#Librerias necesarias para realizar los análisis
library(ggplot2)
library(ggthemes)
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library(tidyverse)
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library(lme4)
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library(car)
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library(RcmdrMisc)
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library(spatstat)
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#Lectura de datos
Datos2<-read.csv("Especies.csv", header=TRUE)
#Histogramas para observar normalidad de datos
#altura
windows()
ggplot(data = Datos2, mapping = aes(x = altura, colour = especie)) +
geom_histogram() +
theme_few() +
labs(title="Altura de especies arbóreas") +
facet_grid(~ especie) +
theme(legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#dbh
windows()
ggplot(data = Datos2, mapping = aes(x = dbh, colour = especie)) +
geom_histogram() +
theme_few() +
labs(title="Diámetro a la altura del pecho de especies arbóreas") +
facet_grid(~ especie) +
theme(legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#cobertura
windows()
ggplot(data = Datos2, mapping = aes(x = cobertura, colour = especie)) +
geom_histogram() +
theme_few() +
labs(title="Cobertura de especies arbóreas") +
facet_grid(~ especie) +
theme(plot.title=element_text(size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Prueba de normalidad Shappiro-Wilk
#altura
shapiro.test(lm(altura~especie, Datos2)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(altura ~ especie, Datos2)$residuals
## W = 0.98539, p-value = 0.001801
#dbh
shapiro.test(lm(dbh~especie, Datos2)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(dbh ~ especie, Datos2)$residuals
## W = 0.95587, p-value = 1.703e-08
#cobertura
shapiro.test(lm(cobertura~especie, Datos2)$residuals)
##
## Shapiro-Wilk normality test
##
## data: lm(cobertura ~ especie, Datos2)$residuals
## W = 0.86375, p-value < 2.2e-16
#Homoscedasticidad prueba Bartlett
#altura
bartlett.test(altura~especie, Datos2)
##
## Bartlett test of homogeneity of variances
##
## data: altura by especie
## Bartlett's K-squared = 4.4002, df = 3, p-value = 0.2214
#dbh
bartlett.test(dbh~especie, Datos2)
##
## Bartlett test of homogeneity of variances
##
## data: dbh by especie
## Bartlett's K-squared = 31.31, df = 3, p-value = 7.313e-07
#cobertura
bartlett.test(cobertura~especie, Datos2)
##
## Bartlett test of homogeneity of variances
##
## data: cobertura by especie
## Bartlett's K-squared = 64.007, df = 3, p-value = 8.18e-14
#Prueba no paramétrica Kruskal-Wallis
#altura entre especies
kruskal.test(altura~ especie, data=Datos2)
##
## Kruskal-Wallis rank sum test
##
## data: altura by especie
## Kruskal-Wallis chi-squared = 131.83, df = 3, p-value < 2.2e-16
#Post hoc
pairwise.wilcox.test(x=Datos2$altura, g= Datos2$especie, p.adjust.method = "holm")
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: Datos2$altura and Datos2$especie
##
## CODI ORXA TETE
## ORXA 1.0e-10 - -
## TETE 4.0e-16 0.63 -
## ZISP < 2e-16 3.1e-06 2.2e-07
##
## P value adjustment method: holm
#DBH entre especies
kruskal.test(dbh~ especie, data=Datos2)
##
## Kruskal-Wallis rank sum test
##
## data: dbh by especie
## Kruskal-Wallis chi-squared = 113.79, df = 3, p-value < 2.2e-16
#Post hoc
pairwise.wilcox.test(x=Datos2$dbh, g= Datos2$especie, p.adjust.method = "holm")
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: Datos2$dbh and Datos2$especie
##
## CODI ORXA TETE
## ORXA 0.0003 - -
## TETE 1.9e-14 0.0427 -
## ZISP < 2e-16 1.0e-07 4.7e-09
##
## P value adjustment method: holm
#Cobertura entre especies
kruskal.test(cobertura~ especie, data=Datos2)
##
## Kruskal-Wallis rank sum test
##
## data: cobertura by especie
## Kruskal-Wallis chi-squared = 103.45, df = 3, p-value < 2.2e-16
#Post hoc
pairwise.wilcox.test(x=Datos2$cobertura, g= Datos2$especie, p.adjust.method = "holm")
##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: Datos2$cobertura and Datos2$especie
##
## CODI ORXA TETE
## ORXA 0.0027 - -
## TETE 1.9e-09 0.1469 -
## ZISP < 2e-16 6.8e-07 3.9e-09
##
## P value adjustment method: holm
#Boxplot de variables explicativas por especie
#Altura
windows()
ggplot(data = Datos2, aes(x = factor(especie), y = altura, colour=especie)) +
geom_boxplot() +
geom_jitter(color="#222424", size=1, alpha=0.3) +
labs(title="Altura de especies arbóreas",
x="Especie", y="Altura en m") +
theme_few()+
theme(axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie", alpha=0)
#DBH
windows()
ggplot(data = Datos2, aes(x = factor(especie), y = dbh, colour=especie)) +
geom_boxplot() +
