1.0000000
3:T2-4:T1 -400.50 -1398.3134 597.3134 0.9831192
4:T2-4:T1 134.75 -863.0634 1132.5634 1.0000000
1:T3-4:T1 -4.25 -1002.0634 993.5634 1.0000000
2:T3-4:T1 -45.00 -1042.8134 952.8134 1.0000000
3:T3-4:T1 -199.50 -1197.3134 798.3134 0.9999943
4:T3-4:T1 10.25 -987.5634 1008.0634 1.0000000
1:T4-4:T1 -182.75 -1180.5634 815.0634 0.9999982
2:T4-4:T1 -52.25 -1050.0634 945.5634 1.0000000
3:T4-4:T1 -324.00 -1321.8134 673.8134 0.9979525
4:T4-4:T1 -72.00 -1069.8134 925.8134 1.0000000
2:T2-1:T2 71.25 -926.5634 1069.0634 1.0000000
3:T2-1:T2 -320.25 -1318.0634 677.5634 0.9981946
4:T2-1:T2 215.00 -782.8134 1212.8134 0.9999849
1:T3-1:T2 76.00 -921.8134 1073.8134 1.0000000
2:T3-1:T2 35.25 -962.5634 1033.0634 1.0000000
3:T3-1:T2 -119.25 -1117.0634 878.5634 1.0000000
4:T3-1:T2 90.50 -907.3134 1088.3134 1.0000000
1:T4-1:T2 -102.50 -1100.3134 895.3134 1.0000000
2:T4-1:T2 28.00 -969.8134 1025.8134 1.0000000
3:T4-1:T2 -243.75 -1241.5634 754.0634 0.9999256
4:T4-1:T2 8.25 -989.5634 1006.0634 1.0000000
3:T2-2:T2 -391.50 -1389.3134 606.3134 0.9862942
4:T2-2:T2 143.75 -854.0634 1141.5634 0.9999999
1:T3-2:T2 4.75 -993.0634 1002.5634 1.0000000
2:T3-2:T2 -36.00 -1033.8134 961.8134 1.0000000
3:T3-2:T2 -190.50 -1188.3134 807.3134 0.9999969
4:T3-2:T2 19.25 -978.5634 1017.0634 1.0000000
1:T4-2:T2 -173.75 -1171.5634 824.0634 0.9999991
2:T4-2:T2 -43.25 -1041.0634 954.5634 1.0000000
3:T4-2:T2 -315.00 -1312.8134 682.8134 0.9984924
4:T4-2:T2 -63.00 -1060.8134 934.8134 1.0000000
4:T2-3:T2 535.25 -462.5634 1533.0634 0.8451948
1:T3-3:T2 396.25 -601.5634 1394.0634 0.9846832
2:T3-3:T2 355.50 -642.3134 1353.3134 0.9946119
3:T3-3:T2 201.00 -796.8134 1198.8134 0.9999937
4:T3-3:T2 410.75 -587.0634 1408.5634 0.9788345
1:T4-3:T2 217.75 -780.0634 1215.5634 0.9999822
2:T4-3:T2 348.25 -649.5634 1346.0634 0.9956293
3:T4-3:T2 76.50 -921.3134 1074.3134 1.0000000
4:T4-3:T2 328.50 -669.3134 1326.3134 0.9976262
1:T3-4:T2 -139.00 -1136.8134 858.8134 1.0000000
2:T3-4:T2 -179.75 -1177.5634 818.0634 0.9999986
3:T3-4:T2 -334.25 -1332.0634 663.5634 0.9971465
4:T3-4:T2 -124.50 -1122.3134 873.3134 1.0000000
1:T4-4:T2 -317.50 -1315.3134 680.3134 0.9983563
2:T4-4:T2 -187.00 -1184.8134 810.8134 0.9999976
3:T4-4:T2 -458.75 -1456.5634 539.0634 0.9469966
4:T4-4:T2 -206.75 -1204.5634 791.0634 0.9999909
2:T3-1:T3 -40.75 -1038.5634 957.0634 1.0000000
3:T3-1:T3 -195.25 -1193.0634 802.5634 0.9999957
4:T3-1:T3 14.50 -983.3134 1012.3134 1.0000000
1:T4-1:T3 -178.50 -1176.3134 819.3134 0.9999987
2:T4-1:T3 -48.00 -1045.8134 949.8134 1.0000000
3:T4-1:T3 -319.75 -1317.5634 678.0634 0.9982249
4:T4-1:T3 -67.75 -1065.5634 930.0634 1.0000000
3:T3-2:T3 -154.50 -1152.3134 843.3134 0.9999998
4:T3-2:T3 55.25 -942.5634 1053.0634 1.0000000
1:T4-2:T3 -137.75 -1135.5634 860.0634 1.0000000
2:T4-2:T3 -7.25 -1005.0634 990.5634 1.0000000
3:T4-2:T3 -279.00 -1276.8134 718.8134 0.9996198
4:T4-2:T3 -27.00 -1024.8134 970.8134 1.0000000
4:T3-3:T3 209.75 -788.0634 1207.5634 0.9999890
1:T4-3:T3 16.75 -981.0634 1014.5634 1.0000000
2:T4-3:T3 147.25 -850.5634 1145.0634 0.9999999
3:T4-3:T3 -124.50 -1122.3134 873.3134 1.0000000
4:T4-3:T3 127.50 -870.3134 1125.3134 1.0000000
1:T4-4:T3 -193.00 -1190.8134 804.8134 0.9999963
2:T4-4:T3 -62.50 -1060.3134 935.3134 1.0000000
3:T4-4:T3 -334.25 -1332.0634 663.5634 0.9971465
4:T4-4:T3 -82.25 -1080.0634 915.5634 1.0000000
2:T4-1:T4 130.50 -867.3134 1128.3134 1.0000000
3:T4-1:T4 -141.25 -1139.0634 856.5634 0.9999999
4:T4-1:T4 110.75 -887.0634 1108.5634 1.0000000
3:T4-2:T4 -271.75 -1269.5634 726.0634 0.9997213
4:T4-2:T4 -19.75 -1017.5634 978.0634 1.0000000
4:T4-3:T4 252.00 -745.8134 1249.8134 0.9998879
Letras do Tukey:
$periodo
$periodo$Letters
4 1 2 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
4 TRUE
1 TRUE
2 TRUE
3 TRUE
$tratamento
$tratamento$Letters
T3 T2 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T3 TRUE
T2 TRUE
T4 TRUE
T1 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
4:T2 4:T3 4:T1 1:T3 2:T2 1:T1 2:T3 2:T4 4:T4 1:T2 1:T4 3:T3 2:T1 3:T4 3:T1 3:T2
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
4:T2 TRUE
4:T3 TRUE
4:T1 TRUE
1:T3 TRUE
2:T2 TRUE
1:T1 TRUE
2:T3 TRUE
2:T4 TRUE
4:T4 TRUE
1:T2 TRUE
1:T4 TRUE
3:T3 TRUE
2:T1 TRUE
3:T4 TRUE
3:T1 TRUE
3:T2 TRUE
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3.148 1.0493 1.074 0.388
tratamento 3 5.676 1.8918 1.937 0.164
periodo:tratamento 9 12.085 1.3428 1.375 0.277
Residuals 16 15.631 0.9769
48 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 5.74 0.699 16 4.26 7.22
2 T1 6.37 0.699 16 4.89 7.85
3 T1 4.78 0.699 16 3.29 6.26
4 T1 5.66 0.699 16 4.18 7.14
1 T2 5.66 0.699 16 4.18 7.14
2 T2 6.03 0.699 16 4.55 7.51
3 T2 5.22 0.699 16 3.74 6.71
4 T2 6.04 0.699 16 4.55 7.52
1 T3 6.04 0.699 16 4.55 7.52
2 T3 6.34 0.699 16 4.86 7.83
3 T3 5.53 0.699 16 4.05 7.01
4 T3 3.79 0.699 16 2.31 5.27
1 T4 3.79 0.699 16 2.31 5.27
2 T4 4.70 0.699 16 3.21 6.18
3 T4 4.42 0.699 16 2.94 5.91
4 T4 5.74 0.699 16 4.26 7.22
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 5.537500e-01 -0.8601659 1.9676659 0.6826183
3-1 -3.175000e-01 -1.7314159 1.0964159 0.9166334
4-1 -8.881784e-16 -1.4139159 1.4139159 1.0000000
3-2 -8.712500e-01 -2.2851659 0.5426659 0.3258970
4-2 -5.537500e-01 -1.9676659 0.8601659 0.6826183
4-3 3.175000e-01 -1.0964159 1.7314159 0.9166334
$tratamento
diff lwr upr p adj
T2-T1 0.10125 -1.312666 1.5151659 0.9968287
T3-T1 -0.21125 -1.625166 1.2026659 0.9729178
T4-T1 -0.97375 -2.387666 0.4401659 0.2396590
T3-T2 -0.31250 -1.726416 1.1014159 0.9200796
T4-T2 -1.07500 -2.488916 0.3389159 0.1724193
T4-T3 -0.76250 -2.176416 0.6514159 0.4366937
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 6.300000e-01 -3.327166 4.587166 0.9999966
3:T1-1:T1 -9.650000e-01 -4.922166 2.992166 0.9994041
4:T1-1:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
1:T2-1:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
2:T2-1:T1 2.900000e-01 -3.667166 4.247166 1.0000000
3:T2-1:T1 -5.150000e-01 -4.472166 3.442166 0.9999998
4:T2-1:T1 2.950000e-01 -3.662166 4.252166 1.0000000
1:T3-1:T1 2.950000e-01 -3.662166 4.252166 1.0000000
2:T3-1:T1 6.050000e-01 -3.352166 4.562166 0.9999980
3:T3-1:T1 -2.100000e-01 -4.167166 3.747166 1.0000000
4:T3-1:T1 -1.950000e+00 -5.907166 2.007166 0.8093383
1:T4-1:T1 -1.950000e+00 -5.907166 2.007166 0.8093383
2:T4-1:T1 -1.045000e+00 -5.002166 2.912166 0.9986029
3:T4-1:T1 -1.315000e+00 -5.272166 2.642166 0.9873784
4:T4-1:T1 -3.552714e-15 -3.957166 3.957166 1.0000000
3:T1-2:T1 -1.595000e+00 -5.552166 2.362166 0.9429056
4:T1-2:T1 -7.100000e-01 -4.667166 3.247166 0.9999841
1:T2-2:T1 -7.100000e-01 -4.667166 3.247166 0.9999841
2:T2-2:T1 -3.400000e-01 -4.297166 3.617166 1.0000000
3:T2-2:T1 -1.145000e+00 -5.102166 2.812166 0.9964816
4:T2-2:T1 -3.350000e-01 -4.292166 3.622166 1.0000000
1:T3-2:T1 -3.350000e-01 -4.292166 3.622166 1.0000000
2:T3-2:T1 -2.500000e-02 -3.982166 3.932166 1.0000000
3:T3-2:T1 -8.400000e-01 -4.797166 3.117166 0.9998770
4:T3-2:T1 -2.580000e+00 -6.537166 1.377166 0.4507708
1:T4-2:T1 -2.580000e+00 -6.537166 1.377166 0.4507708
2:T4-2:T1 -1.675000e+00 -5.632166 2.282166 0.9206831
3:T4-2:T1 -1.945000e+00 -5.902166 2.012166 0.8118002
4:T4-2:T1 -6.300000e-01 -4.587166 3.327166 0.9999966
4:T1-3:T1 8.850000e-01 -3.072166 4.842166 0.9997744
1:T2-3:T1 8.850000e-01 -3.072166 4.842166 0.9997744
2:T2-3:T1 1.255000e+00 -2.702166 5.212166 0.9916546
3:T2-3:T1 4.500000e-01 -3.507166 4.407166 1.0000000
4:T2-3:T1 1.260000e+00 -2.697166 5.217166 0.9913494
1:T3-3:T1 1.260000e+00 -2.697166 5.217166 0.9913494
2:T3-3:T1 1.570000e+00 -2.387166 5.527166 0.9488982
3:T3-3:T1 7.550000e-01 -3.202166 4.712166 0.9999659
4:T3-3:T1 -9.850000e-01 -4.942166 2.972166 0.9992549
1:T4-3:T1 -9.850000e-01 -4.942166 2.972166 0.9992549
2:T4-3:T1 -8.000000e-02 -4.037166 3.877166 1.0000000
3:T4-3:T1 -3.500000e-01 -4.307166 3.607166 1.0000000
4:T4-3:T1 9.650000e-01 -2.992166 4.922166 0.9994041
1:T2-4:T1 0.000000e+00 -3.957166 3.957166 1.0000000
2:T2-4:T1 3.700000e-01 -3.587166 4.327166 1.0000000
3:T2-4:T1 -4.350000e-01 -4.392166 3.522166 1.0000000
4:T2-4:T1 3.750000e-01 -3.582166 4.332166 1.0000000
1:T3-4:T1 3.750000e-01 -3.582166 4.332166 1.0000000
2:T3-4:T1 6.850000e-01 -3.272166 4.642166 0.9999899
3:T3-4:T1 -1.300000e-01 -4.087166 3.827166 1.0000000
4:T3-4:T1 -1.870000e+00 -5.827166 2.087166 0.8469659
1:T4-4:T1 -1.870000e+00 -5.827166 2.087166 0.8469659
2:T4-4:T1 -9.650000e-01 -4.922166 2.992166 0.9994041
3:T4-4:T1 -1.235000e+00 -5.192166 2.722166 0.9927915
4:T4-4:T1 8.000000e-02 -3.877166 4.037166 1.0000000
2:T2-1:T2 3.700000e-01 -3.587166 4.327166 1.0000000
3:T2-1:T2 -4.350000e-01 -4.392166 3.522166 1.0000000
4:T2-1:T2 3.750000e-01 -3.582166 4.332166 1.0000000
1:T3-1:T2 3.750000e-01 -3.582166 4.332166 1.0000000
2:T3-1:T2 6.850000e-01 -3.272166 4.642166 0.9999899
3:T3-1:T2 -1.300000e-01 -4.087166 3.827166 1.0000000
4:T3-1:T2 -1.870000e+00 -5.827166 2.087166 0.8469659
1:T4-1:T2 -1.870000e+00 -5.827166 2.087166 0.8469659
2:T4-1:T2 -9.650000e-01 -4.922166 2.992166 0.9994041
3:T4-1:T2 -1.235000e+00 -5.192166 2.722166 0.9927915
4:T4-1:T2 8.000000e-02 -3.877166 4.037166 1.0000000
3:T2-2:T2 -8.050000e-01 -4.762166 3.152166 0.9999258
4:T2-2:T2 5.000000e-03 -3.952166 3.962166 1.0000000
1:T3-2:T2 5.000000e-03 -3.952166 3.962166 1.0000000
2:T3-2:T2 3.150000e-01 -3.642166 4.272166 1.0000000
3:T3-2:T2 -5.000000e-01 -4.457166 3.457166 0.9999998
4:T3-2:T2 -2.240000e+00 -6.197166 1.717166 0.6488283
1:T4-2:T2 -2.240000e+00 -6.197166 1.717166 0.6488283
2:T4-2:T2 -1.335000e+00 -5.292166 2.622166 0.9856288
3:T4-2:T2 -1.605000e+00 -5.562166 2.352166 0.9403834
4:T4-2:T2 -2.900000e-01 -4.247166 3.667166 1.0000000
4:T2-3:T2 8.100000e-01 -3.147166 4.767166 0.9999201
1:T3-3:T2 8.100000e-01 -3.147166 4.767166 0.9999201
2:T3-3:T2 1.120000e+00 -2.837166 5.077166 0.9971694
3:T3-3:T2 3.050000e-01 -3.652166 4.262166 1.0000000
4:T3-3:T2 -1.435000e+00 -5.392166 2.522166 0.9739693
1:T4-3:T2 -1.435000e+00 -5.392166 2.522166 0.9739693
2:T4-3:T2 -5.300000e-01 -4.487166 3.427166 0.9999997
3:T4-3:T2 -8.000000e-01 -4.757166 3.157166 0.9999311
4:T4-3:T2 5.150000e-01 -3.442166 4.472166 0.9999998
1:T3-4:T2 0.000000e+00 -3.957166 3.957166 1.0000000
2:T3-4:T2 3.100000e-01 -3.647166 4.267166 1.0000000
3:T3-4:T2 -5.050000e-01 -4.462166 3.452166 0.9999998
4:T3-4:T2 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T4-4:T2 -2.245000e+00 -6.202166 1.712166 0.6458700
2:T4-4:T2 -1.340000e+00 -5.297166 2.617166 0.9851637
3:T4-4:T2 -1.610000e+00 -5.567166 2.347166 0.9390952
4:T4-4:T2 -2.950000e-01 -4.252166 3.662166 1.0000000
2:T3-1:T3 3.100000e-01 -3.647166 4.267166 1.0000000
3:T3-1:T3 -5.050000e-01 -4.462166 3.452166 0.9999998
4:T3-1:T3 -2.245000e+00 -6.202166 1.712166 0.6458700
1:T4-1:T3 -2.245000e+00 -6.202166 1.712166 0.6458700
2:T4-1:T3 -1.340000e+00 -5.297166 2.617166 0.9851637
3:T4-1:T3 -1.610000e+00 -5.567166 2.347166 0.9390952
4:T4-1:T3 -2.950000e-01 -4.252166 3.662166 1.0000000
3:T3-2:T3 -8.150000e-01 -4.772166 3.142166 0.9999140
4:T3-2:T3 -2.555000e+00 -6.512166 1.402166 0.4646140
1:T4-2:T3 -2.555000e+00 -6.512166 1.402166 0.4646140
2:T4-2:T3 -1.650000e+00 -5.607166 2.307166 0.9281328
3:T4-2:T3 -1.920000e+00 -5.877166 2.037166 0.8238969
4:T4-2:T3 -6.050000e-01 -4.562166 3.352166 0.9999980
4:T3-3:T3 -1.740000e+00 -5.697166 2.217166 0.8991495
1:T4-3:T3 -1.740000e+00 -5.697166 2.217166 0.8991495
2:T4-3:T3 -8.350000e-01 -4.792166 3.122166 0.9998853
3:T4-3:T3 -1.105000e+00 -5.062166 2.852166 0.9975260
4:T4-3:T3 2.100000e-01 -3.747166 4.167166 1.0000000
1:T4-4:T3 4.440892e-16 -3.957166 3.957166 1.0000000
2:T4-4:T3 9.050000e-01 -3.052166 4.862166 0.9997088
3:T4-4:T3 6.350000e-01 -3.322166 4.592166 0.9999962
4:T4-4:T3 1.950000e+00 -2.007166 5.907166 0.8093383
2:T4-1:T4 9.050000e-01 -3.052166 4.862166 0.9997088
3:T4-1:T4 6.350000e-01 -3.322166 4.592166 0.9999962
4:T4-1:T4 1.950000e+00 -2.007166 5.907166 0.8093383
3:T4-2:T4 -2.700000e-01 -4.227166 3.687166 1.0000000
4:T4-2:T4 1.045000e+00 -2.912166 5.002166 0.9986029
4:T4-3:T4 1.315000e+00 -2.642166 5.272166 0.9873784
Letras do Tukey:
$periodo
$periodo$Letters
2 1 4 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
2 TRUE
1 TRUE
4 TRUE
3 TRUE
$tratamento
$tratamento$Letters
T2 T1 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T1 TRUE
T3 TRUE
T4 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
2:T1 2:T3 4:T2 1:T3 2:T2 1:T1 4:T4 4:T1 1:T2 3:T3 3:T2 3:T1 2:T4 3:T4 4:T3 1:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
2:T1 TRUE
2:T3 TRUE
4:T2 TRUE
1:T3 TRUE
2:T2 TRUE
1:T1 TRUE
4:T4 TRUE
4:T1 TRUE
1:T2 TRUE
3:T3 TRUE
3:T2 TRUE
3:T1 TRUE
2:T4 TRUE
3:T4 TRUE
4:T3 TRUE
1:T4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.02021 0.006735 2.177 0.131
tratamento 3 0.00599 0.001997 0.646 0.597
periodo:tratamento 9 0.01158 0.001287 0.416 0.908
Residuals 16 0.04950 0.003094
48 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 0.120 0.0393 16 0.03632 0.203
2 T1 0.090 0.0393 16 0.00662 0.173
3 T1 0.070 0.0393 16 -0.01338 0.153
4 T1 0.170 0.0393 16 0.08662 0.253
1 T2 0.128 0.0393 16 0.04507 0.212
2 T2 0.145 0.0393 16 0.06162 0.228
3 T2 0.115 0.0393 16 0.03162 0.198
4 T2 0.175 0.0393 16 0.09162 0.258
1 T3 0.150 0.0393 16 0.06662 0.233
2 T3 0.145 0.0393 16 0.06162 0.228
3 T3 0.130 0.0393 16 0.04662 0.213
4 T3 0.135 0.0393 16 0.05162 0.218
1 T4 0.085 0.0393 16 0.00162 0.168
2 T4 0.100 0.0393 16 0.01662 0.183
3 T4 0.080 0.0393 16 -0.00338 0.163
4 T4 0.190 0.0393 16 0.10662 0.273
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -0.0007875 -0.08035582 0.07878082 0.9999915
3-1 -0.0220375 -0.10160582 0.05753082 0.8568434
4-1 0.0467125 -0.03285582 0.12628082 0.3656643
3-2 -0.0212500 -0.10081832 0.05831832 0.8693269
4-2 0.0475000 -0.03206832 0.12706832 0.3518334
4-3 0.0687500 -0.01081832 0.14831832 0.1031126
$tratamento
diff lwr upr p adj
T2-T1 0.0284375 -0.05113082 0.10800582 0.7389940
T3-T1 0.0275750 -0.05199332 0.10714332 0.7562630
T4-T1 0.0013250 -0.07824332 0.08089332 0.9999594
T3-T2 -0.0008625 -0.08043082 0.07870582 0.9999888
T4-T2 -0.0271125 -0.10668082 0.05245582 0.7653800
T4-T3 -0.0262500 -0.10581832 0.05331832 0.7820860
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 -2.970000e-02 -0.2523901 0.1929901 0.9999997
3:T1-1:T1 -4.970000e-02 -0.2723901 0.1729901 0.9997797
4:T1-1:T1 5.030000e-02 -0.1723901 0.2729901 0.9997472
1:T2-1:T1 8.750000e-03 -0.2139401 0.2314401 1.0000000
2:T2-1:T1 2.530000e-02 -0.1973901 0.2479901 1.0000000
3:T2-1:T1 -4.700000e-03 -0.2273901 0.2179901 1.0000000
4:T2-1:T1 5.530000e-02 -0.1673901 0.2779901 0.9992737
1:T3-1:T1 3.030000e-02 -0.1923901 0.2529901 0.9999996
2:T3-1:T1 2.530000e-02 -0.1973901 0.2479901 1.0000000
3:T3-1:T1 1.030000e-02 -0.2123901 0.2329901 1.0000000
4:T3-1:T1 1.530000e-02 -0.2073901 0.2379901 1.0000000
1:T4-1:T1 -3.470000e-02 -0.2573901 0.1879901 0.9999974
2:T4-1:T1 -1.970000e-02 -0.2423901 0.2029901 1.0000000
3:T4-1:T1 -3.970000e-02 -0.2623901 0.1829901 0.9999854
4:T4-1:T1 7.030000e-02 -0.1523901 0.2929901 0.9919968
3:T1-2:T1 -2.000000e-02 -0.2426901 0.2026901 1.0000000
4:T1-2:T1 8.000000e-02 -0.1426901 0.3026901 0.9758418
1:T2-2:T1 3.845000e-02 -0.1842401 0.2611401 0.9999902
2:T2-2:T1 5.500000e-02 -0.1676901 0.2776901 0.9993154
3:T2-2:T1 2.500000e-02 -0.1976901 0.2476901 1.0000000
4:T2-2:T1 8.500000e-02 -0.1376901 0.3076901 0.9614234
1:T3-2:T1 6.000000e-02 -0.1626901 0.2826901 0.9982798
2:T3-2:T1 5.500000e-02 -0.1676901 0.2776901 0.9993154
3:T3-2:T1 4.000000e-02 -0.1826901 0.2626901 0.9999839
4:T3-2:T1 4.500000e-02 -0.1776901 0.2676901 0.9999315
1:T4-2:T1 -5.000000e-03 -0.2276901 0.2176901 1.0000000
2:T4-2:T1 1.000000e-02 -0.2126901 0.2326901 1.0000000
3:T4-2:T1 -1.000000e-02 -0.2326901 0.2126901 1.0000000
4:T4-2:T1 1.000000e-01 -0.1226901 0.3226901 0.8855140
4:T1-3:T1 1.000000e-01 -0.1226901 0.3226901 0.8855140
1:T2-3:T1 5.845000e-02 -0.1642401 0.2811401 0.9986891
2:T2-3:T1 7.500000e-02 -0.1476901 0.2976901 0.9858355
3:T2-3:T1 4.500000e-02 -0.1776901 0.2676901 0.9999315
4:T2-3:T1 1.050000e-01 -0.1176901 0.3276901 0.8488176
1:T3-3:T1 8.000000e-02 -0.1426901 0.3026901 0.9758418
2:T3-3:T1 7.500000e-02 -0.1476901 0.2976901 0.9858355
3:T3-3:T1 6.000000e-02 -0.1626901 0.2826901 0.9982798
4:T3-3:T1 6.500000e-02 -0.1576901 0.2876901 0.9961694
1:T4-3:T1 1.500000e-02 -0.2076901 0.2376901 1.0000000
2:T4-3:T1 3.000000e-02 -0.1926901 0.2526901 0.9999996
3:T4-3:T1 1.000000e-02 -0.2126901 0.2326901 1.0000000
4:T4-3:T1 1.200000e-01 -0.1026901 0.3426901 0.7115193
1:T2-4:T1 -4.155000e-02 -0.2642401 0.1811401 0.9999741
2:T2-4:T1 -2.500000e-02 -0.2476901 0.1976901 1.0000000
3:T2-4:T1 -5.500000e-02 -0.2776901 0.1676901 0.9993154
4:T2-4:T1 5.000000e-03 -0.2176901 0.2276901 1.0000000
1:T3-4:T1 -2.000000e-02 -0.2426901 0.2026901 1.0000000
2:T3-4:T1 -2.500000e-02 -0.2476901 0.1976901 1.0000000
3:T3-4:T1 -4.000000e-02 -0.2626901 0.1826901 0.9999839
4:T3-4:T1 -3.500000e-02 -0.2576901 0.1876901 0.9999971
1:T4-4:T1 -8.500000e-02 -0.3076901 0.1376901 0.9614234
2:T4-4:T1 -7.000000e-02 -0.2926901 0.1526901 0.9923025
3:T4-4:T1 -9.000000e-02 -0.3126901 0.1326901 0.9418336
4:T4-4:T1 2.000000e-02 -0.2026901 0.2426901 1.0000000
2:T2-1:T2 1.655000e-02 -0.2061401 0.2392401 1.0000000
3:T2-1:T2 -1.345000e-02 -0.2361401 0.2092401 1.0000000
4:T2-1:T2 4.655000e-02 -0.1761401 0.2692401 0.9998974
1:T3-1:T2 2.155000e-02 -0.2011401 0.2442401 1.0000000
2:T3-1:T2 1.655000e-02 -0.2061401 0.2392401 1.0000000
3:T3-1:T2 1.550000e-03 -0.2211401 0.2242401 1.0000000
4:T3-1:T2 6.550000e-03 -0.2161401 0.2292401 1.0000000
1:T4-1:T2 -4.345000e-02 -0.2661401 0.1792401 0.9999552
2:T4-1:T2 -2.845000e-02 -0.2511401 0.1942401 0.9999998
3:T4-1:T2 -4.845000e-02 -0.2711401 0.1742401 0.9998359
4:T4-1:T2 6.155000e-02 -0.1611401 0.2842401 0.9977686
3:T2-2:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
4:T2-2:T2 3.000000e-02 -0.1926901 0.2526901 0.9999996
1:T3-2:T2 5.000000e-03 -0.2176901 0.2276901 1.0000000
2:T3-2:T2 -2.775558e-17 -0.2226901 0.2226901 1.0000000
3:T3-2:T2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
4:T3-2:T2 -1.000000e-02 -0.2326901 0.2126901 1.0000000
1:T4-2:T2 -6.000000e-02 -0.2826901 0.1626901 0.9982798
2:T4-2:T2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
3:T4-2:T2 -6.500000e-02 -0.2876901 0.1576901 0.9961694
4:T4-2:T2 4.500000e-02 -0.1776901 0.2676901 0.9999315
4:T2-3:T2 6.000000e-02 -0.1626901 0.2826901 0.9982798
1:T3-3:T2 3.500000e-02 -0.1876901 0.2576901 0.9999971
2:T3-3:T2 3.000000e-02 -0.1926901 0.2526901 0.9999996
3:T3-3:T2 1.500000e-02 -0.2076901 0.2376901 1.0000000
4:T3-3:T2 2.000000e-02 -0.2026901 0.2426901 1.0000000
1:T4-3:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
2:T4-3:T2 -1.500000e-02 -0.2376901 0.2076901 1.0000000
3:T4-3:T2 -3.500000e-02 -0.2576901 0.1876901 0.9999971
4:T4-3:T2 7.500000e-02 -0.1476901 0.2976901 0.9858355
1:T3-4:T2 -2.500000e-02 -0.2476901 0.1976901 1.0000000
2:T3-4:T2 -3.000000e-02 -0.2526901 0.1926901 0.9999996
3:T3-4:T2 -4.500000e-02 -0.2676901 0.1776901 0.9999315
4:T3-4:T2 -4.000000e-02 -0.2626901 0.1826901 0.9999839
1:T4-4:T2 -9.000000e-02 -0.3126901 0.1326901 0.9418336
2:T4-4:T2 -7.500000e-02 -0.2976901 0.1476901 0.9858355
3:T4-4:T2 -9.500000e-02 -0.3176901 0.1276901 0.9165838
4:T4-4:T2 1.500000e-02 -0.2076901 0.2376901 1.0000000
2:T3-1:T3 -5.000000e-03 -0.2276901 0.2176901 1.0000000
3:T3-1:T3 -2.000000e-02 -0.2426901 0.2026901 1.0000000
4:T3-1:T3 -1.500000e-02 -0.2376901 0.2076901 1.0000000
1:T4-1:T3 -6.500000e-02 -0.2876901 0.1576901 0.9961694
2:T4-1:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
3:T4-1:T3 -7.000000e-02 -0.2926901 0.1526901 0.9923025
4:T4-1:T3 4.000000e-02 -0.1826901 0.2626901 0.9999839
3:T3-2:T3 -1.500000e-02 -0.2376901 0.2076901 1.0000000
4:T3-2:T3 -1.000000e-02 -0.2326901 0.2126901 1.0000000
1:T4-2:T3 -6.000000e-02 -0.2826901 0.1626901 0.9982798
2:T4-2:T3 -4.500000e-02 -0.2676901 0.1776901 0.9999315
3:T4-2:T3 -6.500000e-02 -0.2876901 0.1576901 0.9961694
4:T4-2:T3 4.500000e-02 -0.1776901 0.2676901 0.9999315
4:T3-3:T3 5.000000e-03 -0.2176901 0.2276901 1.0000000
1:T4-3:T3 -4.500000e-02 -0.2676901 0.1776901 0.9999315
2:T4-3:T3 -3.000000e-02 -0.2526901 0.1926901 0.9999996
3:T4-3:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
4:T4-3:T3 6.000000e-02 -0.1626901 0.2826901 0.9982798
1:T4-4:T3 -5.000000e-02 -0.2726901 0.1726901 0.9997640
2:T4-4:T3 -3.500000e-02 -0.2576901 0.1876901 0.9999971
3:T4-4:T3 -5.500000e-02 -0.2776901 0.1676901 0.9993154
4:T4-4:T3 5.500000e-02 -0.1676901 0.2776901 0.9993154
2:T4-1:T4 1.500000e-02 -0.2076901 0.2376901 1.0000000
3:T4-1:T4 -5.000000e-03 -0.2276901 0.2176901 1.0000000
4:T4-1:T4 1.050000e-01 -0.1176901 0.3276901 0.8488176
3:T4-2:T4 -2.000000e-02 -0.2426901 0.2026901 1.0000000
4:T4-2:T4 9.000000e-02 -0.1326901 0.3126901 0.9418336
4:T4-3:T4 1.100000e-01 -0.1126901 0.3326901 0.8070205
Letras do Tukey:
$periodo
$periodo$Letters
4 1 2 3
"a" "a" "a" "a"
$periodo$LetterMatrix
a
4 TRUE
1 TRUE
2 TRUE
3 TRUE
$tratamento
$tratamento$Letters
T2 T3 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T4 TRUE
T1 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
4:T4 4:T2 4:T1 1:T3 2:T2 2:T3 4:T3 3:T3 1:T2 1:T1 3:T2 2:T4 2:T1 1:T4 3:T4 3:T1
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
4:T4 TRUE
4:T2 TRUE
4:T1 TRUE
1:T3 TRUE
2:T2 TRUE
2:T3 TRUE
4:T3 TRUE
3:T3 TRUE
1:T2 TRUE
1:T1 TRUE
3:T2 TRUE
2:T4 TRUE
2:T1 TRUE
1:T4 TRUE
3:T4 TRUE
3:T1 TRUE
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.09002 0.03001 192.05 <2e-16 ***
tratamento 3 0.09707 0.03236 207.08 <2e-16 ***
periodo:tratamento 9 0.07108 0.00790 50.55 <2e-16 ***
Residuals 32 0.00500 0.00016
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 0.277 0.00722 32 0.262 0.291
2 T1 0.210 0.00722 32 0.195 0.225
3 T1 0.163 0.00722 32 0.149 0.178
4 T1 0.340 0.00722 32 0.325 0.355
1 T2 0.300 0.00722 32 0.285 0.315
2 T2 0.357 0.00722 32 0.342 0.371
3 T2 0.253 0.00722 32 0.239 0.268
4 T2 0.433 0.00722 32 0.419 0.448
1 T3 0.350 0.00722 32 0.335 0.365
2 T3 0.367 0.00722 32 0.352 0.381
3 T3 0.293 0.00722 32 0.279 0.308
4 T3 0.290 0.00722 32 0.275 0.305
1 T4 0.183 0.00722 32 0.169 0.198
2 T4 0.230 0.00722 32 0.215 0.245
3 T4 0.197 0.00722 32 0.182 0.211
4 T4 0.330 0.00722 32 0.315 0.345
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 0.01335083 -0.0004751829 0.02717685 0.0615041
3-1 -0.05081583 -0.0646418495 -0.03698982 0.0000000
4-1 0.07085083 0.0570248171 0.08467685 0.0000000
3-2 -0.06416667 -0.0779926829 -0.05034065 0.0000000
4-2 0.05750000 0.0436739838 0.07132602 0.0000000
4-3 0.12166667 0.1078406505 0.13549268 0.0000000
$tratamento
diff lwr upr p adj
T2-T1 0.08835083 0.07452482 0.102176850 0.0000000
T3-T1 0.07751750 0.06369148 0.091343516 0.0000000
T4-T1 -0.01248250 -0.02630852 0.001343516 0.0885647
T3-T2 -0.01083333 -0.02465935 0.002992683 0.1675959
T4-T2 -0.10083333 -0.11465935 -0.087007317 0.0000000
T4-T3 -0.09000000 -0.10382602 -0.076173984 0.0000000
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 -0.066596667 -0.104441776 -0.028751557 0.0000243
3:T1-1:T1 -0.113263333 -0.151108443 -0.075418224 0.0000000
4:T1-1:T1 0.063403333 0.025558224 0.101248443 0.0000586
1:T2-1:T1 0.023403333 -0.014441776 0.061248443 0.6308596
2:T2-1:T1 0.080070000 0.042224891 0.117915109 0.0000006
3:T2-1:T1 -0.023263333 -0.061108443 0.014581776 0.6398228
4:T2-1:T1 0.156736667 0.118891557 0.194581776 0.0000000
1:T3-1:T1 0.073403333 0.035558224 0.111248443 0.0000038
2:T3-1:T1 0.090070000 0.052224891 0.127915109 0.0000000
3:T3-1:T1 0.016736667 -0.021108443 0.054581776 0.9477916
4:T3-1:T1 0.013403333 -0.024441776 0.051248443 0.9922893
1:T4-1:T1 -0.093263333 -0.131108443 -0.055418224 0.0000000
2:T4-1:T1 -0.046596667 -0.084441776 -0.008751557 0.0056910
3:T4-1:T1 -0.079930000 -0.117775109 -0.042084891 0.0000007
4:T4-1:T1 0.053403333 0.015558224 0.091248443 0.0009179
3:T1-2:T1 -0.046666667 -0.084511776 -0.008821557 0.0055876
4:T1-2:T1 0.130000000 0.092154891 0.167845109 0.0000000
1:T2-2:T1 0.090000000 0.052154891 0.127845109 0.0000000
2:T2-2:T1 0.146666667 0.108821557 0.184511776 0.0000000
3:T2-2:T1 0.043333333 0.005488224 0.081178443 0.0131979
