R Notebook
Cargar los datos
ruta_archivo <- "C:/Users/shami/Downloads/NUM3R2013(R).xlsx"datos <- read_excel(ruta_archivo)Inspección inicial de los datos
head(datos)summary(datos) año tipo de agua COD. Muestra Lugar Distrito Provincia Aspecto
Min. :1908-10-01 00:00:00.00 Length:243 Min. :3106 Length:243 Length:243 Length:243 Length:243
1st Qu.:2013-10-15 00:00:00.00 Class :character 1st Qu.:3166 Class :character Class :character Class :character Class :character
Median :2013-10-20 00:00:00.00 Mode :character Median :3581 Mode :character Mode :character Mode :character Mode :character
Mean :2013-05-16 05:43:42.22 Mean :3468
3rd Qu.:2013-10-30 00:00:00.00 3rd Qu.:3663
Max. :2014-01-07 00:00:00.00 Max. :3944
NA's :1
Color Olor Sabor polemiel H-PH turbidez(NTU) dureza total Alcalinidad (mg/L) cloruros (mg/L)
Length:243 Length:243 Length:243 Min. :1.800 Min. : 0.000 Min. : 0.00 Length:243 Min. : 0.00
Class :character Class :character Class :character 1st Qu.:7.300 1st Qu.: 1.000 1st Qu.: 31.80 Class :character 1st Qu.: 10.41
Mode :character Mode :character Mode :character Median :7.500 Median : 3.000 Median : 98.42 Mode :character Median : 16.61
Mean :7.543 Mean : 2.918 Mean : 140.21 Mean : 38.21
3rd Qu.:7.900 3rd Qu.: 4.000 3rd Qu.: 196.00 3rd Qu.: 42.74
Max. :9.300 Max. :20.000 Max. :1899.16 Max. :663.62
sulfatos (mg/L) nitratos (mg/L) calcio (mg/L) magnesio (mg/L) solidos totales (mg/L)
Min. : 0.00 Length:243 Min. : 0.000 Min. : 0.00 Min. : 0.00
1st Qu.: 8.00 Class :character 1st Qu.: 4.055 1st Qu.: 1.91 1st Qu.: 24.00
Median : 25.89 Mode :character Median : 21.790 Median : 6.84 Median : 89.29
Mean : 51.72 Mean : 40.634 Mean :11.06 Mean :135.11
3rd Qu.: 78.00 3rd Qu.: 59.845 3rd Qu.:12.96 3rd Qu.:199.51
Max. :380.00 Max. :596.910 Max. :98.90 Max. :967.00
str(datos)tibble [243 × 20] (S3: tbl_df/tbl/data.frame)
$ año : POSIXct[1:243], format: "2013-10-20" "2013-10-20" "2013-10-20" "2013-11-05" ...
$ tipo de agua : chr [1:243] "Agua potable" "Agua potable" "Agua potable" "Agua potable" ...
$ COD. Muestra : num [1:243] 3194 3195 3196 3682 3672 ...
$ Lugar : chr [1:243] "Puno" "Puno" "Puno" "Puno" ...
$ Distrito : chr [1:243] "Ocuviri" "Ocuviri" "Ocuviri" NA ...
$ Provincia : chr [1:243] "Lampa" "Lampa" "Lampa" NA ...
$ Aspecto : chr [1:243] "Liquido" "Liquido" "Liquido" "Liquido" ...
$ Color : chr [1:243] "Incoloro" "Incoloro" "Incoloro" "Incoloro" ...
$ Olor : chr [1:243] "Inodoro" "Inodoro" "Inodoro" "Inodoro" ...
$ Sabor : chr [1:243] "Insipido" "Insipido" "Insipido" "Insipido" ...
$ polemiel H-PH : num [1:243] 7.7 7.5 7.4 7.7 7.3 7.5 7.2 7.3 7.3 7.8 ...
$ turbidez(NTU) : num [1:243] 2 2 3 3 6 2 1 2 5 5 ...
$ dureza total : num [1:243] 76 65.6 65.7 249.7 0 ...
$ Alcalinidad (mg/L) : chr [1:243] "68.26m" "60.24" "38.26" "269.47000000000003" ...
$ cloruros (mg/L) : num [1:243] 29.2 12.3 19.1 42.4 240.1 ...
$ sulfatos (mg/L) : num [1:243] 45 16 25 92 128 44 12 76 76 84 ...
$ nitratos (mg/L) : chr [1:243] "Negativo" "Negativo" "Negativo" "Negativo" ...
$ calcio (mg/L) : num [1:243] 24 11.5 18.2 83.7 0 ...
$ magnesio (mg/L) : num [1:243] 5.09 4.09 6.09 9.86 0 ...
$ solidos totales (mg/L): num [1:243] 59.6 17.9 21.6 348 795 ...Verificar los nombres de las columnas
colnames(datos) [1] "año" "tipo de agua" "COD. Muestra" "Lugar" "Distrito"
[6] "Provincia" "Aspecto" "Color" "Olor" "Sabor"
[11] "polemiel H-PH" "turbidez(NTU)" "dureza total" "Alcalinidad (mg/L)" "cloruros (mg/L)"
[16] "sulfatos (mg/L)" "nitratos (mg/L)" "calcio (mg/L)" "magnesio (mg/L)" "solidos totales (mg/L)"Convertir variables categóricas a factores
datos <- datos %>% mutate("tipo de agua" = as.factor("tipo de agua"),Distrito = as.factor(Distrito) )Ordenar los datos por año, tipo de agua y distrito
datos_ordenados <- datos %>%arrange("tipo de agua", Distrito)Ver los datos ordenados
head(datos_ordenados)Verificar valores faltantes
sum(is.na(datos_ordenados))[1] 61Realizar ANOVA
ruta_Ensamble <-"C:/Users/shami/Downloads/NUM3R2013(R).xlsx"excel_sheets(ruta_Ensamble)[1] "Hoja1"casoDBCA1<-read_excel(ruta_Ensamble)print(casoDBCA1)AÑO<-factor(casoDBCA1$año)TIP<-factor(casoDBCA1$`tipo de agua`)COD<-factor(casoDBCA1$`COD. Muestra`)LUG<-factor(casoDBCA1$Lugar)DIS<-factor(casoDBCA1$Distrito)PROV<-factor(casoDBCA1$Provincia)ASP<-factor(casoDBCA1$Aspecto)COL<-factor(casoDBCA1$Color)OL<-factor(casoDBCA1$Olor)SAB<-factor(casoDBCA1$Sabor)PH<-factor(casoDBCA1$`polemiel H-PH`)TUR<-factor(casoDBCA1$`turbidez(NTU)`)DUR<-factor(casoDBCA1$`dureza total`)ALC<-factor(casoDBCA1$`Alcalinidad (mg/L)`)CLOR<-factor(casoDBCA1$`cloruros (mg/L)`)SUL<-factor(casoDBCA1$`sulfatos (mg/L)`)NIT<-factor(casoDBCA1$`nitratos (mg/L)`)CAL<-factor(casoDBCA1$`calcio (mg/L)`)MAG<-factor(casoDBCA1$`magnesio (mg/L)`)SOLTO<-factor(casoDBCA1$`solidos totales (mg/L)`)TIP<-as.vector(casoDBCA1$`tipo de agua`)PH1 <- as.numeric(as.vector(PH))par(mfrow=c(1,1))boxplot(split(PH1,TUR),xlab="turbidez(NTU)", ylab="polemiel H - PH")
Verificar la conversión
str(datos_ordenados)tibble [243 × 20] (S3: tbl_df/tbl/data.frame)
$ año : POSIXct[1:243], format: "2013-08-27" "2013-08-27" "2013-09-05" "2013-09-05" ...