geom_jitter(color="#222424", size=1, alpha=0.3) +
labs(title="Diámetro a la altura del pecho de especies arbóreas", x="Especie", y="DBH en m") +
theme_few()+
theme(axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie")
#cobertura
windows()
ggplot(data = Datos2, aes(x = factor(especie), y = cobertura, colour=especie)) +
geom_boxplot() +
geom_jitter(color="#222424", size=1, alpha=0.3) +
labs(title="Cobertura de especies arbóreas",
x="Especie", y="Cobertura en m") +
theme_few()+
theme(axis.text.x = element_text(size=rel(1.35)),
axis.title.x = element_text(face="bold", hjust=0.5, size=15),
axis.title.y = element_text(face="bold", hjust=0.5, size=15),
legend.title=element_text(face="bold", color="black", hjust=0.5))+
labs(fill="Especie")
#Modelos explicativos de cobertura a partir de variables explicativas altura, DBH y estadío
#Correlación de Spearman entre variables explicativas contínuas y cobertura
#dbh
x<-Datos2$cobertura
y1<- Datos2$dbh
cor.test(x,y1, method="spearman")
## Warning in cor.test.default(x, y1, method = "spearman"): Cannot compute exact p-
## value with ties
##
## Spearman's rank correlation rho
##
## data: x and y1
## S = 840809, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8658106
regresiondbh<-lm(Datos2$cobertura~Datos2$dbh, data=Datos2)
summary(regresiondbh)
##
## Call:
## lm(formula = Datos2$cobertura ~ Datos2$dbh, data = Datos2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -115.741 -17.450 -3.552 8.055 170.080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.012 3.487 -2.011 0.0452 *
## Datos2$dbh 314.521 14.544 21.625 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 36.13 on 333 degrees of freedom
## Multiple R-squared: 0.5841, Adjusted R-squared: 0.5828
## F-statistic: 467.6 on 1 and 333 DF, p-value: < 2.2e-16
#Ecuación obtenida: y=-7.012+314.521x
#Gráfica de dispersión con línea de regresión lineal
windows()
plot(Datos2$dbh, Datos2$cobertura, xlab="dbh", ylab="Cobertura", col="lightsalmon", main = "Gráfico de dispersión: Cobertura - DBH")
abline(regresiondbh, col="black")
#altura
y2<- Datos2$altura
cor.test(x,y2, method="spearman")
## Warning in cor.test.default(x, y2, method = "spearman"): Cannot compute exact p-
## value with ties
##
## Spearman's rank correlation rho
##
## data: x and y2
## S = 1093087, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8255481
regresionalt<-lm(Datos2$cobertura~Datos2$altura, data=Datos2)
summary(regresionalt)
##
## Call:
## lm(formula = Datos2$cobertura ~ Datos2$altura, data = Datos2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -99.86 -22.34 -1.54 11.03 218.37
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -36.8700 5.8057 -6.351 7.03e-10 ***
## Datos2$altura 6.5081 0.3791 17.168 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 40.81 on 333 degrees of freedom
## Multiple R-squared: 0.4695, Adjusted R-squared: 0.4679
## F-statistic: 294.7 on 1 and 333 DF, p-value: < 2.2e-16
#Ecuación obtenida: y=-36.87+6.508x
#Gráfica de dispersión con línea de regresión lineal
windows()
plot(Datos2$altura, Datos2$cobertura, xlab="Altura", ylab="Cobertura", col="lightsalmon", main = "Gráfico de dispersión: Cobertura - Altura")
abline(regresionalt, col="black")
#Correlación Spearman entre Cobertura, DBH y altura
cor.test(x,y1+y2, method="spearman")
## Warning in cor.test.default(x, y1 + y2, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: x and y1 + y2
## S = 1023616, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8366354
#Regresión lineal
regresionaltdbh<-lm(formula =Datos2$cobertura~Datos2$altura+Datos2$dbh)
summary(regresionaltdbh)
##
## Call:
## lm(formula = Datos2$cobertura ~ Datos2$altura + Datos2$dbh)
##
## Residuals:
## Min 1Q Median 3Q Max
## -99.594 -17.936 -3.000 8.779 175.863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.2262 5.4730 -3.148 0.0018 **
## Datos2$altura 1.4630 0.6071 2.410 0.0165 *
## Datos2$dbh 261.5374 26.3038 9.943 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 35.87 on 332 degrees of freedom
## Multiple R-squared: 0.5912, Adjusted R-squared: 0.5888
## F-statistic: 240.1 on 2 and 332 DF, p-value: < 2.2e-16
#Correlación entre variables explicativas VIF: Factor de inflación de varianza
vif(regresionaltdbh)
## Datos2$altura Datos2$dbh
## 3.318016 3.318016
#GLM
#Documento sin las columnas correspondientes a x, y, y especies
Datos3<-read.csv("Especies_3.csv", header=TRUE)
#Modelo que toma como variables explicativas a todas las variables dentro del documento: altura, DBH y etapa
modelo.completo <- glm(cobertura ~ ., family = gaussian, data = Datos3)
#Evaluación de modelos por criterio AIC
