Medidas repetidas en 2 vías
2024-10-02
\[\text{Valeria Riveros Parra y Juan Felipe Paz Paiba}\] \[\text{Analisis de varianza para medidas repetidas de dos vías}\]
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.2.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.2.3library(ggplot2)
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## expand, pack, unpack# Datos en formato ancho
data_2= read_xlsx ("C:/Users/juanf/Downloads/m_repetidas.xlsx", sheet= "biomasa_2")
data_2## # A tibble: 30 × 14
## dosis_fert `0_días` `05_días` `10_días` `15_días` `20_días` `25_días`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 A 0.0001 2.95 3.31 3.12 3.27 3.86
## 2 A 0.0001 2.53 3.02 3.35 3.41 3.73
## 3 A 0.0001 2.68 3.07 3.39 2.72 3.52
## 4 A 0.0001 2.77 2.91 3.11 3.42 2.99
## 5 A 0.0001 2.83 3.15 3.46 3.15 3.08
## 6 A 0.0001 2.88 3.18 3.27 3.75 3.24
## 7 A 0.0001 2.92 3.21 2.67 3.37 3.81
## 8 A 0.0001 2.86 2.96 3.24 2.9 3.45
## 9 A 0.0001 2.99 3.27 3.04 3.51 3.03
## 10 A 0.0001 3.02 3.14 3.19 3.44 3.57
## # ℹ 20 more rows
## # ℹ 7 more variables: `30_días` <dbl>, `35_días` <dbl>, `40_días` <dbl>,
## # `45_días` <dbl>, `50_días` <dbl>, `55_días` <dbl>, `60_días` <dbl># Gather the columns t1, t2 and t3 into long format.
# Convert id and time into factor variables
data_2_larga <- data_2 %>%
gather(key = "time", value = "biomasa",`0_días`, `05_días`, `10_días`, `15_días`, `20_días`, `25_días`, `30_días`, `35_días`, `40_días`, `45_días`, `50_días`, `55_días`, `60_días`) %>%
convert_as_factor(time)
head(data_2_larga)## # A tibble: 6 × 3
## dosis_fert time biomasa
## <chr> <fct> <dbl>
## 1 A 0_días 0.0001
## 2 A 0_días 0.0001
## 3 A 0_días 0.0001
## 4 A 0_días 0.0001
## 5 A 0_días 0.0001
## 6 A 0_días 0.0001# Inspect some random rows of the data by groups
set.seed(123)
data_2_larga %>% sample_n_by(dosis_fert, time, size = 1)## # A tibble: 39 × 3
## dosis_fert time biomasa
## <chr> <fct> <dbl>
## 1 A 0_días 0.0001
## 2 A 05_días 2.68
## 3 A 10_días 3.14
## 4 A 15_días 3.35
## 5 A 20_días 3.75
## 6 A 25_días 3.08
## 7 A 30_días 3.60
## 8 A 35_días 3.64
## 9 A 40_días 4.59
## 10 A 45_días 4.13
## # ℹ 29 more rowsdata_2_larga %>%
group_by(dosis_fert, time) %>%
get_summary_stats(biomasa, type = "mean_sd")## # A tibble: 39 × 6
## dosis_fert time variable n mean sd
## <chr> <fct> <fct> <dbl> <dbl> <dbl>
## 1 A 0_días biomasa 10 0 0
## 2 A 05_días biomasa 10 2.84 0.15
## 3 A 10_días biomasa 10 3.12 0.133
## 4 A 15_días biomasa 10 3.18 0.224
## 5 A 20_días biomasa 10 3.30 0.301
## 6 A 25_días biomasa 10 3.43 0.326
## 7 A 30_días biomasa 10 3.69 0.27
## 8 A 35_días biomasa 10 3.78 0.221
## 9 A 40_días biomasa 10 4.32 0.24
## 10 A 45_días biomasa 10 4.47 0.361
## # ℹ 29 more rowslibrary(collapsibleTree)## Warning: package 'collapsibleTree' was built under R version 4.2.3# Crear un árbol colapsable
tree <- collapsibleTree(data_2_larga, c("time", "dosis_fert","biomasa"), collapsed = TRUE)
# Visualizar el árbol colapsable
treebxp <- ggboxplot(
