muestreo2
Analisis estadístico muestreo número 2
library(readxl)
library(ggplot2)
library(car)## Loading required package: carDataDM_2 <- read_excel("C:/Users/USUARIO/Downloads/fisio/M2/muestreo_2.xlsx")
names(DM_2) <- c('DEF', 'N', 'T', 'E_A', 'E_C', 'E_T', 'CRC', 'N_H', 'Long', 'AF', 'CRA', 'Diam', 'PF', 'PS', 'PE', 'trt')
DM_2## # A tibble: 16 x 16
## DEF N T E_A E_C E_T CRC N_H Long AF CRA Diam PF
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SD SN 17 21 18 39 36.1 5 11 72.5 91.4 21 7.21
## 2 SD SN 18 22 17 39 35.8 4 11 75.1 90.0 20 7.16
## 3 SD SN 18 21 17 38 35.7 5 11 74.3 95.9 23 7.3
## 4 SD SN 18 24 16 40 36.2 5 11 72.5 92.0 20 7.27
## 5 SD CN 18 21 18 39 38.9 5 11 82.7 82.2 24 7.36
## 6 SD CN 17 21 19 40 40.1 5 11 81.9 89.5 24 7.29
## 7 SD CN 17 18 21 39 41.2 6 12 83.2 91.1 23 7.31
## 8 SD CN 17 23 18 41 39.7 4 12 82.6 87.1 23 7.33
## 9 CD SN 17 25 12 37 38.1 4 10 48.2 80.8 19 5.13
## 10 CD SN 17 28 11 39 37.8 4 11 48.3 84.8 18 4.97
## 11 CD SN 17 25 13 38 37.8 5 10 48.8 79.2 20 5.13
## 12 CD SN 17 27 12 39 37.5 4 10 48.3 88.8 19 5.25
## 13 CD CN 17 20 21 41 38.7 4 11 57.3 89.7 19 5.97
## 14 CD CN 17 22 19 41 39.1 4 11 58.0 85.7 20 5.85
## 15 CD CN 18 18 21 39 39.2 5 10 58.0 91.6 19 6.01
## 16 CD CN 17 23 20 43 38.9 5 11 57.3 84.2 21 5.89
## # ... with 3 more variables: PS <dbl>, PE <dbl>, trt <chr>DM_22 <- as.data.frame(DM_2)
ggplot(data = DM_2, aes(x = DEF, y = T, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = N, y = T, color = N)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_A , color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = N, y = E_A , color = N)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_C, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_T, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = CRC, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = N_H, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = Long, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = AF, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = CRA, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = Diam, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = PF, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = PS, color = DEF)) +
geom_boxplot() +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = PE, color = DEF)) +
geom_boxplot() +
theme_bw()
spT <- shapiro.test(DM_2$T)
spT##
## Shapiro-Wilk normality test
##
## data: DM_2$T
## W = 0.59088, p-value = 1.33e-05spE_A <- shapiro.test(DM_2$E_A)
spE_A##
## Shapiro-Wilk normality test
##
## data: DM_2$E_A
## W = 0.95804, p-value = 0.6264spE_C <- shapiro.test(DM_2$E_C)
spE_C##
## Shapiro-Wilk normality test
##
## data: DM_2$E_C
## W = 0.8903, p-value = 0.05631spE_T <- shapiro.test(DM_2$E_T)
spE_T##
## Shapiro-Wilk normality test
##
## data: DM_2$E_T
## W = 0.91445, p-value = 0.1373spCRC <- shapiro.test(DM_2$CRC)
spCRC##
## Shapiro-Wilk normality test
##
## data: DM_2$CRC
## W = 0.95213, p-value = 0.5241spN_H <- shapiro.test(DM_2$N_H)
spN_H##
## Shapiro-Wilk normality test
##
## data: DM_2$N_H
## W = 0.76031, p-value = 0.0008479spLong <- shapiro.test(DM_2$Long)
spLong##
## Shapiro-Wilk normality test
##
## data: DM_2$Long
## W = 0.77824, p-value = 0.001421spAF <- shapiro.test(DM_2$AF)
spAF##
## Shapiro-Wilk normality test
##
## data: DM_2$AF
## W = 0.8583, p-value = 0.01809spCRA <- shapiro.test(DM_2$CRA)
spCRA##
## Shapiro-Wilk normality test
##
## data: DM_2$CRA
## W = 0.96172, p-value = 0.693spDiam <- shapiro.test(DM_2$Diam)
spDiam##