evaluacion_modelosAIC <- stepwise(modelo.completo, direction='forward/backward', criterion='AIC')
##
## Direction: forward/backward
## Criterion: AIC
##
## Start: AIC=3649.96
## cobertura ~ 1
##
## Df Deviance AIC
## + dbh 1 434712 3358.1
## + altura 1 554461 3439.6
## + etapa 1 684385 3510.1
## <none> 1045195 3650.0
##
## Step: AIC=3358.07
## cobertura ~ dbh
##
## Df Deviance AIC
## + altura 1 427239 3354.3
## <none> 434712 3358.1
## + etapa 1 434257 3359.7
## - dbh 1 1045195 3650.0
##
## Step: AIC=3354.26
## cobertura ~ dbh + altura
##
## Df Deviance AIC
## <none> 427239 3354.3
## + etapa 1 425753 3355.1
## - altura 1 434712 3358.1
## - dbh 1 554461 3439.6
summary(evaluacion_modelosAIC)
##
## Call:
## glm(formula = cobertura ~ dbh + altura, family = gaussian, data = Datos3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -99.594 -17.936 -3.000 8.779 175.863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.2262 5.4730 -3.148 0.0018 **
## dbh 261.5374 26.3038 9.943 <2e-16 ***
## altura 1.4630 0.6071 2.410 0.0165 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1286.863)
##
## Null deviance: 1045195 on 334 degrees of freedom
## Residual deviance: 427239 on 332 degrees of freedom
## AIC: 3354.3
##
## Number of Fisher Scoring iterations: 2
#Correlación entre variables explicativas VIF: Factor de inflación de varianza
vif(evaluacion_modelosAIC)
## dbh altura
## 3.318016 3.318016
#Evaluación de modelos por criterio BIC
evaluacion_modelosBIC <- stepwise(modelo.completo, direction='forward/backward', criterion='BIC')
##
## Direction: forward/backward
## Criterion: BIC
##
## Start: AIC=3653.77
## cobertura ~ 1
##
## Df Deviance AIC
## + dbh 1 434712 3365.7
## + altura 1 554461 3447.2
## + etapa 1 684385 3517.7
## <none> 1045195 3653.8
##
## Step: AIC=3365.7
## cobertura ~ dbh
##
## Df Deviance AIC
## <none> 434712 3365.7
## + altura 1 427239 3365.7
## + etapa 1 434257 3371.2
## - dbh 1 1045195 3653.8
summary(evaluacion_modelosBIC)
##
## Call:
## glm(formula = cobertura ~ dbh, family = gaussian, data = Datos3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -115.741 -17.450 -3.552 8.055 170.080
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.012 3.487 -2.011 0.0452 *
## dbh 314.521 14.544 21.625 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1305.442)
##
## Null deviance: 1045195 on 334 degrees of freedom
## Residual deviance: 434712 on 333 degrees of freedom
## AIC: 3358.1
##
## Number of Fisher Scoring iterations: 2
#Distribución de especies en espacio mapeado
#Tabla
distribucion<-table(Datos2$especie)
distribucion
##
## CODI ORXA TETE ZISP
## 119 63 80 73
#Selección de datos correspondientes únicamente a especie CODI
CODI<-subset(Datos2, especie=="CODI")
CODI
## x y especie etapa altura dbh cobertura
## 1 6.00 16.00 CODI 0 5.0 0.027 5.94
## 2 4.00 16.00 CODI 0 5.0 0.028 3.14
## 3 12.50 21.50 CODI 0 5.7 0.032 5.94
## 4 14.10 10.60 CODI 0 6.5 0.035 3.98
## 5 8.90 4.50 CODI 0 2.5 0.039 8.55
## 6 12.40 12.50 CODI 0 6.5 0.048 15.90
## 7 88.80 45.50 CODI 0 4.5 0.069 3.14
## 8 67.80 9.80 CODI 0 10.0 0.076 28.27
## 9 44.10 63.60 CODI 0 11.0 0.080 33.18
## 10 99.30 50.60 CODI 0 16.5 0.099 34.73
## 11 70.40 22.90 CODI 0 18.0 0.110 9.62
## 12 49.70 5.60 CODI 0 12.0 0.124 44.18
## 13 41.50 6.30 CODI 0 11.0 0.130 38.48
## 14 11.40 6.50 CODI 0 13.8 0.135 25.97
## 15 46.30 60.40 CODI 0 20.0 0.153 19.63
## 16 95.00 3.50 CODI 0 20.0 0.154 19.63
## 17 96.00 58.10 CODI 0 16.0 0.160 82.52
## 18 64.10 56.50 CODI 0 15.0 0.165 63.62
## 19 97.70 6.00 CODI 0 19.0 0.165 19.63
## 20 46.75 84.30 CODI 0 18.0 0.169 50.27
## 21 56.65 62.00 CODI 0 10.0 0.170 33.18
## 22 80.90 29.60 CODI 0 13.0 0.170 33.18
## 23 24.30 55.60 CODI 0 18.0 0.174 35.78
## 24 78.50 66.50 CODI 0 17.0 0.178 38.48
## 25 92.10 82.80 CODI 0 17.5 0.180 60.13
## 26 64.60 16.90 CODI 0 18.0 0.180 28.27
## 27 8.00 52.20 CODI 0 13.0 0.191 42.43
## 28 99.60 56.80 CODI 0 19.0 0.194 117.86
## 29 99.00 56.60 CODI 0 17.0 0.197 123.70
## 30 42.15 81.90 CODI 0 22.0 0.199 63.62
## 31 72.80 29.80 CODI 1 16.0 0.204 23.76
## 32 95.60 45.60 CODI 1 19.0 0.205 50.27
## 33 69.40 45.60 CODI 1 16.5 0.205 23.76
## 34 2.20 42.50 CODI 1 18.0 0.207 52.17
## 35 31.40 28.10 CODI 1 17.0 0.210 78.54
## 36 43.10 12.20 CODI 1 16.0 0.210 95.03
## 37 43.70 61.10 CODI 1 16.0 0.212 70.88
## 38 90.20 46.00 CODI 1 20.0 0.215 28.27
## 39 89.30 43.50 CODI 1 20.0 0.220 44.18
## 40 97.30 32.50 CODI 1 18.0 0.225 86.59
## 41 49.80 48.30 CODI 1 19.0 0.226 132.73
## 42 46.50 53.20 CODI 1 18.0 0.235 122.72
## 43 91.60 1.00 CODI 1 20.0 0.237 38.48
## 44 98.60 1.30 CODI 1 22.0 0.240 28.27
## 45 95.40 7.90 CODI 1 