data_2_larga, x = "time", y = "biomasa",
color = "dosis_fert", palette = "Zissou1"
)
bxp
data_2_larga %>%
group_by(dosis_fert, time) %>%
identify_outliers(biomasa)## # A tibble: 8 × 5
## dosis_fert time biomasa is.outlier is.extreme
## <chr> <fct> <dbl> <lgl> <lgl>
## 1 A 05_días 2.53 TRUE FALSE
## 2 A 15_días 2.67 TRUE FALSE
## 3 A 20_días 2.72 TRUE FALSE
## 4 A 60_días 5.10 TRUE FALSE
## 5 B 30_días 7.41 TRUE FALSE
## 6 B 45_días 8.79 TRUE FALSE
## 7 C 45_días 24.6 TRUE FALSE
## 8 C 45_días 15.7 TRUE FALSEshapiro.test(data_2_larga$biomasa)##
## Shapiro-Wilk normality test
##
## data: data_2_larga$biomasa
## W = 0.908, p-value = 1.2e-14ggqqplot(data_2_larga, "biomasa", ggtheme = theme_bw()) +
facet_grid(time ~ dosis_fert, labeller = "label_both")
#aplicar logaritmos para evitar datos atipicos en la biomasa
data_2_larga$log_biomasa= log(data_2_larga$biomasa)
data_2_larga %>%
group_by(dosis_fert, time) %>%
identify_outliers(log_biomasa)## # A tibble: 10 × 6
## dosis_fert time biomasa log_biomasa is.outlier is.extreme
## <chr> <fct> <dbl> <dbl> <lgl> <lgl>
## 1 A 05_días 2.53 0.928 TRUE FALSE
## 2 A 15_días 2.67 0.982 TRUE FALSE
## 3 A 20_días 2.72 1.00 TRUE FALSE
## 4 A 60_días 5.10 1.63 TRUE FALSE
## 5 B 30_días 7.41 2.00 TRUE FALSE
## 6 B 45_días 8.79 2.17 TRUE FALSE
## 7 B 50_días 8.36 2.12 TRUE FALSE
## 8 C 15_días 7.86 2.06 TRUE FALSE
## 9 C 45_días 24.6 3.20 TRUE FALSE
## 10 C 45_días 15.7 2.75 TRUE FALSEshapiro.test(data_2_larga$log_biomasa)##
## Shapiro-Wilk normality test
##
## data: data_2_larga$log_biomasa
## W = 0.47889, p-value < 2.2e-16ggqqplot(data_2_larga, "log_biomasa", ggtheme = theme_bw()) +
facet_grid(time ~ dosis_fert, labeller = "label_both")
#aplicar raíz cuadrada para evitar datos atipicos en la biomasa
data_2_larga$sqrt_biomasa= sqrt(data_2_larga$biomasa)
data_2_larga %>%
group_by(dosis_fert, time) %>%
identify_outliers(sqrt_biomasa)## # A tibble: 9 × 7
## dosis_fert time biomasa log_biomasa sqrt_biomasa is.outlier is.extreme
## <chr> <fct> <dbl> <dbl> <dbl> <lgl> <lgl>
## 1 A 05_días 2.53 0.928 1.59 TRUE FALSE
## 2 A 15_días 2.67 0.982 1.63 TRUE FALSE
## 3 A 20_días 2.72 1.00 1.65 TRUE FALSE
## 4 A 60_días 5.10 1.63 2.26 TRUE FALSE
## 5 B 30_días 7.41 2.00 2.72 TRUE FALSE
## 6 B 45_días 8.79 2.17 2.96 TRUE FALSE
## 7 C 15_días 7.86 2.06 2.80 TRUE FALSE
## 8 C 45_días 24.6 3.20 4.96 TRUE FALSE
## 9 C 45_días 15.7 2.75 3.96 TRUE FALSEshapiro.test(data_2_larga$sqrt_biomasa)##
## Shapiro-Wilk normality test
##
## data: data_2_larga$sqrt_biomasa
## W = 0.9546, p-value = 1.354e-09ggqqplot(data_2_larga, "sqrt_biomasa", ggtheme = theme_bw()) +
facet_grid(time ~ dosis_fert, labeller = "label_both")
nrow(data_2_larga)## [1] 390data_2_larga$id= rep(seq(1,10),39)# ANOVA
res.aov2C <- anova_test(data = data_2_larga, dv = log_biomasa, wid = id,
within = time, between = dosis_fert)## Warning: The 'wid' column contains duplicate ids across between-subjects