4:T2-2:T1 0.223333333 0.185488224 0.261178443 0.0000000
1:T3-2:T1 0.140000000 0.102154891 0.177845109 0.0000000
2:T3-2:T1 0.156666667 0.118821557 0.194511776 0.0000000
3:T3-2:T1 0.083333333 0.045488224 0.121178443 0.0000003
4:T3-2:T1 0.080000000 0.042154891 0.117845109 0.0000007
1:T4-2:T1 -0.026666667 -0.064511776 0.011178443 0.4243390
2:T4-2:T1 0.020000000 -0.017845109 0.057845109 0.8289662
3:T4-2:T1 -0.013333333 -0.051178443 0.024511776 0.9926678
4:T4-2:T1 0.120000000 0.082154891 0.157845109 0.0000000
4:T1-3:T1 0.176666667 0.138821557 0.214511776 0.0000000
1:T2-3:T1 0.136666667 0.098821557 0.174511776 0.0000000
2:T2-3:T1 0.193333333 0.155488224 0.231178443 0.0000000
3:T2-3:T1 0.090000000 0.052154891 0.127845109 0.0000000
4:T2-3:T1 0.270000000 0.232154891 0.307845109 0.0000000
1:T3-3:T1 0.186666667 0.148821557 0.224511776 0.0000000
2:T3-3:T1 0.203333333 0.165488224 0.241178443 0.0000000
3:T3-3:T1 0.130000000 0.092154891 0.167845109 0.0000000
4:T3-3:T1 0.126666667 0.088821557 0.164511776 0.0000000
1:T4-3:T1 0.020000000 -0.017845109 0.057845109 0.8289662
2:T4-3:T1 0.066666667 0.028821557 0.104511776 0.0000239
3:T4-3:T1 0.033333333 -0.004511776 0.071178443 0.1332726
4:T4-3:T1 0.166666667 0.128821557 0.204511776 0.0000000
1:T2-4:T1 -0.040000000 -0.077845109 -0.002154891 0.0300852
2:T2-4:T1 0.016666667 -0.021178443 0.054511776 0.9494264
3:T2-4:T1 -0.086666667 -0.124511776 -0.048821557 0.0000001
4:T2-4:T1 0.093333333 0.055488224 0.131178443 0.0000000
1:T3-4:T1 0.010000000 -0.027845109 0.047845109 0.9996615
2:T3-4:T1 0.026666667 -0.011178443 0.064511776 0.4243390
3:T3-4:T1 -0.046666667 -0.084511776 -0.008821557 0.0055876
4:T3-4:T1 -0.050000000 -0.087845109 -0.012154891 0.0023062
1:T4-4:T1 -0.156666667 -0.194511776 -0.118821557 0.0000000
2:T4-4:T1 -0.110000000 -0.147845109 -0.072154891 0.0000000
3:T4-4:T1 -0.143333333 -0.181178443 -0.105488224 0.0000000
4:T4-4:T1 -0.010000000 -0.047845109 0.027845109 0.9996615
2:T2-1:T2 0.056666667 0.018821557 0.094511776 0.0003755
3:T2-1:T2 -0.046666667 -0.084511776 -0.008821557 0.0055876
4:T2-1:T2 0.133333333 0.095488224 0.171178443 0.0000000
1:T3-1:T2 0.050000000 0.012154891 0.087845109 0.0023062
2:T3-1:T2 0.066666667 0.028821557 0.104511776 0.0000239
3:T3-1:T2 -0.006666667 -0.044511776 0.031178443 0.9999979
4:T3-1:T2 -0.010000000 -0.047845109 0.027845109 0.9996615
1:T4-1:T2 -0.116666667 -0.154511776 -0.078821557 0.0000000
2:T4-1:T2 -0.070000000 -0.107845109 -0.032154891 0.0000096
3:T4-1:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
4:T4-1:T2 0.030000000 -0.007845109 0.067845109 0.2503711
3:T2-2:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
4:T2-2:T2 0.076666667 0.038821557 0.114511776 0.0000016
1:T3-2:T2 -0.006666667 -0.044511776 0.031178443 0.9999979
2:T3-2:T2 0.010000000 -0.027845109 0.047845109 0.9996615
3:T3-2:T2 -0.063333333 -0.101178443 -0.025488224 0.0000598
4:T3-2:T2 -0.066666667 -0.104511776 -0.028821557 0.0000239
1:T4-2:T2 -0.173333333 -0.211178443 -0.135488224 0.0000000
2:T4-2:T2 -0.126666667 -0.164511776 -0.088821557 0.0000000
3:T4-2:T2 -0.160000000 -0.197845109 -0.122154891 0.0000000
4:T4-2:T2 -0.026666667 -0.064511776 0.011178443 0.4243390
4:T2-3:T2 0.180000000 0.142154891 0.217845109 0.0000000
1:T3-3:T2 0.096666667 0.058821557 0.134511776 0.0000000
2:T3-3:T2 0.113333333 0.075488224 0.151178443 0.0000000
3:T3-3:T2 0.040000000 0.002154891 0.077845109 0.0300852
4:T3-3:T2 0.036666667 -0.001178443 0.074511776 0.0653618
1:T4-3:T2 -0.070000000 -0.107845109 -0.032154891 0.0000096
2:T4-3:T2 -0.023333333 -0.061178443 0.014511776 0.6353449
3:T4-3:T2 -0.056666667 -0.094511776 -0.018821557 0.0003755
4:T4-3:T2 0.076666667 0.038821557 0.114511776 0.0000016
1:T3-4:T2 -0.083333333 -0.121178443 -0.045488224 0.0000003
2:T3-4:T2 -0.066666667 -0.104511776 -0.028821557 0.0000239
3:T3-4:T2 -0.140000000 -0.177845109 -0.102154891 0.0000000
4:T3-4:T2 -0.143333333 -0.181178443 -0.105488224 0.0000000
1:T4-4:T2 -0.250000000 -0.287845109 -0.212154891 0.0000000
2:T4-4:T2 -0.203333333 -0.241178443 -0.165488224 0.0000000
3:T4-4:T2 -0.236666667 -0.274511776 -0.198821557 0.0000000
4:T4-4:T2 -0.103333333 -0.141178443 -0.065488224 0.0000000
2:T3-1:T3 0.016666667 -0.021178443 0.054511776 0.9494264
3:T3-1:T3 -0.056666667 -0.094511776 -0.018821557 0.0003755
4:T3-1:T3 -0.060000000 -0.097845109 -0.022154891 0.0001499
1:T4-1:T3 -0.166666667 -0.204511776 -0.128821557 0.0000000
2:T4-1:T3 -0.120000000 -0.157845109 -0.082154891 0.0000000
3:T4-1:T3 -0.153333333 -0.191178443 -0.115488224 0.0000000
4:T4-1:T3 -0.020000000 -0.057845109 0.017845109 0.8289662
3:T3-2:T3 -0.073333333 -0.111178443 -0.035488224 0.0000039
4:T3-2:T3 -0.076666667 -0.114511776 -0.038821557 0.0000016
1:T4-2:T3 -0.183333333 -0.221178443 -0.145488224 0.0000000
2:T4-2:T3 -0.136666667 -0.174511776 -0.098821557 0.0000000
3:T4-2:T3 -0.170000000 -0.207845109 -0.132154891 0.0000000
4:T4-2:T3 -0.036666667 -0.074511776 0.001178443 0.0653618
4:T3-3:T3 -0.003333333 -0.041178443 0.034511776 1.0000000
1:T4-3:T3 -0.110000000 -0.147845109 -0.072154891 0.0000000
2:T4-3:T3 -0.063333333 -0.101178443 -0.025488224 0.0000598
3:T4-3:T3 -0.096666667 -0.134511776 -0.058821557 0.0000000
4:T4-3:T3 0.036666667 -0.001178443 0.074511776 0.0653618
1:T4-4:T3 -0.106666667 -0.144511776 -0.068821557 0.0000000
2:T4-4:T3 -0.060000000 -0.097845109 -0.022154891 0.0001499
3:T4-4:T3 -0.093333333 -0.131178443 -0.055488224 0.0000000
4:T4-4:T3 0.040000000 0.002154891 0.077845109 0.0300852
2:T4-1:T4 0.046666667 0.008821557 0.084511776 0.0055876
3:T4-1:T4 0.013333333 -0.024511776 0.051178443 0.9926678
4:T4-1:T4 0.146666667 0.108821557 0.184511776 0.0000000
3:T4-2:T4 -0.033333333 -0.071178443 0.004511776 0.1332726
4:T4-2:T4 0.100000000 0.062154891 0.137845109 0.0000000
4:T4-3:T4 0.133333333 0.095488224 0.171178443 0.0000000
Letras do Tukey:
$periodo
4 2 1 3
"a" "b" "b" "c"
$tratamento
T2 T3 T1 T4
"a" "a" "b" "b"
$`periodo:tratamento`
4:T2 2:T3 2:T2 1:T3 4:T1 4:T4 1:T2 3:T3 4:T3 1:T1 3:T2 2:T4 2:T1 3:T4 1:T4 3:T1
"a" "b" "b" "b" "b" "bc" "cd" "cd" "de" "de" "ef" "fg" "gh" "ghi" "hi" "i"
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 3430 1143 0.291 0.831
tratamento 3 16169 5390 1.373 0.287
periodo:tratamento 9 6207 690 0.176 0.994
Residuals 16 62809 3926
48 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 599 44.3 16 506 693
2 T1 630 44.3 16 536 724
3 T1 627 44.3 16 533 720
4 T1 579 44.3 16 485 673
1 T2 591 44.3 16 497 685
2 T2 571 44.3 16 477 665
3 T2 582 44.3 16 488 676
4 T2 581 44.3 16 487 675
1 T3 552 44.3 16 458 646
2 T3 578 44.3 16 484 672
3 T3 564 44.3 16 470 658
4 T3 577 44.3 16 483 670
1 T4 549 44.3 16 455 642
2 T4 562 44.3 16 468 656
3 T4 570 44.3 16 476 664
4 T4 507 44.3 16 413 601
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 12.7127750 -76.91466 102.34021 0.9766460
3-1 13.1073875 -76.52004 102.73482 0.9745182
4-1 -11.9710875 -101.59852 77.65634 0.9803409
3-2 0.3946125 -89.23282 90.02204 0.9999992
4-2 -24.6838625 -114.31129 64.94357 0.8588430
4-3 -25.0784750 -114.70591 64.54896 0.8531598
$tratamento
diff lwr upr p adj
T2-T1 -27.35585 -116.9833 62.27158 0.8184909
T3-T1 -41.22081 -130.8482 48.40662 0.5666118
T4-T1 -61.86686 -151.4943 27.76057 0.2379893
T3-T2 -13.86496 -103.4924 75.76247 0.9701102
T4-T2 -34.51101 -124.1384 55.11642 0.6936451
T4-T3 -20.64605 -110.2735 68.98138 0.9108132
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 30.97120 -219.8716 281.8140 0.9999999
3:T1-1:T1 27.11235 -223.7305 277.9552 1.0000000
4:T1-1:T1 -20.63355 -271.4764 230.2093 1.0000000
1:T2-1:T1 -8.08520 -258.9280 242.7576 1.0000000
2:T2-1:T1 -28.18835 -279.0312 222.6545 1.0000000
3:T2-1:T1 -17.03770 -267.8805 233.8051 1.0000000
4:T2-1:T1 -18.66215 -269.5050 232.1807 1.0000000
1:T3-1:T1 -47.83165 -298.6745 203.0112 0.9999662
2:T3-1:T1 -21.28085 -272.1237 229.5620 1.0000000
3:T3-1:T1 -35.46415 -286.3070 215.3787 0.9999993
4:T3-1:T1 -22.85660 -273.6994 227.9862 1.0000000
1:T4-1:T1 -50.92575 -301.7686 199.9171 0.9999276
2:T4-1:T1 -37.49350 -288.3363 213.3493 0.9999985
3:T4-1:T1 -29.02355 -279.8664 221.8193 1.0000000
4:T4-1:T1 -92.57465 -343.4175 158.2682 0.9701347
3:T1-2:T1 -3.85885 -254.7017 246.9840 1.0000000
4:T1-2:T1 -51.60475 -302.4476 199.2381 0.9999151
1:T2-2:T1 -39.05640 -289.8992 211.7864 0.9999974
2:T2-2:T1 -59.15955 -310.0024 191.6833 0.9995883
3:T2-2:T1 -48.00890 -298.8517 202.8339 0.9999646
4:T2-2:T1 -49.63335 -300.4762 201.2095 0.9999469
1:T3-2:T1 -78.80285 -329.6457 172.0400 0.9923439
2:T3-2:T1 -52.25205 -303.0949 198.5908 0.9999016
3:T3-2:T1 -66.43535 -317.2782 184.4075 0.9985598
4:T3-2:T1 -53.82780 -304.6706 197.0150 0.9998603
1:T4-2:T1 -81.89695 -332.7398 168.9459 0.9891845
2:T4-2:T1 -68.46470 -319.3075 182.3781 0.9980362
3:T4-2:T1 -59.99475 -310.8376 190.8481 0.9995189
4:T4-2:T1 -123.54585 -374.3887 127.2970 0.8098341
4:T1-3:T1 -47.74590 -298.5887 203.0969 0.9999669
1:T2-3:T1 -35.19755 -286.0404 215.6453 0.9999994
2:T2-3:T1 -55.30070 -306.1435 195.5421 0.9998087
3:T2-3:T1 -44.15005 -294.9929 206.6928 0.9999876
4:T2-3:T1 -45.77450 -296.6173 205.0683 0.9999804
1:T3-3:T1 -74.94400 -325.7868 175.8988 0.9952071
2:T3-3:T1 -48.39320 -299.2360 202.4496 0.9999610
3:T3-3:T1 -62.57650 -313.4193 188.2663 0.9992369
4:T3-3:T1 -49.96895 -300.8118 200.8739 0.9999424
1:T4-3:T1 -78.03810 -328.8809 172.8047 0.9929987
2:T4-3:T1 -64.60585 -315.4487 186.2370 0.9989260
3:T4-3:T1 -56.13590 -306.9787 194.7069 0.9997727
4:T4-3:T1 -119.68700 -370.5298 131.1558 0.8387851
1:T2-4:T1 12.54835 -238.2945 263.3912 1.0000000
2:T2-4:T1 -7.55480 -258.3976 243.2880 1.0000000
3:T2-4:T1 3.59585 -247.2470 254.4387 1.0000000
4:T2-4:T1 1.97140 -248.8714 252.8142 1.0000000
1:T3-4:T1 -27.19810 -278.0409 223.6447 1.0000000
2:T3-4:T1 -0.64730 -251.4901 250.1955 1.0000000
3:T3-4:T1 -14.83060 -265.6734 236.0122 1.0000000
4:T3-4:T1 -2.22305 -253.0659 248.6198 1.0000000
1:T4-4:T1 -30.29220 -281.1350 220.5506 0.9999999
2:T4-4:T1 -16.85995 -267.7028 233.9829 1.0000000
3:T4-4:T1 -8.39000 -259.2328 242.4528 1.0000000
4:T4-4:T1 -71.94110 -322.7839 178.9017 0.9967742
2:T2-1:T2 -20.10315 -270.9460 230.7397 1.0000000
3:T2-1:T2 -8.95250 -259.7953 241.8903 1.0000000
4:T2-1:T2 -10.57695 -261.4198 240.2659 1.0000000
1:T3-1:T2 -39.74645 -290.5893 211.0964 0.9999968
2:T3-1:T2 -13.19565 -264.0385 237.6472 1.0000000
3:T3-1:T2 -27.37895 -278.2218 223.4639 1.0000000
4:T3-1:T2 -14.77140 -265.6142 236.0714 1.0000000
1:T4-1:T2 -42.84055 -293.6834 208.0023 0.9999915
2:T4-1:T2 -29.40830 -280.2511 221.4345 0.9999999
3:T4-1:T2 -20.93835 -271.7812 229.9045 1.0000000
4:T4-1:T2 -84.48945 -335.3323 166.3534 0.9858243
3:T2-2:T2 11.15065 -239.6922 261.9935 1.0000000
4:T2-2:T2 9.52620 -241.3166 260.3690 1.0000000
1:T3-2:T2 -19.64330 -270.4861 231.1995 1.0000000
2:T3-2:T2 6.90750 -243.9353 257.7503 1.0000000
3:T3-2:T2 -7.27580 -258.1186 243.5670 1.0000000
4:T3-2:T2 5.33175 -245.5111 256.1746 1.0000000
1:T4-2:T2 -22.73740 -273.5802 228.1054 1.0000000
2:T4-2:T2 -9.30515 -260.1480 241.5377 1.0000000
3:T4-2:T2 -0.83520 -251.6780 250.0076 1.0000000
4:T4-2:T2 -64.38630 -315.2291 186.4565 0.9989641
4:T2-3:T2 -1.62445 -252.4673 249.2184 1.0000000
1:T3-3:T2 -30.79395 -281.6368 220.0489 0.9999999
2:T3-3:T2 -4.24315 -255.0860 246.5997 1.0000000
3:T3-3:T2 -18.42645 -269.2693 232.4164 1.0000000
4:T3-3:T2 -5.81890 -256.6617 245.0239 1.0000000
1:T4-3:T2 -33.88805 -284.7309 216.9548 0.9999996
2:T4-3:T2 -20.45580 -271.2986 230.3870 1.0000000
3:T4-3:T2 -11.98585 -262.8287 238.8570 1.0000000
4:T4-3:T2 -75.53695 -326.3798 175.3059 0.9948346
1:T3-4:T2 -29.16950 -280.0123 221.6733 1.0000000
2:T3-4:T2 -2.61870 -253.4615 248.2241 1.0000000
3:T3-4:T2 -16.80200 -267.6448 234.0408 1.0000000
4:T3-4:T2 -4.19445 -255.0373 246.6484 1.0000000
1:T4-4:T2 -32.26360 -283.1064 218.5792 0.9999998
2:T4-4:T2 -18.83135 -269.6742 232.0115 1.0000000
3:T4-4:T2 -10.36140 -261.2042 240.4814 1.0000000
4:T4-4:T2 -73.91250 -324.7553 176.9303 0.9958030
2:T3-1:T3 26.55080 -224.2920 277.3936 1.0000000
3:T3-1:T3 12.36750 -238.4753 263.2103 1.0000000
4:T3-1:T3 24.97505 -225.8678 275.8179 1.0000000