22.0 0.245 44.18
## 46 37.80 18.00 CODI 1 17.0 0.255 38.48
## 47 41.00 79.50 CODI 1 22.0 0.260 153.94
## 48 86.70 53.30 CODI 1 22.0 0.260 28.27
## 49 97.00 48.80 CODI 1 17.0 0.265 50.27
## 50 60.60 89.70 CODI 1 18.0 0.268 72.38
## 51 47.40 2.50 CODI 1 18.0 0.268 70.88
## 52 89.80 40.60 CODI 1 18.0 0.270 38.48
## 53 29.10 7.60 CODI 1 18.0 0.270 95.03
## 54 69.20 89.10 CODI 1 20.0 0.273 28.27
## 55 93.60 87.60 CODI 1 19.3 0.274 84.95
## 56 52.10 34.00 CODI 1 20.0 0.275 33.18
## 57 20.40 57.80 CODI 1 18.0 0.290 70.88
## 58 52.30 26.30 CODI 1 16.0 0.295 86.59
## 59 48.90 72.00 CODI 1 23.0 0.300 143.14
## 60 76.20 65.50 CODI 1 18.0 0.300 70.88
## 61 39.00 27.00 CODI 1 20.0 0.300 165.13
## 62 8.30 41.10 CODI 1 23.5 0.302 57.41
## 63 59.60 76.60 CODI 1 19.3 0.305 76.20
## 64 41.20 55.70 CODI 1 22.0 0.305 50.27
## 65 13.00 62.50 CODI 1 21.0 0.315 63.62
## 66 68.40 32.30 CODI 1 20.0 0.315 113.10
## 67 72.00 56.00 CODI 1 19.0 0.328 132.73
## 68 16.50 78.90 CODI 1 23.0 0.330 80.12
## 69 34.20 31.60 CODI 1 17.0 0.330 86.59
## 70 18.00 28.80 CODI 1 23.0 0.330 63.62
## 71 16.00 49.50 CODI 1 23.5 0.332 56.08
## 72 59.00 34.20 CODI 1 21.0 0.333 95.03
## 73 73.50 67.40 CODI 1 23.0 0.334 70.88
## 74 56.00 67.90 CODI 1 24.0 0.340 213.82
## 75 97.50 43.40 CODI 1 18.0 0.340 95.03
## 76 20.80 60.90 CODI 1 20.0 0.342 132.73
## 77 79.64 96.12 CODI 1 17.8 0.345 32.67
## 78 14.60 49.20 CODI 1 20.3 0.345 47.78
## 79 88.80 37.30 CODI 1 17.0 0.345 50.27
## 80 64.80 58.00 CODI 1 20.0 0.349 113.10
## 81 74.90 99.40 CODI 1 23.0 0.350 64.33
## 82 95.80 50.90 CODI 1 21.0 0.350 86.59
## 83 81.20 52.30 CODI 1 19.0 0.350 70.88
## 84 32.75 78.95 CODI 1 21.0 0.353 113.10
## 85 12.50 69.95 CODI 1 24.0 0.355 132.73
## 86 48.00 46.80 CODI 1 18.0 0.360 86.59
## 87 14.20 72.80 CODI 1 23.0 0.361 240.53
## 88 34.30 90.35 CODI 1 20.0 0.364 201.06
## 89 49.70 92.70 CODI 1 22.0 0.365 70.88
## 90 56.40 95.70 CODI 1 25.0 0.368 25.97
## 91 72.00 13.90 CODI 1 18.0 0.369 103.87
## 92 55.80 38.20 CODI 1 23.0 0.370 78.54
## 93 23.80 6.50 CODI 1 21.0 0.380 213.82
## 94 9.20 30.80 CODI 1 16.9 0.383 283.53
## 95 12.10 60.20 CODI 1 20.0 0.385 132.73
## 96 2.90 83.80 CODI 1 25.3 0.386 106.60
## 97 26.20 59.70 CODI 1 22.0 0.392 213.82
## 98 93.50 92.60 CODI 1 21.0 0.395 99.40
## 99 13.00 2.30 CODI 1 18.0 0.400 74.66
## 100 46.50 91.50 CODI 1 21.0 0.405 122.72
## 101 45.20 63.90 CODI 1 23.0 0.410 165.13
## 102 97.10 57.10 CODI 1 19.0 0.410 86.59
## 103 30.60 43.70 CODI 1 19.0 0.410 113.10
## 104 17.00 86.00 CODI 1 22.0 0.411 131.71
## 105 76.50 69.30 CODI 1 20.0 0.413 201.06
## 106 19.40 71.25 CODI 1 23.0 0.420 213.82
## 107 36.90 76.80 CODI 1 25.0 0.430 165.13
## 108 13.40 77.40 CODI 1 20.0 0.430 63.62
## 109 32.50 69.70 CODI 1 22.0 0.435 78.54
## 110 6.70 85.10 CODI 1 19.0 0.442 74.66
## 111 8.40 78.30 CODI 1 23.0 0.444 95.03
## 112 12.60 84.90 CODI 1 23.0 0.459 143.14
## 113 25.70 22.50 CODI 1 18.0 0.460 165.13
## 114 83.90 43.50 CODI 1 20.0 0.464 95.03
## 115 98.40 87.10 CODI 1 22.5 0.470 151.75
## 116 3.00 98.00 CODI 1 20.0 0.474 160.61
## 117 70.20 52.20 CODI 1 26.0 0.475 240.53
## 118 21.40 84.40 CODI 1 22.0 0.480 95.03
## 119 75.84 92.00 CODI 1 21.0 0.590 182.65
#Gráfico de dispersión de especie CODI
windows()
ggplot(CODI, aes(x, y)) +geom_point(colour="#F8766D")+labs(title="Distribución de especie CODI")
#Coordenadas x, y de especie CODI
xyCODI <- as.matrix(CODI[,1:2])
dfCODI<-data.frame(xyCODI)
head(xyCODI,3)
## x y
## 1 6.0 16.0
## 2 4.0 16.0
## 3 12.5 21.5
#Creación de objeto ppp para generar patrones de puntos en el espacio (100mx100m=1 hectárea) para realizar conteos por cuadrante
Dis_CODI <- ppp(dfCODI$x, dfCODI$y, c(0,100), c(0,100), unitname=c("metre","metres"))
Dis_CODI
## Planar point pattern: 119 points
## window: rectangle = [0, 100] x [0, 100] metres
plot(Dis_CODI)
#Conteo de árboles por cuadrante, 100 cuadrantes (10x10)
quadratcount(Dis_CODI, 10, 10)
## x
## y [0,10) [10,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80)
## [90,100] 1 0 0 1 2 1 0 3
## [80,90) 2 2 1 0 2 0 2 0
## [70,80) 1 4 0 2 2 1 0 0
## [60,70) 0 3 1 1 4 2 0 4
## [50,60) 1 0 3 0 2 0 2 2
## [40,50) 2 2 0 1 2 0 1 0
## [30,40) 1 0 0 1 0 3 1 0
## [20,30) 0 2 1 2 0 1 0 2
## [10,20) 2 2 0 1 1 0 1 1
## [0,10) 1 2 2 0 3 0 1 0
## x
## y [80,90) [90,100]
## [90,100] 0 1
## [80,90) 0 3
## [70,80) 0 0
## [60,70) 0 0
## [50,60) 2 6
## [40,50) 4 4
## [30,40) 1 1
## [20,30) 1 0
## [10,20) 0 0
## [0,10) 0 5
#Obtención de número de número de cuadrantes con determinado número de árboles
tablaCODI<-table(quadratcount(Dis_CODI, 10, 10))
tablaCODI
##
## 0 1 2 3 4 5 6
## 39 26 22 6 5 1 1
#Asignación de variables para realizar prueba chi-cuadrada de bondad de ajuste
