Dados de produção de gás In vitro - Deborah UFPA
Vagner Ovani; Lumena Takahashi; Simón Pérez-Marquez
02/12/2024
1 - PACOTES
library(ggplot2)
library(qpcR)
library(dplyr)
library(tibble)
library(openxlsx)
library(tidyr)
library(tidyverse)
library(emmeans)#medias e teste de media para modelo misto
library(multcompView) #variancia
library(multcomp)2 - Dados
dados = read.csv("D:/Armazenamento/DATA R/Deborah UFPA/data.csv")
View(dados)
str(dados)'data.frame': 216 obs. of 19 variables:
$ Dieta : chr "TCR1D1" "TCR1D1" "TCR1D1" "TCR2D1" ...
$ Inoculante: num 1 1 1 1 1 1 1 1 1 1 ...
$ Dia : num 1 1 1 1 1 1 1 1 1 1 ...
$ Rep : num 1 1 1 2 2 2 3 3 3 4 ...
$ T0 : num 1.39 -0.9 -0.93 1.05 0.58 1.33 0.84 1.69 1.01 1.16 ...
$ T2 : num 3.38 1.29 1.6 3.29 3.04 4.27 4.67 5.85 4.88 4.06 ...
$ T4 : num 5.98 4.49 4.44 6.51 6.42 ...
$ T6 : num 8.63 7.76 7.56 10.24 10.47 ...
$ T8 : num 13 12.6 13 17.2 17.6 ...
$ T10 : num 18.5 17.4 18.3 23.7 24.7 ...
$ T12 : num 27.4 26.1 27.5 33.8 35.7 ...
$ T15 : num 43.6 41.3 43 49.6 53.6 ...
$ T18 : num 62.5 60.7 62.1 69.7 72.3 ...
$ T21 : num 79.5 77.8 78.5 85.4 86 ...
$ T24 : num 92.5 90.7 91.7 98.1 95.6 ...
$ NetGP24h : num 92.5 90.7 91.7 98.1 95.6 ...
$ MOVD : num 0.84 0.84 0.81 0.81 0.82 0.82 0.81 0.82 0.82 0.79 ...
$ DMS : num 909 916 885 883 901 ...
$ DMO : num 905 907 881 878 889 ...dados$Dieta=as.factor(dados$Dieta)
#Filtro
datamodels= dados %>%
select(Dieta, T0:T24) %>%
group_by(Dieta) %>%
summarise(across(everything(), mean, na.rm = TRUE))
print(datamodels)
#Inversão
data_long <- datamodels %>%
pivot_longer(cols = starts_with("T"),
names_to = "Time",
values_to = "PG") %>%
mutate(Time = as.numeric(gsub("T", "", Time))) %>%
pivot_wider(names_from = Dieta, values_from = PG)
head(data_long)3 - SHAPE DATA - LONG FORMAT
data_long1 <- gather(data_long, key = "id", value = "pg", -Time) #invertendo novamente, para formato longo
data_long1 <- drop_na(data_long1) #remoção de NA
data_long1 <- data_long1 %>% filter(pg >= 0) #valores negativos
print(data_long1)data_long_filtro <- subset(data_long1, !Time %in% c(6, 10, 12,18,21)) #Reduzindo alguns tempos desnecessarios
data_long_filtroThe format for this code to work is wide format, with every column being a substrate.
4 - Ranking dos modelos
Exponencial
exp <- nls(pg ~ (vf*(1-exp(-k*Time))),
data = data_long_filtro,
start = c(vf= 100, k = 0.05),
algorithm = "port",
lower = c(1, 0.001),
upper = c(300, 0.5),
control = nls.control(maxiter = 500, minFactor = 1e-10))
exp_stat_criteria <- as.data.frame(c(deviance(exp), AICc(exp), BIC(exp), Rsq.ad(exp), RMSE(exp)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(exp_stat_criteria) <- "Exponencial"explagonencial
explag <- nls(pg ~ (vf*(1-exp(-k*(Time-LAG)))),
data = data_long_filtro,
start = c(vf= 200, k = 0.05, LAG = 0.5),
algorithm = "port",
lower = c(1, 0.001, 0.01),
upper = c(300, 0.5, 1),
control = nls.control(maxiter = 500, minFactor = 1e-10)
)
explag_stat_criteria <- as.data.frame(c(deviance(explag), AICc(explag), BIC(explag), Rsq.ad(explag), RMSE(explag)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(explag_stat_criteria) <- "explagonencial"Gompertz
gom <- nls(pg ~ (vf*exp(-exp(1-k*(Time-l)))),
data = data_long_filtro,
algorithm = "port",
start = c(vf= 200, k = 0.05, l = 0.1),
lower = c(0,0,0))
gom_stat_criteria <- as.data.frame(c(deviance(gom), AICc(gom), BIC(gom), Rsq.ad(gom), RMSE(gom)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(gom_stat_criteria) <- "Gompertz"France
fra <- nls(pg ~ vf*(1-exp(-k*(Time-l)-d*(sqrt(Time)-sqrt(l)))),
data = data_long_filtro,
algorithm = "port",
start = c(vf= 100, k = 0.01, l = 0, d = 0.01),
lower = c(0, 0, 0, 0),
upper = c(200, 1, 1, 1)
)
fra_stat_criteria <- as.data.frame(c(deviance(fra), AICc(fra), BIC(fra), Rsq.ad(fra), RMSE(fra)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(fra_stat_criteria) <- "France"Schofield U
schu <- nls(pg ~ vf/(1+exp(2-4*k*(Time-l))),
data = data_long_filtro,
algorithm = "port",
start = c(vf= 100, k = 0.05, l = 0.2))
schu_stat_criteria <- as.data.frame(c(deviance(schu), AICc(schu), BIC(schu), Rsq.ad(schu), RMSE(schu)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(schu_stat_criteria) <- "Schofield U"Wang
wan <- nls(pg ~ vf*((1-exp(-k*Time))/(1+exp(log(1/d)-k*Time))),
data = data_long_filtro,
algorithm = "port",
start = c(vf=200, k = 0.01, d = 1),
lower = c(0,0,0.1),
upper = c(300,0.5,10)
)
wan_stat_criteria <- as.data.frame(c(deviance(wan), AICc(wan), BIC(wan), Rsq.ad(wan), RMSE(wan)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(wan_stat_criteria) <- "Wang"Wang L
wanl <- nls(pg ~ vf*(1-exp(-k*(Time-l)))/(1+exp(log(1/d)-k*(Time-l))),
data = data_long_filtro,
algorithm = "port",
start = c(vf=200, k = 0.05, d = 0.5, l = 0.5),
lower = c(0, 0, 0, 0),
upper = c(200, 1, 1, 1)
)
wanl_stat_criteria <- as.data.frame(c(deviance(wanl), AICc(wanl), BIC(wanl), Rsq.ad(wanl), RMSE(wanl)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(wanl_stat_criteria) <- "Wang L"Abreu
abr <- nls(pg ~ vf1*(1-exp(-k1*Time))+vf2*exp(-exp(1+k2*exp(1)*(L-Time))),
data = data_long_filtro,
algorithm = "port",
start = c(vf1 = 155, k1 = 0.005, L = 0.5, vf2 = 50, k2 = 0.05),
lower = c(0,0,0,0,0),
upper = c(300,0.5,1,300,0.5)
)
abr_stat_criteria <- as.data.frame(c(deviance(abr), AICc(abr), BIC(abr), Rsq.ad(abr), RMSE(abr)), row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(abr_stat_criteria) <- "Abreu"5 - Resultado Criterios Estatisticos
RSS AIC BIC RSQ RMSE
Abreu 34715.086 3008.123 3032.101 0.8776115 9.179320
Exponencial 41925.027 3079.752 3091.786 0.8595863 10.087605
explagonencial 30866.804 2955.626 2971.652 0.9250198 8.655601
France 37505.028 3037.921 3057.928 0.9020921 9.541049
Gompertz 9680.537 2477.886 2493.911 0.9825880 4.847314
Schofield U 9988.227 2490.777 2506.803 0.9811540 4.923745
Wang 12519.267 2583.832 2599.858 0.9743364 5.512401
Wang L 9683.330 2480.044 2500.051 0.9824672 4.848013tibble [3,708 × 4] (S3: tbl_df/tbl/data.frame)
$ t : num [1:3708] 0 0 0 0 0 0 0 0 0 2 ...
$ assay : Factor w/ 72 levels "LBLHR1D1","LBLHR1D2",..: 1 1 1 1 1 1 1 1 1 1 ...
$ modelo: Factor w/ 9 levels "abr","exp","explag",..: 6 2 3 5 4 7 8 9 1 6 ...
$ pg : num [1:3708] 1.613 0 -4.756 0.498 -5.151 ...6 - Plot
7 - Avaliação dos Modelos - Ranking
criteria <- as.data.frame(t(stat_criteria_results))
criteria <- rownames_to_column(criteria, "criteria")
criteria
write.xlsx(criteria, file = "D:/Armazenamento/DATA R/Deborah UFPA/criteria.xlsx", sheetName = "Dados", rownames = FALSE)
criteria %>%
mutate(across(c(RSS:RMSE), rank)) -> ranked
ranked$TOT <- (ranked$RSS+ranked$AIC+ranked$BIC+ranked$RMSE-ranked$RSQ)
ranked
write.xlsx(ranked, file = "D:/Armazenamento/DATA R/Deborah UFPA/ranked.xlsx", sheetName = "Dados", rownames = FALSE)Gompertz foi o melhor modelo.
8 - Aplicando o modelo Gompertz para análise do perfil de fermentação
\(\displaystyle V= vf*exp^(-exp^{1-k(t-l)})\)
data_long = drop_na(data_long)
print(data_long)
mod_exp_pg <- lapply(names(data_long)[c(2:73)],
function(s){
nls(substitute(p ~ (vf*exp(-exp(1-k*(Time-l)))),
list(p = as.name(s))),
data = data_long,
algorithm = "port",
start = c(vf= 200, k = 0.05, l = 0.1),
lower = c(0,0,0))
}
)
results_pg<-as.data.frame(lapply(mod_exp_pg,coef))
names(results_pg) <- names(data_long[2:73])
results_pg <- as.data.frame(t(results_pg))
results_pg <- rownames_to_column(results_pg, "substrate")
View(results_pg)Calcular t0.5
results_pg = results_pg %>% mutate(t0.5 = l + (1 - log(log(2))) / k)
results_pgCalcular o mu0.5
results_pg = results_pg %>% mutate(mu0.5 = log(2) * k)
results_pg$mu0.5=results_pg$mu0.5*100
#k= fractional rate of gas production
results_pg$k1=results_pg$k*100 #PORCENTAGEM
print(results_pg)Juntar dados
#Filtro
dados.total= dados %>%
group_by(Dieta,Inoculante,Dia) %>%
summarise(across(everything(), mean, na.rm = TRUE))`summarise()` has grouped output by 'Dieta', 'Inoculante'. You can override using the `.groups` argument.print(dados.total)
data_combinado = cbind(dados.total, results_pg)
View(data_combinado)9 - Análise estatistica
Anova
data_all=data_combinado[,-20]
str(data_all)gropd_df [72 × 25] (S3: grouped_df/tbl_df/tbl/data.frame)
$ Dieta : Factor w/ 72 levels "LBLHR1D1","LBLHR1D2",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Inoculante: num [1:72] 3 3 3 3 3 3 3 3 3 3 ...
$ Dia : num [1:72] 1 2 3 4 5 6 1 2 3 4 ...
$ Rep : num [1:72] 1 1 1 1 1 1 2 2 2 2 ...
$ T0 : num [1:72] 1.613 1.213 -0.443 -0.28 1.547 ...
$ T2 : num [1:72] 4.29 3.56 1.47 1.08 2.6 ...
$ T4 : num [1:72] 7.343 7.253 2.413 0.853 7.947 ...
$ T6 : num [1:72] 10.4 12.64 8.28 4.18 11.49 ...
$ T8 : num [1:72] 16.2 20.7 19.1 11.7 18.2 ...
$ T10 : num [1:72] 23.1 28.9 31.4 21.4 25.7 ...
$ T12 : num [1:72] 32.6 39.1 46.6 33.1 37.2 ...
$ T15 : num [1:72] 47.6 55.7 68 53.4 59 ...
$ T18 : num [1:72] 65.6 71.9 82.6 70.6 76.6 ...
$ T21 : num [1:72] 81 85.2 93.5 84.1 90.2 ...
$ T24 : num [1:72] 91.9 94.5 100.4 92.4 99.3 ...
$ NetGP24h : num [1:72] 91.9 94.5 100.4 92.4 99.3 ...