1:T4-1:T3 -3.09410 -253.9369 247.7487 1.0000000
2:T4-1:T3 10.33815 -240.5047 261.1810 1.0000000
3:T4-1:T3 18.80810 -232.0347 269.6509 1.0000000
4:T4-1:T3 -44.74300 -295.5858 206.0998 0.9999853
3:T3-2:T3 -14.18330 -265.0261 236.6595 1.0000000
4:T3-2:T3 -1.57575 -252.4186 249.2671 1.0000000
1:T4-2:T3 -29.64490 -280.4877 221.1979 0.9999999
2:T4-2:T3 -16.21265 -267.0555 234.6302 1.0000000
3:T4-2:T3 -7.74270 -258.5855 243.1001 1.0000000
4:T4-2:T3 -71.29380 -322.1366 179.5490 0.9970495
4:T3-3:T3 12.60755 -238.2353 263.4504 1.0000000
1:T4-3:T3 -15.46160 -266.3044 235.3812 1.0000000
2:T4-3:T3 -2.02935 -252.8722 248.8135 1.0000000
3:T4-3:T3 6.44060 -244.4022 257.2834 1.0000000
4:T4-3:T3 -57.11050 -307.9533 193.7323 0.9997233
1:T4-4:T3 -28.06915 -278.9120 222.7737 1.0000000
2:T4-4:T3 -14.63690 -265.4797 236.2059 1.0000000
3:T4-4:T3 -6.16695 -257.0098 244.6759 1.0000000
4:T4-4:T3 -69.71805 -320.5609 181.1248 0.9976398
2:T4-1:T4 13.43225 -237.4106 264.2751 1.0000000
3:T4-1:T4 21.90220 -228.9406 272.7450 1.0000000
4:T4-1:T4 -41.64890 -292.4917 209.1939 0.9999941
3:T4-2:T4 8.46995 -242.3729 259.3128 1.0000000
4:T4-2:T4 -55.08115 -305.9240 195.7617 0.9998173
4:T4-3:T4 -63.55110 -314.3939 187.2917 0.9990988
Letras do Tukey:
$periodo
$periodo$Letters
3 2 1 4
"a" "a" "a" "a"
$periodo$LetterMatrix
a
3 TRUE
2 TRUE
1 TRUE
4 TRUE
$tratamento
$tratamento$Letters
T1 T2 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T1 TRUE
T2 TRUE
T3 TRUE
T4 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
2:T1 3:T1 1:T1 1:T2 3:T2 4:T2 4:T1 2:T3 4:T3 2:T2 3:T4 3:T3 2:T4 1:T3 1:T4 4:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
2:T1 TRUE
3:T1 TRUE
1:T1 TRUE
1:T2 TRUE
3:T2 TRUE
4:T2 TRUE
4:T1 TRUE
2:T3 TRUE
4:T3 TRUE
2:T2 TRUE
3:T4 TRUE
3:T3 TRUE
2:T4 TRUE
1:T3 TRUE
1:T4 TRUE
4:T4 TRUE
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
periodo 3 0.2134 0.07114 3.087 0.041 *
tratamento 3 0.0022 0.00072 0.031 0.992
periodo:tratamento 9 0.0015 0.00017 0.007 1.000
Residuals 32 0.7373 0.02304
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
periodo tratamento emmean SE df lower.CL upper.CL
1 T1 0.2415 0.0876 32 0.0630 0.420
2 T1 0.0869 0.0876 32 -0.0916 0.265
3 T1 0.0847 0.0876 32 -0.0938 0.263
4 T1 0.1074 0.0876 32 -0.0711 0.286
1 T2 0.2428 0.0876 32 0.0643 0.421
2 T2 0.0975 0.0876 32 -0.0810 0.276
3 T2 0.0949 0.0876 32 -0.0836 0.273
4 T2 0.1156 0.0876 32 -0.0629 0.294
1 T3 0.2608 0.0876 32 0.0823 0.439
2 T3 0.0830 0.0876 32 -0.0955 0.262
3 T3 0.0885 0.0876 32 -0.0900 0.267
4 T3 0.1147 0.0876 32 -0.0638 0.293
1 T4 0.2394 0.0876 32 0.0609 0.418
2 T4 0.0801 0.0876 32 -0.0984 0.259
3 T4 0.0798 0.0876 32 -0.0988 0.258
4 T4 0.0845 0.0876 32 -0.0940 0.263
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$periodo
diff lwr upr p adj
2-1 -1.592404e-01 -0.3271360 0.008655137 0.0680796
3-1 -1.591483e-01 -0.3270439 0.008747220 0.0682991
4-1 -1.405546e-01 -0.3084501 0.027340970 0.1270168
3-2 9.208333e-05 -0.1678035 0.167987637 1.0000000
4-2 1.868583e-02 -0.1492097 0.186581387 0.9902907
4-3 1.859375e-02 -0.1493018 0.186489304 0.9904301
$tratamento
diff lwr upr p adj
T2-T1 0.0075937500 -0.1603018 0.1754893 0.9993279
T3-T1 0.0066620833 -0.1612335 0.1745576 0.9995455
T4-T1 -0.0091691667 -0.1770647 0.1587264 0.9988201
T3-T2 -0.0009316667 -0.1688272 0.1669639 0.9999988
T4-T2 -0.0167629167 -0.1846585 0.1511326 0.9929396
T4-T3 -0.0158312500 -0.1837268 0.1520643 0.9940334
$`periodo:tratamento`
diff lwr upr p adj
2:T1-1:T1 -0.154600000 -0.6141702 0.3049702 0.9953426
3:T1-1:T1 -0.156781667 -0.6163519 0.3027886 0.9946459
4:T1-1:T1 -0.134058333 -0.5936286 0.3255119 0.9989616
1:T2-1:T1 0.001333333 -0.4582369 0.4609036 1.0000000
2:T2-1:T1 -0.143975000 -0.6035452 0.3155952 0.9977586
3:T2-1:T1 -0.146581667 -0.6061519 0.3129886 0.9972954
4:T2-1:T1 -0.125841667 -0.5854119 0.3337286 0.9994895
1:T3-1:T1 0.019338333 -0.4402319 0.4789086 1.0000000
2:T3-1:T1 -0.158425000 -0.6179952 0.3011452 0.9940667
3:T3-1:T1 -0.152928333 -0.6124986 0.3066419 0.9958244
4:T3-1:T1 -0.126776667 -0.5863469 0.3327936 0.9994445
1:T4-1:T1 -0.002081667 -0.4616519 0.4574886 1.0000000
2:T4-1:T1 -0.161371667 -0.6209419 0.2981986 0.9929008
3:T4-1:T1 -0.161711667 -0.6212819 0.2978586 0.9927551
4:T4-1:T1 -0.156951667 -0.6165219 0.3026186 0.9945883
3:T1-2:T1 -0.002181667 -0.4617519 0.4573886 1.0000000
4:T1-2:T1 0.020541667 -0.4390286 0.4801119 1.0000000
1:T2-2:T1 0.155933333 -0.3036369 0.6155036 0.9949263
2:T2-2:T1 0.010625000 -0.4489452 0.4701952 1.0000000
3:T2-2:T1 0.008018333 -0.4515519 0.4675886 1.0000000
4:T2-2:T1 0.028758333 -0.4308119 0.4883286 1.0000000
1:T3-2:T1 0.173938333 -0.2856319 0.6335086 0.9856898
2:T3-2:T1 -0.003825000 -0.4633952 0.4557452 1.0000000
3:T3-2:T1 0.001671667 -0.4578986 0.4612419 1.0000000
4:T3-2:T1 0.027823333 -0.4317469 0.4873936 1.0000000
1:T4-2:T1 0.152518333 -0.3070519 0.6120886 0.9959360
2:T4-2:T1 -0.006771667 -0.4663419 0.4527986 1.0000000
3:T4-2:T1 -0.007111667 -0.4666819 0.4524586 1.0000000
4:T4-2:T1 -0.002351667 -0.4619219 0.4572186 1.0000000
4:T1-3:T1 0.022723333 -0.4368469 0.4822936 1.0000000
1:T2-3:T1 0.158115000 -0.3014552 0.6176852 0.9941797
2:T2-3:T1 0.012806667 -0.4467636 0.4723769 1.0000000
3:T2-3:T1 0.010200000 -0.4493702 0.4697702 1.0000000
4:T2-3:T1 0.030940000 -0.4286302 0.4905102 1.0000000
1:T3-3:T1 0.176120000 -0.2834502 0.6356902 0.9839965
2:T3-3:T1 -0.001643333 -0.4612136 0.4579269 1.0000000
3:T3-3:T1 0.003853333 -0.4557169 0.4634236 1.0000000
4:T3-3:T1 0.030005000 -0.4295652 0.4895752 1.0000000
1:T4-3:T1 0.154700000 -0.3048702 0.6142702 0.9953124
2:T4-3:T1 -0.004590000 -0.4641602 0.4549802 1.0000000
3:T4-3:T1 -0.004930000 -0.4645002 0.4546402 1.0000000
4:T4-3:T1 -0.000170000 -0.4597402 0.4594002 1.0000000
1:T2-4:T1 0.135391667 -0.3241786 0.5949619 0.9988423
2:T2-4:T1 -0.009916667 -0.4694869 0.4496536 1.0000000
3:T2-4:T1 -0.012523333 -0.4720936 0.4470469 1.0000000
4:T2-4:T1 0.008216667 -0.4513536 0.4677869 1.0000000
1:T3-4:T1 0.153396667 -0.3061736 0.6129669 0.9956937
2:T3-4:T1 -0.024366667 -0.4839369 0.4352036 1.0000000
3:T3-4:T1 -0.018870000 -0.4784402 0.4407002 1.0000000
4:T3-4:T1 0.007281667 -0.4522886 0.4668519 1.0000000
1:T4-4:T1 0.131976667 -0.3275936 0.5915469 0.9991269
2:T4-4:T1 -0.027313333 -0.4868836 0.4322569 1.0000000
3:T4-4:T1 -0.027653333 -0.4872236 0.4319169 1.0000000
4:T4-4:T1 -0.022893333 -0.4824636 0.4366769 1.0000000
2:T2-1:T2 -0.145308333 -0.6048786 0.3142619 0.9975308
3:T2-1:T2 -0.147915000 -0.6074852 0.3116552 0.9970291
4:T2-1:T2 -0.127175000 -0.5867452 0.3323952 0.9994244
1:T3-1:T2 0.018005000 -0.4415652 0.4775752 1.0000000
2:T3-1:T2 -0.159758333 -0.6193286 0.2998119 0.9935601
3:T3-1:T2 -0.154261667 -0.6138319 0.3053086 0.9954436
4:T3-1:T2 -0.128110000 -0.5876802 0.3314602 0.9993745
1:T4-1:T2 -0.003415000 -0.4629852 0.4561552 1.0000000
2:T4-1:T2 -0.162705000 -0.6222752 0.2968652 0.9923155
3:T4-1:T2 -0.163045000 -0.6226152 0.2965252 0.9921602
4:T4-1:T2 -0.158285000 -0.6178552 0.3012852 0.9941180
3:T2-2:T2 -0.002606667 -0.4621769 0.4569636 1.0000000
4:T2-2:T2 0.018133333 -0.4414369 0.4777036 1.0000000
1:T3-2:T2 0.163313333 -0.2962569 0.6228836 0.9920358
2:T3-2:T2 -0.014450000 -0.4740202 0.4451202 1.0000000
3:T3-2:T2 -0.008953333 -0.4685236 0.4506169 1.0000000
4:T3-2:T2 0.017198333 -0.4423719 0.4767686 1.0000000
1:T4-2:T2 0.141893333 -0.3176769 0.6014636 0.9980788
2:T4-2:T2 -0.017396667 -0.4769669 0.4421736 1.0000000
3:T4-2:T2 -0.017736667 -0.4773069 0.4418336 1.0000000
4:T4-2:T2 -0.012976667 -0.4725469 0.4465936 1.0000000
4:T2-3:T2 0.020740000 -0.4388302 0.4803102 1.0000000
1:T3-3:T2 0.165920000 -0.2936502 0.6254902 0.9907434
2:T3-3:T2 -0.011843333 -0.4714136 0.4477269 1.0000000
3:T3-3:T2 -0.006346667 -0.4659169 0.4532236 1.0000000
4:T3-3:T2 0.019805000 -0.4397652 0.4793752 1.0000000
1:T4-3:T2 0.144500000 -0.3150702 0.6040702 0.9976711
2:T4-3:T2 -0.014790000 -0.4743602 0.4447802 1.0000000
3:T4-3:T2 -0.015130000 -0.4747002 0.4444402 1.0000000
4:T4-3:T2 -0.010370000 -0.4699402 0.4492002 1.0000000
1:T3-4:T2 0.145180000 -0.3143902 0.6047502 0.9975535
2:T3-4:T2 -0.032583333 -0.4921536 0.4269869 1.0000000
3:T3-4:T2 -0.027086667 -0.4866569 0.4324836 1.0000000
4:T3-4:T2 -0.000935000 -0.4605052 0.4586352 1.0000000
1:T4-4:T2 0.123760000 -0.3358102 0.5833302 0.9995784
2:T4-4:T2 -0.035530000 -0.4951002 0.4240402 1.0000000
3:T4-4:T2 -0.035870000 -0.4954402 0.4237002 1.0000000
4:T4-4:T2 -0.031110000 -0.4906802 0.4284602 1.0000000
2:T3-1:T3 -0.177763333 -0.6373336 0.2818069 0.9826215
3:T3-1:T3 -0.172266667 -0.6318369 0.2873036 0.9868897
4:T3-1:T3 -0.146115000 -0.6056852 0.3134552 0.9973837
1:T4-1:T3 -0.021420000 -0.4809902 0.4381502 1.0000000
2:T4-1:T3 -0.180710000 -0.6402802 0.2788602 0.9799294
3:T4-1:T3 -0.181050000 -0.6406202 0.2785202 0.9795993
4:T4-1:T3 -0.176290000 -0.6358602 0.2832802 0.9838583
3:T3-2:T3 0.005496667 -0.4540736 0.4650669 1.0000000
4:T3-2:T3 0.031648333 -0.4279219 0.4912186 1.0000000
1:T4-2:T3 0.156343333 -0.3032269 0.6159136 0.9947923
2:T4-2:T3 -0.002946667 -0.4625169 0.4566236 1.0000000
3:T4-2:T3 -0.003286667 -0.4628569 0.4562836 1.0000000
4:T4-2:T3 0.001473333 -0.4580969 0.4610436 1.0000000
4:T3-3:T3 0.026151667 -0.4334186 0.4857219 1.0000000
1:T4-3:T3 0.150846667 -0.3087236 0.6104169 0.9963661
2:T4-3:T3 -0.008443333 -0.4680136 0.4511269 1.0000000
3:T4-3:T3 -0.008783333 -0.4683536 0.4507869 1.0000000
4:T4-3:T3 -0.004023333 -0.4635936 0.4555469 1.0000000
1:T4-4:T3 0.124695000 -0.3348752 0.5842652 0.9995403
2:T4-4:T3 -0.034595000 -0.4941652 0.4249752 1.0000000
3:T4-4:T3 -0.034935000 -0.4945052 0.4246352 1.0000000
4:T4-4:T3 -0.030175000 -0.4897452 0.4293952 1.0000000
2:T4-1:T4 -0.159290000 -0.6188602 0.3002802 0.9937419
3:T4-1:T4 -0.159630000 -0.6192002 0.2999402 0.9936104
4:T4-1:T4 -0.154870000 -0.6144402 0.3047002 0.9952606
3:T4-2:T4 -0.000340000 -0.4599102 0.4592302 1.0000000
4:T4-2:T4 0.004420000 -0.4551502 0.4639902 1.0000000
4:T4-3:T4 0.004760000 -0.4548102 0.4643302 1.0000000
Letras do Tukey:
$periodo
$periodo$Letters
1 4 3 2
"a" "a" "a" "a"
$periodo$LetterMatrix
a
1 TRUE
4 TRUE
3 TRUE
2 TRUE
$tratamento
$tratamento$Letters
T2 T3 T1 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T1 TRUE
T4 TRUE
$`periodo:tratamento`
$`periodo:tratamento`$Letters
1:T3 1:T2 1:T1 1:T4 4:T2 4:T3 4:T1 2:T2 3:T2 3:T3 2:T1 3:T1 4:T4 2:T3 2:T4 3:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`periodo:tratamento`$LetterMatrix
a
1:T3 TRUE
1:T2 TRUE
1:T1 TRUE
1:T4 TRUE
4:T2 TRUE
4:T3 TRUE
4:T1 TRUE
2:T2 TRUE
3:T2 TRUE
3:T3 TRUE
2:T1 TRUE
3:T1 TRUE
4:T4 TRUE
2:T3 TRUE
2:T4 TRUE
3:T4 TRUE
12 Tempo x Tratamento
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
modelo <- aov(as.formula(paste(var, "~ tempo * tratamento")), data = database)
res <- residuals(modelo)
shapiro_p <- shapiro.test(res)$p.value
levene_p <- leveneTest(as.formula(paste(var, "~ tempo * tratamento")), data = database)[1, "Pr(>F)"]
data.frame(Variavel = var,
p_Shapiro = round(shapiro_p, 4),
p_Levene = round(levene_p, 4))
})
resultado_testes = do.call(rbind, testes)
print(resultado_testes)
# Anova e tukey
for (var in variables) {
cat("\n\n########################### Variável:", var, "##########################\n")
formula <- as.formula(paste(var, "~ tempo * tratamento"))
mod <- aov(formula, data = database)
cat("ANOVA:\n")
print(summary(mod))
cat("\nMédias (emmeans):\n")
medias <- emmeans(mod, ~ tempo * tratamento)
print(summary(medias))
cat("\nTukey HSD:\n")
tukey <- TukeyHSD(mod)
print(tukey)
cat("\nLetras do Tukey:\n")
letras <- multcompLetters4(mod, tukey)
print(letras)
}
########################### Variável: efluente ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 3 349201 116400 8.111 0.00018 ***
tratamento 3 4359 1453 0.101 0.95892
tempo:tratamento 9 9756 1084 0.076 0.99985
Residuals 48 688848 14351
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
24 T1 297 59.9 48 176.6 417
48 T1 209 59.9 48 88.3 329
72 T1 401 59.9 48 280.8 522
96 T1 310 59.9 48 189.6 430
24 T2 292 59.9 48 172.1 413
48 T2 160 59.9 48 39.6 280
72 T2 400 59.9 48 279.6 520