$ tipo de agua : Factor w/ 1 level "tipo de agua": 1 1 1 1 1 1 1 1 1 1 ...
$ COD. Muestra : num [1:243] 3354 3569 3354 3580 3612 ...
$ Lugar : chr [1:243] "Planta de tratamiento" "Planta de tratamiento" "Planta de tratamiento" "Planta de tratamiento" ...
$ Distrito : Factor w/ 44 levels "Alto Selva Alegre",..: 1 1 1 1 2 2 3 3 4 4 ...
$ Provincia : chr [1:243] "Arequipa" "Arequipa" "Arequipa" "Arequipa" ...
$ Aspecto : chr [1:243] "LÃquido" "LÃquido" "LÃquido" "LÃquido" ...
$ Color : chr [1:243] "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" ...
$ Olor : chr [1:243] "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" "CaracterÃstico al agua residual" ...
$ Sabor : chr [1:243] "0" "0" "0" "0" ...
$ polemiel H-PH : num [1:243] 7.8 7.8 8 8 4.5 7.4 7.9 7.9 7.3 7 ...
$ turbidez(NTU) : num [1:243] 0 0 0 0 8 3 0 0 20 14 ...
$ dureza total : num [1:243] 0 0 0 0 249 ...
$ Alcalinidad (mg/L) : chr [1:243] "0" "0" "0" "0" ...
$ cloruros (mg/L) : num [1:243] 0 0 0 0 23 ...
$ sulfatos (mg/L) : num [1:243] 0 0 0 0 107 4 8 8 8 8 ...
$ nitratos (mg/L) : chr [1:243] "0" "0" "0" "0" ...
$ calcio (mg/L) : num [1:243] 0 0 0 0 46.1 ...
$ magnesio (mg/L) : num [1:243] 0 0 0 0 35.2 ...
$ solidos totales (mg/L): num [1:243] 0 0 0 0 192 ...Crear el modelo de ANOVA
aov.PH1 <- aov(PH1 ~ TUR, data = datos_ordenados)Resumen del modelo para verificar
summary(aov.PH1) Df Sum Sq Mean Sq F value Pr(>F)
TUR 13 29.96 2.3046 3.059 0.000342 ***
Residuals 229 172.54 0.7534
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Realizar el análisis post-hoc con TukeyHSD
resultado_tukey <- TukeyHSD(aov.PH1)
print(resultado_tukey) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = PH1 ~ TUR, data = datos_ordenados)
$TUR
diff lwr upr p adj
1-0 0.865405405 0.1941856 1.5366252 0.0015120
2-0 0.841081081 0.1698612 1.5123009 0.0024752
3-0 0.925306122 0.2982344 1.5523779 0.0000973
4-0 0.860000000 0.1097483 1.6102517 0.0097046
5-0 0.960000000 0.2422952 1.6777048 0.0007672
6-0 0.531428571 -0.3823756 1.4452328 0.7835907
7-0 0.660000000 -1.1015456 2.4215456 0.9921045
8-0 -0.040000000 -1.8015456 1.7215456 1.0000000
10-0 0.610000000 -1.5222098 2.7422098 0.9994747
11-0 0.260000000 -2.7192861 3.2392861 1.0000000
12-0 1.660000000 -1.3192861 4.6392861 0.8304819
14-0 0.160000000 -2.8192861 3.1392861 1.0000000
20-0 0.460000000 -2.5192861 3.4392861 0.9999996
2-1 -0.024324324 -0.7084949 0.6598463 1.0000000
3-1 0.059900717 -0.5810145 0.7008160 1.0000000
4-1 -0.005405405 -0.7672656 0.7564547 1.0000000
5-1 0.094594595 -0.6352365 0.8244257 1.0000000
6-1 -0.333976834 -1.2573356 0.5893820 0.9943290
7-1 -0.205405405 -1.9719263 1.5611155 1.0000000
8-1 -0.905405405 -2.6719263 0.8611155 0.9002031
10-1 -0.255405405 -2.3917275 1.8809167 1.0000000
11-1 -0.605405405 -3.5876360 2.3768252 0.9999892
12-1 0.794594595 -2.1876360 3.7768252 0.9997574
14-1 -0.705405405 -3.6876360 2.2768252 0.9999361
20-1 -0.405405405 -3.3876360 2.5768252 0.9999999
3-2 0.084225041 -0.5566902 0.7251403 0.9999999
4-2 0.018918919 -0.7429412 0.7807791 1.0000000
5-2 0.118918919 -0.6109122 0.8487501 0.9999992
6-2 -0.309652510 -1.2330113 0.6137063 0.9972821
7-2 -0.181081081 -1.9476020 1.5854398 1.0000000
8-2 -0.881081081 -2.6476020 0.8854398 0.9172604
10-2 -0.231081081 -2.3674031 1.9052410 1.0000000
11-2 -0.581081081 -3.5633116 2.4011495 0.9999933
12-2 0.818918919 -2.1633116 3.8011495 0.9996626
14-2 -0.681081081 -3.6633116 2.3011495 0.9999572
20-2 -0.381081081 -3.3633116 2.6011495 1.0000000
4-3 -0.065306122 -0.7885722 0.6579600 1.0000000
5-3 0.034693878 -0.6547526 0.7241403 1.0000000
6-3 -0.393877551 -1.2856591 0.4979040 0.9669716
7-3 -0.265306122 -2.0155287 1.4849165 0.9999997
8-3 -0.965306122 -2.7155287 0.7849165 0.8404951
10-3 -0.315306122 -2.4381709 1.8075587 0.9999998
11-3 -0.665306122 -3.6379114 2.3072991 0.9999661
12-3 0.734693878 -2.2379114 3.7072991 0.9998949
14-3 -0.765306122 -3.7379114 2.2072991 0.9998340
20-3 -0.465306122 -3.4379114 2.5072991 0.9999995
5-4 0.100000000 -0.7031158 0.9031158 1.0000000
6-4 -0.328571429 -1.3108814 0.6537386 0.9973511
7-4 -0.200000000 -1.9980372 1.5980372 1.0000000
8-4 -0.900000000 -2.6980372 0.8980372 0.9151734
10-4 -0.250000000 -2.4124554 1.9124554 1.0000000
11-4 -0.600000000 -3.6010066 2.4010066 0.9999910