$ MOVD : num [1:72] 0.797 0.817 0.83 0.85 0.853 ...
$ DMS : num [1:72] 871 876 909 932 931 ...
$ DMO : num [1:72] 866 878 905 925 926 ...
$ vf : num [1:72] 152 128 110 109 133 ...
$ k : num [1:72] 0.0948 0.1146 0.1833 0.1665 0.1239 ...
$ l : num [1:72] 5.87 4.63 5.71 6.94 5.47 ...
$ t0.5 : num [1:72] 20.3 16.6 13.2 15.2 16.5 ...
$ mu0.5 : num [1:72] 6.57 7.94 12.71 11.54 8.59 ...
$ k1 : num [1:72] 9.48 11.46 18.33 16.65 12.39 ...
- attr(*, "groups")= tibble [72 × 3] (S3: tbl_df/tbl/data.frame)
..$ Dieta : Factor w/ 72 levels "LBLHR1D1","LBLHR1D2",..: 1 2 3 4 5 6 7 8 9 10 ...
..$ Inoculante: num [1:72] 3 3 3 3 3 3 3 3 3 3 ...
..$ .rows : list<int> [1:72]
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.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUEdata_all$Inoculante <- as.factor(data_all$Inoculante)
data_all$Dia <- as.factor(data_all$Dia)
# Filtrar nomes das colunas numéricas
variaveis_dependentes = names(data_all)[sapply(data_all, is.numeric) & !(names(data_all) %in% names(data_all)[1:15])]
variaveis_dependentes [1] "NetGP24h" "MOVD" "DMS" "DMO" "vf" "k" "l" "t0.5" "mu0.5" "k1" #Função para ANOVA
anova_model_all <- function(data, variaveis_dependentes) {
resultados_anova <- lapply(variaveis_dependentes, function(var) {
mod <- aov(as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")), data = data)
anova_res <- summary(mod)
list(
variavel = var,
model = mod,
anova = anova_res
)
})
return(resultados_anova)
}
resultados_anova <- anova_model_all(data_all, variaveis_dependentes)
resultados_anova[[1]]
[[1]]$variavel
[1] "NetGP24h"
[[1]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 90.2536 1030.8552 337.2843 919.0450
Deg. of Freedom 2 5 10 54
Residual standard error: 4.125452
Estimated effects may be unbalanced
[[1]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 90.3 45.13 2.652 0.0797 .
Dia 5 1030.9 206.17 12.114 6.89e-08 ***
Inoculante:Dia 10 337.3 33.73 1.982 0.0537 .
Residuals 54 919.0 17.02
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[2]]
[[2]]$variavel
[1] "MOVD"
[[2]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 0.00011875 0.06185359 0.00385301 0.00746319
Deg. of Freedom 2 5 10 54
Residual standard error: 0.01175616
Estimated effects may be unbalanced
[[2]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 0.00012 0.000059 0.430 0.65297
Dia 5 0.06185 0.012371 89.508 < 2e-16 ***
Inoculante:Dia 10 0.00385 0.000385 2.788 0.00745 **
Residuals 54 0.00746 0.000138
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[3]]
[[3]]$variavel
[1] "DMS"
[[3]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 420.49 67633.51 4792.70 7090.89
Deg. of Freedom 2 5 10 54
Residual standard error: 11.45918
Estimated effects may be unbalanced
[[3]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 420 210 1.601 0.211102
Dia 5 67634 13527 103.011 < 2e-16 ***
Inoculante:Dia 10 4793 479 3.650 0.000915 ***
Residuals 54 7091 131
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[4]]
[[4]]$variavel
[1] "DMO"
[[4]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 313.29 58246.12 4059.26 6890.05
Deg. of Freedom 2 5 10 54
Residual standard error: 11.29573
Estimated effects may be unbalanced
[[4]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 313 157 1.228 0.30101
Dia 5 58246 11649 91.299 < 2e-16 ***
Inoculante:Dia 10 4059 406 3.181 0.00284 **
Residuals 54 6890 128
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[5]]
[[5]]$variavel
[1] "vf"
[[5]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 67.009 13211.479 1280.915 3944.380
Deg. of Freedom 2 5 10 54
Residual standard error: 8.546582
Estimated effects may be unbalanced
[[5]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 67 33.5 0.459 0.6346
Dia 5 13211 2642.3 36.174 4.49e-16 ***
Inoculante:Dia 10 1281 128.1 1.754 0.0923 .
Residuals 54 3944 73.0
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[6]]
[[6]]$variavel
[1] "k"
[[6]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 0.00102289 0.05310690 0.00577267 0.00801822
Deg. of Freedom 2 5 10 54
Residual standard error: 0.01218546
Estimated effects may be unbalanced
[[6]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 0.00102 0.000511 3.444 0.03909 *
Dia 5 0.05311 0.010621 71.531 < 2e-16 ***
Inoculante:Dia 10 0.00577 0.000577 3.888 0.00052 ***
Residuals 54 0.00802 0.000148
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[7]]
[[7]]$variavel
[1] "l"
[[7]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 0.376653 7.924345 9.116099 19.973292
Deg. of Freedom 2 5 10 54
Residual standard error: 0.6081741
Estimated effects may be unbalanced
[[7]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 0.377 0.1883 0.509 0.60386
Dia 5 7.924 1.5849 4.285 0.00235 **
Inoculante:Dia 10 9.116 0.9116 2.465 0.01652 *
Residuals 54 19.973 0.3699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[8]]
[[8]]$variavel
[1] "t0.5"
[[8]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 2.2925 356.2682 59.1779 95.1347
Deg. of Freedom 2 5 10 54
Residual standard error: 1.327311
Estimated effects may be unbalanced
[[8]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 2.3 1.15 0.651 0.52576
Dia 5 356.3 71.25 40.445 < 2e-16 ***
Inoculante:Dia 10 59.2 5.92 3.359 0.00184 **
Residuals 54 95.1 1.76
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[9]]
[[9]]$variavel
[1] "mu0.5"
[[9]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 4.91450 255.15372 27.73497 38.52377
Deg. of Freedom 2 5 10 54
Residual standard error: 0.8446319
Estimated effects may be unbalanced
[[9]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 4.91 2.46 3.444 0.03909 *
Dia 5 255.15 51.03 71.531 < 2e-16 ***
Inoculante:Dia 10 27.73 2.77 3.888 0.00052 ***
Residuals 54 38.52 0.71
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
[[10]]
[[10]]$variavel
[1] "k1"
[[10]]$model
Call:
aov(formula = as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")),
data = data)
Terms:
Inoculante Dia Inoculante:Dia Residuals
Sum of Squares 10.2289 531.0690 57.7267 80.1822
Deg. of Freedom 2 5 10 54
Residual standard error: 1.218546
Estimated effects may be unbalanced
[[10]]$anova
Df Sum Sq Mean Sq F value Pr(>F)
Inoculante 2 10.2 5.11 3.444 0.03909 *
Dia 5 531.1 106.21 71.531 < 2e-16 ***
Inoculante:Dia 10 57.7 5.77 3.888 0.00052 ***
Residuals 54 80.2 1.48
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Regressões no Fator Dia
regressao_dia_all <- function(data, variaveis_dependentes) {
data$Dia_num <- as.numeric(as.character(data$Dia))
resultados_regressao <- lapply(variaveis_dependentes, function(var) {
mod_linear <- lm(as.formula(paste(var, "~ Dia_num")), data = data)
mod_quad <- lm(as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
mod_cubic <- lm(as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
list(
variavel = var,
regressao = list(
linear = summary(mod_linear),
quadratica = summary(mod_quad),
cubica = summary(mod_cubic)
)
)
})
return(resultados_regressao)
}
resultados_regressao = regressao_dia_all(data_all, variaveis_dependentes)
resultados_regressao[[1]]
[[1]]$variavel
[1] "NetGP24h"
[[1]]$regressao
[[1]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-10.4856 -2.9806 0.0139 3.1125 9.8154
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.7773 1.2046 74.529 < 2e-16 ***
Dia_num 2.1503 0.3093 6.952 1.53e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.482 on 70 degrees of freedom
Multiple R-squared: 0.4084, Adjusted R-squared: 0.4
F-statistic: 48.33 on 1 and 70 DF, p-value: 1.528e-09
[[1]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-10.5376 -2.8038 0.4224 2.7173 9.9497
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.3034 0.5286 184.073 < 2e-16 ***
poly(Dia_num, 2)1 31.1613 4.4854 6.947 1.66e-09 ***
poly(Dia_num, 2)2 4.2648 4.4854 0.951 0.345
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.485 on 69 degrees of freedom
Multiple R-squared: 0.4161, Adjusted R-squared: 0.3992
F-statistic: 24.58 on 2 and 69 DF, p-value: 8.691e-09
[[1]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-10.5313 -2.8116 0.4162 2.7198 9.9585
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.30343 0.53249 182.734 < 2e-16 ***
poly(Dia_num, 3)1 31.16129 4.51830 6.897 2.18e-09 ***
poly(Dia_num, 3)2 4.26480 4.51830 0.944 0.349
poly(Dia_num, 3)3 -0.05831 4.51830 -0.013 0.990
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.518 on 68 degrees of freedom
Multiple R-squared: 0.4161, Adjusted R-squared: 0.3903
F-statistic: 16.15 on 3 and 68 DF, p-value: 4.976e-08
[[2]]
[[2]]$variavel
[1] "MOVD"
[[2]]$regressao
[[2]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.040230 -0.007131 -0.000464 0.009477 0.030238
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7828611 0.0037050 211.30 <2e-16 ***
Dia_num 0.0169008 0.0009514 17.77 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01379 on 70 degrees of freedom
Multiple R-squared: 0.8185, Adjusted R-squared: 0.8159
F-statistic: 315.6 on 1 and 70 DF, p-value: < 2.2e-16
