Hoja Numero 1: DATOS MUESTREO FINAL
df_g <- read_excel("C:/Users/JuanSebH2/Desktop/Muestreo Virulencia SS 1 7-06-24 (1).xlsx",
sheet = "DATOS MUESTREO FINAL")
df_g= data.frame(df_g); df_g
## Trt N_p N_t P_f_r P_s_r P_f_a P_s_a N_a
## 1 Samacá 6 9 13.68 2.170 83.08 10.08 0
## 2 Samacá 1 5 13.85 2.378 94.91 10.50 0
## 3 Samacá 9 6 19.10 3.290 99.72 11.24 0
## 4 Samacá 10 6 18.91 2.780 85.89 9.12 0
## 5 Samacá 4 6 12.34 2.000 83.98 10.19 0
## 6 Ventaquemada 1 6 16.00 2.600 50.00 6.12 4
## 7 Ventaquemada 3 2 8.00 0.990 35.00 4.71 1
## 8 Ventaquemada 5 9 16.00 2.360 69.00 8.97 34
## 9 Ventaquemada 6 5 12.00 2.120 61.00 7.84 17
## 10 Ventaquemada 10 4 8.00 1.070 36.00 5.11 16
## 11 Motavita 1 7 15.00 1.810 53.00 8.90 0
## 12 Motavita 2 7 23.00 3.490 91.00 10.46 0
## 13 Motavita 4 10 17.00 1.870 41.00 3.96 0
## 14 Motavita 7 7 13.00 1.820 66.00 9.19 0
## 15 Motavita 10 7 22.00 4.510 81.00 11.72 0
## 16 Toca 4 6 17.00 2.490 99.00 10.92 0
## 17 Toca 5 5 12.00 1.450 60.00 5.78 0
## 18 Toca 6 7 11.00 1.690 99.00 10.42 0
## 19 Toca 9 7 23.00 4.490 86.00 9.09 0
## 20 Toca 10 8 16.00 2.700 91.00 9.69 0
## 21 Soracá 1 9 18.00 3.010 78.00 8.95 0
## 22 Soracá 2 9 20.00 3.370 125.00 12.62 0
## 23 Soracá 4 3 8.00 1.340 72.00 7.79 0
## 24 Soracá 6 7 15.00 2.070 67.00 7.78 0
## 25 Soracá 7 4 14.00 1.750 86.00 8.85 0
## 26 Sibaté 3 8 12.00 1.420 34.00 4.91 0
## 27 Sibaté 4 5 14.00 1.520 39.00 5.01 0
## 28 Sibaté 5 8 18.00 2.930 91.00 10.34 0
## 29 Sibaté 8 10 18.00 2.420 78.00 8.16 0
## 30 Sibaté 9 7 19.00 2.960 105.00 11.94 0
## 31 Control 1 10 21.00 3.450 105.00 10.38 0
## 32 Control 5 9 17.00 2.500 82.00 11.24 0
## 33 Control 6 15 18.00 2.580 103.00 10.62 0
## 34 Control 7 11 13.00 2.120 81.00 9.30 0
## 35 Control 8 9 17.00 2.500 91.00 9.96 0
Estadisticos_N_t= df_g |>
group_by(Trt) |>
summarise(
Media= mean(N_t),
Min= min(N_t),
Max= max(N_t),
Var= sd(N_t)
); Estadisticos_N_t
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 10.8 9 15 2.49
## 2 Motavita 7.6 7 10 1.34
## 3 Samacá 6.4 5 9 1.52
## 4 Sibaté 7.6 5 10 1.82
## 5 Soracá 6.4 3 9 2.79
## 6 Toca 6.6 5 8 1.14
## 7 Ventaquemada 5.2 2 9 2.59
#Numero de tuberculo
ggplot(data= df_g, aes(x=Trt, y= N_t, color= Trt))+
geom_boxplot()+
ylab("Numero de tuberculos")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza Numero de tuberculos
N_t= aov(N_t~Trt, data= df_g)
Anova_N_t=anova(N_t);Anova_N_t
## Analysis of Variance Table
##
## Response: N_t
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 94.571 15.762 3.7528 0.007258 **
## Residuals 28 117.600 4.200
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(N_t)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_t ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -3.200000e+00 -7.311551 0.91155051 0.2089451
## Samacá-Control -4.400000e+00 -8.511551 -0.28844949 0.0299971
## Sibaté-Control -3.200000e+00 -7.311551 0.91155051 0.2089451
## Soracá-Control -4.400000e+00 -8.511551 -0.28844949 0.0299971
## Toca-Control -4.200000e+00 -8.311551 -0.08844949 0.0428529
## Ventaquemada-Control -5.600000e+00 -9.711551 -1.48844949 0.0029880
## Samacá-Motavita -1.200000e+00 -5.311551 2.91155051 0.9650740
## Sibaté-Motavita -8.881784e-16 -4.111551 4.11155051 1.0000000
## Soracá-Motavita -1.200000e+00 -5.311551 2.91155051 0.9650740
## Toca-Motavita -1.000000e+00 -5.111551 3.11155051 0.9859087
## Ventaquemada-Motavita -2.400000e+00 -6.511551 1.71155051 0.5266448
## Sibaté-Samacá 1.200000e+00 -2.911551 5.31155051 0.9650740
## Soracá-Samacá 4.440892e-15 -4.111551 4.11155051 1.0000000
## Toca-Samacá 2.000000e-01 -3.911551 4.31155051 0.9999986
## Ventaquemada-Samacá -1.200000e+00 -5.311551 2.91155051 0.9650740
## Soracá-Sibaté -1.200000e+00 -5.311551 2.91155051 0.9650740
## Toca-Sibaté -1.000000e+00 -5.111551 3.11155051 0.9859087
## Ventaquemada-Sibaté -2.400000e+00 -6.511551 1.71155051 0.5266448
## Toca-Soracá 2.000000e-01 -3.911551 4.31155051 0.9999986
## Ventaquemada-Soracá -1.200000e+00 -5.311551 2.91155051 0.9650740
## Ventaquemada-Toca -1.400000e+00 -5.511551 2.71155051 0.9287883
#Diferencias encontradas entre: Samacá-Control; Soracá-Control; Toca-Control; Ventaquemada-Control
#Grafico de las diferencias
plot(TukeyHSD(N_t))
T_N_t=TukeyC(N_t,'Trt')
plot(T_N_t)
Estadisticos_P_f_r= df_g |>
group_by(Trt) |>
summarise(
Media= mean(P_f_r),
Min= min(P_f_r),
Max= max(P_f_r),
Var= sd(P_f_r)
); Estadisticos_P_f_r
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 17.2 13 21 2.86
## 2 Motavita 18 13 23 4.36
## 3 Samacá 15.6 12.3 19.1 3.19
## 4 Sibaté 16.2 12 19 3.03
## 5 Soracá 15 8 20 4.58
## 6 Toca 15.8 11 23 4.76
## 7 Ventaquemada 12 8 16 4
#Peso fresco de la raiz
ggplot(data= df_g, aes(x=Trt, y= P_f_r, color= Trt))+
geom_boxplot()+
ylab("Peso fresco de la raiz")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza peso fresco de la raiz
P_f_r= aov(P_f_r~Trt, data= df_g)
Anova_P_f_r=anova(P_f_r);Anova_P_f_r
## Analysis of Variance Table
##
## Response: P_f_r
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 109.97 18.328 1.2075 0.3316
## Residuals 28 424.98 15.178
TukeyHSD(P_f_r)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = P_f_r ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 0.800 -7.016027 8.616027 0.9998872
## Samacá-Control -1.624 -9.440027 6.192027 0.9938399
## Sibaté-Control -1.000 -8.816027 6.816027 0.9995888
## Soracá-Control -2.200 -10.016027 5.616027 0.9706916
## Toca-Control -1.400 -9.216027 6.416027 0.9972531
## Ventaquemada-Control -5.200 -13.016027 2.616027 0.3742255
## Samacá-Motavita -2.424 -10.240027 5.392027 0.9534440
## Sibaté-Motavita -1.800 -9.616027 6.016027 0.9893805
## Soracá-Motavita -3.000 -10.816027 4.816027 0.8814256
## Toca-Motavita -2.200 -10.016027 5.616027 0.9706916
## Ventaquemada-Motavita -6.000 -13.816027 1.816027 0.2218150
## Sibaté-Samacá 0.624 -7.192027 8.440027 0.9999738
## Soracá-Samacá -0.576 -8.392027 7.240027 0.9999837
## Toca-Samacá 0.224 -7.592027 8.040027 0.9999999
## Ventaquemada-Samacá -3.576 -11.392027 4.240027 0.7697476
## Soracá-Sibaté -1.200 -9.016027 6.616027 0.9988381
## Toca-Sibaté -0.400 -8.216027 7.416027 0.9999981
## Ventaquemada-Sibaté -4.200 -12.016027 3.616027 0.6187517
## Toca-Soracá 0.800 -7.016027 8.616027 0.9998872
## Ventaquemada-Soracá -3.000 -10.816027 4.816027 0.8814256
## Ventaquemada-Toca -3.800 -11.616027 4.016027 0.7179349
#Diferencias encontradas entre: No se encontraron diferencias
#Grafico de las diferencias
plot(TukeyHSD(P_f_r))
T_P_f_r=TukeyC(P_f_r,'Trt')
plot(T_P_f_r)
Estadisticos_P_s_r= df_g |>
group_by(Trt) |>
summarise(
Media= mean(P_s_r),
Min= min(P_s_r),
Max= max(P_s_r),
Var= sd(P_s_r)
); Estadisticos_P_s_r
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 2.63 2.12 3.45 0.492
## 2 Motavita 2.7 1.81 4.51 1.24
## 3 Samacá 2.52 2 3.29 0.518
## 4 Sibaté 2.25 1.42 2.96 0.745
## 5 Soracá 2.31 1.34 3.37 0.855
## 6 Toca 2.56 1.45 4.49 1.20
## 7 Ventaquemada 1.83 0.99 2.6 0.749
#Peso seco de la raiz
ggplot(data= df_g, aes(x=Trt, y= P_s_r, color= Trt))+
geom_boxplot()+
ylab("Peso seco de la raiz")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza peso seco de la raiz