12-4 0.800000000 -2.2010066 3.8010066 0.9997560
14-4 -0.700000000 -3.7010066 2.3010066 0.9999455
20-4 -0.400000000 -3.4010066 2.6010066 0.9999999
6-5 -0.428571429 -1.3862538 0.5291109 0.9633062
7-5 -0.300000000 -2.0847017 1.4847017 0.9999989
8-5 -1.000000000 -2.7847017 0.7847017 0.8247761
10-5 -0.350000000 -2.5013800 1.8013800 0.9999992
11-5 -0.700000000 -3.6930358 2.2930358 0.9999438
12-5 0.700000000 -2.2930358 3.6930358 0.9999438
14-5 -0.800000000 -3.7930358 2.1930358 0.9997488
20-5 -0.500000000 -3.4930358 2.4930358 0.9999990
7-6 0.128571429 -1.7436193 2.0007622 1.0000000
8-6 -0.571428571 -2.4436193 1.3007622 0.9989587
10-6 0.078571429 -2.1459227 2.3030655 1.0000000
11-6 -0.271428571 -3.3174426 2.7745854 1.0000000
12-6 1.128571429 -1.9174426 4.1745854 0.9928849
14-6 -0.371428571 -3.4174426 2.6745854 1.0000000
20-6 -0.071428571 -3.1174426 2.9745854 1.0000000
8-7 -0.700000000 -3.1027282 1.7027282 0.9993622
10-7 -0.050000000 -2.7363318 2.6363318 1.0000000
11-7 -0.400000000 -3.7979709 2.9979709 1.0000000
12-7 1.000000000 -2.3979709 4.3979709 0.9992901
14-7 -0.500000000 -3.8979709 2.8979709 0.9999998
20-7 -0.200000000 -3.5979709 3.1979709 1.0000000
10-8 0.650000000 -2.0363318 3.3363318 0.9999173
11-8 0.300000000 -3.0979709 3.6979709 1.0000000
12-8 1.700000000 -1.6979709 5.0979709 0.9154654
14-8 0.200000000 -3.1979709 3.5979709 1.0000000
20-8 0.500000000 -2.8979709 3.8979709 0.9999998
11-10 -0.350000000 -3.9540923 3.2540923 1.0000000
12-10 1.050000000 -2.5540923 4.6540923 0.9993622
14-10 -0.450000000 -4.0540923 3.1540923 1.0000000
20-10 -0.150000000 -3.7540923 3.4540923 1.0000000
12-11 1.400000000 -2.7616474 5.5616474 0.9971963
14-11 -0.100000000 -4.2616474 4.0616474 1.0000000
20-11 0.200000000 -3.9616474 4.3616474 1.0000000
14-12 -1.500000000 -5.6616474 2.6616474 0.9945146
20-12 -1.200000000 -5.3616474 2.9616474 0.9994286
20-14 0.300000000 -3.8616474 4.4616474 1.0000000aov(PH1 ~ TUR, data = datos_ordenados)Call:
aov(formula = PH1 ~ TUR, data = datos_ordenados)
Terms:
TUR Residuals
Sum of Squares 29.96003 172.53627
Deg. of Freedom 13 229
Residual standard error: 0.8680055
Estimated effects may be unbalancedaov.PH1 <- aov(PH1 ~ TUR,data = datos_ordenados)aov(aov.PH1)Call:
aov(formula = aov.PH1)
Terms:
TUR Residuals
Sum of Squares 29.96003 172.53627
Deg. of Freedom 13 229
Residual standard error: 0.8680055
Estimated effects may be unbalancedaov.PH1 <- aov(PH1 ~ TUR,data = datos_ordenados)modelo <- aov(PH1 ~ TUR,data = datos_ordenados)
print(resultado_tukey) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = PH1 ~ TUR, data = datos_ordenados)
$TUR
diff lwr upr p adj
1-0 0.865405405 0.1941856 1.5366252 0.0015120
2-0 0.841081081 0.1698612 1.5123009 0.0024752
3-0 0.925306122 0.2982344 1.5523779 0.0000973
4-0 0.860000000 0.1097483 1.6102517 0.0097046
5-0 0.960000000 0.2422952 1.6777048 0.0007672
6-0 0.531428571 -0.3823756 1.4452328 0.7835907
7-0 0.660000000 -1.1015456 2.4215456 0.9921045
8-0 -0.040000000 -1.8015456 1.7215456 1.0000000
10-0 0.610000000 -1.5222098 2.7422098 0.9994747
11-0 0.260000000 -2.7192861 3.2392861 1.0000000
12-0 1.660000000 -1.3192861 4.6392861 0.8304819
14-0 0.160000000 -2.8192861 3.1392861 1.0000000
20-0 0.460000000 -2.5192861 3.4392861 0.9999996
2-1 -0.024324324 -0.7084949 0.6598463 1.0000000
3-1 0.059900717 -0.5810145 0.7008160 1.0000000
4-1 -0.005405405 -0.7672656 0.7564547 1.0000000
5-1 0.094594595 -0.6352365 0.8244257 1.0000000
6-1 -0.333976834 -1.2573356 0.5893820 0.9943290
7-1 -0.205405405 -1.9719263 1.5611155 1.0000000
8-1 -0.905405405 -2.6719263 0.8611155 0.9002031
10-1 -0.255405405 -2.3917275 1.8809167 1.0000000
11-1 -0.605405405 -3.5876360 2.3768252 0.9999892
12-1 0.794594595 -2.1876360 3.7768252 0.9997574
14-1 -0.705405405 -3.6876360 2.2768252 0.9999361
20-1 -0.405405405 -3.3876360 2.5768252 0.9999999
3-2 0.084225041 -0.5566902 0.7251403 0.9999999
4-2 0.018918919 -0.7429412 0.7807791 1.0000000
5-2 0.118918919 -0.6109122 0.8487501 0.9999992
6-2 -0.309652510 -1.2330113 0.6137063 0.9972821
7-2 -0.181081081 -1.9476020 1.5854398 1.0000000
8-2 -0.881081081 -2.6476020 0.8854398 0.9172604
10-2 -0.231081081 -2.3674031 1.9052410 1.0000000
11-2 -0.581081081 -3.5633116 2.4011495 0.9999933
12-2 0.818918919 -2.1633116 3.8011495 0.9996626