#Número de árboles
xCODI<-c(0,1,2,3,4,5,6)
#Número de cuadrantes con determinados árboles
ObsCODI<-c(39,26,22,6,5,1,1)
NCODI<-ObsCODI*xCODI
mCODI<-sum(NCODI)/100
#Cálculo de probabilidad esperada, poisson
P_espCODI<- c(dpois(0,mCODI),dpois(1,mCODI),dpois(2,mCODI),dpois(3,mCODI),dpois(4,mCODI),dpois(5,mCODI),dpois(6,mCODI))
ECODI<-c(P_espCODI*sum(NCODI))
#Prueba chi-cuadrada de bondad de ajuste
chisq.test(ObsCODI, p = ECODI, rescale.p = TRUE)
## Warning in chisq.test(ObsCODI, p = ECODI, rescale.p = TRUE): Chi-squared
## approximation may be incorrect
##
## Chi-squared test for given probabilities
##
## data: ObsCODI
## X-squared = 15.147, df = 6, p-value = 0.01915
#Prueba chi-cuadrada de bondad de ajuste con quadrat.test
fitx1 <- ppm(Dis_CODI, ~x, Poisson())
quadrat.test(Dis_CODI, 10, 10, fit=fitx1)
## Warning: Some expected counts are small; chi^2 approximation may be inaccurate
##
## Chi-squared test of CSR using quadrat counts
##
## data: Dis_CODI
## X2 = 140.66, df = 99, p-value = 0.007568
## alternative hypothesis: two.sided
##
## Quadrats: 10 by 10 grid of tiles
#Selección de datos correspondientes únicamente a especie ORXA
ORXA<-subset(Datos2, especie=="ORXA")
ORXA
## x y especie etapa altura dbh cobertura
## 120 80.80 95.70 ORXA 0 4.4 0.025 0.79
## 121 1.00 54.50 ORXA 0 5.4 0.026 1.77
## 122 4.80 27.50 ORXA 0 5.5 0.030 4.91
## 123 67.50 68.10 ORXA 0 18.0 0.037 86.59
## 124 63.70 87.30 ORXA 0 5.2 0.040 4.91
## 125 20.40 12.60 ORXA 0 8.5 0.042 3.14
## 126 74.40 11.60 ORXA 0 8.0 0.050 12.57
## 127 81.20 21.20 ORXA 0 7.5 0.051 2.41
## 128 89.40 4.00 ORXA 0 5.0 0.051 3.14
## 129 10.40 63.90 ORXA 0 7.0 0.052 5.31
## 130 5.40 3.00 ORXA 0 10.3 0.059 3.14
## 131 99.80 9.80 ORXA 0 10.0 0.071 12.57
## 132 18.90 65.50 ORXA 0 10.0 0.080 7.07
## 133 30.70 13.00 ORXA 0 14.0 0.086 12.57
## 134 47.15 71.45 ORXA 0 5.5 0.094 12.57
## 135 78.80 20.60 ORXA 0 9.0 0.100 38.48
## 136 47.70 91.25 ORXA 0 11.0 0.105 25.97
## 137 74.60 52.50 ORXA 0 10.0 0.107 50.27
## 138 84.30 37.30 ORXA 0 11.0 0.110 23.76
## 139 83.70 27.90 ORXA 0 5.5 0.110 1.23
## 140 11.20 70.05 ORXA 0 9.0 0.112 8.30
## 141 67.00 37.70 ORXA 0 9.0 0.113 15.90
## 142 83.00 13.70 ORXA 0 8.0 0.120 33.18
## 143 63.80 14.00 ORXA 0 12.0 0.120 12.57
## 144 52.00 1.90 ORXA 0 9.0 0.130 23.76
## 145 91.90 17.10 ORXA 0 10.0 0.136 12.57
## 146 92.70 27.50 ORXA 0 13.5 0.139 19.63
## 147 12.30 2.90 ORXA 0 15.0 0.157 31.17
## 148 17.90 53.80 ORXA 0 12.0 0.165 25.97
## 149 25.10 66.70 ORXA 0 7.6 0.171 12.57
## 150 25.70 18.00 ORXA 0 12.0 0.179 63.62
## 151 97.70 38.10 ORXA 0 12.0 0.180 44.18
## 152 19.95 64.75 ORXA 0 14.0 0.184 41.28
## 153 98.90 89.20 ORXA 0 16.5 0.200 56.75
## 154 19.00 51.65 ORXA 0 18.0 0.220 67.20
## 155 70.80 12.00 ORXA 0 15.0 0.220 63.62
## 156 33.60 77.30 ORXA 0 14.0 0.222 70.88
## 157 76.20 70.20 ORXA 0 15.0 0.235 108.43
## 158 86.10 25.80 ORXA 0 15.0 0.255 56.75
## 159 66.00 34.00 ORXA 1 18.0 0.260 95.03
## 160 60.20 51.10 ORXA 1 18.0 0.266 113.10
## 161 45.90 56.80 ORXA 1 12.0 0.270 48.40
## 162 94.50 12.40 ORXA 1 15.0 0.275 38.48
## 163 46.10 89.15 ORXA 1 17.0 0.280 38.48
## 164 22.00 30.60 ORXA 1 13.0 0.280 78.54
## 165 68.80 6.50 ORXA 1 16.0 0.285 63.62
## 166 73.00 34.60 ORXA 1 18.0 0.295 95.03
## 167 88.20 46.60 ORXA 1 18.0 0.296 143.14
## 168 98.80 32.60 ORXA 1 18.0 0.300 103.87
## 169 44.50 45.50 ORXA 1 18.0 0.305 170.87
## 170 43.50 45.70 ORXA 1 13.0 0.310 56.75
## 171 6.40 91.10 ORXA 1 18.0 0.343 77.76
## 172 49.00 97.90 ORXA 1 20.0 0.345 176.71
## 173 1.60 4.80 ORXA 1 19.0 0.375 95.03
## 174 33.00 19.70 ORXA 1 20.0 0.380 176.71
## 175 78.50 87.50 ORXA 1 21.0 0.381 101.18
## 176 68.00 27.50 ORXA 1 20.0 0.410 63.62
## 177 36.70 13.30 ORXA 1 18.0 0.425 283.53
## 178 59.70 40.40 ORXA 1 18.0 0.440 226.98
## 179 37.50 8.30 ORXA 1 19.0 0.450 95.03
## 180 23.60 30.40 ORXA 1 18.0 0.500 298.65
## 181 43.80 78.80 ORXA 1 27.0 0.564 113.10
## 182 92.10 37.00 ORXA 1 17.0 0.640 78.54
#Gráfico de dispersión de especie ORXA
windows()
ggplot(ORXA, aes(x, y)) +geom_point(colour="#7CAE00")+labs(title="Distribución de especie ORXA")
#Coordenadas x, y de especie ORXA
xyORXA <- as.matrix(ORXA[,1:2])
dfORXA<-data.frame(xyORXA)
head(xyORXA,3)
## x y
## 120 80.8 95.7
## 121 1.0 54.5
## 122 4.8 27.5
#Creación de objeto ppp para generar patrones de puntos en el espacio (100mx100m=1 hectárea) para realizar conteos por cuadrante
Dis_ORXA <- ppp(dfORXA$x, dfORXA$y, c(0,100), c(0,100), unitname=c("metre","metres"))
Dis_ORXA
## Planar point pattern: 63 points
## window: rectangle = [0, 100] x [0, 100] metres
plot(Dis_ORXA)