P_s_r= aov(P_s_r~Trt, data= df_g)
Anova_P_s_r=anova(P_s_r);Anova_P_s_r
## Analysis of Variance Table
##
## Response: P_s_r
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 2.7161 0.45268 0.5945 0.732
## Residuals 28 21.3202 0.76143
TukeyHSD(P_s_r)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = P_s_r ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 0.0700 -1.680642 1.8206422 0.9999996
## Samacá-Control -0.1064 -1.857042 1.6442422 0.9999948
## Sibaté-Control -0.3800 -2.130642 1.3706422 0.9922228
## Soracá-Control -0.3220 -2.072642 1.4286422 0.9968214
## Toca-Control -0.0660 -1.816642 1.6846422 0.9999997
## Ventaquemada-Control -0.8020 -2.552642 0.9486422 0.7687086
## Samacá-Motavita -0.1764 -1.927042 1.5742422 0.9998971
## Sibaté-Motavita -0.4500 -2.200642 1.3006422 0.9813228
## Soracá-Motavita -0.3920 -2.142642 1.3586422 0.9908344
## Toca-Motavita -0.1360 -1.886642 1.6146422 0.9999777
## Ventaquemada-Motavita -0.8720 -2.622642 0.8786422 0.6954216
## Sibaté-Samacá -0.2736 -2.024242 1.4770422 0.9987157
## Soracá-Samacá -0.2156 -1.966242 1.5350422 0.9996699
## Toca-Samacá 0.0404 -1.710242 1.7910422 1.0000000
## Ventaquemada-Samacá -0.6956 -2.446242 1.0550422 0.8637229
## Soracá-Sibaté 0.0580 -1.692642 1.8086422 0.9999999
## Toca-Sibaté 0.3140 -1.436642 2.0646422 0.9972324
## Ventaquemada-Sibaté -0.4220 -2.172642 1.3286422 0.9865419
## Toca-Soracá 0.2560 -1.494642 2.0066422 0.9991180
## Ventaquemada-Soracá -0.4800 -2.230642 1.2706422 0.9742285
## Ventaquemada-Toca -0.7360 -2.486642 1.0146422 0.8304486
#Diferencias encontradas entre: No se encontraron diferencias
#Grafico de las diferencias
plot(TukeyHSD(P_s_r))
T_P_s_r=TukeyC(P_s_r,'Trt')
plot(T_P_s_r)
Estadisticos_P_f_a= df_g |>
group_by(Trt) |>
summarise(
Media= mean(P_f_a),
Min= min(P_f_a),
Max= max(P_f_a),
Var= sd(P_f_a)
); Estadisticos_P_f_a
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 92.4 81 105 11.3
## 2 Motavita 66.4 41 91 20.3
## 3 Samacá 89.5 83.1 99.7 7.39
## 4 Sibaté 69.4 34 105 31.6
## 5 Soracá 85.6 67 125 23.1
## 6 Toca 87 60 99 16.1
## 7 Ventaquemada 50.2 35 69 15.0
#Peso fresco de la parte aerea
ggplot(data= df_g, aes(x=Trt, y= P_f_a, color= Trt))+
geom_boxplot()+
ylab("Peso fresco de la parte aerea")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza peso fresco de la parte aerea
P_f_a= aov(P_f_a~Trt, data= df_g)
Anova_P_f_a=anova(P_f_a);Anova_P_f_a
## Analysis of Variance Table
##
## Response: P_f_a
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 7279 1213.17 3.2549 0.01484 *
## Residuals 28 10436 372.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(P_f_a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = P_f_a ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -26.000 -64.73198 12.7319813 0.3639643
## Samacá-Control -2.884 -41.61598 35.8479813 0.9999826
## Sibaté-Control -23.000 -61.73198 15.7319813 0.5068153
## Soracá-Control -6.800 -45.53198 31.9319813 0.9975410
## Toca-Control -5.400 -44.13198 33.3319813 0.9993276
## Ventaquemada-Control -42.200 -80.93198 -3.4680187 0.0259498
## Samacá-Motavita 23.116 -15.61598 61.8479813 0.5009718
## Sibaté-Motavita 3.000 -35.73198 41.7319813 0.9999781
## Soracá-Motavita 19.200 -19.53198 57.9319813 0.6999694
## Toca-Motavita 20.600 -18.13198 59.3319813 0.6296463
## Ventaquemada-Motavita -16.200 -54.93198 22.5319813 0.8337162
## Sibaté-Samacá -20.116 -58.84798 18.6159813 0.6542643
## Soracá-Samacá -3.916 -42.64798 34.8159813 0.9998950
## Toca-Samacá -2.516 -41.24798 36.2159813 0.9999923
## Ventaquemada-Samacá -39.316 -78.04798 -0.5840187 0.0448867
## Soracá-Sibaté 16.200 -22.53198 54.9319813 0.8337162
## Toca-Sibaté 17.600 -21.13198 56.3319813 0.7751490
## Ventaquemada-Sibaté -19.200 -57.93198 19.5319813 0.6999694
## Toca-Soracá 1.400 -37.33198 40.1319813 0.9999998
## Ventaquemada-Soracá -35.400 -74.13198 3.3319813 0.0904166
## Ventaquemada-Toca -36.800 -75.53198 1.9319813 0.0708499
#Diferencias encontradas entre: Control-Ventaquemada; Samaca-ventaquemada
#Grafico de las diferencias
plot(TukeyHSD(P_f_a))
T_P_f_a=TukeyC(P_f_a,'Trt')
plot(T_P_f_a)
Estadisticos_P_s_a= df_g |>
group_by(Trt) |>
summarise(
Media= mean(P_s_a),
Min= min(P_s_a),
Max= max(P_s_a),
Var= sd(P_s_a)
); Estadisticos_P_s_a
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 10.3 9.3 11.2 0.726
## 2 Motavita 8.85 3.96 11.7 2.95
## 3 Samacá 10.2 9.12 11.2 0.766
## 4 Sibaté 8.07 4.91 11.9 3.14
## 5 Soracá 9.20 7.78 12.6 1.99
## 6 Toca 9.18 5.78 10.9 2.02
## 7 Ventaquemada 6.55 4.71 8.97 1.81
#Peso seco de la parte aerea
ggplot(data= df_g, aes(x=Trt, y= P_s_a, color= Trt))+
geom_boxplot()+
ylab("Peso seco de la parte aerea")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza peso seco de la parte aerea
P_s_a= aov(P_s_a~Trt, data= df_g)
Anova_P_s_a=anova(P_s_a);Anova_P_s_a
## Analysis of Variance Table
##
## Response: P_s_a
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 50.479 8.4131 1.896 0.1167
## Residuals 28 124.247 4.4374
TukeyHSD(P_s_a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = P_s_a ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -1.454 -5.680154 2.7721536 0.9254580
## Samacá-Control -0.074 -4.300154 4.1521536 1.0000000
## Sibaté-Control -2.228 -6.454154 1.9981536 0.6388677
## Soracá-Control -1.102 -5.328154 3.1241536 0.9799261
## Toca-Control -1.120 -5.346154 3.1061536 0.9782296
## Ventaquemada-Control -3.750 -7.976154 0.4761536 0.1076678
## Samacá-Motavita 1.380 -2.846154 5.6061536 0.9409800
## Sibaté-Motavita -0.774 -5.000154 3.4521536 0.9968954
## Soracá-Motavita 0.352 -3.874154 4.5781536 0.9999663
## Toca-Motavita 0.334 -3.892154 4.5601536 0.9999753
## Ventaquemada-Motavita -2.296 -6.522154 1.9301536 0.6069767
## Sibaté-Samacá -2.154 -6.380154 2.0721536 0.6731408
## Soracá-Samacá -1.028 -5.254154 3.1981536 0.9858998
## Toca-Samacá -1.046 -5.272154 3.1801536 0.9845879
## Ventaquemada-Samacá -3.676 -7.902154 0.5501536 0.1204580
## Soracá-Sibaté 1.126 -3.100154 5.3521536 0.9776417
## Toca-Sibaté 1.108 -3.118154 5.3341536 0.9793717
## Ventaquemada-Sibaté -1.522 -5.748154 2.7041536 0.9091185
## Toca-Soracá -0.018 -4.244154 4.2081536 1.0000000
## Ventaquemada-Soracá -2.648 -6.874154 1.5781536 0.4440624
## Ventaquemada-Toca -2.630 -6.856154 1.5961536 0.4520611
#Diferencias encontradas entre: No se encontraron diferencias
#Grafico de las diferencias
plot(TukeyHSD(P_s_a))
T_P_s_a=TukeyC(P_s_a,'Trt')
plot(T_P_s_a)
Estadisticos_N_a= df_g |>
group_by(Trt) |>
summarise(
Media= mean(N_a),
Min= min(N_a),
Max= max(N_a),
Var= sd(N_a)
); Estadisticos_N_a
## # A tibble: 7 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Control 0 0 0 0
## 2 Motavita 0 0 0 0
## 3 Samacá 0 0 0 0
## 4 Sibaté 0 0 0 0
## 5 Soracá 0 0 0 0
## 6 Toca 0 0 0 0
## 7 Ventaquemada 14.4 1 34 13.0
#Numero de agallas
ggplot(data= df_g, aes(x=Trt, y= N_a, color= Trt))+
geom_boxplot()+
ylab("Numero de agallas")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza Numero de agallas
N_a= aov(N_a~Trt, data= df_g)
Anova_N_a=anova(N_a);Anova_N_a
## Analysis of Variance Table
##
## Response: N_a
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 888.69 148.114 6.0881 0.0003571 ***