96 T2 302 59.9 48 181.8 423
24 T3 292 59.9 48 172.1 413
48 T3 189 59.9 48 68.3 309
72 T3 378 59.9 48 257.1 498
96 T3 351 59.9 48 230.6 471
24 T4 312 59.9 48 192.1 433
48 T4 202 59.9 48 81.6 322
72 T4 405 59.9 48 284.6 525
96 T4 326 59.9 48 206.1 447
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -108.7500 -221.47019 3.970194 0.0622779
72-24 97.3125 -15.40769 210.032694 0.1128509
96-24 23.8125 -88.90769 136.532694 0.9427304
72-48 206.0625 93.34231 318.782694 0.0000735
96-48 132.5625 19.84231 245.282694 0.0152349
96-72 -73.5000 -186.22019 39.220194 0.3170677
$tratamento
diff lwr upr p adj
T2-T1 -15.5625 -128.28269 97.15769 0.9828466
T3-T1 -1.8125 -114.53269 110.90769 0.9999714
T4-T1 7.2500 -105.47019 119.97019 0.9981917
T3-T2 13.7500 -98.97019 126.47019 0.9880304
T4-T2 22.8125 -89.90769 135.53269 0.9491390
T4-T3 9.0625 -103.65769 121.78269 0.9964913
$`tempo:tratamento`
diff lwr upr p adj
48:T1-24:T1 -8.825000e+01 -394.27506 217.77506 0.9994557
72:T1-24:T1 1.042500e+02 -201.77506 410.27506 0.9965969
96:T1-24:T1 1.300000e+01 -293.02506 319.02506 1.0000000
24:T2-24:T1 -4.500000e+00 -310.52506 301.52506 1.0000000
48:T2-24:T1 -1.370000e+02 -443.02506 169.02506 0.9570449
72:T2-24:T1 1.030000e+02 -203.02506 409.02506 0.9970005
96:T2-24:T1 5.250000e+00 -300.77506 311.27506 1.0000000
24:T3-24:T1 -4.500000e+00 -310.52506 301.52506 1.0000000
48:T3-24:T1 -1.082500e+02 -414.27506 197.77506 0.9949891
72:T3-24:T1 8.050000e+01 -225.52506 386.52506 0.9998160
96:T3-24:T1 5.400000e+01 -252.02506 360.02506 0.9999989
24:T4-24:T1 1.550000e+01 -290.52506 321.52506 1.0000000
48:T4-24:T1 -9.500000e+01 -401.02506 211.02506 0.9987471
72:T4-24:T1 1.080000e+02 -198.02506 414.02506 0.9951052
96:T4-24:T1 2.950000e+01 -276.52506 335.52506 1.0000000
72:T1-48:T1 1.925000e+02 -113.52506 498.52506 0.6441955
96:T1-48:T1 1.012500e+02 -204.77506 407.27506 0.9974975
24:T2-48:T1 8.375000e+01 -222.27506 389.77506 0.9997048
48:T2-48:T1 -4.875000e+01 -354.77506 257.27506 0.9999997
72:T2-48:T1 1.912500e+02 -114.77506 497.27506 0.6541814
96:T2-48:T1 9.350000e+01 -212.52506 399.52506 0.9989505
24:T3-48:T1 8.375000e+01 -222.27506 389.77506 0.9997048
48:T3-48:T1 -2.000000e+01 -326.02506 286.02506 1.0000000
72:T3-48:T1 1.687500e+02 -137.27506 474.77506 0.8175211
96:T3-48:T1 1.422500e+02 -163.77506 448.27506 0.9423177
24:T4-48:T1 1.037500e+02 -202.27506 409.77506 0.9967635
48:T4-48:T1 -6.750000e+00 -312.77506 299.27506 1.0000000
72:T4-48:T1 1.962500e+02 -109.77506 502.27506 0.6139681
96:T4-48:T1 1.177500e+02 -188.27506 423.77506 0.9885817
96:T1-72:T1 -9.125000e+01 -397.27506 214.77506 0.9992022
24:T2-72:T1 -1.087500e+02 -414.77506 197.27506 0.9947500
48:T2-72:T1 -2.412500e+02 -547.27506 64.77506 0.2781969
72:T2-72:T1 -1.250000e+00 -307.27506 304.77506 1.0000000
96:T2-72:T1 -9.900000e+01 -405.02506 207.02506 0.9980331
24:T3-72:T1 -1.087500e+02 -414.77506 197.27506 0.9947500
48:T3-72:T1 -2.125000e+02 -518.52506 93.52506 0.4824475
72:T3-72:T1 -2.375000e+01 -329.77506 282.27506 1.0000000
96:T3-72:T1 -5.025000e+01 -356.27506 255.77506 0.9999996
24:T4-72:T1 -8.875000e+01 -394.77506 217.27506 0.9994191
48:T4-72:T1 -1.992500e+02 -505.27506 106.77506 0.5895946
72:T4-72:T1 3.750000e+00 -302.27506 309.77506 1.0000000
96:T4-72:T1 -7.475000e+01 -380.77506 231.27506 0.9999257
24:T2-96:T1 -1.750000e+01 -323.52506 288.52506 1.0000000
48:T2-96:T1 -1.500000e+02 -456.02506 156.02506 0.9147076
72:T2-96:T1 9.000000e+01 -216.02506 396.02506 0.9993181
96:T2-96:T1 -7.750000e+00 -313.77506 298.27506 1.0000000
24:T3-96:T1 -1.750000e+01 -323.52506 288.52506 1.0000000
48:T3-96:T1 -1.212500e+02 -427.27506 184.77506 0.9850017
72:T3-96:T1 6.750000e+01 -238.52506 373.52506 0.9999795
96:T3-96:T1 4.100000e+01 -265.02506 347.02506 1.0000000
24:T4-96:T1 2.500000e+00 -303.52506 308.52506 1.0000000
48:T4-96:T1 -1.080000e+02 -414.02506 198.02506 0.9951052
72:T4-96:T1 9.500000e+01 -211.02506 401.02506 0.9987471
96:T4-96:T1 1.650000e+01 -289.52506 322.52506 1.0000000
48:T2-24:T2 -1.325000e+02 -438.52506 173.52506 0.9673280
72:T2-24:T2 1.075000e+02 -198.52506 413.52506 0.9953308
96:T2-24:T2 9.750000e+00 -296.27506 315.77506 1.0000000
24:T3-24:T2 -5.684342e-14 -306.02506 306.02506 1.0000000
48:T3-24:T2 -1.037500e+02 -409.77506 202.27506 0.9967635
72:T3-24:T2 8.500000e+01 -221.02506 391.02506 0.9996484
96:T3-24:T2 5.850000e+01 -247.52506 364.52506 0.9999969
24:T4-24:T2 2.000000e+01 -286.02506 326.02506 1.0000000
48:T4-24:T2 -9.050000e+01 -396.52506 215.52506 0.9992736
72:T4-24:T2 1.125000e+02 -193.52506 418.52506 0.9926413
96:T4-24:T2 3.400000e+01 -272.02506 340.02506 1.0000000
72:T2-48:T2 2.400000e+02 -66.02506 546.02506 0.2858159
96:T2-48:T2 1.422500e+02 -163.77506 448.27506 0.9423177
24:T3-48:T2 1.325000e+02 -173.52506 438.52506 0.9673280
48:T3-48:T2 2.875000e+01 -277.27506 334.77506 1.0000000
72:T3-48:T2 2.175000e+02 -88.52506 523.52506 0.4432735
96:T3-48:T2 1.910000e+02 -115.02506 497.02506 0.6561720
24:T4-48:T2 1.525000e+02 -153.52506 458.52506 0.9042346
48:T4-48:T2 4.200000e+01 -264.02506 348.02506 1.0000000
72:T4-48:T2 2.450000e+02 -61.02506 551.02506 0.2561317
96:T4-48:T2 1.665000e+02 -139.52506 472.52506 0.8313786
96:T2-72:T2 -9.775000e+01 -403.77506 208.27506 0.9982861
24:T3-72:T2 -1.075000e+02 -413.52506 198.52506 0.9953308
48:T3-72:T2 -2.112500e+02 -517.27506 94.77506 0.4923958
72:T3-72:T2 -2.250000e+01 -328.52506 283.52506 1.0000000
96:T3-72:T2 -4.900000e+01 -355.02506 257.02506 0.9999997
24:T4-72:T2 -8.750000e+01 -393.52506 218.52506 0.9995069
48:T4-72:T2 -1.980000e+02 -504.02506 108.02506 0.5997636
72:T4-72:T2 5.000000e+00 -301.02506 311.02506 1.0000000
96:T4-72:T2 -7.350000e+01 -379.52506 232.52506 0.9999398
24:T3-96:T2 -9.750000e+00 -315.77506 296.27506 1.0000000
48:T3-96:T2 -1.135000e+02 -419.52506 192.52506 0.9919756
72:T3-96:T2 7.525000e+01 -230.77506 381.27506 0.9999193
96:T3-96:T2 4.875000e+01 -257.27506 354.77506 0.9999997
24:T4-96:T2 1.025000e+01 -295.77506 316.27506 1.0000000
48:T4-96:T2 -1.002500e+02 -406.27506 205.77506 0.9977490
72:T4-96:T2 1.027500e+02 -203.27506 408.77506 0.9970762
96:T4-96:T2 2.425000e+01 -281.77506 330.27506 1.0000000
48:T3-24:T3 -1.037500e+02 -409.77506 202.27506 0.9967635
72:T3-24:T3 8.500000e+01 -221.02506 391.02506 0.9996484
96:T3-24:T3 5.850000e+01 -247.52506 364.52506 0.9999969
24:T4-24:T3 2.000000e+01 -286.02506 326.02506 1.0000000
48:T4-24:T3 -9.050000e+01 -396.52506 215.52506 0.9992736
72:T4-24:T3 1.125000e+02 -193.52506 418.52506 0.9926413
96:T4-24:T3 3.400000e+01 -272.02506 340.02506 1.0000000
72:T3-48:T3 1.887500e+02 -117.27506 494.77506 0.6739752
96:T3-48:T3 1.622500e+02 -143.77506 468.27506 0.8559899
24:T4-48:T3 1.237500e+02 -182.27506 429.77506 0.9819444
48:T4-48:T3 1.325000e+01 -292.77506 319.27506 1.0000000
72:T4-48:T3 2.162500e+02 -89.77506 522.27506 0.4529636
96:T4-48:T3 1.377500e+02 -168.27506 443.77506 0.9551269
96:T3-72:T3 -2.650000e+01 -332.52506 279.52506 1.0000000
24:T4-72:T3 -6.500000e+01 -371.02506 241.02506 0.9999874
48:T4-72:T3 -1.755000e+02 -481.52506 130.52506 0.7727910
72:T4-72:T3 2.750000e+01 -278.52506 333.52506 1.0000000
96:T4-72:T3 -5.100000e+01 -357.02506 255.02506 0.9999995
24:T4-96:T3 -3.850000e+01 -344.52506 267.52506 1.0000000
48:T4-96:T3 -1.490000e+02 -455.02506 157.02506 0.9186800
72:T4-96:T3 5.400000e+01 -252.02506 360.02506 0.9999989
96:T4-96:T3 -2.450000e+01 -330.52506 281.52506 1.0000000
48:T4-24:T4 -1.105000e+02 -416.52506 195.52506 0.9938381
72:T4-24:T4 9.250000e+01 -213.52506 398.52506 0.9990697
96:T4-24:T4 1.400000e+01 -292.02506 320.02506 1.0000000
72:T4-48:T4 2.030000e+02 -103.02506 509.02506 0.5590491
96:T4-48:T4 1.245000e+02 -181.52506 430.52506 0.9809387
96:T4-72:T4 -7.850000e+01 -384.52506 227.52506 0.9998644
Letras do Tukey:
$tempo
72 96 24 48
"a" "a" "ab" "b"
$tratamento
$tratamento$Letters
T4 T1 T3 T2
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T4 TRUE
T1 TRUE
T3 TRUE
T2 TRUE
$`tempo:tratamento`
$`tempo:tratamento`$Letters
72:T4 72:T1 72:T2 72:T3 96:T3 96:T4 24:T4 96:T1 96:T2 24:T1 24:T2 24:T3 48:T1 48:T4 48:T3 48:T2
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`tempo:tratamento`$LetterMatrix
a
72:T4 TRUE
72:T1 TRUE
72:T2 TRUE
72:T3 TRUE
96:T3 TRUE
96:T4 TRUE
24:T4 TRUE
96:T1 TRUE
96:T2 TRUE
24:T1 TRUE
24:T2 TRUE
24:T3 TRUE
48:T1 TRUE
48:T4 TRUE
48:T3 TRUE
48:T2 TRUE
########################### Variável: gas_total ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 3 3330824 1110275 13.325 1.88e-06 ***
tratamento 3 157390 52463 0.630 0.599
tempo:tratamento 9 1345330 149481 1.794 0.094 .
Residuals 48 3999568 83324
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
16 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
24 T1 1796 144 48 1506 2087
48 T1 1380 144 48 1089 1670
72 T1 797 144 48 507 1087
96 T1 1653 144 48 1363 1943
24 T2 1474 144 48 1183 1764
48 T2 1475 144 48 1185 1765
72 T2 1266 144 48 976 1556
96 T2 1783 144 48 1493 2073
24 T3 1632 144 48 1342 1923
48 T3 1324 144 48 1034 1614
72 T3 1403 144 48 1113 1693
96 T3 1755 144 48 1465 2045
24 T4 1867 144 48 1577 2157
48 T4 1291 144 48 1001 1581
72 T4 1089 144 48 799 1379
96 T4 1476 144 48 1185 1766
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -325.0000 -596.61052 -53.38948 0.0131437
72-24 -553.3750 -824.98552 -281.76448 0.0000110
96-24 -25.6875 -297.29802 245.92302 0.9943283
72-48 -228.3750 -499.98552 43.23552 0.1277064
96-48 299.3125 27.70198 570.92302 0.0255581
96-72 527.6875 256.07698 799.29802 0.0000262
$tratamento
diff lwr upr p adj
T2-T1 92.9375 -178.6730 364.5480 0.7992604
T3-T1 122.0625 -149.5480 393.6730 0.6323047
T4-T1 23.9375 -247.6730 295.5480 0.9953951
T3-T2 29.1250 -242.4855 300.7355 0.9917919
T4-T2 -69.0000 -340.6105 202.6105 0.9056390
T4-T3 -98.1250 -369.7355 173.4855 0.7717616
$`tempo:tratamento`
diff lwr upr p adj
48:T1-24:T1 -417.00 -1154.3978 320.3978 0.7894936
72:T1-24:T1 -999.25 -1736.6478 -261.8522 0.0011146
96:T1-24:T1 -143.50 -880.8978 593.8978 0.9999960
24:T2-24:T1 -323.00 -1060.3978 414.3978 0.9640232
48:T2-24:T1 -321.25 -1058.6478 416.1478 0.9656038
72:T2-24:T1 -530.25 -1267.6478 207.1478 0.4236949
96:T2-24:T1 -13.50 -750.8978 723.8978 1.0000000
24:T3-24:T1 -164.00 -901.3978 573.3978 0.9999773
48:T3-24:T1 -472.75 -1210.1478 264.6478 0.6144183
72:T3-24:T1 -393.25 -1130.6478 344.1478 0.8506630
96:T3-24:T1 -41.50 -778.8978 695.8978 1.0000000
24:T4-24:T1 70.25 -667.1478 807.6478 1.0000000
48:T4-24:T1 -505.75 -1243.1478 231.6478 0.5032799
72:T4-24:T1 -707.50 -1444.8978 29.8978 0.0724624
96:T4-24:T1 -321.00 -1058.3978 416.3978 0.9658253
72:T1-48:T1 -582.25 -1319.6478 155.1478 0.2758606
96:T1-48:T1 273.50 -463.8978 1010.8978 0.9919726
24:T2-48:T1 94.00 -643.3978 831.3978 1.0000000
48:T2-48:T1 95.75 -641.6478 833.1478 1.0000000
72:T2-48:T1 -113.25 -850.6478 624.1478 0.9999998
96:T2-48:T1 403.50 -333.8978 1140.8978 0.8255636
24:T3-48:T1 253.00 -484.3978 990.3978 0.9963349
48:T3-48:T1 -55.75 -793.1478 681.6478 1.0000000
72:T3-48:T1 23.75 -713.6478 761.1478 1.0000000
96:T3-48:T1 375.50 -361.8978 1112.8978 0.8890527
24:T4-48:T1 487.25 -250.1478 1224.6478 0.5654665
48:T4-48:T1 -88.75 -826.1478 648.6478 1.0000000
72:T4-48:T1 -290.50 -1027.8978 446.8978 0.9857700
96:T4-48:T1 96.00 -641.3978 833.3978 1.0000000
96:T1-72:T1 855.75 118.3522 1593.1478 0.0099367
24:T2-72:T1 676.25 -61.1478 1413.6478 0.1047465
48:T2-72:T1 678.00 -59.3978 1415.3978 0.1026643
72:T2-72:T1 469.00 -268.3978 1206.3978 0.6270029
96:T2-72:T1 985.75 248.3522 1723.1478 0.0013802
24:T3-72:T1 835.25 97.8522 1572.6478 0.0133386
48:T3-72:T1 526.50 -210.8978 1263.8978 0.4355688
72:T3-72:T1 606.00 -131.3978 1343.3978 0.2207667
96:T3-72:T1 957.75 220.3522 1695.1478 0.0021401
24:T4-72:T1 1069.50 332.1022 1806.8978 0.0003594
48:T4-72:T1 493.50 -243.8978 1230.8978 0.5443590
72:T4-72:T1 291.75 -445.6478 1029.1478 0.9851958
96:T4-72:T1 678.25 -59.1478 1415.6478 0.1023697
24:T2-96:T1 -179.50 -916.8978 557.8978 0.9999288
48:T2-96:T1 -177.75 -915.1478 559.6478 0.9999370
72:T2-96:T1 -386.75 -1124.1478 350.6478 0.8654857
96:T2-96:T1 130.00 -607.3978 867.3978 0.9999989