#Conteo de árboles por cuadrante, 100 cuadrantes (10x10)
quadratcount(Dis_ORXA, 10, 10)
## x
## y [0,10) [10,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80)
## [90,100] 1 0 0 0 2 0 0 0
## [80,90) 0 0 0 0 1 0 1 1
## [70,80) 0 1 0 1 2 0 0 1
## [60,70) 0 3 1 0 0 0 1 0
## [50,60) 1 2 0 0 1 0 1 1
## [40,50) 0 0 0 0 2 1 0 0
## [30,40) 0 0 2 0 0 0 2 1
## [20,30) 1 0 0 0 0 0 1 1
## [10,20) 0 0 2 3 0 0 1 2
## [0,10) 2 1 0 1 0 1 1 0
## x
## y [80,90) [90,100]
## [90,100] 1 0
## [80,90) 0 1
## [70,80) 0 0
## [60,70) 0 0
## [50,60) 0 0
## [40,50) 1 0
## [30,40) 1 3
## [20,30) 3 1
## [10,20) 1 2
## [0,10) 1 1
#Obtención de número de número de cuadrantes con determinado número de árboles
tablaORXA<-table(quadratcount(Dis_ORXA, 10, 10))
tablaORXA
##
## 0 1 2 3
## 55 31 10 4
#Asignación de variables para realizar prueba chi-cuadrada de bondad de ajuste
#Número de árboles
xORXA<-c(0,1,2,3)
#Número de cuadrantes con determinados árboles
ObsORXA<-c(55,31,10,4)
NORXA<-ObsORXA*xORXA
mORXA<-sum(NORXA)/100
#Cálculo de probabilidad esperada, poisson
P_espORXA<- c(dpois(0,mORXA),dpois(1,mORXA),dpois(2,mORXA),dpois(3,mORXA))
EORXA<-c(P_espORXA*sum(NORXA))
#Prueba chi-cuadrada de bondad de ajuste
chisq.test(ObsORXA, p = EORXA, rescale.p = TRUE)
## Warning in chisq.test(ObsORXA, p = EORXA, rescale.p = TRUE): Chi-squared
## approximation may be incorrect
##
## Chi-squared test for given probabilities
##
## data: ObsORXA
## X-squared = 1.7017, df = 3, p-value = 0.6366
#Prueba chi-cuadrada de bondad de ajuste con quadrat.test
fitx2 <- ppm(Dis_ORXA, ~x, Poisson())
quadrat.test(Dis_ORXA, 10, 10, fit=fitx2)
## Warning: Some expected counts are small; chi^2 approximation may be inaccurate
##
## Chi-squared test of CSR using quadrat counts
##
## data: Dis_ORXA
## X2 = 106.84, df = 99, p-value = 0.555
## alternative hypothesis: two.sided
##
## Quadrats: 10 by 10 grid of tiles
#Selección de datos correspondientes únicamente a especie TETE
TETE<-subset(Datos2, especie=="TETE")
TETE
## x y especie etapa altura dbh cobertura
## 183 44.00 56.8 TETE 0 5.00 0.026 1.43
## 184 21.65 89.6 TETE 0 4.90 0.035 2.99
## 185 33.70 17.2 TETE 0 6.00 0.052 1.77
## 186 13.50 18.0 TETE 0 15.00 0.053 50.27
## 187 43.40 7.0 TETE 0 6.00 0.056 9.62
## 188 2.30 88.9 TETE 0 6.50 0.057 3.14
## 189 43.40 17.2 TETE 0 7.00 0.063 19.63
## 190 57.00 28.5 TETE 0 8.00 0.066 19.63
## 191 47.10 25.5 TETE 0 8.00 0.070 23.76
## 192 33.00 4.9 TETE 0 9.50 0.070 9.62
## 193 32.00 7.5 TETE 0 12.00 0.075 113.10
## 194 66.80 72.3 TETE 0 16.00 0.083 62.91
## 195 56.70 75.7 TETE 0 7.10 0.085 28.27
## 196 41.90 11.2 TETE 0 10.00 0.086 28.27
## 197 79.60 69.8 TETE 0 11.00 0.089 56.75
## 198 48.20 3.2 TETE 0 2.50 0.090 1.23
## 199 87.20 37.0 TETE 0 10.00 0.091 19.63
## 200 60.70 32.5 TETE 0 11.00 0.092 23.76
## 201 78.80 41.3 TETE 0 16.00 0.096 19.63
## 202 25.90 37.6 TETE 0 5.00 0.099 7.07
## 203 21.40 25.4 TETE 0 10.00 0.100 50.27
## 204 34.10 2.9 TETE 0 9.00 0.100 19.63
## 205 18.30 53.7 TETE 0 12.00 0.102 23.76
## 206 41.60 1.4 TETE 0 9.00 0.105 28.27
## 207 52.70 34.3 TETE 0 12.00 0.111 9.62
## 208 14.95 50.1 TETE 0 14.00 0.120 12.88
## 209 60.70 64.8 TETE 0 13.00 0.122 33.18
## 210 68.80 66.4 TETE 0 11.00 0.123 33.18
## 211 47.90 71.2 TETE 0 15.00 0.124 44.18
## 212 84.10 89.1 TETE 0 10.85 0.125 22.48
## 213 53.30 46.4 TETE 0 14.00 0.125 28.27
## 214 94.00 32.2 TETE 0 12.00 0.130 23.76
## 215 58.40 17.5 TETE 0 12.00 0.132 33.18
## 216 42.00 27.0 TETE 0 15.00 0.133 28.27
## 217 86.60 91.3 TETE 0 11.50 0.134 22.90
## 218 64.60 51.6 TETE 0 11.50 0.134 56.75
## 219 70.50 68.7 TETE 0 14.00 0.135 33.18
## 220 52.20 7.7 TETE 0 16.00 0.135 28.27
## 221 83.50 91.6 TETE 0 9.00 0.137 18.10
## 222 86.25 89.1 TETE 0 12.00 0.138 21.24
## 223 76.40 75.8 TETE 0 16.70 0.139 49.64
## 224 26.90 18.0 TETE 0 15.00 0.139 50.27
## 225 81.00 75.6 TETE 0 15.50 0.140 30.68
## 226 68.00 87.5 TETE 0 16.00 0.145 33.18
## 227 3.90 78.4 TETE 0 16.00 0.145 23.33
## 228 60.30 38.9 TETE 0 13.00 0.145 38.48
## 229 33.90 3.9 TETE 0 15.00 0.145 28.27
## 230 56.30 54.6 TETE 0 14.00 0.149 41.28
## 231 75.75 98.8 TETE 0 14.00 0.150 30.19
## 232 25.55 70.5 TETE 0 14.00 0.153 63.62
## 233 11.00 59.7 TETE 0 12.00 0.156 11.04
## 234 28.10 2.2 TETE 1 15.00 0.160 63.62
## 235 26.30 4.4 TETE 1 12.00 0.160 28.27
## 236 17.80 20.2 TETE 1 17.00 0.162 29.71
## 237 70.80 87.7 TETE 1 21.00 0.166 23.76
## 238 21.60 35.2 TETE 1 17.00 0.170 86.59
## 239 13.80 33.2 TETE 1 9.50 0.171 34.73
## 240 89.10 97.0 TETE 1 12.50 0.172 36.58
## 241 59.30 66.8 TETE 1 11.00 0.175 15.90
## 242 62.80 67.8 TETE 1 13.00 0.177 113.10
## 243 76.50 51.4 TETE 1 15.00 0.184 