## Residuals 28 681.20 24.329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(N_a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = N_a ~ Trt, data = df_g)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 2.220446e-15 -9.895535 9.895535 1.0000000
## Samacá-Control 1.509903e-14 -9.895535 9.895535 1.0000000
## Sibaté-Control 1.776357e-15 -9.895535 9.895535 1.0000000
## Soracá-Control 5.329071e-15 -9.895535 9.895535 1.0000000
## Toca-Control 1.776357e-15 -9.895535 9.895535 1.0000000
## Ventaquemada-Control 1.440000e+01 4.504465 24.295535 0.0013777
## Samacá-Motavita 1.287859e-14 -9.895535 9.895535 1.0000000
## Sibaté-Motavita -4.440892e-16 -9.895535 9.895535 1.0000000
## Soracá-Motavita 3.108624e-15 -9.895535 9.895535 1.0000000
## Toca-Motavita -4.440892e-16 -9.895535 9.895535 1.0000000
## Ventaquemada-Motavita 1.440000e+01 4.504465 24.295535 0.0013777
## Sibaté-Samacá -1.332268e-14 -9.895535 9.895535 1.0000000
## Soracá-Samacá -9.769963e-15 -9.895535 9.895535 1.0000000
## Toca-Samacá -1.332268e-14 -9.895535 9.895535 1.0000000
## Ventaquemada-Samacá 1.440000e+01 4.504465 24.295535 0.0013777
## Soracá-Sibaté 3.552714e-15 -9.895535 9.895535 1.0000000
## Toca-Sibaté 0.000000e+00 -9.895535 9.895535 1.0000000
## Ventaquemada-Sibaté 1.440000e+01 4.504465 24.295535 0.0013777
## Toca-Soracá -3.552714e-15 -9.895535 9.895535 1.0000000
## Ventaquemada-Soracá 1.440000e+01 4.504465 24.295535 0.0013777
## Ventaquemada-Toca 1.440000e+01 4.504465 24.295535 0.0013777
#Diferencias encontradas entre: Ventaquemada con todo los otros tratamientos
#Grafico de las diferencias
plot(TukeyHSD(N_a))
T_N_a=TukeyC(N_a,'Trt')
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
## Warning in qt(sig.level, aux_mt$coef[, 3]): NaNs produced
plot(T_N_a)
Hoja Numero 2: Diametro de los Quistosoros en mm
#Quistosoros
T_q<- read_excel("C:/Users/JuanSebH2/Desktop/Muestreo Virulencia SS 1 7-06-24 (1).xlsx",
sheet = "T_Q")
T_q= data.frame(T_q); T_q
## Trt D_1 D_2 P_d
## 1 Samaca 34.44 24.09 29.265
## 2 Samaca 12.94 17.37 15.155
## 3 Samaca 11.83 10.53 11.180
## 4 Samaca 19.88 13.39 16.635
## 5 Samaca 13.33 9.29 11.310
## 6 Samaca 29.82 26.88 28.350
## 7 Samaca 37.90 33.73 35.815
## 8 Samaca 9.23 9.32 9.275
## 9 Samaca 32.98 31.16 32.070
## 10 Samaca 25.17 26.20 25.685
## 11 Samaca 21.24 23.14 22.190
## 12 Samaca 31.97 30.02 30.995
## 13 Samaca 47.15 28.31 37.730
## 14 Samaca 21.86 42.27 32.065
## 15 Samaca 35.97 40.15 38.060
## 16 Samaca 32.93 28.58 30.755
## 17 Samaca 16.45 18.17 17.310
## 18 Samaca 24.03 21.55 22.790
## 19 Samaca 36.20 53.71 44.955
## 20 Samaca 32.15 28.45 30.300
## 21 Samaca 33.49 26.49 29.990
## 22 Samaca 24.80 24.65 24.725
## 23 Samaca 30.79 33.57 32.180
## 24 Samaca 39.13 38.06 38.595
## 25 Samaca 34.59 23.94 29.265
## 26 Samaca 28.58 32.80 30.690
## 27 Samaca 48.68 28.41 38.545
## 28 Samaca 19.53 48.27 33.900
## 29 Samaca 46.63 50.39 48.510
## 30 Samaca 27.46 27.78 27.620
## 31 Soraca 38.59 39.74 39.165
## 32 Soraca 31.35 37.42 34.385
## 33 Soraca 22.46 22.55 22.505
## 34 Soraca 18.21 11.99 15.100
## 35 Soraca 23.53 22.33 22.930
## 36 Soraca 12.23 24.83 18.530
## 37 Soraca 14.75 14.45 14.600
## 38 Soraca 10.94 21.02 15.980
## 39 Soraca 21.89 8.12 15.005
## 40 Soraca 12.48 14.53 13.505
## 41 Soraca 11.02 17.55 14.285
## 42 Soraca 13.23 17.31 15.270
## 43 Soraca 11.96 17.36 14.660
## 44 Soraca 9.71 11.40 10.555
## 45 Soraca 7.88 18.56 13.220
## 46 Soraca 12.24 13.10 12.670
## 47 Soraca 8.55 12.48 10.515
## 48 Soraca 26.87 31.47 29.170
## 49 Soraca 22.57 20.08 21.325
## 50 Soraca 12.53 19.27 15.900
## 51 Soraca 24.04 16.31 20.175
## 52 Soraca 16.93 28.25 22.590
## 53 Soraca 29.60 28.84 29.220
## 54 Soraca 26.10 19.16 22.630
## 55 Soraca 18.45 28.21 23.330
## 56 Soraca 14.94 25.74 20.340
## 57 Soraca 42.88 64.18 53.530
## 58 Soraca 31.61 68.41 50.010
## 59 Soraca 18.13 20.97 19.550
## 60 Soraca 14.41 13.71 14.060
## 61 Sibate 58.63 39.26 48.945
## 62 Sibate 38.81 37.66 38.235
## 63 Sibate 38.68 48.40 43.540
## 64 Sibate 40.29 51.77 46.030
## 65 Sibate 21.11 22.40 21.755
## 66 Sibate 18.57 16.94 17.755
## 67 Sibate 12.73 10.48 11.605
## 68 Sibate 15.30 10.67 12.985
## 69 Sibate 8.34 10.46 9.400
## 70 Sibate 18.32 23.17 20.745
## 71 Sibate 36.12 35.10 35.610
## 72 Sibate 24.10 29.54 26.820
## 73 Sibate 24.36 23.66 24.010
## 74 Sibate 24.57 15.51 20.040
## 75 Sibate 31.73 69.42 50.575
## 76 Sibate 48.56 30.39 39.475
## 77 Sibate 44.03 31.00 37.515
## 78 Sibate 50.98 27.53 39.255
## 79 Sibate 21.59 45.46 33.525
## 80 Sibate 19.23 23.08 21.155
## 81 Sibate 19.40 22.34 20.870
## 82 Sibate 21.39 25.08 23.235
## 83 Sibate 22.73 14.70 18.715
## 84 Sibate 35.10 18.41 26.755
## 85 Sibate 29.40 31.27 30.335
## 86 Sibate 36.37 19.37 27.870
## 87 Sibate 49.16 39.26 44.210
## 88 Sibate 47.56 39.59 43.575
## 89 Sibate 51.82 37.28 44.550
## 90 Sibate 25.30 41.85 33.575
## 91 Motavita 36.02 33.87 34.945
## 92 Motavita 22.28 19.40 20.840
## 93 Motavita 31.26 23.39 27.325
## 94 Motavita 19.73 23.74 21.735
## 95 Motavita 57.37 45.59 51.480
## 96 Motavita 28.64 40.02 34.330
## 97 Motavita 24.24 13.76 19.000
## 98 Motavita 28.37 23.05 25.710
## 99 Motavita 17.59 16.44 17.015
## 100 Motavita 47.03 30.35 38.690
## 101 Motavita 51.53 38.86 45.195
## 102 Motavita 28.28 50.40 39.340
## 103 Motavita 18.76 13.02 15.890
## 104 Motavita 21.16 15.31 18.235
## 105 Motavita 54.47 29.10 41.785
## 106 Motavita 13.84 15.67 14.755
## 107 Motavita 46.59 41.27 43.930
## 108 Motavita 20.82 21.28 21.050
## 109 Motavita 15.77 14.90 15.335
## 110 Motavita 31.50 29.21 30.355
## 111 Motavita 28.93 42.36 35.645
## 112 Motavita 26.10 20.98 23.540
## 113 Motavita 27.60 42.68 35.140
## 114 Motavita 28.08 30.35 29.215
## 115 Motavita 38.89 21.79 30.340
## 116 Motavita 31.02 34.29 32.655
## 117 Motavita 17.28 38.07 27.675
## 118 Motavita 12.28 14.47 13.375
## 119 Motavita 30.46 33.18 31.820
## 120 Motavita 41.50 36.13 38.815
## 121 Ventaquemada 65.35 48.32 56.835
## 122 Ventaquemada 43.08 74.01 58.545
## 123 Ventaquemada 29.60 31.85 30.725
## 124 Ventaquemada 24.09 28.60 26.345
## 125 Ventaquemada 34.57 36.92 35.745
## 126 Ventaquemada 38.11 29.54 33.825
## 127 Ventaquemada 43.70 40.78 42.240
## 128 Ventaquemada 33.47 44.30 38.885
## 129 Ventaquemada 36.68 40.19 38.435
## 130 Ventaquemada 67.52 51.75 59.635
## 131 Ventaquemada 23.81 28.20 26.005
## 132 Ventaquemada 30.60 41.99 36.295
## 133 Ventaquemada 30.12 28.45 29.285
## 134 Ventaquemada 18.09 24.95 21.520
## 135 Ventaquemada 26.15 19.24 22.695
## 136 Ventaquemada 18.87 22.49 20.680
## 137 Ventaquemada 17.04 22.95 19.995
## 138 Ventaquemada 19.52 17.65 18.585
## 139 Ventaquemada 16.33 11.31 13.820
## 140 Ventaquemada 16.17 11.45 13.810
## 141 Ventaquemada 17.33 20.84 19.085
## 142 Ventaquemada 41.61 65.19 53.400
## 143 Ventaquemada 32.15 32.79 32.470
## 144 Ventaquemada 24.71 25.92 25.315
## 145 Ventaquemada 39.00 33.34 36.170
## 146 Ventaquemada 37.02 40.09 38.555
## 147 Ventaquemada 49.91 49.85 49.880
## 148 Ventaquemada 24.78 33.70 29.240
## 149 Ventaquemada 30.50 41.92 36.210
## 150 Ventaquemada 52.02 52.46 52.240
## 151 Toca 29.13 36.94 33.035
## 152 Toca 17.22 15.39 16.305
## 153 Toca 18.76 16.49 17.625
## 154 Toca 11.47 8.37 9.920
## 155 Toca 10.42 11.26 10.840
## 156 Toca 28.55 31.76 30.155
## 157 Toca 39.20 34.73 36.965
## 158 Toca 87.52 51.61 69.565
## 159 Toca 32.06 31.44 31.750
## 160 Toca 49.57 31.42 40.495
## 161 Toca 57.71 46.42 52.065
## 162 Toca 25.71 27.40 26.555
## 163 Toca 38.60 28.77 33.685
## 164 Toca 31.41 38.64 35.025
## 165 Toca 49.76 47.13 48.445
## 166 Toca 28.34 25.90 27.120
## 167 Toca 12.26 15.36 13.810
## 168 Toca 29.15 21.06 25.105
## 169 Toca 27.71 37.50 32.605
## 170 Toca 12.65 15.72 14.185
## 171 Toca 33.74 31.98 32.860
## 172 Toca 21.98 29.54 25.760
## 173 Toca 17.78 20.76 19.270
## 174 Toca 20.30 30.40 25.350
## 175 Toca 28.04 34.03 31.035
## 176 Toca 21.38 24.40 22.890
## 177 Toca 32.60 31.01 31.805
## 178 Toca 24.54 38.09 31.315
## 179 Toca 29.93 47.27 38.600
## 180 Toca 25.01 30.42 27.715
Estadisticos_D_1= T_q |>