24:T3-96:T1 -20.50 -757.8978 716.8978 1.0000000
48:T3-96:T1 -329.25 -1066.6478 408.1478 0.9579418
72:T3-96:T1 -249.75 -987.1478 487.6478 0.9967966
96:T3-96:T1 102.00 -635.3978 839.3978 1.0000000
24:T4-96:T1 213.75 -523.6478 951.1478 0.9994223
48:T4-96:T1 -362.25 -1099.6478 375.1478 0.9133444
72:T4-96:T1 -564.00 -1301.3978 173.3978 0.3237536
96:T4-96:T1 -177.50 -914.8978 559.8978 0.9999381
48:T2-24:T2 1.75 -735.6478 739.1478 1.0000000
72:T2-24:T2 -207.25 -944.6478 530.1478 0.9995962
96:T2-24:T2 309.50 -427.8978 1046.8978 0.9749188
24:T3-24:T2 159.00 -578.3978 896.3978 0.9999847
48:T3-24:T2 -149.75 -887.1478 587.6478 0.9999930
72:T3-24:T2 -70.25 -807.6478 667.1478 1.0000000
96:T3-24:T2 281.50 -455.8978 1018.8978 0.9894042
24:T4-24:T2 393.25 -344.1478 1130.6478 0.8506630
48:T4-24:T2 -182.75 -920.1478 554.6478 0.9999111
72:T4-24:T2 -384.50 -1121.8978 352.8978 0.8704128
96:T4-24:T2 2.00 -735.3978 739.3978 1.0000000
72:T2-48:T2 -209.00 -946.3978 528.3978 0.9995546
96:T2-48:T2 307.75 -429.6478 1045.1478 0.9761234
24:T3-48:T2 157.25 -580.1478 894.6478 0.9999868
48:T3-48:T2 -151.50 -888.8978 585.8978 0.9999919
72:T3-48:T2 -72.00 -809.3978 665.3978 1.0000000
96:T3-48:T2 279.75 -457.6478 1017.1478 0.9900158
24:T4-48:T2 391.50 -345.8978 1128.8978 0.8547390
48:T4-48:T2 -184.50 -921.8978 552.8978 0.9999000
72:T4-48:T2 -386.25 -1123.6478 351.1478 0.8665898
96:T4-48:T2 0.25 -737.1478 737.6478 1.0000000
96:T2-72:T2 516.75 -220.6478 1254.1478 0.4670060
24:T3-72:T2 366.25 -371.1478 1103.6478 0.9064075
48:T3-72:T2 57.50 -679.8978 794.8978 1.0000000
72:T3-72:T2 137.00 -600.3978 874.3978 0.9999979
96:T3-72:T2 488.75 -248.6478 1226.1478 0.5603963
24:T4-72:T2 600.50 -136.8978 1337.8978 0.2327837
48:T4-72:T2 24.50 -712.8978 761.8978 1.0000000
72:T4-72:T2 -177.25 -914.6478 560.1478 0.9999392
96:T4-72:T2 209.25 -528.1478 946.6478 0.9995484
24:T3-96:T2 -150.50 -887.8978 586.8978 0.9999925
48:T3-96:T2 -459.25 -1196.6478 278.1478 0.6594168
72:T3-96:T2 -379.75 -1117.1478 357.6478 0.8804648
96:T3-96:T2 -28.00 -765.3978 709.3978 1.0000000
24:T4-96:T2 83.75 -653.6478 821.1478 1.0000000
48:T4-96:T2 -492.25 -1229.6478 245.1478 0.5485758
72:T4-96:T2 -694.00 -1431.3978 43.3978 0.0851818
96:T4-96:T2 -307.50 -1044.8978 429.8978 0.9762918
48:T3-24:T3 -308.75 -1046.1478 428.6478 0.9754406
72:T3-24:T3 -229.25 -966.6478 508.1478 0.9987265
96:T3-24:T3 122.50 -614.8978 859.8978 0.9999995
24:T4-24:T3 234.25 -503.1478 971.6478 0.9983856
48:T4-24:T3 -341.75 -1079.1478 395.6478 0.9436145
72:T4-24:T3 -543.50 -1280.8978 193.8978 0.3828703
96:T4-24:T3 -157.00 -894.3978 580.3978 0.9999870
72:T3-48:T3 79.50 -657.8978 816.8978 1.0000000
96:T3-48:T3 431.25 -306.1478 1168.6478 0.7481317
24:T4-48:T3 543.00 -194.3978 1280.3978 0.3843763
48:T4-48:T3 -33.00 -770.3978 704.3978 1.0000000
72:T4-48:T3 -234.75 -972.1478 502.6478 0.9983476
96:T4-48:T3 151.75 -585.6478 889.1478 0.9999917
96:T3-72:T3 351.75 -385.6478 1089.1478 0.9299373
24:T4-72:T3 463.50 -273.8978 1200.8978 0.6453515
48:T4-72:T3 -112.50 -849.8978 624.8978 0.9999999
72:T4-72:T3 -314.25 -1051.6478 423.1478 0.9714168
96:T4-72:T3 72.25 -665.1478 809.6478 1.0000000
24:T4-96:T3 111.75 -625.6478 849.1478 0.9999999
48:T4-96:T3 -464.25 -1201.6478 273.1478 0.6428584
72:T4-96:T3 -666.00 -1403.3978 71.3978 0.1176558
96:T4-96:T3 -279.50 -1016.8978 457.8978 0.9901008
48:T4-24:T4 -576.00 -1313.3978 161.3978 0.2917316
72:T4-24:T4 -777.75 -1515.1478 -40.3522 0.0295319
96:T4-24:T4 -391.25 -1128.6478 346.1478 0.8553162
72:T4-48:T4 -201.75 -939.1478 535.6478 0.9997058
96:T4-48:T4 184.75 -552.6478 922.1478 0.9998983
96:T4-72:T4 386.50 -350.8978 1123.8978 0.8660384
Letras do Tukey:
$tempo
24 96 48 72
"a" "a" "b" "b"
$tratamento
$tratamento$Letters
T3 T2 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T3 TRUE
T2 TRUE
T4 TRUE
T1 TRUE
$`tempo:tratamento`
24:T4 24:T1 96:T2 96:T3 96:T1 24:T3 96:T4 48:T2 24:T2 72:T3 48:T1 48:T3 48:T4 72:T2 72:T4 72:T1
"a" "ab" "ab" "ab" "ab" "ab" "abc" "abc" "abc" "abc" "abc" "abc" "abc" "abc" "bc" "c"
########################### Variável: ch4_effic ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 5.874 5.874 5.691 0.0253 *
tratamento 3 5.676 1.892 1.833 0.1681
tempo:tratamento 3 0.219 0.073 0.071 0.9750
Residuals 24 24.771 1.032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
72 T1 5.25 0.508 24 4.20 6.30
96 T1 6.02 0.508 24 4.97 7.07
72 T2 5.42 0.508 24 4.37 6.47
96 T2 6.05 0.508 24 5.00 7.10
72 T3 4.92 0.508 24 3.87 5.97
96 T3 5.93 0.508 24 4.88 6.98
72 T4 4.16 0.508 24 3.11 5.20
96 T4 5.17 0.508 24 4.12 6.22
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
96-72 0.856875 0.1155415 1.598209 0.0252956
$tratamento
diff lwr upr p adj
T2-T1 0.10125 -1.300048 1.5025478 0.9971165
T3-T1 -0.21125 -1.612548 1.1900478 0.9752233
T4-T1 -0.97375 -2.375048 0.4275478 0.2477197
T3-T2 -0.31250 -1.713798 1.0887978 0.9262342
T4-T2 -1.07500 -2.476298 0.3262978 0.1766108
T4-T3 -0.76250 -2.163798 0.6387978 0.4524458
$`tempo:tratamento`
diff lwr upr p adj
96:T1-72:T1 0.7675 -1.611718 3.1467179 0.9574626
72:T2-72:T1 0.1700 -2.209218 2.5492179 0.9999972
96:T2-72:T1 0.8000 -1.579218 3.1792179 0.9474312
72:T3-72:T1 -0.3350 -2.714218 2.0442179 0.9997173
96:T3-72:T1 0.6800 -1.699218 3.0592179 0.9777358
72:T4-72:T1 -1.0975 -3.476718 1.2817179 0.7855227
96:T4-72:T1 -0.0825 -2.461718 2.2967179 1.0000000
72:T2-96:T1 -0.5975 -2.976718 1.7817179 0.9893118
96:T2-96:T1 0.0325 -2.346718 2.4117179 1.0000000
72:T3-96:T1 -1.1025 -3.481718 1.2767179 0.7818120
96:T3-96:T1 -0.0875 -2.466718 2.2917179 1.0000000
72:T4-96:T1 -1.8650 -4.244218 0.5142179 0.2055744
96:T4-96:T1 -0.8500 -3.229218 1.5292179 0.9290910
96:T2-72:T2 0.6300 -1.749218 3.0092179 0.9854963
72:T3-72:T2 -0.5050 -2.884218 1.8742179 0.9960942
96:T3-72:T2 0.5100 -1.869218 2.8892179 0.9958512
72:T4-72:T2 -1.2675 -3.646718 1.1117179 0.6480034
96:T4-72:T2 -0.2525 -2.631718 2.1267179 0.9999574
72:T3-96:T2 -1.1350 -3.514218 1.2442179 0.7570843
96:T3-96:T2 -0.1200 -2.499218 2.2592179 0.9999997
72:T4-96:T2 -1.8975 -4.276718 0.4817179 0.1897629
96:T4-96:T2 -0.8825 -3.261718 1.4967179 0.9152115
96:T3-72:T3 1.0150 -1.364218 3.3942179 0.8426178
72:T4-72:T3 -0.7625 -3.141718 1.6167179 0.9588789
96:T4-72:T3 0.2525 -2.126718 2.6317179 0.9999574
72:T4-96:T3 -1.7775 -4.156718 0.6017179 0.2530470
96:T4-96:T3 -0.7625 -3.141718 1.6167179 0.9588789
96:T4-72:T4 1.0150 -1.364218 3.3942179 0.8426178
Letras do Tukey:
$tempo
96 72
"a" "b"
$tratamento
$tratamento$Letters
T2 T1 T3 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T1 TRUE
T3 TRUE
T4 TRUE
$`tempo:tratamento`
$`tempo:tratamento`$Letters
96:T2 96:T1 96:T3 72:T2 72:T1 96:T4 72:T3 72:T4
"a" "a" "a" "a" "a" "a" "a" "a"
$`tempo:tratamento`$LetterMatrix
a
96:T2 TRUE
96:T1 TRUE
96:T3 TRUE
72:T2 TRUE
72:T1 TRUE
96:T4 TRUE
72:T3 TRUE
72:T4 TRUE
########################### Variável: ch4_24h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 0.03768 0.03768 21.769 9.72e-05 ***
tratamento 3 0.00599 0.00200 1.154 0.348
tempo:tratamento 3 0.00206 0.00069 0.398 0.756
Residuals 24 0.04154 0.00173
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
72 T1 0.0648 0.0208 24 0.0218 0.108
96 T1 0.1601 0.0208 24 0.1171 0.203
72 T2 0.1100 0.0208 24 0.0671 0.153
96 T2 0.1717 0.0208 24 0.1288 0.215
72 T3 0.1075 0.0208 24 0.0646 0.150
96 T3 0.1725 0.0208 24 0.1296 0.215
72 T4 0.0875 0.0208 24 0.0446 0.130
96 T4 0.1400 0.0208 24 0.0971 0.183
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
96-72 0.06863125 0.0382718 0.0989907 9.72e-05
$tratamento
diff lwr upr p adj
T2-T1 0.0284375 -0.02894912 0.08582412 0.5312388
T3-T1 0.0275750 -0.02981162 0.08496162 0.5562812
T4-T1 0.0013250 -0.05606162 0.05871162 0.9999045
T3-T2 -0.0008625 -0.05824912 0.05652412 0.9999736
T4-T2 -0.0271125 -0.08449912 0.03027412 0.5697856
T4-T3 -0.0262500 -0.08363662 0.03113662 0.5950505
$`tempo:tratamento`
diff lwr upr p adj
96:T1-72:T1 0.095300 -0.002134875 0.19273487 0.0584109
72:T2-72:T1 0.045225 -0.052209875 0.14265987 0.7804484
96:T2-72:T1 0.106950 0.009515125 0.20438487 0.0244072
72:T3-72:T1 0.042725 -0.054709875 0.14015987 0.8239731
96:T3-72:T1 0.107725 0.010290125 0.20515987 0.0229877
72:T4-72:T1 0.022725 -0.074709875 0.12015987 0.9930892
96:T4-72:T1 0.075225 -0.022209875 0.17265987 0.2200416
72:T2-96:T1 -0.050075 -0.147509875 0.04735987 0.6859709
96:T2-96:T1 0.011650 -0.085784875 0.10908487 0.9999048
72:T3-96:T1 -0.052575 -0.150009875 0.04485987 0.6339928
96:T3-96:T1 0.012425 -0.085009875 0.10985987 0.9998535
72:T4-96:T1 -0.072575 -0.170009875 0.02485987 0.2561695
96:T4-96:T1 -0.020075 -0.117509875 0.07735987 0.9967478
96:T2-72:T2 0.061725 -0.035709875 0.15915987 0.4436161
72:T3-72:T2 -0.002500 -0.099934875 0.09493487 1.0000000
96:T3-72:T2 0.062500 -0.034934875 0.15993487 0.4283771
72:T4-72:T2 -0.022500 -0.119934875 0.07493487 0.9934884
96:T4-72:T2 0.030000 -0.067434875 0.12743487 0.9666809
72:T3-96:T2 -0.064225 -0.161659875 0.03320987 0.3953277
96:T3-96:T2 0.000775 -0.096659875 0.09820987 1.0000000
72:T4-96:T2 -0.084225 -0.181659875 0.01320987 0.1256521
96:T4-96:T2 -0.031725 -0.129159875 0.06570987 0.9553708
96:T3-72:T3 0.065000 -0.032434875 0.16243487 0.3809046
72:T4-72:T3 -0.020000 -0.117434875 0.07743487 0.9968222
96:T4-72:T3 0.032500 -0.064934875 0.12993487 0.9495207
72:T4-96:T3 -0.085000 -0.182434875 0.01243487 0.1193921
96:T4-96:T3 -0.032500 -0.129934875 0.06493487 0.9495207
96:T4-72:T4 0.052500 -0.044934875 0.14993487 0.6355699
Letras do Tukey:
$tempo
96 72
"a" "b"
$tratamento
$tratamento$Letters
T2 T3 T4 T1
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T4 TRUE
T1 TRUE
$`tempo:tratamento`
96:T3 96:T2 96:T1 96:T4 72:T2 72:T3 72:T4 72:T1
"a" "a" "ab" "ab" "ab" "ab" "ab" "b"
########################### Variável: ch4_48h ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 2 0.00000 0.00000 0.000 0.999873
tratamento 3 0.09707 0.03236 7.081 0.000735 ***
tempo:tratamento 6 0.00162 0.00027 0.059 0.999073
Residuals 36 0.16449 0.00457
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
48 T1 0.250 0.0338 36 0.181 0.319
72 T1 0.249 0.0338 36 0.180 0.318
96 T1 0.243 0.0338 36 0.175 0.312
48 T2 0.330 0.0338 36 0.261 0.399
72 T2 0.340 0.0338 36 0.271 0.409
96 T2 0.338 0.0338 36 0.269 0.406
48 T3 0.320 0.0338 36 0.251 0.389
72 T3 0.333 0.0338 36 0.264 0.401
96 T3 0.323 0.0338 36 0.254 0.391
48 T4 0.242 0.0338 36 0.174 0.311
72 T4 0.222 0.0338 36 0.154 0.291
96 T4 0.240 0.0338 36 0.171 0.309
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
72-48 0.0003775 -0.05803744 0.05879244 0.9998624
96-48 0.0002325 -0.05818244 0.05864744 0.9999478
96-72 -0.0001450 -0.05855994 0.05826994 0.9999797
$tratamento
diff lwr upr p adj
T2-T1 0.08835083 0.014029708 0.16267196 0.0144872
T3-T1 0.07751750 0.003196374 0.15183863 0.0381844
T4-T1 -0.01248250 -0.086803626 0.06183863 0.9687472
T3-T2 -0.01083333 -0.085154459 0.06348779 0.9791550
T4-T2 -0.10083333 -0.175154459 -0.02651221 0.0043435
T4-T3 -0.09000000 -0.164321126 -0.01567887 0.0124163
$`tempo:tratamento`
diff lwr upr p adj
72:T1-48:T1 -0.0009900 -0.16781662 0.16583662 1.0000000
96:T1-48:T1 -0.0065700 -0.17339662 0.16025662 1.0000000
48:T2-48:T1 0.0799975 -0.08682912 0.24682412 0.8682595
72:T2-48:T1 0.0899975 -0.07682912 0.25682412 0.7615117
96:T2-48:T1 0.0874975 -0.07932912 0.25432412 0.7909945
48:T3-48:T1 0.0699975 -0.09682912 0.23682412 0.9407873
72:T3-48:T1 0.0824975 -0.08432912 0.24932412 0.8445423
96:T3-48:T1 0.0724975 -0.09432912 0.23932412 0.9260290
48:T4-48:T1 -0.0075025 -0.17432912 0.15932412 1.0000000
72:T4-48:T1 -0.0275025 -0.19432912 0.13932412 0.9999825
96:T4-48:T1 -0.0100025 -0.17682912 0.15682412 1.0000000
96:T1-72:T1 -0.0055800 -0.17240662 0.16124662 1.0000000
48:T2-72:T1 0.0809875 -0.08583912 0.24781412 0.8591254
72:T2-72:T1 0.0909875 -0.07583912 0.25781412 0.7494044
96:T2-72:T1 0.0884875 -0.07833912 0.25531412 0.7795156
48:T3-72:T1 0.0709875 -0.09583912 0.23781412 0.9352055
72:T3-72:T1 0.0834875 -0.08333912 0.25031412 0.8345652
96:T3-72:T1 0.0734875 -0.09333912 0.24031412 0.9195710