## variables. Automatic unique id will be createdres.aov2C## ANOVA Table (type II tests)
##
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 dosis_fert 2 27 15847.65 3.59e-42 * 0.977
## 2 time 12 324 38726.64 0.00e+00 * 0.999
## 3 dosis_fert:time 24 324 68.45 1.38e-111 * 0.830
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 time 3.29e-05 2.12e-17 *
## 2 dosis_fert:time 3.29e-05 2.12e-17 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF]
## 1 time 0.462 5.55, 149.85 3.96e-234 * 0.596 7.15, 193.08
## 2 dosis_fert:time 0.462 11.1, 149.85 6.54e-53 * 0.596 14.3, 193.08
## p[HF] p[HF]<.05
## 1 6.24e-301 *
## 2 1.72e-67 *# Obtener la tabla de ANOVA
get_anova_table(res.aov2C)## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dosis_fert 2.00 27.00 15847.65 3.59e-42 * 0.977
## 2 time 5.55 149.85 38726.64 3.96e-234 * 0.999
## 3 dosis_fert:time 11.10 149.85 68.45 6.54e-53 * 0.830# Hay 3 hipotesis: Factor, Tiempo e interacción
# Hay interacción
# Hay efecto del tiempo; Hay efecto del tratamiento# Effect of time at each level of treatment
one.way2 <- data_2_larga %>%
group_by(dosis_fert) %>%
anova_test(dv = log_biomasa, wid = id, within = time) %>%
get_anova_table() %>%
adjust_pvalue(method = "bonferroni")
one.way2## # A tibble: 3 × 9
## dosis_fert Effect DFn DFd F p `p<.05` ges p.adj
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 A time 12 108 18719. 3.26e-173 * 0.999 9.78e-173
## 2 B time 12 108 19325. 5.84e-174 * 1 1.75e-173
## 3 C time 12 108 8438. 1.53e-154 * 0.999 4.59e-154# Convertir las columnas a factores si es necesario
data_2_larga$time <- as.factor(data_2_larga$time)
data_2_larga$dosis_fert <- as.factor(data_2_larga$dosis_fert)
# Realizar el ANOVA para la dosis de fertilización
modelo_anova_dosis <- aov(log_biomasa ~ dosis_fert, data = data_2_larga)
summary(modelo_anova_dosis)## Df Sum Sq Mean Sq F value Pr(>F)
## dosis_fert 2 111 55.39 5.996 0.00273 **
## Residuals 387 3575 9.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Realizar la prueba de comparaciones múltiples de Tukey para la dosis de fertilización
tukey_result_dosis <- TukeyHSD(modelo_anova_dosis)
# Mostrar los resultados de la prueba de Tukey para la dosis de fertilización
print(tukey_result_dosis)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log_biomasa ~ dosis_fert, data = data_2_larga)
##
## $dosis_fert
## diff lwr upr p adj
## B-A 0.7742015 -0.1127937 1.661197 0.1010691
## C-A 1.2974150 0.4104197 2.184410 0.0018531
## C-B 0.5232134 -0.3637818 1.410209 0.3482609# Pairwise comparisons between time points
pwc2 <- data_2_larga %>%
group_by(dosis_fert) %>%
pairwise_t_test(
log_biomasa ~ time, paired = TRUE,
p.adjust.method = "bonferroni"
)
pwc2## # A tibble: 234 × 11
## dosis_fert .y. group1 group2 n1 n2 statistic df p p.adj
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 A log_b… 0_días 05_dí… 10 10 -599. 9 5.12e-22 3.99e-20