group_by(Trt) |>
summarise(
Media= mean(D_1),
Min= min(D_1),
Max= max(D_1),
Var= sd(D_1)
); Estadisticos_D_1
## # A tibble: 6 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Motavita 29.9 12.3 57.4 12.1
## 2 Samaca 28.7 9.23 48.7 10.4
## 3 Sibate 31.1 8.34 58.6 13.3
## 4 Soraca 19.3 7.88 42.9 8.97
## 5 Toca 29.8 10.4 87.5 15.7
## 6 Ventaquemada 32.7 16.2 67.5 13.5
#Diametro 1
ggplot(data= T_q, aes(x=Trt, y= D_1, color= Trt))+
geom_boxplot()+
ylab("Diametro 1 (mm)")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza Diametro 1
D_1= aov(D_1~Trt, data= T_q)
Anova_D_1=anova(D_1);Anova_D_1
## Analysis of Variance Table
##
## Response: D_1
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 5 3372 674.41 4.2965 0.001038 **
## Residuals 174 27312 156.97
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(D_1)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = D_1 ~ Trt, data = T_q)
##
## $Trt
## diff lwr upr p adj
## Samaca-Motavita -1.208000 -10.530087 8.11408731 0.9990404
## Sibate-Motavita 1.229667 -8.092421 10.55175398 0.9989541
## Soraca-Motavita -10.577000 -19.899087 -1.25491269 0.0161259
## Toca-Motavita -0.163000 -9.485087 9.15908731 1.0000000
## Ventaquemada-Motavita 2.817000 -6.505087 12.13908731 0.9529601
## Sibate-Samaca 2.437667 -6.884421 11.75975398 0.9746924
## Soraca-Samaca -9.369000 -18.691087 -0.04691269 0.0480655
## Toca-Samaca 1.045000 -8.277087 10.36708731 0.9995258
## Ventaquemada-Samaca 4.025000 -5.297087 13.34708731 0.8143604
## Soraca-Sibate -11.806667 -21.128754 -2.48457935 0.0046081
## Toca-Sibate -1.392667 -10.714754 7.92942065 0.9980956
## Ventaquemada-Sibate 1.587333 -7.734754 10.90942065 0.9964491
## Toca-Soraca 10.414000 1.091913 19.73608731 0.0188422
## Ventaquemada-Soraca 13.394000 4.071913 22.71608731 0.0007569
## Ventaquemada-Toca 2.980000 -6.342087 12.30208731 0.9406263
#Diferencias encontradas entre: Soraca con todo los otros tratamientos
#Grafico de las diferencias
plot(TukeyHSD(D_1))
T_D_1=TukeyC(D_1,'Trt')
plot(T_D_1)
Estadisticos_D_2= T_q |>
group_by(Trt) |>
summarise(
Media= mean(D_2),
Min= min(D_2),
Max= max(D_2),
Var= sd(D_2)
); Estadisticos_D_2
## # A tibble: 6 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Motavita 28.4 13.0 50.4 10.9
## 2 Samaca 28.4 9.29 53.7 11.2
## 3 Sibate 29.7 10.5 69.4 13.7
## 4 Soraca 23.6 8.12 68.4 13.8
## 5 Toca 29.7 8.37 51.6 10.9
## 6 Ventaquemada 35.0 11.3 74.0 14.6
#Diametro 2
ggplot(data= T_q, aes(x=Trt, y= D_2, color= Trt))+
geom_boxplot()+
ylab("Diametro 2 (mm)")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza Diametro 2
D_2= aov(D_2~Trt, data= T_q)
Anova_D_2=anova(D_2);Anova_D_2
## Analysis of Variance Table
##
## Response: D_2
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 5 2001 400.20 2.5082 0.03198 *
## Residuals 174 27763 159.56
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(D_2)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = D_2 ~ Trt, data = T_q)
##
## $Trt
## diff lwr upr p adj
## Samaca-Motavita -0.075333333 -9.474040 9.323374 1.0000000
## Sibate-Motavita 1.270666667 -8.128040 10.669374 0.9988220
## Soraca-Motavita -4.786333333 -14.185040 4.612374 0.6853456
## Toca-Motavita 1.276000000 -8.122707 10.674707 0.9987980
## Ventaquemada-Motavita 6.603666667 -2.795040 16.002374 0.3326186
## Sibate-Samaca 1.346000000 -8.052707 10.744707 0.9984453
## Soraca-Samaca -4.711000000 -14.109707 4.687707 0.6997960
## Toca-Samaca 1.351333333 -8.047374 10.750040 0.9984154
## Ventaquemada-Samaca 6.679000000 -2.719707 16.077707 0.3198867
## Soraca-Sibate -6.057000000 -15.455707 3.341707 0.4321819
## Toca-Sibate 0.005333333 -9.393374 9.404040 1.0000000
## Ventaquemada-Sibate 5.333000000 -4.065707 14.731707 0.5764799
## Toca-Soraca 6.062333333 -3.336374 15.461040 0.4311581
## Ventaquemada-Soraca 11.390000000 1.991293 20.788707 0.0078713
## Ventaquemada-Toca 5.327666667 -4.071040 14.726374 0.5775603
#Diferencias encontradas entre: Ventaquemada-Soraca
#Grafico de las diferencias
plot(TukeyHSD(D_2))
T_D_2=TukeyC(D_2,'Trt')
plot(T_D_2)
Estadisticos_P_d= T_q |>
group_by(Trt) |>
summarise(
Media= mean(P_d),
Min= min(P_d),
Max= max(P_d),
Var= sd(P_d)
); Estadisticos_P_d
## # A tibble: 6 × 5
## Trt Media Min Max Var
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Motavita 29.2 13.4 51.5 10.2
## 2 Samaca 28.5 9.28 48.5 9.65
## 3 Sibate 30.4 9.4 50.6 11.8
## 4 Soraca 21.5 10.5 53.5 10.7
## 5 Toca 29.7 9.92 69.6 12.6
## 6 Ventaquemada 33.9 13.8 59.6 13.2
#Diametro 2
ggplot(data= T_q, aes(x=Trt, y= P_d, color= Trt))+
geom_boxplot()+
ylab("Promedio de diametro (mm)")+
xlab("Tratamiento")+
theme_bw()
#Analisis de varianza Diametro 2
P_d= aov(P_d~Trt, data= T_q)
Anova_P_d=anova(P_d);Anova_P_d
## Analysis of Variance Table
##
## Response: P_d
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 5 2488.1 497.61 3.8169 0.002635 **
## Residuals 174 22684.9 130.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(P_d)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = P_d ~ Trt, data = T_q)
##
## $Trt
## diff lwr upr p adj
## Samaca-Motavita -0.6416667 -9.1374507 7.8541174 0.9999320
## Sibate-Motavita 1.2501667 -7.2456174 9.7459507 0.9982288
## Soraca-Motavita -7.6816667 -16.1774507 0.8141174 0.1013628
## Toca-Motavita 0.5565000 -7.9392841 9.0522841 0.9999664
## Ventaquemada-Motavita 4.7103333 -3.7854507 13.2061174 0.6011678
## Sibate-Samaca 1.8918333 -6.6039507 10.3876174 0.9876519
## Soraca-Samaca -7.0400000 -15.5357841 1.4557841 0.1662044
## Toca-Samaca 1.1981667 -7.2976174 9.6939507 0.9985559
## Ventaquemada-Samaca 5.3520000 -3.1437841 13.8477841 0.4585999
## Soraca-Sibate -8.9318333 -17.4276174 -0.4360493 0.0330828
## Toca-Sibate -0.6936667 -9.1894507 7.8021174 0.9999000
## Ventaquemada-Sibate 3.4601667 -5.0356174 11.9559507 0.8488178
## Toca-Soraca 8.2381667 -0.2576174 16.7339507 0.0631116
## Ventaquemada-Soraca 12.3920000 3.8962159 20.8877841 0.0005921
## Ventaquemada-Toca 4.1538333 -4.3419507 12.6496174 0.7215872
#Diferencias encontradas entre: Ventaquemada-Soraca, Sibate-Soraca
#Grafico de las diferencias
plot(TukeyHSD(P_d))
T_P_d=TukeyC(P_d,'Trt')
plot(T_P_d)
Hoja Numero 3: ALTURA Y CRC