14-2 -0.681081081 -3.6633116 2.3011495 0.9999572
20-2 -0.381081081 -3.3633116 2.6011495 1.0000000
4-3 -0.065306122 -0.7885722 0.6579600 1.0000000
5-3 0.034693878 -0.6547526 0.7241403 1.0000000
6-3 -0.393877551 -1.2856591 0.4979040 0.9669716
7-3 -0.265306122 -2.0155287 1.4849165 0.9999997
8-3 -0.965306122 -2.7155287 0.7849165 0.8404951
10-3 -0.315306122 -2.4381709 1.8075587 0.9999998
11-3 -0.665306122 -3.6379114 2.3072991 0.9999661
12-3 0.734693878 -2.2379114 3.7072991 0.9998949
14-3 -0.765306122 -3.7379114 2.2072991 0.9998340
20-3 -0.465306122 -3.4379114 2.5072991 0.9999995
5-4 0.100000000 -0.7031158 0.9031158 1.0000000
6-4 -0.328571429 -1.3108814 0.6537386 0.9973511
7-4 -0.200000000 -1.9980372 1.5980372 1.0000000
8-4 -0.900000000 -2.6980372 0.8980372 0.9151734
10-4 -0.250000000 -2.4124554 1.9124554 1.0000000
11-4 -0.600000000 -3.6010066 2.4010066 0.9999910
12-4 0.800000000 -2.2010066 3.8010066 0.9997560
14-4 -0.700000000 -3.7010066 2.3010066 0.9999455
20-4 -0.400000000 -3.4010066 2.6010066 0.9999999
6-5 -0.428571429 -1.3862538 0.5291109 0.9633062
7-5 -0.300000000 -2.0847017 1.4847017 0.9999989
8-5 -1.000000000 -2.7847017 0.7847017 0.8247761
10-5 -0.350000000 -2.5013800 1.8013800 0.9999992
11-5 -0.700000000 -3.6930358 2.2930358 0.9999438
12-5 0.700000000 -2.2930358 3.6930358 0.9999438
14-5 -0.800000000 -3.7930358 2.1930358 0.9997488
20-5 -0.500000000 -3.4930358 2.4930358 0.9999990
7-6 0.128571429 -1.7436193 2.0007622 1.0000000
8-6 -0.571428571 -2.4436193 1.3007622 0.9989587
10-6 0.078571429 -2.1459227 2.3030655 1.0000000
11-6 -0.271428571 -3.3174426 2.7745854 1.0000000
12-6 1.128571429 -1.9174426 4.1745854 0.9928849
14-6 -0.371428571 -3.4174426 2.6745854 1.0000000
20-6 -0.071428571 -3.1174426 2.9745854 1.0000000
8-7 -0.700000000 -3.1027282 1.7027282 0.9993622
10-7 -0.050000000 -2.7363318 2.6363318 1.0000000
11-7 -0.400000000 -3.7979709 2.9979709 1.0000000
12-7 1.000000000 -2.3979709 4.3979709 0.9992901
14-7 -0.500000000 -3.8979709 2.8979709 0.9999998
20-7 -0.200000000 -3.5979709 3.1979709 1.0000000
10-8 0.650000000 -2.0363318 3.3363318 0.9999173
11-8 0.300000000 -3.0979709 3.6979709 1.0000000
12-8 1.700000000 -1.6979709 5.0979709 0.9154654
14-8 0.200000000 -3.1979709 3.5979709 1.0000000
20-8 0.500000000 -2.8979709 3.8979709 0.9999998
11-10 -0.350000000 -3.9540923 3.2540923 1.0000000
12-10 1.050000000 -2.5540923 4.6540923 0.9993622
14-10 -0.450000000 -4.0540923 3.1540923 1.0000000
20-10 -0.150000000 -3.7540923 3.4540923 1.0000000
12-11 1.400000000 -2.7616474 5.5616474 0.9971963
14-11 -0.100000000 -4.2616474 4.0616474 1.0000000
20-11 0.200000000 -3.9616474 4.3616474 1.0000000
14-12 -1.500000000 -5.6616474 2.6616474 0.9945146
20-12 -1.200000000 -5.3616474 2.9616474 0.9994286
20-14 0.300000000 -3.8616474 4.4616474 1.0000000class(aov.PH1)[1] "aov" "lm" datos_ordenados$`turbidez(NTU)` <- as.factor(datos_ordenados$`turbidez(NTU)`)resultado_tukey <- TukeyHSD(aov.PH1)
print(resultado_tukey) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = PH1 ~ TUR, data = datos_ordenados)
$TUR
diff lwr upr p adj
1-0 0.865405405 0.1941856 1.5366252 0.0015120
2-0 0.841081081 0.1698612 1.5123009 0.0024752
3-0 0.925306122 0.2982344 1.5523779 0.0000973
4-0 0.860000000 0.1097483 1.6102517 0.0097046
5-0 0.960000000 0.2422952 1.6777048 0.0007672
6-0 0.531428571 -0.3823756 1.4452328 0.7835907
7-0 0.660000000 -1.1015456 2.4215456 0.9921045
8-0 -0.040000000 -1.8015456 1.7215456 1.0000000
10-0 0.610000000 -1.5222098 2.7422098 0.9994747
11-0 0.260000000 -2.7192861 3.2392861 1.0000000
12-0 1.660000000 -1.3192861 4.6392861 0.8304819
14-0 0.160000000 -2.8192861 3.1392861 1.0000000
20-0 0.460000000 -2.5192861 3.4392861 0.9999996
2-1 -0.024324324 -0.7084949 0.6598463 1.0000000
3-1 0.059900717 -0.5810145 0.7008160 1.0000000
4-1 -0.005405405 -0.7672656 0.7564547 1.0000000
5-1 0.094594595 -0.6352365 0.8244257 1.0000000
6-1 -0.333976834 -1.2573356 0.5893820 0.9943290
7-1 -0.205405405 -1.9719263 1.5611155 1.0000000
8-1 -0.905405405 -2.6719263 0.8611155 0.9002031
10-1 -0.255405405 -2.3917275 1.8809167 1.0000000
11-1 -0.605405405 -3.5876360 2.3768252 0.9999892
12-1 0.794594595 -2.1876360 3.7768252 0.9997574
14-1 -0.705405405 -3.6876360 2.2768252 0.9999361