## 2 A log_b… 0_días 10_dí… 10 10 -765. 9 5.68e-23 4.43e-21
## 3 A log_b… 0_días 15_dí… 10 10 -447. 9 7.14e-21 5.57e-19
## 4 A log_b… 0_días 20_dí… 10 10 -348. 9 6.75e-20 5.26e-18
## 5 A log_b… 0_días 25_dí… 10 10 -344. 9 7.64e-20 5.96e-18
## 6 A log_b… 0_días 30_dí… 10 10 -445. 9 7.42e-21 5.79e-19
## 7 A log_b… 0_días 35_dí… 10 10 -565. 9 8.65e-22 6.75e-20
## 8 A log_b… 0_días 40_dí… 10 10 -607. 9 4.56e-22 3.56e-20
## 9 A log_b… 0_días 45_dí… 10 10 -422. 9 1.19e-20 9.28e-19
## 10 A log_b… 0_días 50_dí… 10 10 -453. 9 6.29e-21 4.91e-19
## # ℹ 224 more rows
## # ℹ 1 more variable: p.adj.signif <chr># Realizar el ANOVA para el tiempo
modelo_anova_tiempo <- aov(log_biomasa ~ time, data = data_2_larga)
summary(modelo_anova_tiempo)## Df Sum Sq Mean Sq F value Pr(>F)
## time 12 3560 296.68 888.1 <2e-16 ***
## Residuals 377 126 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Realizar la prueba de comparaciones múltiples de Tukey para el tiempo
tukey_result_tiempo <- TukeyHSD(modelo_anova_tiempo)
# Mostrar los resultados de la prueba de Tukey para el tiempo
print(tukey_result_tiempo)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = log_biomasa ~ time, data = data_2_larga)
##
## $time
## diff lwr upr p adj
## 05_días-0_días 11.000880548 10.503299554 11.4984615 0.0000000
## 10_días-0_días 11.095078459 10.597497465 11.5926595 0.0000000
## 15_días-0_días 11.123851950 10.626270956 11.6214329 0.0000000
## 20_días-0_días 11.117002081 10.619421087 11.6145831 0.0000000
## 25_días-0_días 11.201814314 10.704233320 11.6993953 0.0000000
## 30_días-0_días 11.230506748 10.732925754 11.7280877 0.0000000
## 35_días-0_días 11.291040612 10.793459618 11.7886216 0.0000000
## 40_días-0_días 11.437480183 10.939899189 11.9350612 0.0000000
## 45_días-0_días 11.462798204 10.965217210 11.9603792 0.0000000
## 50_días-0_días 11.500908623 11.003327629 11.9984896 0.0000000
## 55_días-0_días 11.580099810 11.082518816 12.0776808 0.0000000
## 60_días-0_días 11.709345430 11.211764435 12.2069264 0.0000000
## 10_días-05_días 0.094197911 -0.403383083 0.5917789 0.9999898
## 15_días-05_días 0.122971402 -0.374609592 0.6205524 0.9998193
## 20_días-05_días 0.116121533 -0.381459462 0.6137025 0.9999012
## 25_días-05_días 0.200933766 -0.296647228 0.6985148 0.9809345
## 30_días-05_días 0.229626200 -0.267954795 0.7272072 0.9463479
## 35_días-05_días 0.290160064 -0.207420931 0.7877411 0.7679244
## 40_días-05_días 0.436599635 -0.060981359 0.9341806 0.1537267
## 45_días-05_días 0.461917656 -0.035663338 0.9594987 0.0992773
## 50_días-05_días 0.500028074 0.002447080 0.9976091 0.0475651
## 55_días-05_días 0.579219262 0.081638267 1.0768003 0.0078797
## 60_días-05_días 0.708464881 0.210883887 1.2060459 0.0002156
## 15_días-10_días 0.028773491 -0.468807503 0.5263545 1.0000000
## 20_días-10_días 0.021923622 -0.475657372 0.5195046 1.0000000
## 25_días-10_días 0.106735855 -0.390845139 0.6043168 0.9999600
## 30_días-10_días 0.135428289 -0.362152705 0.6330093 0.9995113
## 35_días-10_días 0.195962153 -0.301618841 0.6935431 0.9845134
## 40_días-10_días 0.342401724 -0.155179270 0.8399827 0.5225218
## 45_días-10_días 0.367719745 -0.129861249 