Ac <- read_excel("C:/Users/JuanSebH2/Desktop/Muestreo Virulencia SS 1 7-06-24 (1).xlsx", sheet = "ALTURA Y CRC")
Ac=data.frame(Ac);Ac
## Muestreo Trt Altura CRC
## 1 Mayo_8 Ventaquemada 26.4 39.9
## 2 Mayo_8 Ventaquemada 25.5 43.5
## 3 Mayo_8 Ventaquemada 23.1 39.4
## 4 Mayo_8 Ventaquemada 21.0 41.7
## 5 Mayo_8 Ventaquemada 26.1 45.8
## 6 Mayo_8 Ventaquemada 23.9 31.7
## 7 Mayo_8 Ventaquemada 21.0 39.8
## 8 Mayo_8 Ventaquemada 22.2 44.9
## 9 Mayo_8 Ventaquemada 25.7 48.3
## 10 Mayo_8 Ventaquemada 23.5 39.2
## 11 Mayo_8 Soraca 44.0 45.8
## 12 Mayo_8 Soraca 35.5 37.3
## 13 Mayo_8 Soraca 31.1 40.1
## 14 Mayo_8 Soraca 36.7 45.8
## 15 Mayo_8 Soraca 30.7 43.2
## 16 Mayo_8 Soraca 27.0 32.2
## 17 Mayo_8 Soraca 21.6 40.5
## 18 Mayo_8 Soraca 28.9 45.4
## 19 Mayo_8 Soraca 30.1 45.0
## 20 Mayo_8 Soraca 30.3 40.9
## 21 Mayo_8 Samaca 35.1 47.5
## 22 Mayo_8 Samaca 28.6 43.7
## 23 Mayo_8 Samaca 31.4 35.8
## 24 Mayo_8 Samaca 34.8 39.3
## 25 Mayo_8 Samaca 27.5 39.0
## 26 Mayo_8 Samaca 33.1 32.7
## 27 Mayo_8 Samaca 37.0 42.1
## 28 Mayo_8 Samaca 24.1 45.8
## 29 Mayo_8 Samaca 34.3 41.1
## 30 Mayo_8 Samaca 27.3 41.7
## 31 Mayo_8 Motavita 28.7 44.5
## 32 Mayo_8 Motavita 31.6 41.4
## 33 Mayo_8 Motavita 28.7 49.0
## 34 Mayo_8 Motavita 31.0 40.1
## 35 Mayo_8 Motavita 29.0 36.2
## 36 Mayo_8 Motavita 37.4 35.5
## 37 Mayo_8 Motavita 32.4 40.8
## 38 Mayo_8 Motavita 23.2 38.5
## 39 Mayo_8 Motavita 37.0 41.7
## 40 Mayo_8 Motavita 35.2 37.1
## 41 Mayo_8 Sibate 26.2 44.2
## 42 Mayo_8 Sibate 27.2 46.6
## 43 Mayo_8 Sibate 22.6 38.1
## 44 Mayo_8 Sibate 21.5 43.5
## 45 Mayo_8 Sibate 30.4 38.5
## 46 Mayo_8 Sibate 21.6 41.3
## 47 Mayo_8 Sibate 26.0 41.6
## 48 Mayo_8 Sibate 23.0 39.8
## 49 Mayo_8 Sibate 30.5 40.6
## 50 Mayo_8 Sibate 31.4 38.2
## 51 Mayo_8 Toca 25.5 47.9
## 52 Mayo_8 Toca 19.3 44.1
## 53 Mayo_8 Toca 31.0 40.0
## 54 Mayo_8 Toca 26.0 34.7
## 55 Mayo_8 Toca 23.1 42.2
## 56 Mayo_8 Toca 24.9 42.8
## 57 Mayo_8 Toca 37.5 45.3
## 58 Mayo_8 Toca 31.5 41.0
## 59 Mayo_8 Toca 29.4 42.9
## 60 Mayo_8 Toca 31.2 42.9
## 61 Mayo_8 Control 28.5 44.1
## 62 Mayo_8 Control 31.5 39.4
## 63 Mayo_8 Control 27.0 49.7
## 64 Mayo_8 Control 32.1 43.3
## 65 Mayo_8 Control 39.2 41.0
## 66 Mayo_8 Control 30.5 41.2
## 67 Mayo_8 Control 29.0 38.9
## 68 Mayo_8 Control 31.5 39.9
## 69 Mayo_8 Control 26.1 37.7
## 70 Mayo_8 Control 34.5 40.6
## 71 Mayo_17 Ventaquemada 26.5 38.8
## 72 Mayo_17 Ventaquemada 26.0 46.3
## 73 Mayo_17 Ventaquemada 24.2 26.5
## 74 Mayo_17 Ventaquemada 22.0 27.6
## 75 Mayo_17 Ventaquemada 27.3 44.4
## 76 Mayo_17 Ventaquemada 21.4 54.1
## 77 Mayo_17 Ventaquemada 21.5 30.2
## 78 Mayo_17 Ventaquemada 23.7 45.8
## 79 Mayo_17 Ventaquemada 21.9 35.4
## 80 Mayo_17 Ventaquemada 25.9 37.0
## 81 Mayo_17 Soraca 48.0 31.5
## 82 Mayo_17 Soraca 38.2 37.1
## 83 Mayo_17 Soraca 31.6 39.5
## 84 Mayo_17 Soraca 37.1 43.7
## 85 Mayo_17 Soraca 31.5 38.9
## 86 Mayo_17 Soraca 28.5 38.2
## 87 Mayo_17 Soraca 22.4 48.1
## 88 Mayo_17 Soraca 30.1 47.7
## 89 Mayo_17 Soraca 32.1 43.6
## 90 Mayo_17 Soraca 32.0 42.3
## 91 Mayo_17 Samaca 37.0 38.1
## 92 Mayo_17 Samaca 30.6 41.5
## 93 Mayo_17 Samaca 32.9 41.9
## 94 Mayo_17 Samaca 36.2 40.5
## 95 Mayo_17 Samaca 29.1 32.6
## 96 Mayo_17 Samaca 34.2 45.0
## 97 Mayo_17 Samaca 37.4 43.9
## 98 Mayo_17 Samaca 26.1 49.0
## 99 Mayo_17 Samaca 36.3 38.6
## 100 Mayo_17 Samaca 30.1 43.1
## 101 Mayo_17 Motavita 30.3 46.6
## 102 Mayo_17 Motavita 34.2 41.0
## 103 Mayo_17 Motavita 29.5 45.4
## 104 Mayo_17 Motavita 28.2 40.3
## 105 Mayo_17 Motavita 30.0 34.5
## 106 Mayo_17 Motavita 37.9 39.6
## 107 Mayo_17 Motavita 33.6 39.8
## 108 Mayo_17 Motavita 24.0 31.1
## 109 Mayo_17 Motavita 39.0 46.0
## 110 Mayo_17 Motavita 38.8 39.4
## 111 Mayo_17 Sibate 28.5 36.8
## 112 Mayo_17 Sibate 28.3 42.9
## 113 Mayo_17 Sibate 23.0 33.0
## 114 Mayo_17 Sibate 31.1 42.5
## 115 Mayo_17 Sibate 34.0 40.0
## 116 Mayo_17 Sibate 27.4 40.0
## 117 Mayo_17 Sibate 26.5 39.4
## 118 Mayo_17 Sibate 24.0 41.1
## 119 Mayo_17 Sibate 31.5 46.5
## 120 Mayo_17 Sibate 32.6 34.6
## 121 Mayo_17 Toca 25.6 45.7
## 122 Mayo_17 Toca 20.9 45.4
## 123 Mayo_17 Toca 32.9 38.0
## 124 Mayo_17 Toca 27.2 40.4
## 125 Mayo_17 Toca 24.0 49.1
## 126 Mayo_17 Toca 27.7 43.1
## 127 Mayo_17 Toca 38.5 46.0
## 128 Mayo_17 Toca 31.9 41.0
## 129 Mayo_17 Toca 28.8 44.8
## 130 Mayo_17 Toca 33.1 42.9
## 131 Mayo_17 Control 28.5 39.1
## 132 Mayo_17 Control 31.9 39.1
## 133 Mayo_17 Control 28.0 44.5
## 134 Mayo_17 Control 33.0 44.5
## 135 Mayo_17 Control 41.0 38.8
## 136 Mayo_17 Control 33.0 42.7
## 137 Mayo_17 Control 28.6 40.6
## 138 Mayo_17 Control 32.0 40.5
## 139 Mayo_17 Control 27.7 34.5
## 140 Mayo_17 Control 34.7 37.8
## 141 Mayo_24 Ventaquemada 30.0 46.8
## 142 Mayo_24 Ventaquemada 28.5 50.6
## 143 Mayo_24 Ventaquemada 25.0 46.2
## 144 Mayo_24 Ventaquemada 25.0 47.2
## 145 Mayo_24 Ventaquemada 31.0 33.8
## 146 Mayo_24 Ventaquemada 26.3 21.3
## 147 Mayo_24 Ventaquemada 24.5 42.1
## 148 Mayo_24 Ventaquemada 27.0 49.5
## 149 Mayo_24 Ventaquemada 28.0 49.4
## 150 Mayo_24 Ventaquemada 26.0 34.7
## 151 Mayo_24 Soraca 55.1 49.5
## 152 Mayo_24 Soraca 41.1 43.1
## 153 Mayo_24 Soraca 37.5 46.8
## 154 Mayo_24 Soraca 40.0 47.3
## 155 Mayo_24 Soraca 36.5 49.2
## 156 Mayo_24 Soraca 28.5 46.1
## 157 Mayo_24 Soraca 25.0 53.7
## 158 Mayo_24 Soraca 32.5 52.8
## 159 Mayo_24 Soraca 35.0 48.0
## 160 Mayo_24 Soraca 35.5 41.8
## 161 Mayo_24 Samaca 42.5 32.1
## 162 Mayo_24 Samaca 36.0 48.2
## 163 Mayo_24 Samaca 34.5 31.7
## 164 Mayo_24 Samaca 40.5 45.1
## 165 Mayo_24 Samaca 32.5 48.7
## 166 Mayo_24 Samaca 37.0 30.8
## 167 Mayo_24 Samaca 40.5 47.4
## 168 Mayo_24 Samaca 27.0 43.2
## 169 Mayo_24 Samaca 39.0 45.2
## 170 Mayo_24 Samaca 32.0 45.2
## 171 Mayo_24 Motavita 32.5 30.9
## 172 Mayo_24 Motavita 40.0 32.9
## 173 Mayo_24 Motavita 32.0 45.4
## 174 Mayo_24 Motavita 33.9 48.2
## 175 Mayo_24 Motavita 33.0 20.7
## 176 Mayo_24 Motavita 41.6 35.6
## 177 Mayo_24 Motavita 36.0 29.5
## 178 Mayo_24 Motavita 27.0 31.3
## 179 Mayo_24 Motavita 36.6 41.7
## 180 Mayo_24 Motavita 43.0 37.1
## 181 Mayo_24 Sibate 33.0 27.6
## 182 Mayo_24 Sibate 30.0 29.3
## 183 Mayo_24 Sibate 21.5 39.7
## 184 Mayo_24 Sibate 31.5 29.3
## 185 Mayo_24 Sibate 36.2 34.3
## 186 Mayo_24 Sibate 33.5 41.5
## 187 Mayo_24 Sibate 30.5 33.8
## 188 Mayo_24 Sibate 28.0 37.3
## 189 Mayo_24 Sibate 35.0 38.0
## 190 Mayo_24 Sibate 34.2 27.4
## 191 Mayo_24 Toca 30.5 50.0
## 192 Mayo_24 Toca 25.1 50.4
## 193 Mayo_24 Toca 35.5 41.8
## 194 Mayo_24 Toca 31.5 47.8
## 195 Mayo_24 Toca 30.0 31.1
## 196 Mayo_24 Toca 31.0 31.6
## 197 Mayo_24 Toca 42.0 49.3
## 198 Mayo_24 Toca 34.0 44.6
## 199 Mayo_24 Toca 32.0 50.3
## 200 Mayo_24 Toca 36.0 36.4
## 201 Mayo_24 Control 33.5 41.0
## 202 Mayo_24 Control 36.1 47.6
## 203 Mayo_24 Control 32.5 42.8
## 204 Mayo_24 Control 37.5 47.1
## 205 Mayo_24 Control 45.1 43.1
## 206 Mayo_24 Control 35.3 31.6
## 207 Mayo_24 Control 33.5 31.1
## 208 Mayo_24 Control 36.2 23.6
## 209 Mayo_24 Control 30.5 29.9
## 210 Mayo_24 Control 39.0 44.7
Ac$Muestreo <- factor(Ac$Muestreo, levels = c("Mayo_8", "Mayo_17", "Mayo_24"))
Ac_g= Ac |>
group_by(Muestreo, Trt) |>
summarise(
Media= mean(Altura),
Min= min(Altura),
Max= max(Altura),
Var= sd(Altura)
)