[[2]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.036163 -0.006843 0.000762 0.008345 0.031021
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.842014 0.001571 535.945 <2e-16 ***
poly(Dia_num, 2)1 0.244916 0.013331 18.372 <2e-16 ***
poly(Dia_num, 2)2 0.032284 0.013331 2.422 0.0181 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01333 on 69 degrees of freedom
Multiple R-squared: 0.8327, Adjusted R-squared: 0.8278
F-statistic: 171.7 on 2 and 69 DF, p-value: < 2.2e-16
[[2]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.038505 -0.005334 0.001455 0.007722 0.028082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.842014 0.001534 548.886 <2e-16 ***
poly(Dia_num, 3)1 0.244916 0.013017 18.815 <2e-16 ***
poly(Dia_num, 3)2 0.032284 0.013017 2.480 0.0156 *
poly(Dia_num, 3)3 0.027218 0.013017 2.091 0.0403 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01302 on 68 degrees of freedom
Multiple R-squared: 0.8428, Adjusted R-squared: 0.8359
F-statistic: 121.5 on 3 and 68 DF, p-value: < 2.2e-16
[[3]]
[[3]]$variavel
[1] "DMS"
[[3]]$regressao
[[3]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-46.267 -5.214 2.596 8.224 36.430
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 849.2685 3.7305 227.66 <2e-16 ***
Dia_num 17.7884 0.9579 18.57 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.88 on 70 degrees of freedom
Multiple R-squared: 0.8313, Adjusted R-squared: 0.8289
F-statistic: 344.9 on 1 and 70 DF, p-value: < 2.2e-16
[[3]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-45.681 -4.738 2.429 7.816 35.697
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 911.528 1.646 553.648 <2e-16 ***
poly(Dia_num, 2)1 257.778 13.970 18.452 <2e-16 ***
poly(Dia_num, 2)2 4.653 13.970 0.333 0.74
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.97 on 69 degrees of freedom
Multiple R-squared: 0.8315, Adjusted R-squared: 0.8267
F-statistic: 170.3 on 2 and 69 DF, p-value: < 2.2e-16
[[3]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-43.043 -6.862 2.270 7.629 32.400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 911.528 1.600 569.858 <2e-16 ***
poly(Dia_num, 3)1 257.778 13.573 18.992 <2e-16 ***
poly(Dia_num, 3)2 4.653 13.573 0.343 0.7328
poly(Dia_num, 3)3 -30.651 13.573 -2.258 0.0271 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.57 on 68 degrees of freedom
Multiple R-squared: 0.8433, Adjusted R-squared: 0.8364
F-statistic: 122 on 3 and 68 DF, p-value: < 2.2e-16
[[4]]
[[4]]$variavel
[1] "DMO"
[[4]]$regressao
[[4]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-42.887 -5.179 1.799 6.431 30.637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 850.2434 3.5112 242.15 <2e-16 ***
Dia_num 16.5558 0.9016 18.36 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.07 on 70 degrees of freedom
Multiple R-squared: 0.8281, Adjusted R-squared: 0.8256
F-statistic: 337.2 on 1 and 70 DF, p-value: < 2.2e-16
[[4]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-43.468 -5.651 1.765 6.540 31.364
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 908.19 1.55 586.117 <2e-16 ***
poly(Dia_num, 2)1 239.92 13.15 18.247 <2e-16 ***
poly(Dia_num, 2)2 -4.61 13.15 -0.351 0.727
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.15 on 69 degrees of freedom
Multiple R-squared: 0.8284, Adjusted R-squared: 0.8234
F-statistic: 166.5 on 2 and 69 DF, p-value: < 2.2e-16
[[4]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-41.548 -4.368 0.838 7.565 28.964
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 908.189 1.528 594.386 <2e-16 ***
poly(Dia_num, 3)1 239.916 12.965 18.505 <2e-16 ***
poly(Dia_num, 3)2 -4.610 12.965 -0.356 0.7233
poly(Dia_num, 3)3 -22.308 12.965 -1.721 0.0899 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.97 on 68 degrees of freedom
Multiple R-squared: 0.8356, Adjusted R-squared: 0.8283
F-statistic: 115.2 on 3 and 68 DF, p-value: < 2.2e-16
[[5]]
[[5]]$variavel
[1] "vf"
[[5]]$regressao
[[5]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-29.777 -9.195 -0.238 8.072 32.980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 142.0655 3.6560 38.858 < 2e-16 ***
Dia_num -5.1402 0.9388 -5.475 6.42e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.6 on 70 degrees of freedom
Multiple R-squared: 0.2999, Adjusted R-squared: 0.2899
F-statistic: 29.98 on 1 and 70 DF, p-value: 6.425e-07
[[5]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-20.465 -6.712 -1.543 3.620 35.308
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.075 1.228 101.032 < 2e-16 ***
poly(Dia_num, 2)1 -74.488 10.421 -7.148 7.16e-10 ***
poly(Dia_num, 2)2 73.910 10.421 7.093 9.03e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 10.42 on 69 degrees of freedom
Multiple R-squared: 0.5951, Adjusted R-squared: 0.5833
F-statistic: 50.7 on 2 and 69 DF, p-value: 2.846e-14
[[5]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-19.619 -5.981 -1.414 3.873 36.788
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 124.075 1.229 100.950 < 2e-16 ***
poly(Dia_num, 3)1 -74.488 10.429 -7.142 7.86e-10 ***
poly(Dia_num, 3)2 73.910 10.429 7.087 9.89e-10 ***
poly(Dia_num, 3)3 -9.827 10.429 -0.942 0.349
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 10.43 on 68 degrees of freedom
Multiple R-squared: 0.6003, Adjusted R-squared: 0.5827
F-statistic: 34.04 on 3 and 68 DF, p-value: 1.497e-13
[[6]]
[[6]]$variavel
[1] "k"
[[6]]$regressao
[[6]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.044735 -0.019024 0.000465 0.016430 0.050255
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.099370 0.006071 16.368 < 2e-16 ***
Dia_num 0.012382 0.001559 7.943 2.33e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02259 on 70 degrees of freedom
Multiple R-squared: 0.4741, Adjusted R-squared: 0.4665
F-statistic: 63.09 on 1 and 70 DF, p-value: 2.325e-11
[[6]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.040889 -0.007794 0.001380 0.012195 0.036363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.142708 0.002178 65.524 < 2e-16 ***
poly(Dia_num, 2)1 0.179438 0.018481 9.710 1.55e-14 ***
poly(Dia_num, 2)2 -0.110259 0.018481 -5.966 9.35e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01848 on 69 degrees of freedom
Multiple R-squared: 0.653, Adjusted R-squared: 0.643
F-statistic: 64.94 on 2 and 69 DF, p-value: < 2.2e-16
[[6]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-0.040799 -0.007924 0.001348 0.012155 0.036259
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.142708 0.002194 65.050 < 2e-16 ***
poly(Dia_num, 3)1 0.179438 0.018615 9.639 2.40e-14 ***
poly(Dia_num, 3)2 -0.110259 0.018615 -5.923 1.16e-07 ***
poly(Dia_num, 3)3 0.001214 0.018615 0.065 0.948
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01862 on 68 degrees of freedom
Multiple R-squared: 0.6531, Adjusted R-squared: 0.6378
F-statistic: 42.67 on 3 and 68 DF, p-value: 1.265e-15
[[7]]
[[7]]$variavel
[1] "l"
[[7]]$regressao
[[7]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-2.04228 -0.33838 0.06733 0.38693 1.75347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.70633 0.19433 29.364 <2e-16 ***
Dia_num -0.06126 0.04990 -1.228 0.224
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7231 on 70 degrees of freedom
Multiple R-squared: 0.02108, Adjusted R-squared: 0.007091
F-statistic: 1.507 on 1 and 70 DF, p-value: 0.2237
[[7]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-2.0510 -0.3079 0.0699 0.3717 1.7971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.49192 0.08574 64.050 <2e-16 ***
poly(Dia_num, 2)1 -0.88770 0.72757 -1.220 0.227
poly(Dia_num, 2)2 -0.27685 0.72757 -0.381 0.705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7276 on 69 degrees of freedom
Multiple R-squared: 0.02313, Adjusted R-squared: -0.00519
F-statistic: 0.8167 on 2 and 69 DF, p-value: 0.4461
[[7]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-1.87295 -0.33443 0.02747 0.43132 1.66990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.4919 0.0847 64.836 <2e-16 ***
poly(Dia_num, 3)1 -0.8877 0.7187 -1.235 0.221
poly(Dia_num, 3)2 -0.2768 0.7187 -0.385 0.701
poly(Dia_num, 3)3 -1.1822 0.7187 -1.645 0.105
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7187 on 68 degrees of freedom
Multiple R-squared: 0.0605, Adjusted R-squared: 0.01905
F-statistic: 1.46 on 3 and 68 DF, p-value: 0.2333
[[8]]
[[8]]$variavel
[1] "t0.5"
[[8]]$regressao
[[8]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-3.0434 -1.6462 -0.0949 1.5654 4.2296
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.4518 0.5154 37.742 < 2e-16 ***
Dia_num -1.1029 0.1323 -8.333 4.44e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.918 on 70 degrees of freedom
Multiple R-squared: 0.498, Adjusted R-squared: 0.4908
F-statistic: 69.45 on 1 and 70 DF, p-value: 4.44e-12
[[8]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-3.3286 -1.2179 -0.2792 1.1032 4.3261
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.5918 0.1896 82.224 < 2e-16 ***
poly(Dia_num, 2)1 -15.9819 1.6090 -9.933 6.16e-15 ***