20-1 -0.405405405 -3.3876360 2.5768252 0.9999999
3-2 0.084225041 -0.5566902 0.7251403 0.9999999
4-2 0.018918919 -0.7429412 0.7807791 1.0000000
5-2 0.118918919 -0.6109122 0.8487501 0.9999992
6-2 -0.309652510 -1.2330113 0.6137063 0.9972821
7-2 -0.181081081 -1.9476020 1.5854398 1.0000000
8-2 -0.881081081 -2.6476020 0.8854398 0.9172604
10-2 -0.231081081 -2.3674031 1.9052410 1.0000000
11-2 -0.581081081 -3.5633116 2.4011495 0.9999933
12-2 0.818918919 -2.1633116 3.8011495 0.9996626
14-2 -0.681081081 -3.6633116 2.3011495 0.9999572
20-2 -0.381081081 -3.3633116 2.6011495 1.0000000
4-3 -0.065306122 -0.7885722 0.6579600 1.0000000
5-3 0.034693878 -0.6547526 0.7241403 1.0000000
6-3 -0.393877551 -1.2856591 0.4979040 0.9669716
7-3 -0.265306122 -2.0155287 1.4849165 0.9999997
8-3 -0.965306122 -2.7155287 0.7849165 0.8404951
10-3 -0.315306122 -2.4381709 1.8075587 0.9999998
11-3 -0.665306122 -3.6379114 2.3072991 0.9999661
12-3 0.734693878 -2.2379114 3.7072991 0.9998949
14-3 -0.765306122 -3.7379114 2.2072991 0.9998340
20-3 -0.465306122 -3.4379114 2.5072991 0.9999995
5-4 0.100000000 -0.7031158 0.9031158 1.0000000
6-4 -0.328571429 -1.3108814 0.6537386 0.9973511
7-4 -0.200000000 -1.9980372 1.5980372 1.0000000
8-4 -0.900000000 -2.6980372 0.8980372 0.9151734
10-4 -0.250000000 -2.4124554 1.9124554 1.0000000
11-4 -0.600000000 -3.6010066 2.4010066 0.9999910
12-4 0.800000000 -2.2010066 3.8010066 0.9997560
14-4 -0.700000000 -3.7010066 2.3010066 0.9999455
20-4 -0.400000000 -3.4010066 2.6010066 0.9999999
6-5 -0.428571429 -1.3862538 0.5291109 0.9633062
7-5 -0.300000000 -2.0847017 1.4847017 0.9999989
8-5 -1.000000000 -2.7847017 0.7847017 0.8247761
10-5 -0.350000000 -2.5013800 1.8013800 0.9999992
11-5 -0.700000000 -3.6930358 2.2930358 0.9999438
12-5 0.700000000 -2.2930358 3.6930358 0.9999438
14-5 -0.800000000 -3.7930358 2.1930358 0.9997488
20-5 -0.500000000 -3.4930358 2.4930358 0.9999990
7-6 0.128571429 -1.7436193 2.0007622 1.0000000
8-6 -0.571428571 -2.4436193 1.3007622 0.9989587
10-6 0.078571429 -2.1459227 2.3030655 1.0000000
11-6 -0.271428571 -3.3174426 2.7745854 1.0000000
12-6 1.128571429 -1.9174426 4.1745854 0.9928849
14-6 -0.371428571 -3.4174426 2.6745854 1.0000000
20-6 -0.071428571 -3.1174426 2.9745854 1.0000000
8-7 -0.700000000 -3.1027282 1.7027282 0.9993622
10-7 -0.050000000 -2.7363318 2.6363318 1.0000000
11-7 -0.400000000 -3.7979709 2.9979709 1.0000000
12-7 1.000000000 -2.3979709 4.3979709 0.9992901
14-7 -0.500000000 -3.8979709 2.8979709 0.9999998
20-7 -0.200000000 -3.5979709 3.1979709 1.0000000
10-8 0.650000000 -2.0363318 3.3363318 0.9999173
11-8 0.300000000 -3.0979709 3.6979709 1.0000000
12-8 1.700000000 -1.6979709 5.0979709 0.9154654
14-8 0.200000000 -3.1979709 3.5979709 1.0000000
20-8 0.500000000 -2.8979709 3.8979709 0.9999998
11-10 -0.350000000 -3.9540923 3.2540923 1.0000000
12-10 1.050000000 -2.5540923 4.6540923 0.9993622
14-10 -0.450000000 -4.0540923 3.1540923 1.0000000
20-10 -0.150000000 -3.7540923 3.4540923 1.0000000
12-11 1.400000000 -2.7616474 5.5616474 0.9971963
14-11 -0.100000000 -4.2616474 4.0616474 1.0000000
20-11 0.200000000 -3.9616474 4.3616474 1.0000000
14-12 -1.500000000 -5.6616474 2.6616474 0.9945146
20-12 -1.200000000 -5.3616474 2.9616474 0.9994286
20-14 0.300000000 -3.8616474 4.4616474 1.0000000TukeyHSD(aov.PH1) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = PH1 ~ TUR, data = datos_ordenados)
$TUR
diff lwr upr p adj
1-0 0.865405405 0.1941856 1.5366252 0.0015120
2-0 0.841081081 0.1698612 1.5123009 0.0024752
3-0 0.925306122 0.2982344 1.5523779 0.0000973
4-0 0.860000000 0.1097483 1.6102517 0.0097046
5-0 0.960000000 0.2422952 1.6777048 0.0007672
6-0 0.531428571 -0.3823756 1.4452328 0.7835907
7-0 0.660000000 -1.1015456 2.4215456 0.9921045
8-0 -0.040000000 -1.8015456 1.7215456 1.0000000
10-0 0.610000000 -1.5222098 2.7422098 0.9994747
11-0 0.260000000 -2.7192861 3.2392861 1.0000000
12-0 1.660000000 -1.3192861 4.6392861 0.8304819
14-0 0.160000000 -2.8192861 3.1392861 1.0000000
20-0 0.460000000 -2.5192861 3.4392861 0.9999996
2-1 -0.024324324 -0.7084949 0.6598463 1.0000000
3-1 0.059900717 -0.5810145 0.7008160 1.0000000
4-1 -0.005405405 -0.7672656 0.7564547 1.0000000
5-1 0.094594595 -0.6352365 0.8244257 1.0000000
6-1 -0.333976834 -1.2573356 0.5893820 0.9943290