70.88
## 244 70.20 69.6 TETE 1 17.00 0.188 56.75
## 245 83.80 76.6 TETE 1 8.00 0.190 0.79
## 246 68.20 51.5 TETE 1 12.00 0.190 44.18
## 247 63.10 68.7 TETE 1 18.00 0.195 44.18
## 248 60.40 45.8 TETE 1 17.00 0.195 38.48
## 249 79.00 51.3 TETE 1 16.00 0.209 70.88
## 250 92.20 75.5 TETE 1 15.10 0.212 50.90
## 251 63.30 86.8 TETE 1 15.00 0.213 35.78
## 252 69.40 80.5 TETE 1 16.50 0.225 38.48
## 253 33.10 3.9 TETE 1 15.00 0.225 56.75
## 254 56.10 52.0 TETE 1 20.00 0.230 74.66
## 255 59.80 61.4 TETE 1 16.00 0.240 50.27
## 256 51.30 45.0 TETE 1 20.00 0.242 44.18
## 257 63.60 26.5 TETE 1 16.00 0.260 113.10
## 258 56.20 24.0 TETE 1 13.00 0.280 50.27
## 259 83.30 87.7 TETE 1 19.00 0.308 60.82
## 260 81.50 77.8 TETE 1 22.00 0.315 86.59
## 261 74.70 11.6 TETE 1 14.00 0.395 38.48
## 262 84.40 0.9 TETE 1 22.00 0.430 213.82
#Gráfico de dispersión de especie TETE
windows()
ggplot(TETE, aes(x, y)) +geom_point(colour="#00BFC4")+labs(title="Distribución de especie TETE")
#Coordenadas x, y de especie TETE
xyTETE <- as.matrix(TETE[,1:2])
dfTETE<-data.frame(xyTETE)
head(xyTETE,3)
## x y
## 183 44.00 56.8
## 184 21.65 89.6
## 185 33.70 17.2
#Creación de objeto ppp para generar patrones de puntos en el espacio (100mx100m=1 hectárea) para realizar conteos por cuadrante
Dis_TETE <- ppp(dfTETE$x, dfTETE$y, c(0,100), c(0,100), unitname=c("metre","metres"))
Dis_TETE
## Planar point pattern: 80 points
## window: rectangle = [0, 100] x [0, 100] metres
plot(Dis_TETE)
#Conteo de árboles por cuadrante, 100 cuadrantes (10x10)
quadratcount(Dis_TETE, 10, 10)
## x
## y [0,10) [10,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80)
## [90,100] 0 0 0 0 0 0 0 1
## [80,90) 1 0 1 0 0 0 3 1
## [70,80) 1 0 1 0 1 1 1 1
## [60,70) 0 0 0 0 0 2 4 3
## [50,60) 0 3 0 0 1 2 2 2
## [40,50) 0 0 0 0 0 2 1 1
## [30,40) 0 1 2 0 0 1 2 0
## [20,30) 0 1 1 0 2 2 1 0
## [10,20) 0 1 1 1 2 1 0 1
## [0,10) 0 0 2 5 3 1 0 0
## x
## y [80,90) [90,100]
## [90,100] 3 0
## [80,90) 3 0
## [70,80) 3 1
## [60,70) 0 0
## [50,60) 0 0
## [40,50) 0 0
## [30,40) 1 1
## [20,30) 0 0
## [10,20) 0 0
## [0,10) 1 0
#Obtención de número de número de cuadrantes con determinado número de árboles
tablaTETE<-table(quadratcount(Dis_TETE, 10, 10))
tablaTETE
##
## 0 1 2 3 4 5
## 52 28 11 7 1 1
#Asignación de variables para realizar prueba chi-cuadrada de bondad de ajuste
#Número de árboles
xTETE<-c(0,1,2,3,4,5)
#Número de cuadrantes con determinados árboles
ObsTETE<-c(52,28,11,7,1,1)
NTETE<-ObsTETE*xTETE
mTETE<-sum(NTETE)/100
#Cálculo de probabilidad esperada, poisson
P_espTETE<- c(dpois(0,mTETE),dpois(1,mTETE),dpois(2,mTETE),dpois(3,mTETE),dpois(4,mTETE),dpois(5,mTETE))
ETETE<-c(P_espTETE*sum(NTETE))
#Prueba chi-cuadrada de bondad de ajuste
chisq.test(ObsTETE, p = ETETE, rescale.p = TRUE)
## Warning in chisq.test(ObsTETE, p = ETETE, rescale.p = TRUE): Chi-squared
## approximation may be incorrect
##
## Chi-squared test for given probabilities
##
## data: ObsTETE
## X-squared = 12.617, df = 5, p-value = 0.02724
#Prueba chi-cuadrada de bondad de ajuste con quadrat.test
fitx3 <- ppm(Dis_TETE, ~x, Poisson())
quadrat.test(Dis_TETE, 10, 10, fit=fitx3)
## Warning: Some expected counts are small; chi^2 approximation may be inaccurate
##
## Chi-squared test of CSR using quadrat counts
##
## data: Dis_TETE
## X2 = 140, df = 99, p-value = 0.008457
## alternative hypothesis: two.sided
##
## Quadrats: 10 by 10 grid of tiles
#Selección de datos correspondientes únicamente a especie ZISP
ZISP<-subset(Datos2, especie=="ZISP")
ZISP
## x y especie etapa altura dbh cobertura
## 263 39.80 91.20 ZISP 0 3.95 0.025 3.46
## 264 14.50 85.70 ZISP 0 4.70 0.025 3.14
## 265 3.50 53.00 ZISP 0 5.40 0.025 3.98
## 266 39.00 84.00 ZISP 0 4.50 0.026 4.91
## 267 22.00 86.00 ZISP 0 7.00 0.026 3.46
## 268 18.70 67.90 ZISP 0 4.00 0.026 0.95
## 269 13.20 8.10 ZISP 0 4.00 0.026 2.54
## 270 14.00 6.00 ZISP 0 3.70 0.026 3.14
## 271 36.20 87.40 ZISP 0 4.80 0.027 4.91
## 272 10.40 59.42 ZISP 0 4.42 0.027 2.66
## 273 9.80 14.30 ZISP 0 3.90 0.027 3.14
## 274 73.30 2.40 ZISP 0 3.80 0.027 4.91
## 275 11.20 2.40 ZISP 0 3.50 0.027 4.52
## 276 10.20 5.50 ZISP 0 4.75 0.027 2.99
## 277 97.30 50.90 ZISP 0 11.30 0.028 10.18
## 278 0.70 25.80 ZISP 0 5.50 0.028 7.07
## 279 3.20 28.50 ZISP 0 5.50 0.029 1.54
## 280 13.60 4.00 ZISP 0 5.10 0.029 8.30
## 281 28.48 83.60 ZISP 0 4.98 0.030 1.14
## 282 13.80 33.20 ZISP 0 5.20 0.030 5.31
## 283 10.90 11.50 ZISP 0 5.10 0.030 3.63
## 284 9.70 4.20 ZISP 0 5.90 0.030 2.01
## 285 36.80 97.17 ZISP 0 5.30 0.036 4.71
## 286 25.50 0.10 ZISP 0 5.50 0.036 3.14
## 287 34.60 86.65 ZISP 0 6.50 0.037 6.61
## 288 28.40 79.70 ZISP 0 5.50 