poly(Dia_num, 2)2 8.8777 1.6090 5.517 5.61e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.609 on 69 degrees of freedom
Multiple R-squared: 0.6517, Adjusted R-squared: 0.6416
F-statistic: 64.55 on 2 and 69 DF, p-value: < 2.2e-16
[[8]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-3.4989 -1.2882 -0.2327 0.9329 4.5644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.5918 0.1897 82.204 < 2e-16 ***
poly(Dia_num, 3)1 -15.9819 1.6094 -9.930 7.27e-15 ***
poly(Dia_num, 3)2 8.8777 1.6094 5.516 5.83e-07 ***
poly(Dia_num, 3)3 -1.5827 1.6094 -0.983 0.329
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.609 on 68 degrees of freedom
Multiple R-squared: 0.6566, Adjusted R-squared: 0.6414
F-statistic: 43.33 on 3 and 68 DF, p-value: 8.978e-16
[[9]]
[[9]]$variavel
[1] "mu0.5"
[[9]]$regressao
[[9]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-3.1008 -1.3186 0.0322 1.1388 3.4834
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.8878 0.4208 16.368 < 2e-16 ***
Dia_num 0.8583 0.1081 7.943 2.33e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.566 on 70 degrees of freedom
Multiple R-squared: 0.4741, Adjusted R-squared: 0.4665
F-statistic: 63.09 on 1 and 70 DF, p-value: 2.325e-11
[[9]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-2.83424 -0.54022 0.09567 0.84527 2.52050
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.892 0.151 65.524 < 2e-16 ***
poly(Dia_num, 2)1 12.438 1.281 9.710 1.55e-14 ***
poly(Dia_num, 2)2 -7.643 1.281 -5.966 9.35e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.281 on 69 degrees of freedom
Multiple R-squared: 0.653, Adjusted R-squared: 0.643
F-statistic: 64.94 on 2 and 69 DF, p-value: < 2.2e-16
[[9]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-2.82799 -0.54927 0.09344 0.84254 2.51326
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.89178 0.15207 65.050 < 2e-16 ***
poly(Dia_num, 3)1 12.43769 1.29032 9.639 2.40e-14 ***
poly(Dia_num, 3)2 -7.64261 1.29032 -5.923 1.16e-07 ***
poly(Dia_num, 3)3 0.08413 1.29032 0.065 0.948
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.29 on 68 degrees of freedom
Multiple R-squared: 0.6531, Adjusted R-squared: 0.6378
F-statistic: 42.67 on 3 and 68 DF, p-value: 1.265e-15
[[10]]
[[10]]$variavel
[1] "k1"
[[10]]$regressao
[[10]]$regressao$linear
Call:
lm(formula = as.formula(paste(var, "~ Dia_num")), data = data)
Residuals:
Min 1Q Median 3Q Max
-4.4735 -1.9024 0.0465 1.6430 5.0255
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.9370 0.6071 16.368 < 2e-16 ***
Dia_num 1.2382 0.1559 7.943 2.33e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.259 on 70 degrees of freedom
Multiple R-squared: 0.4741, Adjusted R-squared: 0.4665
F-statistic: 63.09 on 1 and 70 DF, p-value: 2.325e-11
[[10]]$regressao$quadratica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-4.0889 -0.7794 0.1380 1.2195 3.6363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.2708 0.2178 65.524 < 2e-16 ***
poly(Dia_num, 2)1 17.9438 1.8481 9.710 1.55e-14 ***
poly(Dia_num, 2)2 -11.0259 1.8481 -5.966 9.35e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.848 on 69 degrees of freedom
Multiple R-squared: 0.653, Adjusted R-squared: 0.643
F-statistic: 64.94 on 2 and 69 DF, p-value: < 2.2e-16
[[10]]$regressao$cubica
Call:
lm(formula = as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
Residuals:
Min 1Q Median 3Q Max
-4.0799 -0.7924 0.1348 1.2155 3.6259
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.2708 0.2194 65.050 < 2e-16 ***
poly(Dia_num, 3)1 17.9438 1.8615 9.639 2.40e-14 ***
poly(Dia_num, 3)2 -11.0259 1.8615 -5.923 1.16e-07 ***
poly(Dia_num, 3)3 0.1214 1.8615 0.065 0.948
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.862 on 68 degrees of freedom
Multiple R-squared: 0.6531, Adjusted R-squared: 0.6378
F-statistic: 42.67 on 3 and 68 DF, p-value: 1.265e-15Plot PG
str(data_all)gropd_df [72 × 25] (S3: grouped_df/tbl_df/tbl/data.frame)
$ Dieta : Factor w/ 72 levels "LBLHR1D1","LBLHR1D2",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Inoculante: Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
$ Dia : Factor w/ 6 levels "1","2","3","4",..: 1 2 3 4 5 6 1 2 3 4 ...
$ Rep : num [1:72] 1 1 1 1 1 1 2 2 2 2 ...
$ T0 : num [1:72] 1.613 1.213 -0.443 -0.28 1.547 ...
$ T2 : num [1:72] 4.29 3.56 1.47 1.08 2.6 ...
$ T4 : num [1:72] 7.343 7.253 2.413 0.853 7.947 ...
$ T6 : num [1:72] 10.4 12.64 8.28 4.18 11.49 ...
$ T8 : num [1:72] 16.2 20.7 19.1 11.7 18.2 ...
$ T10 : num [1:72] 23.1 28.9 31.4 21.4 25.7 ...
$ T12 : num [1:72] 32.6 39.1 46.6 33.1 37.2 ...
$ T15 : num [1:72] 47.6 55.7 68 53.4 59 ...
$ T18 : num [1:72] 65.6 71.9 82.6 70.6 76.6 ...
$ T21 : num [1:72] 81 85.2 93.5 84.1 90.2 ...
$ T24 : num [1:72] 91.9 94.5 100.4 92.4 99.3 ...
$ NetGP24h : num [1:72] 91.9 94.5 100.4 92.4 99.3 ...
$ MOVD : num [1:72] 0.797 0.817 0.83 0.85 0.853 ...
$ DMS : num [1:72] 871 876 909 932 931 ...
$ DMO : num [1:72] 866 878 905 925 926 ...
$ vf : num [1:72] 152 128 110 109 133 ...
$ k : num [1:72] 0.0948 0.1146 0.1833 0.1665 0.1239 ...
$ l : num [1:72] 5.87 4.63 5.71 6.94 5.47 ...
$ t0.5 : num [1:72] 20.3 16.6 13.2 15.2 16.5 ...
$ mu0.5 : num [1:72] 6.57 7.94 12.71 11.54 8.59 ...
$ k1 : num [1:72] 9.48 11.46 18.33 16.65 12.39 ...
- attr(*, "groups")= tibble [72 × 3] (S3: tbl_df/tbl/data.frame)
..$ Dieta : Factor w/ 72 levels "LBLHR1D1","LBLHR1D2",..: 1 2 3 4 5 6 7 8 9 10 ...
..$ Inoculante: Factor w/ 3 levels "1","2","3": 3 3 3 3 3 3 3 3 3 3 ...
..$ .rows : list<int> [1:72]
.. ..$ : int 1
.. ..$ : int 2
.. ..$ : int 3
.. ..$ : int 4
.. ..$ : int 5
.. ..$ : int 6
.. ..$ : int 7
.. ..$ : int 8
.. ..$ : int 9
.. ..$ : int 10
.. ..$ : int 11
.. ..$ : int 12
.. ..$ : int 13
.. ..$ : int 14
.. ..$ : int 15
.. ..$ : int 16
.. ..$ : int 17
.. ..$ : int 18
.. ..$ : int 19
.. ..$ : int 20
.. ..$ : int 21
.. ..$ : int 22
.. ..$ : int 23
.. ..$ : int 24
.. ..$ : int 25
.. ..$ : int 26
.. ..$ : int 27
.. ..$ : int 28
.. ..$ : int 29
.. ..$ : int 30
.. ..$ : int 31
.. ..$ : int 32
.. ..$ : int 33
.. ..$ : int 34
.. ..$ : int 35
.. ..$ : int 36
.. ..$ : int 37
.. ..$ : int 38
.. ..$ : int 39
.. ..$ : int 40
.. ..$ : int 41
.. ..$ : int 42
.. ..$ : int 43
.. ..$ : int 44
.. ..$ : int 45
.. ..$ : int 46
.. ..$ : int 47
.. ..$ : int 48
.. ..$ : int 49
.. ..$ : int 50
.. ..$ : int 51
.. ..$ : int 52
.. ..$ : int 53
.. ..$ : int 54
.. ..$ : int 55
.. ..$ : int 56
.. ..$ : int 57
.. ..$ : int 58
.. ..$ : int 59
.. ..$ : int 60
.. ..$ : int 61
.. ..$ : int 62
.. ..$ : int 63
.. ..$ : int 64
.. ..$ : int 65
.. ..$ : int 66
.. ..$ : int 67
.. ..$ : int 68
.. ..$ : int 69
.. ..$ : int 70
.. ..$ : int 71
.. ..$ : int 72
.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUEm1=lm(NetGP24h~as.numeric(Dia),data=data_all)
summary(m1)
Call:
lm(formula = NetGP24h ~ as.numeric(Dia), data = data_all)
Residuals:
Min 1Q Median 3Q Max
-10.4856 -2.9806 0.0139 3.1125 9.8154
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 89.7773 1.2046 74.529 < 2e-16 ***
as.numeric(Dia) 2.1503 0.3093 6.952 1.53e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.482 on 70 degrees of freedom
Multiple R-squared: 0.4084, Adjusted R-squared: 0.4
F-statistic: 48.33 on 1 and 70 DF, p-value: 1.528e-09plot1 = ggplot(data_all, aes(x = as.numeric(Dia), y = NetGP24h, group = Inoculante, color = as.factor(Inoculante))) +
geom_smooth(method = 'lm', formula = y ~ x, se = TRUE, size = 0.25, aes(linetype = as.factor(Inoculante))) +
geom_point(size = 1) +
scale_x_continuous(name = expression(paste("Tempo de fermentação (Dias)")),
breaks = 1:6,labels = c("15 dias", "30 dias", "45 dias", "60 dias", "75 dias", "90 dias"))+
scale_y_continuous(name = expression(paste("Produção de gás (mL ", g^-1, "MS)")),breaks = seq(40, 140, 20),limits = c(40, 140))+
scale_color_manual(name = "Inoculante",values = c("blue4", "red4", "green4"),labels = c("TC", "LB", "LHLB"))+
scale_linetype_manual(name = "Inoculante",values = c(1, 1, 1),labels = c("TC", "LB", "LHLB"))
plot1
plot2=plot1+ theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid"),
panel.background = element_rect(fill = "transparent"),
axis.ticks = element_line(colour = "black", size = 0.25),
axis.title.x = element_text(size = 14, color = "black"),
axis.title.y = element_text(size = 14, color = "black"),
axis.text.x = element_text(size = 12, angle = 25, hjust = 1, color = "black"),
axis.text.y = element_text(size = 12, color = "black"),
legend.background = element_rect(fill = "transparent", size=0.5, linetype="solid",colour ="black"),
legend.position = c(0.15, 0.85),legend.key.size = unit(0.42, 'cm'),
legend.text = element_text(size = 13),
legend.title = element_text(size = 14))+
annotate(geom="text", y=70, x=3.5,label=expression(paste("y = 2.15x + 89.7 ", R^2, " = 0.41")),size=5,color="black")+
annotate("text",size=5, x=5, y=140,label="P values")+
annotate("text",size=4, x=5, y=133,label= "Inoculante = 0.080")+
annotate("text",size=4, x=5, y=127,label= "Dias = <0.01")+
annotate("text",size=4, x=5, y=121,label= "I * D = 0.054")+
coord_fixed(ratio = 6/140)