## `summarise()` has grouped output by 'Muestreo'. You can override using the
## `.groups` argument.
Ac_g
## # A tibble: 21 × 6
## # Groups: Muestreo [3]
## Muestreo Trt Media Min Max Var
## <fct> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Mayo_8 Control 31.0 26.1 39.2 3.83
## 2 Mayo_8 Motavita 31.4 23.2 37.4 4.35
## 3 Mayo_8 Samaca 31.3 24.1 37 4.23
## 4 Mayo_8 Sibate 26.0 21.5 31.4 3.81
## 5 Mayo_8 Soraca 31.6 21.6 44 6.04
## 6 Mayo_8 Toca 27.9 19.3 37.5 5.20
## 7 Mayo_8 Ventaquemada 23.8 21 26.4 2.04
## 8 Mayo_17 Control 31.8 27.7 41 4.06
## 9 Mayo_17 Motavita 32.6 24 39 5.01
## 10 Mayo_17 Samaca 33.0 26.1 37.4 3.87
## # ℹ 11 more rows
ggplot(Ac, aes(x= Trt, y= Altura, color= Trt))+
geom_boxplot()+
xlab("Tratamiento")+
facet_wrap(~Muestreo)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size= 7))
#Filtrando por fechas
Ac_g8m=Ac |>
filter(Muestreo=="Mayo_8");Ac_g8m
## Muestreo Trt Altura CRC
## 1 Mayo_8 Ventaquemada 26.4 39.9
## 2 Mayo_8 Ventaquemada 25.5 43.5
## 3 Mayo_8 Ventaquemada 23.1 39.4
## 4 Mayo_8 Ventaquemada 21.0 41.7
## 5 Mayo_8 Ventaquemada 26.1 45.8
## 6 Mayo_8 Ventaquemada 23.9 31.7
## 7 Mayo_8 Ventaquemada 21.0 39.8
## 8 Mayo_8 Ventaquemada 22.2 44.9
## 9 Mayo_8 Ventaquemada 25.7 48.3
## 10 Mayo_8 Ventaquemada 23.5 39.2
## 11 Mayo_8 Soraca 44.0 45.8
## 12 Mayo_8 Soraca 35.5 37.3
## 13 Mayo_8 Soraca 31.1 40.1
## 14 Mayo_8 Soraca 36.7 45.8
## 15 Mayo_8 Soraca 30.7 43.2
## 16 Mayo_8 Soraca 27.0 32.2
## 17 Mayo_8 Soraca 21.6 40.5
## 18 Mayo_8 Soraca 28.9 45.4
## 19 Mayo_8 Soraca 30.1 45.0
## 20 Mayo_8 Soraca 30.3 40.9
## 21 Mayo_8 Samaca 35.1 47.5
## 22 Mayo_8 Samaca 28.6 43.7
## 23 Mayo_8 Samaca 31.4 35.8
## 24 Mayo_8 Samaca 34.8 39.3
## 25 Mayo_8 Samaca 27.5 39.0
## 26 Mayo_8 Samaca 33.1 32.7
## 27 Mayo_8 Samaca 37.0 42.1
## 28 Mayo_8 Samaca 24.1 45.8
## 29 Mayo_8 Samaca 34.3 41.1
## 30 Mayo_8 Samaca 27.3 41.7
## 31 Mayo_8 Motavita 28.7 44.5
## 32 Mayo_8 Motavita 31.6 41.4
## 33 Mayo_8 Motavita 28.7 49.0
## 34 Mayo_8 Motavita 31.0 40.1
## 35 Mayo_8 Motavita 29.0 36.2
## 36 Mayo_8 Motavita 37.4 35.5
## 37 Mayo_8 Motavita 32.4 40.8
## 38 Mayo_8 Motavita 23.2 38.5
## 39 Mayo_8 Motavita 37.0 41.7
## 40 Mayo_8 Motavita 35.2 37.1
## 41 Mayo_8 Sibate 26.2 44.2
## 42 Mayo_8 Sibate 27.2 46.6
## 43 Mayo_8 Sibate 22.6 38.1
## 44 Mayo_8 Sibate 21.5 43.5
## 45 Mayo_8 Sibate 30.4 38.5
## 46 Mayo_8 Sibate 21.6 41.3
## 47 Mayo_8 Sibate 26.0 41.6
## 48 Mayo_8 Sibate 23.0 39.8
## 49 Mayo_8 Sibate 30.5 40.6
## 50 Mayo_8 Sibate 31.4 38.2
## 51 Mayo_8 Toca 25.5 47.9
## 52 Mayo_8 Toca 19.3 44.1
## 53 Mayo_8 Toca 31.0 40.0
## 54 Mayo_8 Toca 26.0 34.7
## 55 Mayo_8 Toca 23.1 42.2
## 56 Mayo_8 Toca 24.9 42.8
## 57 Mayo_8 Toca 37.5 45.3
## 58 Mayo_8 Toca 31.5 41.0
## 59 Mayo_8 Toca 29.4 42.9
## 60 Mayo_8 Toca 31.2 42.9
## 61 Mayo_8 Control 28.5 44.1
## 62 Mayo_8 Control 31.5 39.4
## 63 Mayo_8 Control 27.0 49.7
## 64 Mayo_8 Control 32.1 43.3
## 65 Mayo_8 Control 39.2 41.0
## 66 Mayo_8 Control 30.5 41.2
## 67 Mayo_8 Control 29.0 38.9
## 68 Mayo_8 Control 31.5 39.9
## 69 Mayo_8 Control 26.1 37.7
## 70 Mayo_8 Control 34.5 40.6
Ac_g17m=Ac |>
filter(Muestreo=="Mayo_17");Ac_g17m
## Muestreo Trt Altura CRC
## 1 Mayo_17 Ventaquemada 26.5 38.8
## 2 Mayo_17 Ventaquemada 26.0 46.3
## 3 Mayo_17 Ventaquemada 24.2 26.5
## 4 Mayo_17 Ventaquemada 22.0 27.6
## 5 Mayo_17 Ventaquemada 27.3 44.4
## 6 Mayo_17 Ventaquemada 21.4 54.1
## 7 Mayo_17 Ventaquemada 21.5 30.2
## 8 Mayo_17 Ventaquemada 23.7 45.8
## 9 Mayo_17 Ventaquemada 21.9 35.4
## 10 Mayo_17 Ventaquemada 25.9 37.0
## 11 Mayo_17 Soraca 48.0 31.5
## 12 Mayo_17 Soraca 38.2 37.1
## 13 Mayo_17 Soraca 31.6 39.5
## 14 Mayo_17 Soraca 37.1 43.7
## 15 Mayo_17 Soraca 31.5 38.9
## 16 Mayo_17 Soraca 28.5 38.2
## 17 Mayo_17 Soraca 22.4 48.1
## 18 Mayo_17 Soraca 30.1 47.7
## 19 Mayo_17 Soraca 32.1 43.6
## 20 Mayo_17 Soraca 32.0 42.3
## 21 Mayo_17 Samaca 37.0 38.1
## 22 Mayo_17 Samaca 30.6 41.5
## 23 Mayo_17 Samaca 32.9 41.9
## 24 Mayo_17 Samaca 36.2 40.5
## 25 Mayo_17 Samaca 29.1 32.6
## 26 Mayo_17 Samaca 34.2 45.0
## 27 Mayo_17 Samaca 37.4 43.9
## 28 Mayo_17 Samaca 26.1 49.0
## 29 Mayo_17 Samaca 36.3 38.6
## 30 Mayo_17 Samaca 30.1 43.1
## 31 Mayo_17 Motavita 30.3 46.6
## 32 Mayo_17 Motavita 34.2 41.0
## 33 Mayo_17 Motavita 29.5 45.4
## 34 Mayo_17 Motavita 28.2 40.3
## 35 Mayo_17 Motavita 30.0 34.5
## 36 Mayo_17 Motavita 37.9 39.6
## 37 Mayo_17 Motavita 33.6 39.8
## 38 Mayo_17 Motavita 24.0 31.1
## 39 Mayo_17 Motavita 39.0 46.0
## 40 Mayo_17 Motavita 38.8 39.4
## 41 Mayo_17 Sibate 28.5 36.8
## 42 Mayo_17 Sibate 28.3 42.9
## 43 Mayo_17 Sibate 23.0 33.0
## 44 Mayo_17 Sibate 31.1 42.5
## 45 Mayo_17 Sibate 34.0 40.0
## 46 Mayo_17 Sibate 27.4 40.0
## 47 Mayo_17 Sibate 26.5 39.4
## 48 Mayo_17 Sibate 24.0 41.1
## 49 Mayo_17 Sibate 31.5 46.5
## 50 Mayo_17 Sibate 32.6 34.6
## 51 Mayo_17 Toca 25.6 45.7
## 52 Mayo_17 Toca 20.9 45.4
## 53 Mayo_17 Toca 32.9 38.0
## 54 Mayo_17 Toca 27.2 40.4
## 55 Mayo_17 Toca 24.0 49.1
## 56 Mayo_17 Toca 27.7 43.1
## 57 Mayo_17 Toca 38.5 46.0
## 58 Mayo_17 Toca 31.9 41.0
## 59 Mayo_17 Toca 28.8 44.8
## 60 Mayo_17 Toca 33.1 42.9
## 61 Mayo_17 Control 28.5 39.1
## 62 Mayo_17 Control 31.9 39.1
## 63 Mayo_17 Control 28.0 44.5
## 64 Mayo_17 Control 33.0 44.5
## 65 Mayo_17 Control 41.0 38.8
## 66 Mayo_17 Control 33.0 42.7
## 67 Mayo_17 Control 28.6 40.6
## 68 Mayo_17 Control 32.0 40.5
## 69 Mayo_17 Control 27.7 34.5
## 70 Mayo_17 Control 34.7 37.8
Ac_g24m=Ac |>
filter(Muestreo=="Mayo_24");Ac_g24m