0.8653007 0.4017149
## 50_días-10_días 0.405830164 -0.091750831 0.9034112 0.2464616
## 55_días-10_días 0.485021351 -0.012559643 0.9826023 0.0642368
## 60_días-10_días 0.614266970 0.116685976 1.1118480 0.0032039
## 20_días-15_días -0.006849869 -0.504430863 0.4907311 1.0000000
## 25_días-15_días 0.077962364 -0.419618630 0.5755434 0.9999988
## 30_días-15_días 0.106654798 -0.390926197 0.6042358 0.9999603
## 35_días-15_días 0.167188662 -0.330392332 0.6647697 0.9961974
## 40_días-15_días 0.313628233 -0.183952761 0.8112092 0.6628932
## 45_días-15_días 0.338946254 -0.158634740 0.8365272 0.5395101
## 50_días-15_días 0.377056673 -0.120524322 0.8746377 0.3600430
## 55_días-15_días 0.456247860 -0.041333134 0.9538289 0.1098958
## 60_días-15_días 0.585493479 0.087912485 1.0830745 0.0067371
## 25_días-20_días 0.084812233 -0.412768761 0.5823932 0.9999968
## 30_días-20_días 0.113504667 -0.384076327 0.6110857 0.9999225
## 35_días-20_días 0.174038531 -0.323542463 0.6716195 0.9945047
## 40_días-20_días 0.320478102 -0.177102892 0.8180591 0.6300582
## 45_días-20_días 0.345796123 -0.151784871 0.8433771 0.5058932
## 50_días-20_días 0.383906542 -0.113674452 0.8814875 0.3308399
## 55_días-20_días 0.463097729 -0.034483265 0.9606787 0.0971741
## 60_días-20_días 0.592343348 0.094762354 1.0899243 0.0056653
## 30_días-25_días 0.028692434 -0.468888560 0.5262734 1.0000000
## 35_días-25_días 0.089226298 -0.408354696 0.5868073 0.9999944
## 40_días-25_días 0.235665869 -0.261915125 0.7332469 0.9353959
## 45_días-25_días 0.260983890 -0.236597104 0.7585649 0.8730040
## 50_días-25_días 0.299094309 -0.198486686 0.7966753 0.7296319
## 55_días-25_días 0.378285496 -0.119295498 0.8758665 0.3547140
## 60_días-25_días 0.507531115 0.009950121 1.0051121 0.0407238
## 35_días-30_días 0.060533864 -0.437047130 0.5581149 0.9999999
## 40_días-30_días 0.206973435 -0.290607559 0.7045544 0.9757524
## 45_días-30_días 0.232291456 -0.265289538 0.7298725 0.9416914
## 50_días-30_días 0.270401875 -0.227179119 0.7679829 0.8427975
## 55_días-30_días 0.349593062 -0.147987932 0.8471741 0.4873969
## 60_días-30_días 0.478838681 -0.018742313 0.9764197 0.0724115
## 40_días-35_días 0.146439571 -0.351141423 0.6440206 0.9989312
## 45_días-35_días 0.171757592 -0.325823402 0.6693386 0.9951264
## 50_días-35_días 0.209868011 -0.287712983 0.7074490 0.9729139
## 55_días-35_días 0.289059198 -0.208521796 0.7866402 0.7724694
## 60_días-35_días 0.418304817 -0.079276177 0.9158858 0.2051887
## 45_días-40_días 0.025318021 -0.472262973 0.5228990 1.0000000
## 50_días-40_días 0.063428440 -0.434152555 0.5610094 0.9999999
## 55_días-40_días 0.142619627 -0.354961367 0.6402006 0.9991776
## 60_días-40_días 0.271865246 -0.225715748 0.7694462 0.8377712
## 50_días-45_días 0.038110418 -0.459470576 0.5356914 1.0000000
## 55_días-45_días 0.117301606 -0.380279389 0.6148826 0.9998901
## 60_días-45_días 0.246547225 -0.251033769 0.7441282 0.9119246
## 55_días-50_días 0.079191187 -0.418389807 0.5767722 0.9999985
## 60_días-50_días 0.208436807 -0.289144188 0.7060178 0.9743475
## 60_días-55_días 0.129245619 -0.368335375 0.6268266 0.9996970# Modelo Lineal Generalizado Mixto
model <- lmer(log_biomasa ~ dosis_fert * time + (1 | id), data = data_2_larga)## boundary (singular) fit: see help('isSingular')# Comparaciones por pares con letras de agrupamiento (Tukey)