plot2
ggsave("Plot_PG.png", plot2, width = 6, height = 5, units = "in", dpi = 300)
Desdobramento de Inoculo vs Dia
DMO=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$DMO,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 313 4 1.228 0.30101
Dias 5 58246 2 91.299 0.00000
Inoculante*Dias 10 4059 5 3.181 0.00284
Residuo 54 6890 3
Total 71 69509 1
------------------------------------------------------------------------
CV = 1.24 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.1408589
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 885.375
b 2 865.0325
b 3 854.6208
------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 905.7425
ab 2 900.345
b 1 883.2533
------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 6 941.5967
a 5 931.1375
a 4 918.6
b 1 885.375
b 3 883.2533
b 2 871.5608
------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 6 947.74
ab 5 939.0742
bc 4 916.115
cd 3 900.345
de 2 888.0675
e 1 865.0325
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 6 951.0258
a 5 934.9525
a 4 929.7917
b 3 905.7425
b 2 883.3633
c 1 854.6208
------------------------------------------------------------------------LAG=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$l,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 0.377 2 0.5092 0.60386
Dias 5 7.924 5 4.2849 0.00235
Inoculante*Dias 10 9.116 4 2.4646 0.01652
Residuo 54 19.973 3
Total 71 37.390 1
------------------------------------------------------------------------
CV = 11.07 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.8117588
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 5.907296
b 2 4.862305
b 3 4.351375
------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 6.099995
a 4 6.079671
a 3 5.440338
a 6 5.211431
a 2 4.862305
a 5 4.840571
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 4 6.164046
a 3 6.061047
a 1 5.772523
a 5 5.688424
ab 6 5.512526
b 2 4.351375
------------------------------------------------------------------------LAG.mean= data_all %>%group_by(Inoculante, Dia) %>% summarise(Media = mean(l))`summarise()` has grouped output by 'Inoculante'. You can override using the `.groups` argument.LAG.mean
LAG=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$l,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 0.377 2 0.5092 0.60386
Dias 5 7.924 5 4.2849 0.00235
Inoculante*Dias 10 9.116 4 2.4646 0.01652
Residuo 54 19.973 3
Total 71 37.390 1
------------------------------------------------------------------------
CV = 11.07 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.8117588
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 5.907296
b 2 4.862305
b 3 4.351375
------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 6.099995
a 4 6.079671
a 3 5.440338
a 6 5.211431
a 2 4.862305
a 5 4.840571
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 4 6.164046
a 3 6.061047
a 1 5.772523
a 5 5.688424
ab 6 5.512526
b 2 4.351375
------------------------------------------------------------------------LAG.mean= data_all %>%group_by(Inoculante, Dia) %>% summarise(Media = mean(l))`summarise()` has grouped output by 'Inoculante'. You can override using the `.groups` argument.LAG.mean
t0.5=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$t0.5,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 2.29 2 0.651 0.52576
Dias 5 356.27 5 40.445 0.00000
Inoculante*Dias 10 59.18 4 3.359 0.00184
Residuo 54 95.13 3
Total 71 512.87 1
------------------------------------------------------------------------
CV = 8.51 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.08380287
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 19.82492
b 2 17.43285
b 3 15.3988
------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 2 19.82492
a 1 18.6488
b 6 14.80994
b 3 14.46717
b 4 13.82384
b 5 13.46908
------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 19.99873
a 2 17.43285
b 3 14.22521
b 5 14.19476
b 4 14.00767
b 6 13.1631
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 20.34552
b 5 15.5183
b 2 15.3988
b 4 13.99989
b 3 13.67225
b 6 13.65139
------------------------------------------------------------------------K=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$k1,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 10.23 4 3.444 0.039095
Dias 5 531.07 3 71.531 0.000000
Inoculante*Dias 10 57.73 5 3.888 0.000520
Residuo 54 80.18 2
Total 71 679.21 1
------------------------------------------------------------------------
CV = 8.54 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.5788561
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 12.39519
ab 2 11.16172
b 1 9.826557
------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 17.97526
b 2 15.6679
b 1 15.37956
------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 16.3278
ab 2 14.90192
b 3 14.03791
------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 2 17.31037
a 3 16.81012
b 1 14.23329
------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 5 16.3278
a 4 16.2701
a 3 15.37956
a 6 14.23329
b 1 10.57198
b 2 9.826557
------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 6 17.31037
a 4 17.2504
a 3 15.6679
a 5 14.90192
b 2 11.16172
b 1 9.917906
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 17.97526
a 4 17.45489
a 6 16.81012
b 5 14.03791
b 2 12.39519
c 1 9.38189
------------------------------------------------------------------------mu0.5=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$mu0.5,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)------------------------------------------------------------------------
Legenda:
FATOR 1: Inoculante
FATOR 2: Dias
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------
GL SQ QM Fc Pr>Fc
Inoculante 2 4.91 3 3.444 0.039095
Dias 5 255.15 5 71.531 0.000000
Inoculante*Dias 10 27.73 4 3.888 0.000520
Residuo 54 38.52 2
Total 71 326.33 1
------------------------------------------------------------------------
CV = 8.54 %
------------------------------------------------------------------------
Teste de normalidade dos residuos (Shapiro-Wilk)
valor-p: 0.5788561
De acordo com o teste de Shapiro-Wilk a 5% de significancia, os residuos podem ser considerados normais.
------------------------------------------------------------------------
Interacao significativa: desdobrando a interacao
------------------------------------------------------------------------
Desdobrando Inoculante dentro de cada nivel de Dias
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 1 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 2 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 8.591694
ab 2 7.736717
b 1 6.81125
------------------------------------------------------------------------
Inoculante dentro do nivel 3 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 12.4595
b 2 10.86016
b 1 10.6603
------------------------------------------------------------------------
Inoculante dentro do nivel 4 de Dias
De acordo com o teste F, as medias desse fator sao estatisticamente iguais.
------------------------------------------------------------------------------------------------------------------------------------------------
Inoculante dentro do nivel 5 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 1 11.31757
ab 2 10.32922
b 3 9.73034
------------------------------------------------------------------------
Inoculante dentro do nivel 6 de Dias
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 2 11.99863
a 3 11.65189
b 1 9.865762
------------------------------------------------------------------------
Desdobrando Dias dentro de cada nivel de Inoculante
------------------------------------------------------------------------
------------------------------------------------------------------------
Quadro da analise de variancia
------------------------------------------------------------------------------------------------------------------------------------------------
Dias dentro do nivel 1 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 5 11.31757
a 4 11.27757
a 3 10.6603
a 6 9.865762
b 1 7.327935
b 2 6.81125
------------------------------------------------------------------------
Dias dentro do nivel 2 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 6 11.99863
a 4 11.95706
a 3 10.86016
a 5 10.32922
b 2 7.736717
b 1 6.874568
------------------------------------------------------------------------
Dias dentro do nivel 3 de Inoculante
------------------------------------------------------------------------
Teste de Tukey
------------------------------------------------------------------------
Grupos Tratamentos Medias
a 3 12.4595
a 4 12.09881
a 6 11.65189
b 5 9.73034
b 2 8.591694
c 1 6.503031
------------------------------------------------------------------------Exportar dados
---
title: "Dados de produção de gás In vitro - Deborah UFPA"
author: "Vagner Ovani; Lumena Takahashi; Simón Pérez-Marquez"
date: "02/12/2024"
output:
  html_notebook:
    toc: TRUE
    toc_depth: 2
    theme: united
---