7-1 -0.205405405 -1.9719263 1.5611155 1.0000000
8-1 -0.905405405 -2.6719263 0.8611155 0.9002031
10-1 -0.255405405 -2.3917275 1.8809167 1.0000000
11-1 -0.605405405 -3.5876360 2.3768252 0.9999892
12-1 0.794594595 -2.1876360 3.7768252 0.9997574
14-1 -0.705405405 -3.6876360 2.2768252 0.9999361
20-1 -0.405405405 -3.3876360 2.5768252 0.9999999
3-2 0.084225041 -0.5566902 0.7251403 0.9999999
4-2 0.018918919 -0.7429412 0.7807791 1.0000000
5-2 0.118918919 -0.6109122 0.8487501 0.9999992
6-2 -0.309652510 -1.2330113 0.6137063 0.9972821
7-2 -0.181081081 -1.9476020 1.5854398 1.0000000
8-2 -0.881081081 -2.6476020 0.8854398 0.9172604
10-2 -0.231081081 -2.3674031 1.9052410 1.0000000
11-2 -0.581081081 -3.5633116 2.4011495 0.9999933
12-2 0.818918919 -2.1633116 3.8011495 0.9996626
14-2 -0.681081081 -3.6633116 2.3011495 0.9999572
20-2 -0.381081081 -3.3633116 2.6011495 1.0000000
4-3 -0.065306122 -0.7885722 0.6579600 1.0000000
5-3 0.034693878 -0.6547526 0.7241403 1.0000000
6-3 -0.393877551 -1.2856591 0.4979040 0.9669716
7-3 -0.265306122 -2.0155287 1.4849165 0.9999997
8-3 -0.965306122 -2.7155287 0.7849165 0.8404951
10-3 -0.315306122 -2.4381709 1.8075587 0.9999998
11-3 -0.665306122 -3.6379114 2.3072991 0.9999661
12-3 0.734693878 -2.2379114 3.7072991 0.9998949
14-3 -0.765306122 -3.7379114 2.2072991 0.9998340
20-3 -0.465306122 -3.4379114 2.5072991 0.9999995
5-4 0.100000000 -0.7031158 0.9031158 1.0000000
6-4 -0.328571429 -1.3108814 0.6537386 0.9973511
7-4 -0.200000000 -1.9980372 1.5980372 1.0000000
8-4 -0.900000000 -2.6980372 0.8980372 0.9151734
10-4 -0.250000000 -2.4124554 1.9124554 1.0000000
11-4 -0.600000000 -3.6010066 2.4010066 0.9999910
12-4 0.800000000 -2.2010066 3.8010066 0.9997560
14-4 -0.700000000 -3.7010066 2.3010066 0.9999455
20-4 -0.400000000 -3.4010066 2.6010066 0.9999999
6-5 -0.428571429 -1.3862538 0.5291109 0.9633062
7-5 -0.300000000 -2.0847017 1.4847017 0.9999989
8-5 -1.000000000 -2.7847017 0.7847017 0.8247761
10-5 -0.350000000 -2.5013800 1.8013800 0.9999992
11-5 -0.700000000 -3.6930358 2.2930358 0.9999438
12-5 0.700000000 -2.2930358 3.6930358 0.9999438
14-5 -0.800000000 -3.7930358 2.1930358 0.9997488
20-5 -0.500000000 -3.4930358 2.4930358 0.9999990
7-6 0.128571429 -1.7436193 2.0007622 1.0000000
8-6 -0.571428571 -2.4436193 1.3007622 0.9989587
10-6 0.078571429 -2.1459227 2.3030655 1.0000000
11-6 -0.271428571 -3.3174426 2.7745854 1.0000000
12-6 1.128571429 -1.9174426 4.1745854 0.9928849
14-6 -0.371428571 -3.4174426 2.6745854 1.0000000
20-6 -0.071428571 -3.1174426 2.9745854 1.0000000
8-7 -0.700000000 -3.1027282 1.7027282 0.9993622
10-7 -0.050000000 -2.7363318 2.6363318 1.0000000
11-7 -0.400000000 -3.7979709 2.9979709 1.0000000
12-7 1.000000000 -2.3979709 4.3979709 0.9992901
14-7 -0.500000000 -3.8979709 2.8979709 0.9999998
20-7 -0.200000000 -3.5979709 3.1979709 1.0000000
10-8 0.650000000 -2.0363318 3.3363318 0.9999173
11-8 0.300000000 -3.0979709 3.6979709 1.0000000
12-8 1.700000000 -1.6979709 5.0979709 0.9154654
14-8 0.200000000 -3.1979709 3.5979709 1.0000000
20-8 0.500000000 -2.8979709 3.8979709 0.9999998
11-10 -0.350000000 -3.9540923 3.2540923 1.0000000
12-10 1.050000000 -2.5540923 4.6540923 0.9993622
14-10 -0.450000000 -4.0540923 3.1540923 1.0000000
20-10 -0.150000000 -3.7540923 3.4540923 1.0000000
12-11 1.400000000 -2.7616474 5.5616474 0.9971963
14-11 -0.100000000 -4.2616474 4.0616474 1.0000000
20-11 0.200000000 -3.9616474 4.3616474 1.0000000
14-12 -1.500000000 -5.6616474 2.6616474 0.9945146
20-12 -1.200000000 -5.3616474 2.9616474 0.9994286
20-14 0.300000000 -3.8616474 4.4616474 1.0000000plot(TukeyHSD(aov.PH1))
resaov<-aov(PH1 ~ TUR + PROV)anova(resaov)Analysis of Variance Table
Response: PH1
Df Sum Sq Mean Sq F value Pr(>F)
TUR 13 48.552 3.7348 12.757 < 2.2e-16 ***
PROV 18 96.864 5.3813 18.382 < 2.2e-16 ***
Residuals 182 53.282 0.2928
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1cv.model(resaov)[1] 7.144381euc.lm <- lm(PH1 ~ TUR + PROV)anova(euc.lm , test="F")Analysis of Variance Table
Response: PH1
Df Sum Sq Mean Sq F value Pr(>F)
TUR 13 48.552 3.7348 12.757 < 2.2e-16 ***
PROV 18 96.864 5.3813 18.382 < 2.2e-16 ***
Residuals 182 53.282 0.2928
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1boxplot(anova(euc.lm , test="F"))
shapiro.test(euc.lm$res)