0.037 5.52
## 289 1.40 84.00 ZISP 0 6.20 0.038 6.61
## 290 12.70 9.30 ZISP 0 11.50 0.040 21.65
## 291 8.30 92.20 ZISP 0 7.50 0.041 3.98
## 292 12.40 10.60 ZISP 0 7.00 0.045 8.30
## 293 42.55 93.40 ZISP 0 5.60 0.046 6.38
## 294 14.70 87.00 ZISP 0 6.50 0.047 7.07
## 295 77.50 58.70 ZISP 0 6.10 0.047 9.62
## 296 21.10 85.90 ZISP 0 6.30 0.049 4.34
## 297 18.60 8.40 ZISP 0 5.70 0.050 4.17
## 298 14.60 10.60 ZISP 0 6.00 0.052 9.62
## 299 11.10 88.00 ZISP 0 6.50 0.053 8.55
## 300 15.30 66.50 ZISP 0 6.30 0.054 13.53
## 301 15.60 22.20 ZISP 0 8.30 0.056 28.27
## 302 8.80 64.80 ZISP 0 7.00 0.060 4.91
## 303 19.60 85.20 ZISP 0 7.50 0.061 9.08
## 304 19.80 78.60 ZISP 0 6.00 0.062 6.61
## 305 19.80 84.80 ZISP 0 7.50 0.064 4.91
## 306 46.00 73.25 ZISP 0 11.00 0.072 7.31
## 307 6.90 93.00 ZISP 0 10.00 0.074 10.41
## 308 66.30 79.80 ZISP 0 6.00 0.075 1.23
## 309 10.70 98.40 ZISP 0 11.00 0.076 11.64
## 310 79.00 68.20 ZISP 0 8.00 0.077 19.63
## 311 23.50 82.60 ZISP 0 9.70 0.083 10.75
## 312 20.65 89.00 ZISP 0 10.00 0.085 12.25
## 313 12.55 80.70 ZISP 0 9.00 0.085 13.69
## 314 27.15 81.60 ZISP 0 12.00 0.090 15.90
## 315 1.80 78.50 ZISP 0 9.50 0.095 31.67
## 316 17.95 71.25 ZISP 1 12.00 0.100 15.90
## 317 11.50 91.80 ZISP 1 10.00 0.102 22.90
## 318 14.00 71.30 ZISP 1 13.00 0.115 9.08
## 319 19.20 61.95 ZISP 1 16.00 0.115 28.27
## 320 8.20 49.30 ZISP 1 11.70 0.120 28.27
## 321 14.10 60.30 ZISP 1 16.00 0.121 12.57
## 322 7.60 61.50 ZISP 1 14.00 0.132 9.90
## 323 15.05 85.80 ZISP 1 13.00 0.140 9.35
## 324 44.40 77.00 ZISP 1 13.00 0.140 28.27
## 325 3.60 67.70 ZISP 1 11.00 0.145 25.52
## 326 7.80 44.50 ZISP 1 9.30 0.160 31.17
## 327 5.00 14.50 ZISP 1 14.00 0.210 35.78
## 328 12.50 68.60 ZISP 1 16.00 0.230 44.18
## 329 26.60 89.50 ZISP 1 18.60 0.285 35.78
## 330 24.90 45.40 ZISP 1 28.00 0.322 188.69
## 331 34.70 94.30 ZISP 1 20.00 0.412 70.88
## 332 50.50 4.70 ZISP 1 18.00 0.415 86.59
## 333 23.30 88.10 ZISP 1 18.00 0.457 132.73
## 334 5.30 11.40 ZISP 1 20.00 0.527 50.27
## 335 29.70 90.70 ZISP 1 22.00 0.560 132.73
#Gráfico de dispersión de especie ZISP
windows()
ggplot(ZISP, aes(x, y)) +geom_point(colour="#C77CFF")+labs(title="Distribución de especie ZISP")
#Coordenadas x, y de especie ZISP
xyZISP <- as.matrix(ZISP[,1:2])
dfZISP<-data.frame(xyZISP)
head(xyZISP,3)
## x y
## 263 39.8 91.2
## 264 14.5 85.7
## 265 3.5 53.0
#Creación de objeto ppp para generar patrones de puntos en el espacio (100mx100m=1 hectárea) para realizar conteos por cuadrante
Dis_ZISP <- ppp(dfZISP$x, dfZISP$y, c(0,100), c(0,100), unitname=c("metre","metres"))
Dis_ZISP
## Planar point pattern: 73 points
## window: rectangle = [0, 100] x [0, 100] metres
plot(Dis_ZISP)
#Conteo de árboles por cuadrante, 100 cuadrantes (10x10)
quadratcount(Dis_ZISP, 10, 10)
## x
## y [0,10) [10,20) [20,30) [30,40) [40,50) [50,60) [60,70) [70,80)
## [90,100] 2 2 1 3 1 0 0 0
## [80,90) 1 7 8 3 0 0 0 0
## [70,80) 1 3 1 0 2 0 1 0
## [60,70) 3 5 0 0 0 0 0 1
## [50,60) 1 1 0 0 0 0 0 1
## [40,50) 2 0 1 0 0 0 0 0
## [30,40) 0 1 0 0 0 0 0 0
## [20,30) 2 1 0 0 0 0 0 0
## [10,20) 3 3 0 0 0 0 0 0
## [0,10) 1 7 1 0 0 1 0 1
## x
## y [80,90) [90,100]
## [90,100] 0 0
## [80,90) 0 0
## [70,80) 0 0
## [60,70) 0 0
## [50,60) 0 1
## [40,50) 0 0
## [30,40) 0 0
## [20,30) 0 0
## [10,20) 0 0
## [0,10) 0 0
#Obtención de número de número de cuadrantes con determinado número de árboles
tablaZISP<-table(quadratcount(Dis_ZISP, 10, 10))
tablaZISP
##
## 0 1 2 3 5 7 8
## 67 18 5 6 1 2 1
#Asignación de variables para realizar prueba chi-cuadrada de bondad de ajuste
#Número de árboles
xZISP<-c(0,1,2,3,5,7,8)
#Número de cuadrantes con determinados árboles
ObsZISP<-c(67,18,5,6,1,2,1)
NZISP<-ObsZISP*xZISP
mZISP<-sum(NZISP)/100
#Cálculo de probabilidad esperada, poisson
P_espZISP<- c(dpois(0,mZISP),dpois(1,mZISP),dpois(2,mZISP),dpois(3,mZISP),dpois(5,mZISP),dpois(7,mZISP),dpois(8,mZISP))
EZISP<-c(P_espZISP*sum(NZISP))
#Prueba chi-cuadrada de bondad de ajuste
chisq.test(ObsZISP, p = EZISP, rescale.p = TRUE)
## Warning in chisq.test(ObsZISP, p = EZISP, rescale.p = TRUE): Chi-squared
## approximation may be incorrect
##
## Chi-squared test for given probabilities
##
## data: ObsZISP
## X-squared = 14106, df = 6, p-value < 2.2e-16
#Prueba chi-cuadrada de bondad de ajuste con quadrat.test
fitx4 <- ppm(Dis_ZISP, ~x, Poisson())
quadrat.test(Dis_ZISP, 10, 10, fit=fitx4)
## Warning: Some expected counts are small; chi^2 approximation may be inaccurate
##
## Chi-squared test of CSR using quadrat counts
##
## data: Dis_ZISP
## X2 = 309.19, df = 99, p-value < 2.2e-16
## alternative hypothesis: two.sided
##
## Quadrats: 10 by 10 grid of tiles