48:T4-72:T1 -0.0065125 -0.17333912 0.16031412 1.0000000
72:T4-72:T1 -0.0265125 -0.19333912 0.14031412 0.9999879
96:T4-72:T1 -0.0090125 -0.17583912 0.15781412 1.0000000
48:T2-96:T1 0.0865675 -0.08025912 0.25339412 0.8015289
72:T2-96:T1 0.0965675 -0.07025912 0.26339412 0.6774754
96:T2-96:T1 0.0940675 -0.07275912 0.26089412 0.7103872
48:T3-96:T1 0.0765675 -0.09025912 0.24339412 0.8972089
72:T3-96:T1 0.0890675 -0.07775912 0.25589412 0.7726690
96:T3-96:T1 0.0790675 -0.08775912 0.24589412 0.8765257
48:T4-96:T1 -0.0009325 -0.16775912 0.16589412 1.0000000
72:T4-96:T1 -0.0209325 -0.18775912 0.14589412 0.9999990
96:T4-96:T1 -0.0034325 -0.17025912 0.16339412 1.0000000
72:T2-48:T2 0.0100000 -0.15682662 0.17682662 1.0000000
96:T2-48:T2 0.0075000 -0.15932662 0.17432662 1.0000000
48:T3-48:T2 -0.0100000 -0.17682662 0.15682662 1.0000000
72:T3-48:T2 0.0025000 -0.16432662 0.16932662 1.0000000
96:T3-48:T2 -0.0075000 -0.17432662 0.15932662 1.0000000
48:T4-48:T2 -0.0875000 -0.25432662 0.07932662 0.7909659
72:T4-48:T2 -0.1075000 -0.27432662 0.05932662 0.5277196
96:T4-48:T2 -0.0900000 -0.25682662 0.07682662 0.7614814
96:T2-72:T2 -0.0025000 -0.16932662 0.16432662 1.0000000
48:T3-72:T2 -0.0200000 -0.18682662 0.14682662 0.9999994
72:T3-72:T2 -0.0075000 -0.17432662 0.15932662 1.0000000
96:T3-72:T2 -0.0175000 -0.18432662 0.14932662 0.9999998
48:T4-72:T2 -0.0975000 -0.26432662 0.06932662 0.6649758
72:T4-72:T2 -0.1175000 -0.28432662 0.04932662 0.3960949
96:T4-72:T2 -0.1000000 -0.26682662 0.06682662 0.6310200
48:T3-96:T2 -0.0175000 -0.18432662 0.14932662 0.9999998
72:T3-96:T2 -0.0050000 -0.17182662 0.16182662 1.0000000
96:T3-96:T2 -0.0150000 -0.18182662 0.15182662 1.0000000
48:T4-96:T2 -0.0950000 -0.26182662 0.07182662 0.6982241
72:T4-96:T2 -0.1150000 -0.28182662 0.05182662 0.4276918
96:T4-96:T2 -0.0975000 -0.26432662 0.06932662 0.6649758
72:T3-48:T3 0.0125000 -0.15432662 0.17932662 1.0000000
96:T3-48:T3 0.0025000 -0.16432662 0.16932662 1.0000000
48:T4-48:T3 -0.0775000 -0.24432662 0.08932662 0.8897582
72:T4-48:T3 -0.0975000 -0.26432662 0.06932662 0.6649758
96:T4-48:T3 -0.0800000 -0.24682662 0.08682662 0.8682369
96:T3-72:T3 -0.0100000 -0.17682662 0.15682662 1.0000000
48:T4-72:T3 -0.0900000 -0.25682662 0.07682662 0.7614814
72:T4-72:T3 -0.1100000 -0.27682662 0.05682662 0.4936987
96:T4-72:T3 -0.0925000 -0.25932662 0.07432662 0.7304831
48:T4-96:T3 -0.0800000 -0.24682662 0.08682662 0.8682369
72:T4-96:T3 -0.1000000 -0.26682662 0.06682662 0.6310200
96:T4-96:T3 -0.0825000 -0.24932662 0.08432662 0.8445175
72:T4-48:T4 -0.0200000 -0.18682662 0.14682662 0.9999994
96:T4-48:T4 -0.0025000 -0.16932662 0.16432662 1.0000000
96:T4-72:T4 0.0175000 -0.14932662 0.18432662 0.9999998
Letras do Tukey:
$tempo
$tempo$Letters
72 96 48
"a" "a" "a"
$tempo$LetterMatrix
a
72 TRUE
96 TRUE
48 TRUE
$tratamento
T2 T3 T1 T4
"a" "a" "b" "b"
$`tempo:tratamento`
$`tempo:tratamento`$Letters
72:T2 96:T2 72:T3 48:T2 96:T3 48:T3 48:T1 72:T1 96:T1 48:T4 96:T4 72:T4
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`tempo:tratamento`$LetterMatrix
a
72:T2 TRUE
96:T2 TRUE
72:T3 TRUE
48:T2 TRUE
96:T3 TRUE
48:T3 TRUE
48:T1 TRUE
72:T1 TRUE
96:T1 TRUE
48:T4 TRUE
96:T4 TRUE
72:T4 TRUE
########################### Variável: dmo ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 1 42080 42080 37.243 2.65e-06 ***
tratamento 3 16169 5390 4.770 0.00955 **
tempo:tratamento 3 3249 1083 0.958 0.42831
Residuals 24 27117 1130
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
48 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
24 T1 645 16.8 24 611 680
48 T1 572 16.8 24 538 607
24 T2 628 16.8 24 593 663
48 T2 535 16.8 24 500 569
24 T3 610 16.8 24 575 644
48 T3 526 16.8 24 491 560
24 T4 567 16.8 24 532 602
48 T4 527 16.8 24 492 562
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
48-24 -72.52585 -97.05379 -47.99791 2.6e-06
$tratamento
diff lwr upr p adj
T2-T1 -27.35585 -73.71953 19.007834 0.3827508
T3-T1 -41.22081 -87.58450 5.142872 0.0938891
T4-T1 -61.86686 -108.23055 -15.503178 0.0060311
T3-T2 -13.86496 -60.22865 32.498722 0.8420986
T4-T2 -34.51101 -80.87470 11.852672 0.1971202
T4-T3 -20.64605 -67.00973 25.717634 0.6154408
$`tempo:tratamento`
diff lwr upr p adj
48:T1-24:T1 -72.894000 -151.61339 5.8253898 0.0837317
24:T2-24:T1 -17.114000 -95.83339 61.6053898 0.9954774
48:T2-24:T1 -110.491700 -189.21109 -31.7723102 0.0022021
24:T3-24:T1 -35.730500 -114.44989 42.9888898 0.7983573
48:T3-24:T1 -119.605125 -198.32451 -40.8857352 0.0008621
24:T4-24:T1 -78.335325 -157.05471 0.3840648 0.0517713
48:T4-24:T1 -118.292400 -197.01179 -39.5730102 0.0009870
24:T2-48:T1 55.780000 -22.93939 134.4993898 0.3105000
48:T2-48:T1 -37.597700 -116.31709 41.1216898 0.7560322
24:T3-48:T1 37.163500 -41.55589 115.8828898 0.7661563
48:T3-48:T1 -46.711125 -125.43051 32.0082648 0.5233813
24:T4-48:T1 -5.441325 -84.16071 73.2780648 0.9999977
48:T4-48:T1 -45.398400 -124.11779 33.3209898 0.5575260
48:T2-24:T2 -93.377700 -172.09709 -14.6583102 0.0123975
24:T3-24:T2 -18.616500 -97.33589 60.1028898 0.9924937
48:T3-24:T2 -102.491125 -181.21051 -23.7717352 0.0049800
24:T4-24:T2 -61.221325 -139.94071 17.4980648 0.2130179
48:T4-24:T2 -101.178400 -179.89779 -22.4590102 0.0056871
24:T3-48:T2 74.761200 -3.95819 153.4805898 0.0711925
48:T3-48:T2 -9.113425 -87.83281 69.6059648 0.9999234
24:T4-48:T2 32.156375 -46.56301 110.8757648 0.8689876
48:T4-48:T2 -7.800700 -86.52009 70.9186898 0.9999733
48:T3-24:T3 -83.874625 -162.59401 -5.1552352 0.0310333
24:T4-24:T3 -42.604825 -121.32421 36.1145648 0.6306448
48:T4-24:T3 -82.561900 -161.28129 -3.8425102 0.0350974
24:T4-48:T3 41.269800 -37.44959 119.9891898 0.6652118
48:T4-48:T3 1.312725 -77.40666 80.0321148 1.0000000
48:T4-24:T4 -39.957075 -118.67646 38.7623148 0.6985692
Letras do Tukey:
$tempo
24 48
"a" "b"
$tratamento
T1 T2 T3 T4
"a" "ab" "ab" "b"
$`tempo:tratamento`
24:T1 24:T2 24:T3 48:T1 24:T4 48:T2 48:T4 48:T3
"a" "a" "ab" "abc" "abc" "bc" "c" "c"
########################### Variável: n_nh3 ##########################
ANOVA:
Df Sum Sq Mean Sq F value Pr(>F)
tempo 2 0.5514 0.27568 24.882 1.64e-07 ***
tratamento 3 0.0022 0.00072 0.065 0.978
tempo:tratamento 6 0.0020 0.00033 0.030 1.000
Residuals 36 0.3989 0.01108
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
32 observations deleted due to missingness
Médias (emmeans):
tempo tratamento emmean SE df lower.CL upper.CL
0 T1 0.0420 0.0526 36 -0.0648 0.149
72 T1 0.0673 0.0526 36 -0.0394 0.174
96 T1 0.2810 0.0526 36 0.1743 0.388
0 T2 0.0382 0.0526 36 -0.0686 0.145
72 T2 0.0777 0.0526 36 -0.0290 0.184
96 T2 0.2972 0.0526 36 0.1905 0.404
0 T3 0.0426 0.0526 36 -0.0641 0.149
72 T3 0.0767 0.0526 36 -0.0300 0.183
96 T3 0.2910 0.0526 36 0.1843 0.398
0 T4 0.0415 0.0526 36 -0.0652 0.148
72 T4 0.0627 0.0526 36 -0.0440 0.169
96 T4 0.2585 0.0526 36 0.1518 0.365
Confidence level used: 0.95
Tukey HSD:
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = formula, data = database)
$tempo
diff lwr upr p adj
72-0 0.0300475 -0.06091686 0.1210119 0.7009119
96-0 0.2408847 0.14992033 0.3318490 0.0000005
96-72 0.2108372 0.11987283 0.3018015 0.0000057
$tratamento
diff lwr upr p adj
T2-T1 0.0075937500 -0.1081399 0.12332740 0.9980024
T3-T1 0.0066620833 -0.1090716 0.12239573 0.9986472
T4-T1 -0.0091691667 -0.1249028 0.10656448 0.9965037
T3-T2 -0.0009316667 -0.1166653 0.11480198 0.9999963
T4-T2 -0.0167629167 -0.1324966 0.09897073 0.9795328
T4-T3 -0.0158312500 -0.1315649 0.09990240 0.9826456
$`tempo:tratamento`
diff lwr upr p adj
72:T1-0:T1 0.02536250 -0.2344216891 0.285146689 0.9999999
96:T1-0:T1 0.23903125 -0.0207529391 0.498815439 0.0955214
0:T2-0:T1 -0.00382375 -0.2636079391 0.255960439 1.0000000
72:T2-0:T1 0.03573250 -0.2240516891 0.295516689 0.9999973
96:T2-0:T1 0.25526625 -0.0045179391 0.515050439 0.0578216
0:T3-0:T1 0.00062750 -0.2591566891 0.260411689 1.0000000
72:T3-0:T1 0.03471250 -0.2250716891 0.294496689 0.9999980
96:T3-0:T1 0.24904000 -0.0107441891 0.508824189 0.0703696
0:T4-0:T1 -0.00043500 -0.2602191891 0.259349189 1.0000000
72:T4-0:T1 0.02075125 -0.2390329391 0.280535439 1.0000000
96:T4-0:T1 0.21657000 -0.0432141891 0.476354189 0.1801470
96:T1-72:T1 0.21366875 -0.0461154391 0.473452939 0.1944077
0:T2-72:T1 -0.02918625 -0.2889704391 0.230597939 0.9999997
72:T2-72:T1 0.01037000 -0.2494141891 0.270154189 1.0000000
96:T2-72:T1 0.22990375 -0.0298804391 0.489687939 0.1247409
0:T3-72:T1 -0.02473500 -0.2845191891 0.235049189 0.9999999
72:T3-72:T1 0.00935000 -0.2504341891 0.269134189 1.0000000
96:T3-72:T1 0.22367750 -0.0361066891 0.483461689 0.1486085
0:T4-72:T1 -0.02579750 -0.2855816891 0.233986689 0.9999999
72:T4-72:T1 -0.00461125 -0.2643954391 0.255172939 1.0000000
96:T4-72:T1 0.19120750 -0.0685766891 0.450991689 0.3332062
0:T2-96:T1 -0.24285500 -0.5026391891 0.016929189 0.0851263
72:T2-96:T1 -0.20329875 -0.4630829391 0.056485439 0.2522297
96:T2-96:T1 0.01623500 -0.2435491891 0.276019189 1.0000000
0:T3-96:T1 -0.23840375 -0.4981879391 0.021380439 0.0973264
72:T3-96:T1 -0.20431875 -0.4641029391 0.055465439 0.2460619
96:T3-96:T1 0.01000875 -0.2497754391 0.269792939 1.0000000
0:T4-96:T1 -0.23946625 -0.4992504391 0.020317939 0.0942869
72:T4-96:T1 -0.21828000 -0.4780641891 0.041504189 0.1721239
96:T4-96:T1 -0.02246125 -0.2822454391 0.237322939 1.0000000
72:T2-0:T2 0.03955625 -0.2202279391 0.299340439 0.9999922
96:T2-0:T2 0.25909000 -0.0006941891 0.518874189 0.0511368
0:T3-0:T2 0.00445125 -0.2553329391 0.264235439 1.0000000
72:T3-0:T2 0.03853625 -0.2212479391 0.298320439 0.9999940
96:T3-0:T2 0.25286375 -0.0069204391 0.512647939 0.0624080
0:T4-0:T2 0.00338875 -0.2563954391 0.263172939 1.0000000
72:T4-0:T2 0.02457500 -0.2352091891 0.284359189 0.9999999
96:T4-0:T2 0.22039375 -0.0393904391 0.480177939 0.1625915
96:T2-72:T2 0.21953375 -0.0402504391 0.479317939 0.1664189
0:T3-72:T2 -0.03510500 -0.2948891891 0.224679189 0.9999977
72:T3-72:T2 -0.00102000 -0.2608041891 0.258764189 1.0000000
96:T3-72:T2 0.21330750 -0.0464766891 0.473091689 0.1962412
0:T4-72:T2 -0.03616750 -0.2959516891 0.223616689 0.9999969
72:T4-72:T2 -0.01498125 -0.2747654391 0.244802939 1.0000000
96:T4-72:T2 0.18083750 -0.0789466891 0.440621689 0.4132876
0:T3-96:T2 -0.25463875 -0.5144229391 0.005145439 0.0589898
72:T3-96:T2 -0.22055375 -0.4803379391 0.039230439 0.1618870
96:T3-96:T2 -0.00622625 -0.2660104391 0.253557939 1.0000000
0:T4-96:T2 -0.25570125 -0.5154854391 0.004082939 0.0570239
72:T4-96:T2 -0.23451500 -0.4942991891 0.025269189 0.1091667
96:T4-96:T2 -0.03869625 -0.2984804391 0.221087939 0.9999938
72:T3-0:T3 0.03408500 -0.2256991891 0.293869189 0.9999983
96:T3-0:T3 0.24841250 -0.0113716891 0.508196689 0.0717577
0:T4-0:T3 -0.00106250 -0.2608466891 0.258721689 1.0000000
72:T4-0:T3 0.02012375 -0.2396604391 0.279907939 1.0000000
96:T4-0:T3 0.21594250 -0.0438416891 0.475726689 0.1831618
96:T3-72:T3 0.21432750 -0.0454566891 0.474111689 0.1910974
0:T4-72:T3 -0.03514750 -0.2949316891 0.224636689 0.9999977
72:T4-72:T3 -0.01396125 -0.2737454391 0.245822939 1.0000000
96:T4-72:T3 0.18185750 -0.0779266891 0.441641689 0.4050309
0:T4-96:T3 -0.24947500 -0.5092591891 0.010309189 0.0694211
72:T4-96:T3 -0.22828875 -0.4880729391 0.031495439 0.1306096
96:T4-96:T3 -0.03247000 -0.2922541891 0.227314189 0.9999990
72:T4-0:T4 0.02118625 -0.2385979391 0.280970439 1.0000000
96:T4-0:T4 0.21700500 -0.0427791891 0.476789189 0.1780794
96:T4-72:T4 0.19581875 -0.0639654391 0.455602939 0.3006435
Letras do Tukey:
$tempo
96 72 0
"a" "b" "b"
$tratamento
$tratamento$Letters
T2 T3 T1 T4
"a" "a" "a" "a"
$tratamento$LetterMatrix
a
T2 TRUE
T3 TRUE
T1 TRUE
T4 TRUE
$`tempo:tratamento`
$`tempo:tratamento`$Letters
96:T2 96:T3 96:T1 96:T4 72:T2 72:T3 72:T1 72:T4 0:T3 0:T1 0:T4 0:T2
"a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a" "a"
$`tempo:tratamento`$LetterMatrix
a
96:T2 TRUE
96:T3 TRUE
96:T1 TRUE
96:T4 TRUE
72:T2 TRUE
72:T3 TRUE
72:T1 TRUE
72:T4 TRUE
0:T3 TRUE
0:T1 TRUE
0:T4 TRUE
0:T2 TRUE
---
title: "RUMENFLOW - Jeraldine"
author: "Vagner Ovani"
date: "27/05/2025"
output:
  html_notebook:
    toc: TRUE
    toc_depth: 2
    theme: united
---