# _*1 - PACOTES*_

```{r}
library(ggplot2)
library(qpcR)
library(dplyr)
library(tibble)
library(openxlsx)
library(tidyr)
library(tidyverse)

library(emmeans)#medias e teste de media para modelo misto
library(multcompView) #variancia
library(multcomp)
```

# _*2 - Dados*_

```{r}
dados = read.csv("D:/Armazenamento/DATA R/Deborah UFPA/data.csv")
View(dados)
str(dados)
dados$Dieta=as.factor(dados$Dieta)


#Filtro
datamodels= dados %>%
  select(Dieta, T0:T24) %>%          
  group_by(Dieta) %>%                
  summarise(across(everything(), mean, na.rm = TRUE))
print(datamodels)

#Inversão
data_long <- datamodels %>%
  pivot_longer(cols = starts_with("T"), 
               names_to = "Time", 
               values_to = "PG") %>%
  mutate(Time = as.numeric(gsub("T", "", Time))) %>%
  pivot_wider(names_from = Dieta, values_from = PG)
head(data_long)
```

# _*3 - SHAPE DATA - LONG FORMAT*_

```{r}
data_long1 <- gather(data_long, key = "id", value = "pg", -Time) #invertendo novamente, para formato longo
data_long1 <- drop_na(data_long1) #remoção de NA
data_long1 <- data_long1 %>% filter(pg >= 0) #valores negativos
print(data_long1)
data_long_filtro <- subset(data_long1, !Time %in% c(6, 10, 12,18,21)) #Reduzindo alguns tempos desnecessarios
data_long_filtro
```
> The format for this code to work is wide format, with every column being a substrate.