Shapiro-Wilk normality test
data: euc.lm$res
W = 0.91438, p-value = 8.874e-10plot(anova(euc.lm , test="F"))
boxplot(PH1 ~ PROV, data = datos_ordenados)
boxplot(PH1 ~ DIS, data = datos_ordenados)
boxplot(PH1 ~ LUG, data = datos_ordenados)
fitb <- fitted(resaov)res_stb <- rstandard(resaov)plot(fitb,res_stb,xlab="Valores predichos", ylab="Residuos estandarizados",abline(h=0))
leveneTest(PH1 ~ TUR, center = "median")Levene's Test for Homogeneity of Variance (center = "median")
Df F value Pr(>F)
group 13 1.2404 0.2517
229 outLSD <-LSD.test(resaov, "TUR",console=TRUE)
Study: resaov ~ "TUR"
LSD t Test for PH1
Mean Square Error: 0.2927568
TUR, means and individual ( 95 %) CI
Alpha: 0.05 ; DF Error: 182
Critical Value of t: 1.973084
Groups according to probability of means differences and alpha level( 0.05 )
Treatments with the same letter are not significantly different.outHSD<-HSD.test(resaov, "TUR",console=TRUE)
Study: resaov ~ "TUR"
HSD Test for PH1
Mean Square Error: 0.2927568
TUR, means
Alpha: 0.05 ; DF Error: 182
Critical Value of Studentized Range: 4.807918
Groups according to probability of means differences and alpha level( 0.05 )
Treatments with the same letter are not significantly different.SNK.test(resaov, "TUR",console=TRUE)
Study: resaov ~ "TUR"
Student Newman Keuls Test
for PH1
Mean Square Error: 0.2927568
TUR, means
Groups according to probability of means differences and alpha level( 0.05 )
Means with the same letter are not significantly different.scheffe.test(resaov, "TUR",console=TRUE)
Study: resaov ~ "TUR"
Scheffe Test for PH1
Mean Square Error : 0.2927568
TUR, means
Alpha: 0.05 ; DF Error: 182
Critical Value of F: 1.774262
Groups according to probability of means differences and alpha level( 0.05 )
Means with the same letter are not significantly different.duncan.test(resaov, "TUR",console=TRUE)
Study: resaov ~ "TUR"
Duncan's new multiple range test
for PH1
Mean Square Error: 0.2927568
TUR, means
Groups according to probability of means differences and alpha level( 0.05 )
Means with the same letter are not significantly different.LSD.test(resaov, "TUR", p.adj= "bon",console=TRUE)
Study: resaov ~ "TUR"
LSD t Test for PH1
P value adjustment method: bonferroni
Mean Square Error: 0.2927568
TUR, means and individual ( 95 %) CI
Alpha: 0.05 ; DF Error: 182
Critical Value of t: 3.517829
Groups according to probability of means differences and alpha level( 0.05 )
Treatments with the same letter are not significantly different.sk <- SK(resaov, which= "TUR", dispersion="se", sig.level=0.05)summary(sk)Goups of means at sig.level = 0.05 LS0tDQp0aXRsZTogIlIgTm90ZWJvb2siDQpvdXRwdXQ6IGh0bWxfbm90ZWJvb2sNCi0tLQ0KDQojIENhcmdhciBsb3MgZGF0b3MNCmBgYHtyfQ0KcnV0YV9hcmNoaXZvIDwtICJDOi9Vc2Vycy9zaGFtaS9Eb3dubG9hZHMvTlVNM1IyMDEzKFIpLnhsc3giDQpgYGANCmBgYHtyfQ0KZGF0b3MgPC0gcmVhZF9leGNlbChydXRhX2FyY2hpdm8pDQpgYGANCg0KIyBJbnNwZWNjacOzbiBpbmljaWFsIGRlIGxvcyBkYXRvcw0KYGBge3J9DQpoZWFkKGRhdG9zKQ0KYGBgDQpgYGB7cn0NCnN1bW1hcnkoZGF0b3MpDQpgYGANCmBgYHtyfQ0Kc3RyKGRhdG9zKQ0KYGBgDQojIFZlcmlmaWNhciBsb3Mgbm9tYnJlcyBkZSBsYXMgY29sdW1uYXMNCmBgYHtyfQ0KY29sbmFtZXMoZGF0b3MpDQpgYGANCiMgQ29udmVydGlyIHZhcmlhYmxlcyBjYXRlZ8OzcmljYXMgYSBmYWN0b3Jlcw0KYGBge3J9DQpkYXRvcyA8LSBkYXRvcyAlPiUgbXV0YXRlKCJ0aXBvIGRlIGFndWEiID0gYXMuZmFjdG9yKCJ0aXBvIGRlIGFndWEiKSxEaXN0cml0byA9IGFzLmZhY3RvcihEaXN0cml0bykgKQ0KYGBgDQojIE9yZGVuYXIgbG9zIGRhdG9zIHBvciBhw7FvLCB0aXBvIGRlIGFndWEgeSBkaXN0cml0bw0KYGBge3J9DQpkYXRvc19vcmRlbmFkb3MgPC0gZGF0b3MgJT4lYXJyYW5nZSgidGlwbyBkZSBhZ3VhIiwgRGlzdHJpdG8pDQpgYGANCiMgVmVyIGxvcyBkYXRvcyBvcmRlbmFkb3MNCmBgYHtyfQ0KaGVhZChkYXRvc19vcmRlbmFkb3MpDQpgYGANCiMgVmVyaWZpY2FyIHZhbG9yZXMgZmFsdGFudGVzDQpgYGB7cn0NCnN1bShpcy5uYShkYXRvc19vcmRlbmFkb3MpKQ0KYGBgDQojIFJlYWxpemFyIEFOT1ZBDQpgYGB7cn0NCnJ1dGFfRW5zYW1ibGUgPC0iQzovVXNlcnMvc2hhbWkvRG93bmxvYWRzL05VTTNSMjAxMyhSKS54bHN4Ig0KYGBgDQpgYGB7cn0NCmV4Y2VsX3NoZWV0cyhydXRhX0Vuc2FtYmxlKQ0KYGBgDQpgYGB7cn0NCmNhc29EQkNBMTwtcmVhZF9leGNlbChydXRhX0Vuc2FtYmxlKQ0KYGBgDQpgYGB7cn0NCnByaW50KGNhc29EQkNBMSkNCmBgYA0KYGBge3J9DQpBw5FPPC1mYWN0b3IoY2Fzb0RCQ0ExJGHDsW8pDQpgYGANCmBgYHtyfQ0KVElQPC1mYWN0b3IoY2Fzb0RCQ0ExJGB0aXBvIGRlIGFndWFgKQ0KYGBgDQpgYGB7cn0NCkNPRDwtZmFjdG9yKGNhc29EQkNBMSRgQ09ELiBNdWVzdHJhYCkNCmBgYA0KYGBge3J9DQpMVUc8LWZhY3RvcihjYXNvREJDQTEkTHVnYXIpDQpgYGANCmBgYHtyfQ0KRElTPC1mYWN0b3IoY2Fzb0RCQ0ExJERpc3RyaXRvKQ0KYGBgDQpgYGB7cn0NClBST1Y8LWZhY3RvcihjYXNvREJDQTEkUHJvdmluY2lhKQ0KYGBgDQpgYGB7cn0NCkFTUDwtZmFjdG9yKGNhc29EQkNBMSRBc3BlY3RvKQ0KYGBgDQpgYGB7cn0NCkNPTDwtZmFjdG9yKGNhc29EQkNBMSRDb2xvcikNCmBgYA0KYGBge3J9DQpPTDwtZmFjdG9yKGNhc29EQkNBMSRPbG9yKQ0KYGBgDQpgYGB7cn0NClNBQjwtZmFjdG9yKGNhc29EQkNBMSRTYWJvcikNCmBgYA0KYGBge3J9DQpQSDwtZmFjdG9yKGNhc29EQkNBMSRgcG9sZW1pZWwgSC1QSGApDQpgYGANCmBgYHtyfQ0KVFVSPC1mYWN0b3IoY2Fzb0RCQ0ExJGB0dXJiaWRleihOVFUpYCkNCmBgYA0KYGBge3J9DQpEVVI8LWZhY3RvcihjYXNvREJDQTEkYGR1cmV6YSB0b3RhbGApDQpgYGANCmBgYHtyfQ0KQUxDPC1mYWN0b3IoY2Fzb0RCQ0ExJGBBbGNhbGluaWRhZCAgKG1nL0wpYCkNCmBgYA0KYGBge3J9DQpDTE9SPC1mYWN0b3IoY2Fzb0RCQ0ExJGBjbG9ydXJvcyAgKG1nL0wpYCkNCmBgYA0KYGBge3J9DQpTVUw8LWZhY3RvcihjYXNvREJDQTEkYHN1bGZhdG9zIChtZy9MKWApDQpgYGANCmBgYHtyfQ0KTklUPC1mYWN0b3IoY2Fzb0RCQ0ExJGBuaXRyYXRvcyAgKG1nL0wpYCkNCmBgYA0KYGBge3J9DQpDQUw8LWZhY3RvcihjYXNvREJDQTEkYGNhbGNpbyAgKG1nL0wpYCkNCmBgYA0KYGBge3J9DQpNQUc8LWZhY3RvcihjYXNvREJDQTEkYG1hZ25lc2lvIChtZy9MKWApDQpgYGANCmBgYHtyfQ0KU09MVE88LWZhY3RvcihjYXNvREJDQTEkYHNvbGlkb3MgdG90YWxlcyAgKG1nL0wpYCkNCmBgYA0KYGBge3J9DQpUSVA8LWFzLnZlY3RvcihjYXNvREJDQTEkYHRpcG8gZGUgYWd1YWApDQpgYGANCmBgYHtyfQ0KUEgxIDwtIGFzLm51bWVyaWMoYXMudmVjdG9yKFBIKSkNCmBgYA0KYGBge3J9DQpwYXIobWZyb3c9YygxLDEpKQ0KYGBgDQpgYGB7cn0NCmJveHBsb3Qoc3BsaXQoUEgxLFRVUikseGxhYj0idHVyYmlkZXooTlRVKSIsIHlsYWI9InBvbGVtaWVsIEggLSBQSCIpDQpgYGANCiMgVmVyaWZpY2FyIGxhIGNvbnZlcnNpw7NuDQpgYGB7cn0NCnN0cihkYXRvc19vcmRlbmFkb3MpDQpgYGANCiMgQ3JlYXIgZWwgbW9kZWxvIGRlIEFOT1ZBDQpgYGB7cn0NCmFvdi5QSDEgPC0gYW92KFBIMSB+IFRVUiwgZGF0YSA9IGRhdG9zX29yZGVuYWRvcykNCmBgYA0KDQojIFJlc3VtZW4gZGVsIG1vZGVsbyBwYXJhIHZlcmlmaWNhcg0KDQpgYGB7cn0NCnN1bW1hcnkoYW92LlBIMSkNCmBgYA0KDQojIFJlYWxpemFyIGVsIGFuw6FsaXNpcyBwb3N0LWhvYyBjb24gVHVrZXlIU0QNCg0KYGBge3J9DQpyZXN1bHRhZG9fdHVrZXkgPC0gVHVrZXlIU0QoYW92LlBIMSkNCnByaW50KHJlc3VsdGFkb190dWtleSkNCmBgYA0KDQpgYGB7cn0NCmFvdihQSDEgfiBUVVIsIGRhdGEgPSBkYXRvc19vcmRlbmFkb3MpDQpgYGANCmBgYHtyfQ0KYW92LlBIMSA8LSBhb3YoUEgxIH4gVFVSLGRhdGEgPSBkYXRvc19vcmRlbmFkb3MpDQpgYGANCmBgYHtyfQ0KYW92KGFvdi5QSDEpDQpgYGANCmBgYHtyfQ0KYW92LlBIMSA8LSBhb3YoUEgxIH4gVFVSLGRhdGEgPSBkYXRvc19vcmRlbmFkb3MpDQpgYGANCmBgYHtyfQ0KbW9kZWxvIDwtIGFvdihQSDEgfiBUVVIsZGF0YSA9IGRhdG9zX29yZGVuYWRvcykNCnByaW50KHJlc3VsdGFkb190dWtleSkNCmBgYA0KDQpgYGB7cn0NCmNsYXNzKGFvdi5QSDEpDQpgYGANCmBgYHtyfQ0KZGF0b3Nfb3JkZW5hZG9zJGB0dXJiaWRleihOVFUpYCA8LSBhcy5mYWN0b3IoZGF0b3Nfb3JkZW5hZG9zJGB0dXJiaWRleihOVFUpYCkNCmBgYA0KDQpgYGB7cn0NCnJlc3VsdGFkb190dWtleSA8LSBUdWtleUhTRChhb3YuUEgxKQ0KcHJpbnQocmVzdWx0YWRvX3R1a2V5KQ0KYGBgDQoNCmBgYHtyfQ0KVHVrZXlIU0QoYW92LlBIMSkNCmBgYA0KDQpgYGB7cn0NCnBsb3QoVHVrZXlIU0QoYW92LlBIMSkpDQpgYGANCg0KYGBge3J9DQpyZXNhb3Y8LWFvdihQSDEgfiBUVVIgKyBQUk9WKQ0KYGBgDQpgYGB7cn0NCmFub3ZhKHJlc2FvdikNCmBgYA0KYGBge3J9DQpjdi5tb2RlbChyZXNhb3YpDQpgYGANCmBgYHtyfQ0KZXVjLmxtIDwtIGxtKFBIMSB+IFRVUiArIFBST1YpDQpgYGANCmBgYHtyfQ0KYW5vdmEoZXVjLmxtICwgdGVzdD0iRiIpDQpgYGANCmBgYHtyfQ0KYm94cGxvdChhbm92YShldWMubG0gLCB0ZXN0PSJGIikpDQpgYGANCmBgYHtyfQ0KIHNoYXBpcm8udGVzdChldWMubG0kcmVzKQ0KYGBgDQpgYGB7cn0NCnBsb3QoYW5vdmEoZXVjLmxtICwgdGVzdD0iRiIpKQ0KYGBgDQpgYGB7cn0NCmJveHBsb3QoUEgxIH4gUFJPViwgZGF0YSA9IGRhdG9zX29yZGVuYWRvcykNCmBgYA0KYGBge3J9DQpib3hwbG90KFBIMSB+IERJUywgZGF0YSA9IGRhdG9zX29yZGVuYWRvcykNCmBgYA0KYGBge3J9DQpib3hwbG90KFBIMSB+IExVRywgZGF0YSA9IGRhdG9zX29yZGVuYWRvcykNCmBgYA0KDQpgYGB7cn0NCmZpdGIgPC0gZml0dGVkKHJlc2FvdikNCmBgYA0KYGBge3J9DQpyZXNfc3RiIDwtIHJzdGFuZGFyZChyZXNhb3YpDQpgYGANCmBgYHtyfQ0KcGxvdChmaXRiLHJlc19zdGIseGxhYj0iVmFsb3JlcyBwcmVkaWNob3MiLCB5bGFiPSJSZXNpZHVvcyBlc3RhbmRhcml6YWRvcyIsYWJsaW5lKGg9MCkpDQpgYGANCmBgYHtyfQ0KbGV2ZW5lVGVzdChQSDEgfiBUVVIsIGNlbnRlciA9ICJtZWRpYW4iKQ0KYGBgDQpgYGB7cn0NCm91dExTRCA8LUxTRC50ZXN0KHJlc2FvdiwgIlRVUiIsY29uc29sZT1UUlVFKQ0KYGBgDQpgYGB7cn0NCm91dEhTRDwtSFNELnRlc3QocmVzYW92LCAiVFVSIixjb25zb2xlPVRSVUUpDQpgYGANCmBgYHtyfQ0KU05LLnRlc3QocmVzYW92LCAiVFVSIixjb25zb2xlPVRSVUUpDQpgYGANCmBgYHtyfQ0Kc2NoZWZmZS50ZXN0KHJlc2FvdiwgIlRVUiIsY29uc29sZT1UUlVFKQ0KYGBgDQpgYGB7cn0NCmR1bmNhbi50ZXN0KHJlc2FvdiwgIlRVUiIsY29uc29sZT1UUlVFKQ0KYGBgDQpgYGB7cn0NCkxTRC50ZXN0KHJlc2FvdiwgIlRVUiIsIHAuYWRqPSAiYm9uIixjb25zb2xlPVRSVUUpDQpgYGANCmBgYHtyfQ0Kc2sgPC0gU0socmVzYW92LCB3aGljaD0gIlRVUiIsICBkaXNwZXJzaW9uPSJzZSIsIHNpZy5sZXZlbD0wLjA1KQ0KYGBgDQpgYGB7cn0NCnN1bW1hcnkoc2spDQpgYGANCg0KDQoNCg0KDQoNCg==