comp <- glht(model, linfct = mcp(time = "Tukey"))## Warning in mcp2matrix(model, linfct = linfct): covariate interactions found --
## default contrast might be inappropriatesummary(comp)## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log_biomasa ~ dosis_fert * time + (1 | id), data = data_2_larga)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 05_días - 0_días == 0 10.25341 0.03832 267.606 <0.01 ***
## 10_días - 0_días == 0 10.34771 0.03832 270.067 <0.01 ***
## 15_días - 0_días == 0 10.36654 0.03832 270.558 <0.01 ***
## 20_días - 0_días == 0 10.39892 0.03832 271.403 <0.01 ***
## 25_días - 0_días == 0 10.43790 0.03832 272.421 <0.01 ***
## 30_días - 0_días == 0 10.51241 0.03832 274.365 <0.01 ***
## 35_días - 0_días == 0 10.53867 0.03832 275.051 <0.01 ***
## 40_días - 0_días == 0 10.67216 0.03832 278.535 <0.01 ***
## 45_días - 0_días == 0 10.70418 0.03832 279.370 <0.01 ***
## 50_días - 0_días == 0 10.74669 0.03832 280.480 <0.01 ***
## 55_días - 0_días == 0 10.83565 0.03832 282.802 <0.01 ***
## 60_días - 0_días == 0 10.95958 0.03832 286.036 <0.01 ***
## 10_días - 05_días == 0 0.09429 0.03832 2.461 0.4006
## 15_días - 05_días == 0 0.11313 0.03832 2.953 0.1401
## 20_días - 05_días == 0 0.14551 0.03832 3.798 <0.01 **
## 25_días - 05_días == 0 0.18448 0.03832 4.815 <0.01 ***
## 30_días - 05_días == 0 0.25900 0.03832 6.760 <0.01 ***
## 35_días - 05_días == 0 0.28526 0.03832 7.445 <0.01 ***
## 40_días - 05_días == 0 0.41875 0.03832 10.929 <0.01 ***
## 45_días - 05_días == 0 0.45077 0.03832 11.765 <0.01 ***
## 50_días - 05_días == 0 0.49328 0.03832 12.874 <0.01 ***
## 55_días - 05_días == 0 0.58224 0.03832 15.196 <0.01 ***
## 60_días - 05_días == 0 0.70617 0.03832 18.430 <0.01 ***
## 15_días - 10_días == 0 0.01883 0.03832 0.492 1.0000
## 20_días - 10_días == 0 0.05121 0.03832 1.337 0.9825
## 25_días - 10_días == 0 0.09019 0.03832 2.354 0.4770
## 30_días - 10_días == 0 0.16470 0.03832 4.299 <0.01 **
## 35_días - 10_días == 0 0.19096 0.03832 4.984 <0.01 ***
## 40_días - 10_días == 0 0.32445 0.03832 8.468 <0.01 ***
## 45_días - 10_días == 0 0.35647 0.03832 9.304 <0.01 ***
## 50_días - 10_días == 0 0.39899 0.03832 10.413 <0.01 ***
## 55_días - 10_días == 0 0.48794 0.03832 12.735 <0.01 ***
## 60_días - 10_días == 0 0.61187 0.03832 15.969 <0.01 ***
## 20_días - 15_días == 0 0.03238 0.03832 0.845 0.9998
## 25_días - 15_días == 0 0.07136 0.03832 1.862 0.8179
## 30_días - 15_días == 0 0.14587 0.03832 3.807 <0.01 **
## 35_días - 15_días == 0 0.17213 0.03832 4.492 <0.01 ***
## 40_días - 15_días == 0 0.30562 0.03832 7.976 <0.01 ***
## 45_días - 15_días == 0 0.33764 0.03832 8.812 <0.01 ***
## 50_días - 15_días == 