## Muestreo Trt Altura CRC
## 1 Mayo_24 Ventaquemada 30.0 46.8
## 2 Mayo_24 Ventaquemada 28.5 50.6
## 3 Mayo_24 Ventaquemada 25.0 46.2
## 4 Mayo_24 Ventaquemada 25.0 47.2
## 5 Mayo_24 Ventaquemada 31.0 33.8
## 6 Mayo_24 Ventaquemada 26.3 21.3
## 7 Mayo_24 Ventaquemada 24.5 42.1
## 8 Mayo_24 Ventaquemada 27.0 49.5
## 9 Mayo_24 Ventaquemada 28.0 49.4
## 10 Mayo_24 Ventaquemada 26.0 34.7
## 11 Mayo_24 Soraca 55.1 49.5
## 12 Mayo_24 Soraca 41.1 43.1
## 13 Mayo_24 Soraca 37.5 46.8
## 14 Mayo_24 Soraca 40.0 47.3
## 15 Mayo_24 Soraca 36.5 49.2
## 16 Mayo_24 Soraca 28.5 46.1
## 17 Mayo_24 Soraca 25.0 53.7
## 18 Mayo_24 Soraca 32.5 52.8
## 19 Mayo_24 Soraca 35.0 48.0
## 20 Mayo_24 Soraca 35.5 41.8
## 21 Mayo_24 Samaca 42.5 32.1
## 22 Mayo_24 Samaca 36.0 48.2
## 23 Mayo_24 Samaca 34.5 31.7
## 24 Mayo_24 Samaca 40.5 45.1
## 25 Mayo_24 Samaca 32.5 48.7
## 26 Mayo_24 Samaca 37.0 30.8
## 27 Mayo_24 Samaca 40.5 47.4
## 28 Mayo_24 Samaca 27.0 43.2
## 29 Mayo_24 Samaca 39.0 45.2
## 30 Mayo_24 Samaca 32.0 45.2
## 31 Mayo_24 Motavita 32.5 30.9
## 32 Mayo_24 Motavita 40.0 32.9
## 33 Mayo_24 Motavita 32.0 45.4
## 34 Mayo_24 Motavita 33.9 48.2
## 35 Mayo_24 Motavita 33.0 20.7
## 36 Mayo_24 Motavita 41.6 35.6
## 37 Mayo_24 Motavita 36.0 29.5
## 38 Mayo_24 Motavita 27.0 31.3
## 39 Mayo_24 Motavita 36.6 41.7
## 40 Mayo_24 Motavita 43.0 37.1
## 41 Mayo_24 Sibate 33.0 27.6
## 42 Mayo_24 Sibate 30.0 29.3
## 43 Mayo_24 Sibate 21.5 39.7
## 44 Mayo_24 Sibate 31.5 29.3
## 45 Mayo_24 Sibate 36.2 34.3
## 46 Mayo_24 Sibate 33.5 41.5
## 47 Mayo_24 Sibate 30.5 33.8
## 48 Mayo_24 Sibate 28.0 37.3
## 49 Mayo_24 Sibate 35.0 38.0
## 50 Mayo_24 Sibate 34.2 27.4
## 51 Mayo_24 Toca 30.5 50.0
## 52 Mayo_24 Toca 25.1 50.4
## 53 Mayo_24 Toca 35.5 41.8
## 54 Mayo_24 Toca 31.5 47.8
## 55 Mayo_24 Toca 30.0 31.1
## 56 Mayo_24 Toca 31.0 31.6
## 57 Mayo_24 Toca 42.0 49.3
## 58 Mayo_24 Toca 34.0 44.6
## 59 Mayo_24 Toca 32.0 50.3
## 60 Mayo_24 Toca 36.0 36.4
## 61 Mayo_24 Control 33.5 41.0
## 62 Mayo_24 Control 36.1 47.6
## 63 Mayo_24 Control 32.5 42.8
## 64 Mayo_24 Control 37.5 47.1
## 65 Mayo_24 Control 45.1 43.1
## 66 Mayo_24 Control 35.3 31.6
## 67 Mayo_24 Control 33.5 31.1
## 68 Mayo_24 Control 36.2 23.6
## 69 Mayo_24 Control 30.5 29.9
## 70 Mayo_24 Control 39.0 44.7
#Muestreo 8 de mayo Altura y CRC
Altura_8m <- ggplot(Ac_g8m, aes(x = Trt, y = Altura, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
CRC_8m <- ggplot(Ac_g8m, aes(x = Trt, y = CRC, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
grid.arrange(Altura_8m, CRC_8m, ncol = 2)
#Analisis de Altura
A_g8m= aov(Altura~Trt, data= Ac_g8m)
Anova_A_g8m=anova(A_g8m);Anova_A_g8m
## Analysis of Variance Table
##
## Response: Altura
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 584.15 97.358 5.0998 0.0002534 ***
## Residuals 63 1202.70 19.091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(A_g8m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Altura ~ Trt, data = Ac_g8m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 0.43 -5.5211011 6.3811011 0.9999897
## Samaca-Control 0.33 -5.6211011 6.2811011 0.9999979
## Sibate-Control -4.95 -10.9011011 1.0011011 0.1652443
## Soraca-Control 0.60 -5.3511011 6.5511011 0.9999265
## Toca-Control -3.05 -9.0011011 2.9011011 0.7070926
## Ventaquemada-Control -7.15 -13.1011011 -1.1988989 0.0089083
## Samaca-Motavita -0.10 -6.0511011 5.8511011 1.0000000
## Sibate-Motavita -5.38 -11.3311011 0.5711011 0.1020154
## Soraca-Motavita 0.17 -5.7811011 6.1211011 1.0000000
## Toca-Motavita -3.48 -9.4311011 2.4711011 0.5655444
## Ventaquemada-Motavita -7.58 -13.5311011 -1.6288989 0.0045049
## Sibate-Samaca -5.28 -11.2311011 0.6711011 0.1146268
## Soraca-Samaca 0.27 -5.6811011 6.2211011 0.9999994
## Toca-Samaca -3.38 -9.3311011 2.5711011 0.5991223
## Ventaquemada-Samaca -7.48 -13.4311011 -1.5288989 0.0052928
## Soraca-Sibate 5.55 -0.4011011 11.5011011 0.0831897
## Toca-Sibate 1.90 -4.0511011 7.8511011 0.9581241
## Ventaquemada-Sibate -2.20 -8.1511011 3.7511011 0.9177121
## Toca-Soraca -3.65 -9.6011011 2.3011011 0.5086649
## Ventaquemada-Soraca -7.75 -13.7011011 -1.7988989 0.0034134
## Ventaquemada-Toca -4.10 -10.0511011 1.8511011 0.3663947
#Diferencias encontradas entre: Ventaquemada con Motavita, Soraca y Samaca
#Grafico de las diferencias
plot(TukeyHSD(A_g8m))
T_A_g8m=TukeyC(A_g8m,'Trt')
plot(T_A_g8m)
#Analisis CRC
C_g8m= aov(CRC~Trt, data= Ac_g8m)
Anova_C_g8m=anova(C_g8m);Anova_C_g8m
## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 21.88 3.647 0.2344 0.9637
## Residuals 63 980.40 15.562
TukeyHSD(C_g8m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRC ~ Trt, data = Ac_g8m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -1.10 -6.473052 4.273052 0.9957948
## Samaca-Control -0.71 -6.083052 4.663052 0.9996448
## Sibate-Control -0.34 -5.713052 5.033052 0.9999953
## Soraca-Control 0.04 -5.333052 5.413052 1.0000000
## Toca-Control 0.80 -4.573052 6.173052 0.9992947
## Ventaquemada-Control -0.16 -5.533052 5.213052 0.9999999
## Samaca-Motavita 0.39 -4.983052 5.763052 0.9999894
## Sibate-Motavita 0.76 -4.613052 6.133052 0.9994743
## Soraca-Motavita 1.14 -4.233052 6.513052 0.9948903
## Toca-Motavita 1.90 -3.473052 7.273052 0.9325578
## Ventaquemada-Motavita 0.94 -4.433052 6.313052 0.9982411
## Sibate-Samaca 0.37 -5.003052 5.743052 0.9999923
## Soraca-Samaca 0.75 -4.623052 6.123052 0.9995128
## Toca-Samaca 1.51 -3.863052 6.883052 0.9776307
## Ventaquemada-Samaca 0.55 -4.823052 5.923052 0.9999197
## Soraca-Sibate 0.38 -4.993052 5.753052 0.9999909
## Toca-Sibate 1.14 -4.233052 6.513052 0.9948903
## Ventaquemada-Sibate 0.18 -5.193052 5.553052 0.9999999
## Toca-Soraca 0.76 -4.613052 6.133052 0.9994743
## Ventaquemada-Soraca -0.20 -5.573052 5.173052 0.9999998
## Ventaquemada-Toca -0.96 -6.333052 4.413052 0.9980208
#Diferencias encontradas entre: No se encuentran diferencias
#Grafico de las diferencias
plot(TukeyHSD(C_g8m))
T_C_g8m=TukeyC(C_g8m,'Trt')
plot(T_C_g8m)
#Muestreo 17 de mayo Altura y CRC
Altura_17m <- ggplot(Ac_g17m, aes(x = Trt, y = Altura, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
CRC_17m <- ggplot(Ac_g17m, aes(x = Trt, y = CRC, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
grid.arrange(Altura_17m, CRC_17m, ncol = 2)
#Analisis de Altura
A_g17m= aov(Altura~Trt, data= Ac_g17m)
Anova_A_g17m=anova(A_g17m);Anova_A_g17m
## Analysis of Variance Table
##
## Response: Altura
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 661.03 110.172 5.2296 0.0002012 ***
## Residuals 63 1327.22 21.067
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(A_g17m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Altura ~ Trt, data = Ac_g17m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 0.71 -5.54158 6.96158 0.9998525