# _*4 - Ranking dos modelos*_

## _*Exponencial*_

```{r}
exp <- nls(pg ~ (vf*(1-exp(-k*Time))),
          data = data_long_filtro,
          start = c(vf= 100, k = 0.05),
          algorithm = "port",
           lower = c(1, 0.001), 
           upper = c(300, 0.5),
          control = nls.control(maxiter = 500, minFactor = 1e-10))

exp_stat_criteria <- as.data.frame(c(deviance(exp), AICc(exp), BIC(exp), Rsq.ad(exp), RMSE(exp)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(exp_stat_criteria) <- "Exponencial"
```

## _*explagonencial*_

```{r}
explag <- nls(pg ~ (vf*(1-exp(-k*(Time-LAG)))),
          data = data_long_filtro,
          start = c(vf= 200, k = 0.05, LAG = 0.5),
          algorithm = "port",
           lower = c(1, 0.001, 0.01), 
           upper = c(300, 0.5, 1),
          control = nls.control(maxiter = 500, minFactor = 1e-10)
          )

explag_stat_criteria <- as.data.frame(c(deviance(explag), AICc(explag), BIC(explag), Rsq.ad(explag), RMSE(explag)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(explag_stat_criteria) <- "explagonencial"
```

## _*Gompertz*_

```{r}
gom <- nls(pg ~ (vf*exp(-exp(1-k*(Time-l)))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf= 200, k = 0.05, l = 0.1),
          lower = c(0,0,0))

gom_stat_criteria <- as.data.frame(c(deviance(gom), AICc(gom), BIC(gom), Rsq.ad(gom), RMSE(gom)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(gom_stat_criteria) <- "Gompertz"
```

## _*France*_

```{r}
fra <- nls(pg ~ vf*(1-exp(-k*(Time-l)-d*(sqrt(Time)-sqrt(l)))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf= 100, k = 0.01, l = 0, d = 0.01),
          lower = c(0, 0, 0, 0),
          upper = c(200, 1, 1, 1)
          )

fra_stat_criteria <- as.data.frame(c(deviance(fra), AICc(fra), BIC(fra), Rsq.ad(fra), RMSE(fra)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(fra_stat_criteria) <- "France"
```

## _*Schofield U*_

```{r}
schu <- nls(pg ~ vf/(1+exp(2-4*k*(Time-l))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf= 100, k = 0.05, l = 0.2))

schu_stat_criteria <- as.data.frame(c(deviance(schu), AICc(schu), BIC(schu), Rsq.ad(schu), RMSE(schu)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(schu_stat_criteria) <- "Schofield U"
```

## _*Wang*_

```{r}
wan <- nls(pg ~ vf*((1-exp(-k*Time))/(1+exp(log(1/d)-k*Time))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf=200, k = 0.01, d = 1),
          lower = c(0,0,0.1),
          upper = c(300,0.5,10)
          )

wan_stat_criteria <- as.data.frame(c(deviance(wan), AICc(wan), BIC(wan), Rsq.ad(wan), RMSE(wan)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(wan_stat_criteria) <- "Wang"
```

## _*Wang L*_

```{r}
wanl <- nls(pg ~ vf*(1-exp(-k*(Time-l)))/(1+exp(log(1/d)-k*(Time-l))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf=200, k = 0.05, d = 0.5, l = 0.5),
          lower = c(0, 0, 0, 0),
          upper = c(200, 1, 1, 1)
          )

wanl_stat_criteria <- as.data.frame(c(deviance(wanl), AICc(wanl), BIC(wanl), Rsq.ad(wanl), RMSE(wanl)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(wanl_stat_criteria) <- "Wang L"
```

## _*Abreu*_

```{r}
abr <- nls(pg ~ vf1*(1-exp(-k1*Time))+vf2*exp(-exp(1+k2*exp(1)*(L-Time))),
          data = data_long_filtro,
          algorithm = "port",
          start = c(vf1 = 155, k1 = 0.005, L = 0.5, vf2 = 50, k2 = 0.05),
          lower = c(0,0,0,0,0),
          upper = c(300,0.5,1,300,0.5)
          )

abr_stat_criteria <- as.data.frame(c(deviance(abr), AICc(abr), BIC(abr), Rsq.ad(abr), RMSE(abr)),  row.names = c("RSS","AIC","BIC","RSQ","RMSE" ))
colnames(abr_stat_criteria) <- "Abreu"
```

# _*5 - Resultado Criterios Estatisticos*_

```{r echo=FALSE}
stat_criteria_results <- cbind(abr_stat_criteria,
                               exp_stat_criteria,
                               explag_stat_criteria,
                               fra_stat_criteria,
                               gom_stat_criteria,
                               schu_stat_criteria,
                               wan_stat_criteria,
                               wanl_stat_criteria)
t(stat_criteria_results) #t from transpose

preditos <- cbind(data_long_filtro,
      as.data.frame(predict(exp)),
      as.data.frame(predict(explag)),
      as.data.frame(predict(gom)),
      as.data.frame(predict(fra)),
      as.data.frame(predict(schu)),
      as.data.frame(predict(wan)),
      as.data.frame(predict(wanl)),
      as.data.frame(predict(abr)))
colnames(preditos) <- c("t","assay","obs", "exp", "explag", "gom", "fra", "schu", "wan", "wanl", "abr")
preditos

preditos %>% pivot_longer(c(3:11), names_to = "modelo",values_to = "pg") -> long
print(long)
long$modelo <- as.factor(long$modelo)
long$assay <- as.factor(long$assay)
str(long)

write.xlsx(long, file = "D:/Armazenamento/DATA R/Deborah UFPA/long.xlsx", sheetName = "Dados", rownames = FALSE)
```