## Shapiro-Wilk normality test
##
## data: DM_2$Diam
## W = 0.88747, p-value = 0.05081spPF <- shapiro.test(DM_2$PF)
spPF##
## Shapiro-Wilk normality test
##
## data: DM_2$PF
## W = 0.80577, p-value = 0.003253spPS <- shapiro.test(DM_2$PS)
spPS##
## Shapiro-Wilk normality test
##
## data: DM_2$PS
## W = 0.92794, p-value = 0.2261spPE <- shapiro.test(DM_2$PE)
spPE##
## Shapiro-Wilk normality test
##
## data: DM_2$PE
## W = 0.89213, p-value = 0.06019### PARA TEMPERATURA, NÚMERO DE HOJAS, LONGITUD, AREA FOLIAR, DIAMETRO, PESO FRESCO
# T
leveneTest(T ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 0.3333 0.8015
## 12kruskal.test(T ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: T by trt
## Kruskal-Wallis chi-squared = 5.1818, df = 3, p-value = 0.159pairwise.wilcox.test(x = DM_2$T, g = DM_2$trt, p.adjust.method = "holm" )## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: DM_2$T and DM_2$trt
##
## T1 T2 T3
## T2 1.00 - -
## T3 0.36 1.00 -
## T4 1.00 1.00 1.00
##
## P value adjustment method: holm# NH
leveneTest(N_H ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 0.4 0.7555
## 12kruskal.test(N_H ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: N_H by trt
## Kruskal-Wallis chi-squared = 3.0625, df = 3, p-value = 0.3821pairwise.wilcox.test(x = DM_2$N_H, g = DM_2$trt, p.adjust.method = "holm" )## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: DM_2$N_H and DM_2$trt
##
## T1 T2 T3
## T2 1 - -
## T3 1 1 -
## T4 1 1 1
##
## P value adjustment method: holm#LONGITUD
leveneTest(Long ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 1.3333 0.3096
## 12kruskal.test(Long ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: Long by trt
## Kruskal-Wallis chi-squared = 8.4821, df = 3, p-value = 0.03703pairwise.wilcox.test(x = DM_2$Long, g = DM_2$trt, p.adjust.method = "holm" )## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: DM_2$Long and DM_2$trt
##
## T1 T2 T3
## T2 0.54 - -
## T3 0.30 0.28 -
## T4 0.54 0.53 0.54
##
## P value adjustment method: holm#AREA FOLIAR
leveneTest(AF ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 8.8267 0.002308 **
## 12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1kruskal.test(AF ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: AF by trt
## Kruskal-Wallis chi-squared = 14.118, df = 3, p-value = 0.002749pairwise.wilcox.test(x = DM_2$AF, g = DM_2$trt, p.adjust.method = "holm" )##
## Pairwise comparisons using Wilcoxon rank sum exact test
##
## data: DM_2$AF and DM_2$trt
##
## T1 T2 T3
## T2 0.17 - -
## T3 0.17 0.17 -
## T4 0.17 0.17 0.17
##
## P value adjustment method: holm#DIAMETRO
leveneTest(Diam ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 0.5789 0.6399
## 12kruskal.test(Diam ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: Diam by trt
## Kruskal-Wallis chi-squared = 11.153, df = 3, p-value = 0.01093pairwise.wilcox.test(x = DM_2$Diam, g = DM_2$trt, p.adjust.method = "holm" )## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: DM_2$Diam and DM_2$trt
##
## T1 T2 T3
## T2 0.21 - -
## T3 0.21 0.16 -
## T4 0.46 0.16 0.46
##
## P value adjustment method: holm#PESO SECO
leveneTest(PS ~ trt, data = DM_2, center = "median")## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 1.5927 0.2427