## Samaca-Control 1.15 -5.10158 7.40158 0.9976711
## Sibate-Control -3.15 -9.40158 3.10158 0.7231278
## Soraca-Control 1.31 -4.94158 7.56158 0.9952247
## Toca-Control -2.78 -9.03158 3.47158 0.8233089
## Ventaquemada-Control -7.80 -14.05158 -1.54842 0.0057787
## Samaca-Motavita 0.44 -5.81158 6.69158 0.9999912
## Sibate-Motavita -3.86 -10.11158 2.39158 0.5005518
## Soraca-Motavita 0.60 -5.65158 6.85158 0.9999450
## Toca-Motavita -3.49 -9.74158 2.76158 0.6184326
## Ventaquemada-Motavita -8.51 -14.76158 -2.25842 0.0018999
## Sibate-Samaca -4.30 -10.55158 1.95158 0.3683644
## Soraca-Samaca 0.16 -6.09158 6.41158 1.0000000
## Toca-Samaca -3.93 -10.18158 2.32158 0.4786117
## Ventaquemada-Samaca -8.95 -15.20158 -2.69842 0.0009243
## Soraca-Sibate 4.46 -1.79158 10.71158 0.3247888
## Toca-Sibate 0.37 -5.88158 6.62158 0.9999969
## Ventaquemada-Sibate -4.65 -10.90158 1.60158 0.2770091
## Toca-Soraca -4.09 -10.34158 2.16158 0.4295641
## Ventaquemada-Soraca -9.11 -15.36158 -2.85842 0.0007075
## Ventaquemada-Toca -5.02 -11.27158 1.23158 0.1974198
#Diferencias encontradas entre: Ventaquemada con Motavita, Soraca y Samaca
#Grafico de las diferencias
plot(TukeyHSD(A_g17m))
T_A_g17m=TukeyC(A_g17m,'Trt')
plot(T_A_g17m)
#Analisis CRC
C_g17m= aov(CRC~Trt, data= Ac_g17m)
Anova_C_g17m=anova(C_g17m);Anova_C_g17m
## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 150.48 25.080 0.9358 0.4759
## Residuals 63 1688.50 26.802
TukeyHSD(C_g17m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRC ~ Trt, data = Ac_g17m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control 0.16 -6.891292 7.211292 1.0000000
## Samaca-Control 1.21 -5.841292 8.261292 0.9984220
## Sibate-Control -0.53 -7.581292 6.521292 0.9999870
## Soraca-Control 0.85 -6.201292 7.901292 0.9997914
## Toca-Control 3.43 -3.621292 10.481292 0.7545422
## Ventaquemada-Control -1.60 -8.651292 5.451292 0.9926580
## Samaca-Motavita 1.05 -6.001292 8.101292 0.9992942
## Sibate-Motavita -0.69 -7.741292 6.361292 0.9999384
## Soraca-Motavita 0.69 -6.361292 7.741292 0.9999384
## Toca-Motavita 3.27 -3.781292 10.321292 0.7931659
## Ventaquemada-Motavita -1.76 -8.811292 5.291292 0.9878378
## Sibate-Samaca -1.74 -8.791292 5.311292 0.9885445
## Soraca-Samaca -0.36 -7.411292 6.691292 0.9999987
## Toca-Samaca 2.22 -4.831292 9.271292 0.9608415
## Ventaquemada-Samaca -2.81 -9.861292 4.241292 0.8862684
## Soraca-Sibate 1.38 -5.671292 8.431292 0.9967167
## Toca-Sibate 3.96 -3.091292 11.011292 0.6117959
## Ventaquemada-Sibate -1.07 -8.121292 5.981292 0.9992139
## Toca-Soraca 2.58 -4.471292 9.631292 0.9213853
## Ventaquemada-Soraca -2.45 -9.501292 4.601292 0.9377539
## Ventaquemada-Toca -5.03 -12.081292 2.021292 0.3249126
#Diferencias encontradas entre: No se encuentran diferencias
#Grafico de las diferencias
plot(TukeyHSD(C_g17m))
T_C_g17m=TukeyC(C_g17m,'Trt')
plot(T_C_g17m)
#Muestreo 24 de mayo Altura y CRC
Altura_24m <- ggplot(Ac_g24m, aes(x = Trt, y = Altura, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
CRC_24m <- ggplot(Ac_g24m, aes(x = Trt, y = CRC, color = Trt)) +
geom_boxplot() +
xlab("Tratamiento") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 7))
grid.arrange(Altura_24m, CRC_24m, ncol = 2)
#Analisis de Altura
A_g24m= aov(Altura~Trt, data= Ac_g24m)
Anova_A_g24m=anova(A_g24m);Anova_A_g24m
## Analysis of Variance Table
##
## Response: Altura
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 728.1 121.350 4.9161 0.0003518 ***
## Residuals 63 1555.1 24.684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(A_g24m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Altura ~ Trt, data = Ac_g24m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -0.36 -7.127053 6.407053 0.9999983
## Samaca-Control 0.23 -6.537053 6.997053 0.9999999
## Sibate-Control -4.58 -11.347053 2.187053 0.3879752
## Soraca-Control 0.75 -6.017053 7.517053 0.9998721
## Toca-Control -3.16 -9.927053 3.607053 0.7878508
## Ventaquemada-Control -8.79 -15.557053 -2.022947 0.0035267
## Samaca-Motavita 0.59 -6.177053 7.357053 0.9999688
## Sibate-Motavita -4.22 -10.987053 2.547053 0.4884405
## Soraca-Motavita 1.11 -5.657053 7.877053 0.9987766
## Toca-Motavita -2.80 -9.567053 3.967053 0.8671343
## Ventaquemada-Motavita -8.43 -15.197053 -1.662947 0.0058862
## Sibate-Samaca -4.81 -11.577053 1.957053 0.3291196
## Soraca-Samaca 0.52 -6.247053 7.287053 0.9999852
## Toca-Samaca -3.39 -10.157053 3.377053 0.7284743
## Ventaquemada-Samaca -9.02 -15.787053 -2.252947 0.0025228
## Soraca-Sibate 5.33 -1.437053 12.097053 0.2163262
## Toca-Sibate 1.42 -5.347053 8.187053 0.9951882
## Ventaquemada-Sibate -4.21 -10.977053 2.557053 0.4913368
## Toca-Soraca -3.91 -10.677053 2.857053 0.5794742
## Ventaquemada-Soraca -9.54 -16.307053 -2.772947 0.0011590
## Ventaquemada-Toca -5.63 -12.397053 1.137053 0.1650423
#Diferencias encontradas entre: Ventaquemada con Motavita, Soraca, Samaca y control
#Grafico de las diferencias
plot(TukeyHSD(A_g24m))
T_A_g24m=TukeyC(A_g24m,'Trt')
plot(T_A_g24m)
#Analisis CRC
C_g24m= aov(CRC~Trt, data= Ac_g24m)
Anova_C_g24m=anova(C_g24m);Anova_C_g24m
## Analysis of Variance Table
##
## Response: CRC
## Df Sum Sq Mean Sq F value Pr(>F)
## Trt 6 1423.5 237.24 4.3667 0.0009514 ***
## Residuals 63 3422.8 54.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(C_g24m)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = CRC ~ Trt, data = Ac_g24m)
##
## $Trt
## diff lwr upr p adj
## Motavita-Control -2.92 -12.9593893 7.119389 0.9734324
## Samaca-Control 3.51 -6.5293893 13.549389 0.9359564
## Sibate-Control -4.43 -14.4693893 5.609389 0.8284843
## Soraca-Control 9.58 -0.4593893 19.619389 0.0709388
## Toca-Control 5.08 -4.9593893 15.119389 0.7191927
## Ventaquemada-Control 3.91 -6.1293893 13.949389 0.8967969
## Samaca-Motavita 6.43 -3.6093893 16.469389 0.4557137
## Sibate-Motavita -1.51 -11.5493893 8.529389 0.9992526
## Soraca-Motavita 12.50 2.4606107 22.539389 0.0059224
## Toca-Motavita 8.00 -2.0393893 18.039389 0.2048270
## Ventaquemada-Motavita 6.83 -3.2093893 16.869389 0.3816725
## Sibate-Samaca -7.94 -17.9793893 2.099389 0.2122294
## Soraca-Samaca 6.07 -3.9693893 16.109389 0.5259359
## Toca-Samaca 1.57 -8.4693893 11.609389 0.9990668
## Ventaquemada-Samaca 0.40 -9.6393893 10.439389 0.9999997
## Soraca-Sibate 14.01 3.9706107 24.049389 0.0013418
## Toca-Sibate 9.51 -0.5293893 19.549389 0.0747079
## Ventaquemada-Sibate 8.34 -1.6993893 18.379389 0.1663457
## Toca-Soraca -4.50 -14.5393893 5.539389 0.8178704
## Ventaquemada-Soraca -5.67 -15.7093893 4.369389 0.6054742
## Ventaquemada-Toca -1.17 -11.2093893 8.869389 0.9998286
#Diferencias encontradas entre: Soraca contra motavita y sivate
#Grafico de las diferencias
plot(TukeyHSD(C_g24m))
T_C_g24m=TukeyC(C_g24m,'Trt')
plot(T_C_g24m)