***
***

# **1 Packages**

```{r}
library(car)
library(emmeans)
library(multcompView)
```

***
***

# **2 DATA**

```{r}
database=read.csv("D:/Armazenamento/8 - DATA R/Rumenflow/rumenflow2.csv")
str(database)

database$fermentador=as.factor(database$fermentador)
database$periodo=as.factor(database$periodo)
database$tempo=as.factor(database$tempo)
database$tratamento=as.factor(database$tratamento)
View(database)
```

***
***

# **3 Fermentador**

```{r}
names(database)
# Variáveis a serem analisadas
variables = c("efluente","gas_total","ch4_effic","ch4_24h","ch4_48h","dmo", "n_nh3")
variables


# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ fermentador")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ fermentador")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ fermentador"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ fermentador)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **4 Periodo**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ periodo")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ periodo")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ periodo"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ periodo)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **5 Tempo**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ tempo")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ tempo")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ tempo"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ tempo)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **6 Tratamento**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ tratamento")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ tratamento")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ tratamento"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ tratamento)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **7 Fermentador x Periodo**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ fermentador * periodo")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * periodo")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ fermentador * periodo"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ fermentador * periodo)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **8 Fermentador x Tempo**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ fermentador * tempo")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * tempo")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ fermentador * tempo"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ fermentador * tempo)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **9 Periodo x Tempo**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ periodo * tempo")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ periodo * tempo")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ periodo * tempo"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ periodo * tempo)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```
# **10 Fermentador x Tratamento**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ fermentador * tratamento")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ fermentador * tratamento")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ fermentador * tratamento"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ fermentador * tratamento)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **11 Periodo x Tratamento**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ periodo * tratamento")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ periodo * tratamento")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ periodo * tratamento"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ periodo * tratamento)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```

# **12 Tempo x Tratamento**

```{r}
# Normalidade e homocedasticidade
testes = lapply(variables, function(var) {
  modelo <- aov(as.formula(paste(var, "~ tempo * tratamento")), data = database)
  res <- residuals(modelo)
  shapiro_p <- shapiro.test(res)$p.value
  levene_p <- leveneTest(as.formula(paste(var, "~ tempo * tratamento")), data = database)[1, "Pr(>F)"]
  
  data.frame(Variavel = var,
             p_Shapiro = round(shapiro_p, 4),
             p_Levene = round(levene_p, 4))
})

resultado_testes = do.call(rbind, testes)
print(resultado_testes)

# Anova e tukey

for (var in variables) {
  cat("\n\n########################### Variável:", var, "##########################\n")
  formula <- as.formula(paste(var, "~ tempo * tratamento"))
  mod <- aov(formula, data = database)
  
  cat("ANOVA:\n")
  print(summary(mod))
  
  cat("\nMédias (emmeans):\n")
  medias <- emmeans(mod, ~ tempo * tratamento)
  print(summary(medias))
  
  cat("\nTukey HSD:\n")
  tukey <- TukeyHSD(mod)
  print(tukey)
  
  cat("\nLetras do Tukey:\n")
  letras <- multcompLetters4(mod, tukey)
  print(letras)
}
```