0 0.38016 0.03832 9.922 <0.01 ***
## 55_días - 15_días == 0 0.46911 0.03832 12.243 <0.01 ***
## 60_días - 15_días == 0 0.59304 0.03832 15.478 <0.01 ***
## 25_días - 20_días == 0 0.03898 0.03832 1.017 0.9985
## 30_días - 20_días == 0 0.11349 0.03832 2.962 0.1365
## 35_días - 20_días == 0 0.13975 0.03832 3.647 0.0167 *
## 40_días - 20_días == 0 0.27324 0.03832 7.131 <0.01 ***
## 45_días - 20_días == 0 0.30526 0.03832 7.967 <0.01 ***
## 50_días - 20_días == 0 0.34777 0.03832 9.077 <0.01 ***
## 55_días - 20_días == 0 0.43673 0.03832 11.398 <0.01 ***
## 60_días - 20_días == 0 0.56066 0.03832 14.633 <0.01 ***
## 30_días - 25_días == 0 0.07451 0.03832 1.945 0.7692
## 35_días - 25_días == 0 0.10077 0.03832 2.630 0.2919
## 40_días - 25_días == 0 0.23426 0.03832 6.114 <0.01 ***
## 45_días - 25_días == 0 0.26628 0.03832 6.950 <0.01 ***
## 50_días - 25_días == 0 0.30880 0.03832 8.059 <0.01 ***
## 55_días - 25_días == 0 0.39775 0.03832 10.381 <0.01 ***
## 60_días - 25_días == 0 0.52168 0.03832 13.616 <0.01 ***
## 35_días - 30_días == 0 0.02626 0.03832 0.685 1.0000
## 40_días - 30_días == 0 0.15975 0.03832 4.169 <0.01 **
## 45_días - 30_días == 0 0.19177 0.03832 5.005 <0.01 ***
## 50_días - 30_días == 0 0.23429 0.03832 6.115 <0.01 ***
## 55_días - 30_días == 0 0.32324 0.03832 8.436 <0.01 ***
## 60_días - 30_días == 0 0.44717 0.03832 11.671 <0.01 ***
## 40_días - 35_días == 0 0.13349 0.03832 3.484 0.0286 *
## 45_días - 35_días == 0 0.16551 0.03832 4.320 <0.01 **
## 50_días - 35_días == 0 0.20802 0.03832 5.429 <0.01 ***
## 55_días - 35_días == 0 0.29698 0.03832 7.751 <0.01 ***
## 60_días - 35_días == 0 0.42091 0.03832 10.985 <0.01 ***
## 45_días - 40_días == 0 0.03202 0.03832 0.836 0.9998
## 50_días - 40_días == 0 0.07454 0.03832 1.945 0.7686
## 55_días - 40_días == 0 0.16349 0.03832 4.267 <0.01 **
## 60_días - 40_días == 0 0.28742 0.03832 7.501 <0.01 ***
## 50_días - 45_días == 0 0.04252 0.03832 1.110 0.9967
## 55_días - 45_días == 0 0.13147 0.03832 3.431 0.0337 *
## 60_días - 45_días == 0 0.25540 0.03832 6.666 <0.01 ***
## 55_días - 50_días == 0 0.08896 0.03832 2.322 0.5012
## 60_días - 50_días == 0 0.21289 0.03832 5.556 <0.01 ***
## 60_días - 55_días == 0 0.12393 0.03832 3.234 0.0637 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)# Obtener las letras de agrupamiento
cld_result <- cld(comp, level = 0.05)## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > abseps
## Warning in RET$pfunction("adjusted", ...): Completion with error > absepsprint(cld_result)## 0_días 05_días 10_días 15_días 20_días 25_días 30_días 35_días 40_días 45_días
## "a" "b" "bc" "bc" "cd" "ce" "de" "e" "f" "f"
## 50_días 55_días 60_días
## "fg" "gh" "h"#Grupos significativamente diferentes:
#0_días, 05_días, 35_días.
#Grupos similares:
#10_días y 15_días.
#20_días, 25_días y 30_días.
#40_días y 45_días.
#50_días es similar a 40_días y 45_días.
#55_días y 60_días.
No me quizo cargar en el knit, sin embargo
presento la Visualization del box plots with p-values