# _*6 - Plot*_

```{r echo=FALSE}
ggplot(long, aes(t, pg, linetype = modelo, colour = modelo, group = modelo))+
  geom_point()+
  geom_line()+
  facet_wrap(~ modelo, scales = "free") +  
  theme_minimal() +
  labs(x = "Tempo (h)", y = "PG", title = "Gráfico dos Modelos") +
  theme(legend.position = "bottom") 
```

# _*7 - Avaliação dos Modelos - Ranking*_


```{r}
criteria <- as.data.frame(t(stat_criteria_results))
criteria <- rownames_to_column(criteria, "criteria")
criteria

write.xlsx(criteria, file = "D:/Armazenamento/DATA R/Deborah UFPA/criteria.xlsx", sheetName = "Dados", rownames = FALSE)

criteria %>%
   mutate(across(c(RSS:RMSE), rank)) -> ranked
ranked$TOT <- (ranked$RSS+ranked$AIC+ranked$BIC+ranked$RMSE-ranked$RSQ)
ranked

write.xlsx(ranked, file = "D:/Armazenamento/DATA R/Deborah UFPA/ranked.xlsx", sheetName = "Dados", rownames = FALSE)
```

>Gompertz foi o melhor modelo.

# _*8 - Aplicando o modelo Gompertz para análise do perfil de fermentação*_

$\displaystyle V= vf*exp^(-exp^{1-k(t-l)})$

```{r}
data_long = drop_na(data_long)
print(data_long)

mod_exp_pg <- lapply(names(data_long)[c(2:73)],
                   function(s){
                     nls(substitute(p ~ (vf*exp(-exp(1-k*(Time-l)))),
                                    list(p = as.name(s))),
                         data = data_long,
                         algorithm = "port",
          start = c(vf= 200, k = 0.05, l = 0.1),
          lower = c(0,0,0))
                     }
                   )

results_pg<-as.data.frame(lapply(mod_exp_pg,coef))
names(results_pg) <- names(data_long[2:73])
results_pg <- as.data.frame(t(results_pg))
results_pg <- rownames_to_column(results_pg, "substrate")
View(results_pg)
```

# _*Calcular t0.5*_

```{r}
results_pg = results_pg %>% mutate(t0.5 = l + (1 - log(log(2))) / k)
results_pg
```

# _*Calcular o mu0.5*_

```{r}
results_pg = results_pg %>% mutate(mu0.5 = log(2) * k)
results_pg$mu0.5=results_pg$mu0.5*100

#k= fractional rate of gas production
results_pg$k1=results_pg$k*100 #PORCENTAGEM

print(results_pg)
```

# _*Juntar dados*_

```{r}
#Filtro
dados.total= dados %>%
  group_by(Dieta,Inoculante,Dia) %>%                
  summarise(across(everything(), mean, na.rm = TRUE))
print(dados.total)


data_combinado = cbind(dados.total, results_pg)
View(data_combinado)
```

# _*9 - Análise estatistica*_

## _*Anova*_ 

```{r}
data_all=data_combinado[,-20]
str(data_all)
data_all$Inoculante <- as.factor(data_all$Inoculante)
data_all$Dia <- as.factor(data_all$Dia)

# Filtrar nomes das colunas numéricas 
variaveis_dependentes = names(data_all)[sapply(data_all, is.numeric) & !(names(data_all) %in% names(data_all)[1:15])]
variaveis_dependentes

#Função para ANOVA
anova_model_all <- function(data, variaveis_dependentes) {
  resultados_anova <- lapply(variaveis_dependentes, function(var) {
    mod <- aov(as.formula(paste(var, "~ Inoculante + Dia + Inoculante*Dia")), data = data)
    anova_res <- summary(mod)
    list(
      variavel = var,
      model = mod,
      anova = anova_res
    )
  })
  return(resultados_anova)
}
resultados_anova <- anova_model_all(data_all, variaveis_dependentes)
resultados_anova
```

## _*Regressões no Fator Dia*_ 

```{r}
regressao_dia_all <- function(data, variaveis_dependentes) {
  data$Dia_num <- as.numeric(as.character(data$Dia))
  resultados_regressao <- lapply(variaveis_dependentes, function(var) {
    mod_linear <- lm(as.formula(paste(var, "~ Dia_num")), data = data)
    mod_quad <- lm(as.formula(paste(var, "~ poly(Dia_num, 2)")), data = data)
    mod_cubic <- lm(as.formula(paste(var, "~ poly(Dia_num, 3)")), data = data)
    list(
      variavel = var,
      regressao = list(
        linear = summary(mod_linear),
        quadratica = summary(mod_quad),
        cubica = summary(mod_cubic)
      )
    )
  })
  return(resultados_regressao)
}

resultados_regressao = regressao_dia_all(data_all, variaveis_dependentes)
resultados_regressao
```

## _*Plot PG*_ 

```{r}
str(data_all)

m1=lm(NetGP24h~as.numeric(Dia),data=data_all)
summary(m1)

plot1 = ggplot(data_all, aes(x = as.numeric(Dia), y = NetGP24h, group = Inoculante, color = as.factor(Inoculante))) +
  geom_smooth(method = 'lm', formula = y ~ x, se = TRUE, size = 0.25, aes(linetype = as.factor(Inoculante))) + 
  geom_point(size = 1) +
  scale_x_continuous(name = expression(paste("Tempo de fermentação (Dias)")), 
                     breaks = 1:6,labels = c("15 dias", "30 dias", "45 dias", "60 dias", "75 dias", "90 dias"))+
  scale_y_continuous(name = expression(paste("Produção de gás (mL ", g^-1, "MS)")),breaks = seq(40, 140, 20),limits = c(40, 140))+
  scale_color_manual(name = "Inoculante",values = c("blue4", "red4", "green4"),labels = c("TC", "LB", "LHLB"))+
  scale_linetype_manual(name = "Inoculante",values = c(1, 1, 1),labels = c("TC", "LB", "LHLB"))
plot1
plot2=plot1+ theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid"),
  panel.background = element_rect(fill = "transparent"),
  axis.ticks = element_line(colour = "black", size = 0.25),
  axis.title.x = element_text(size = 14, color = "black"),
  axis.title.y = element_text(size = 14, color = "black"),
  axis.text.x = element_text(size = 12,  angle = 25, hjust = 1, color = "black"),
  axis.text.y = element_text(size = 12, color = "black"),
  legend.background = element_rect(fill = "transparent", size=0.5, linetype="solid",colour ="black"),
  legend.position = c(0.15, 0.85),legend.key.size = unit(0.42, 'cm'),
  legend.text = element_text(size = 13),
  legend.title = element_text(size = 14))+
annotate(geom="text", y=70, x=3.5,label=expression(paste("y = 2.15x + 89.7  ", R^2, " = 0.41")),size=5,color="black")+
annotate("text",size=5, x=5, y=140,label="P values")+
annotate("text",size=4, x=5, y=133,label= "Inoculante = 0.080")+
annotate("text",size=4, x=5, y=127,label= "Dias = <0.01")+
annotate("text",size=4, x=5, y=121,label= "I * D = 0.054")+
coord_fixed(ratio = 6/140)
plot2

ggsave("Plot_PG.png", plot2, width = 6, height = 5, units = "in", dpi = 300)
```

## _*Desdobramento de Inoculo vs Dia*_

```{r}

DMO=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$DMO,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)

LAG=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$l,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)
LAG.mean= data_all %>%group_by(Inoculante, Dia) %>% summarise(Media = mean(l))
LAG.mean

LAG=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$l,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)
LAG.mean= data_all %>%group_by(Inoculante, Dia) %>% summarise(Media = mean(l))
LAG.mean

t0.5=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$t0.5,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)

K=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$k1,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)

mu0.5=fat2.dic(data_all$Inoculante,data_all$Dia, data_all$mu0.5,quali = c(TRUE, TRUE),mcomp = "tukey",fac.names = c("Inoculante", "Dias"),sigT = 0.05,sigF = 0.05,unfold = NULL)
```

## _*Exportar dados*_

```{r}
write.xlsx(data_combinado, file = "D:/Armazenamento/DATA R/Deborah UFPA/Dados UFPA.xlsx", sheetName = "Dados", rownames = FALSE)
```