## 12kruskal.test(PS ~ trt, data = DM_2)##
## Kruskal-Wallis rank sum test
##
## data: PS by trt
## Kruskal-Wallis chi-squared = 12.465, df = 3, p-value = 0.005949pairwise.wilcox.test(x = DM_2$PS, g = DM_2$trt, p.adjust.method = "holm" )## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties##
## Pairwise comparisons using Wilcoxon rank sum exact test
##
## data: DM_2$PS and DM_2$trt
##
## T1 T2 T3
## T2 0.17 - -
## T3 0.17 0.17 -
## T4 0.34 0.17 0.17
##
## P value adjustment method: holm#############################
### TEST DE LEVENE
leveneTest(y = DM_2$T, group = DM_2$trt, center = "median")## Warning in leveneTest.default(y = DM_2$T, group = DM_2$trt, center = "median"):
## DM_2$trt coerced to factor.## Levene's Test for Homogeneity of Variance (center = "median")
## Df F value Pr(>F)
## group 3 0.3333 0.8015
## 12###GRÁFICAS DE INTERACCIÓN
#T
ggplot(data = DM_2, aes(x = N, y = T, colour = DEF,
group = DEF)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = T, colour = N,
group = N)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
#E_A
ggplot(data = DM_2, aes(x = N, y = E_A, colour = DEF,
group = DEF)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_A, colour = N,
group = N)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
#E_C
ggplot(data = DM_2, aes(x = N, y = E_C, colour = DEF,
group = DEF)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_C, colour = N,
group = N)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
#E_T
ggplot(data = DM_2, aes(x = N, y = E_T, colour = DEF,
group = DEF)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
ggplot(data = DM_2, aes(x = DEF, y = E_T, colour = N,
group = N)) +
stat_summary(fun = mean, geom = "point") +
stat_summary(fun = mean, geom = "line") +
labs(y = 'Temperatura') +
theme_bw()
### Anovas
#T
anovaT <- aov(DM_2$T ~ DM_2$N * DM_2$DEF)
summary(anovaT) ########## esta mal, porque los datos no tienen normalidad## Df Sum Sq Mean Sq F value Pr(>F)
## DM_2$N 1 0.0625 0.0625 0.333 0.574
## DM_2$DEF 1 0.5625 0.5625 3.000 0.109
## DM_2$N:DM_2$DEF 1 0.5625 0.5625 3.000 0.109
## Residuals 12 2.2500 0.1875#E_A
anovaE_A <- aov(DM_2$E_A ~ DM_2$N * DM_2$DEF)
summary(anovaE_A)## Df Sum Sq Mean Sq F value Pr(>F)
## DM_2$N 1 45.56 45.56 13.584 0.00312 **
## DM_2$DEF 1 18.06 18.06 5.385 0.03873 *
## DM_2$N:DM_2$DEF 1 18.06 18.06 5.385 0.03873 *
## Residuals 12 40.25 3.35
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#E_C
anovaE_C <- aov(DM_2$E_C ~ DM_2$N * DM_2$DEF)
summary(anovaE_C)## Df Sum Sq Mean Sq F value Pr(>F)
## DM_2$N 1 105.06 105.06 98.88 3.81e-07 ***
## DM_2$DEF 1 14.06 14.06 13.23 0.0034 **
## DM_2$N:DM_2$DEF 1 39.06 39.06 36.77 5.64e-05 ***
## Residuals 12 12.75 1.06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#E_T
anovaE_T <- aov(DM_2$E_T ~ DM_2$N * DM_2$DEF)
summary(anovaE_T)## Df Sum Sq Mean Sq F value Pr(>F)
## DM_2$N 1 12.25 12.250 9.484 0.00955 **
## DM_2$DEF 1 0.25 0.250 0.194 0.66780
## DM_2$N:DM_2$DEF 1 4.00 4.000 3.097 0.10389
## Residuals 12 15.50 1.292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### comprobar supuestos
#T
plot(anovaT, 1)
plot(anovaT, 2)
#E_A
plot(anovaE_A, 1)
plot(anovaE_A, 2)
#E_C
plot(anovaE_C, 1)
plot(anovaE_C, 2)
#E_T
plot(anovaE_T, 1)
plot(anovaE_T, 2)
resistencia <- c(15.29, 15.89, 16.02, 16.56, 15.46, 16.91, 16.99, 17.27, 16.85,
16.35, 17.23, 17.81, 17.74, 18.02, 18.37, 12.07, 12.42, 12.73,
13.02, 12.05, 12.92, 13.01, 12.21, 13.49, 14.01, 13.30, 12.82,
12.49, 13.55, 14.53)
templado <- c(rep(c("rapido", "lento"), c(15,15)))
grosor <- rep(c(8, 16, 24), each = 5, times = 2)
datos <- data.frame(templado = templado, grosor = as.factor(grosor),
resistencia = resistencia)
head(datos)## templado grosor resistencia
## 1 rapido 8 15.29
## 2 rapido 8 15.89
## 3 rapido 8 16.02
## 4 rapido 8 16.56
## 5 rapido 8 15.46
## 6 rapido 16 16.91