Ecosistema
Rodrigo Angulo A00834667
2025-08-21
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library(gplots)##
## Adjuntando el paquete: 'gplots'## The following object is masked from 'package:stats':
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## lowesslibrary(readxl)
library(lmtest)## Cargando paquete requerido: zoo##
## Adjuntando el paquete: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numericlibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(plm)##
## Adjuntando el paquete: 'plm'
##
## The following objects are masked from 'package:dplyr':
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## between, lag, leaddf1 <- read_excel("C:\\Users\\admin\\Downloads\\hogares.xlsx")Generar conjunto de Datos de Panel
panel_1 <- pdata.frame(df1, index = c("HogarID", "Año"))
plotmeans(Ingreso ~ HogarID, main= "Prueba de Heterogeneidad entre hogares para el Ingreso", data = panel_1)
Modelo 1- Regresión agrupada (pooled) Solo se toma si la linea de la prueba de heterogeneidad sale horizontal
pooled <- plm(Ingreso ~ Satisfacción, data= panel_1, model="pooling")
summary(pooled)## Pooling Model
##
## Call:
## plm(formula = Ingreso ~ Satisfacción, data = panel_1, model = "pooling")
##
## Balanced Panel: n = 100, T = 10, N = 1000
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -20196.53 -5106.46 -575.98 5095.02 23468.66
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## (Intercept) 10597.75 976.80 10.850 < 2.2e-16 ***
## Satisfacción 2890.77 166.68 17.343 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 6.8145e+10
## Residual Sum of Squares: 5.2364e+10
## R-Squared: 0.23158
## Adj. R-Squared: 0.23081
## F-statistic: 300.772 on 1 and 998 DF, p-value: < 2.22e-16#Modelo 2 _ Efectos Fijos (within) #Cuando las diferencias no observadas son constantes en el tiempo
within <- plm(Ingreso ~ Satisfacción, data= panel_1, model="within")
summary(within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = Ingreso ~ Satisfacción, data = panel_1, model = "within")
##
## Balanced Panel: n = 100, T = 10, N = 1000
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -15591.951 -3123.123 -74.284 3010.168 13134.979
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## Satisfacción 1698.14 132.73 12.794 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2.3013e+10
## Residual Sum of Squares: 1.9469e+10
## R-Squared: 0.15403
## Adj. R-Squared: 0.05993
## F-statistic: 163.687 on 1 and 899 DF, p-value: < 2.22e-16#Prueba de los modelos
pFtest(within,pooled)##
## F test for individual effects
##
## data: Ingreso ~ Satisfacción
## F = 15.343, df1 = 99, df2 = 899, p-value < 2.2e-16
## alternative hypothesis: significant effects#Modelo 3. Efectos Aleatorios - (Cuando las diferencias no observadas son Aleatorias)
#3.1-Método Walhus
walhus <- plm(Ingreso ~ Satisfacción, data= panel_1, model="random", random.method = "walhus")
summary(walhus)## Oneway (individual) effect Random Effect Model
## (Wallace-Hussain's transformation)
##
## Call:
## plm(formula = Ingreso ~ Satisfacción, data = panel_1, model = "random",
## random.method = "walhus")
##
## Balanced Panel: n = 100, T = 10, N = 1000
##
## Effects:
## var std.dev share
## idiosyncratic 23574420 4855 0.45
## individual 28789336 5366 0.55
## theta: 0.7249
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -16507.33 -3220.23 -147.96 3184.91 15215.46
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 16632.69 925.15 17.978 < 2.2e-16 ***
## Satisfacción 1831.41 131.69 13.907 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2.6429e+10
## Residual Sum of Squares: 2.2139e+10
## R-Squared: 0.16233
## Adj. R-Squared: 0.16149
## Chisq: 193.404 on 1 DF, p-value: < 2.22e-16amemiya <- plm(Ingreso ~ Satisfacción, data= panel_1, model="random", random.method = "amemiya")
summary(amemiya)## Oneway (individual) effect Random Effect Model
## (Amemiya's transformation)
##
## Call:
## plm(formula = Ingreso ~ Satisfacción, data = panel_1, model = "random",
## random.method = "amemiya")
##
## Balanced Panel: n = 100, T = 10, N = 1000
##
## Effects:
## var std.dev share
## idiosyncratic 21631698 4651 0.393
## individual 33418160 5781 0.607
## theta: 0.7534
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -16370.54 -3188.47 -210.78 3188.52 14905.18
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 16777.35 953.98 17.587 < 2.2e-16 ***
## Satisfacción 1806.01 130.63 13.825 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2.5757e+10
## Residual Sum of Squares: 2.1617e+10
## R-Squared: 0.16074
## Adj. R-Squared: 0.1599
## Chisq: 191.14 on 1 DF, p-value: < 2.22e-16#3.3-Método Nerlove
nerlove <- plm(Ingreso ~ Satisfacción, data= panel_1, model="random", random.method = "nerlove")
summary(nerlove)## Oneway (individual) effect Random Effect Model
## (Nerlove's transformation)
##
## Call:
## plm(formula = Ingreso ~ Satisfacción, data = panel_1, model = "random",
## random.method = "nerlove")
##
## Balanced Panel: n = 100, T = 10, N = 1000
##
## Effects:
## var std.dev share
## idiosyncratic 19468528 4412 0.351
## individual 35940737 5995 0.649
## theta: 0.7733
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -16275.51 -3113.76 -212.49 3188.29 14690.19
##
## Coefficients:
## Estimate Std. Error z-value Pr(>|z|)
## (Intercept) 16869.92 981.37 17.190 < 2.2e-16 ***
## Satisfacción 1789.76 129.95 13.773 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 2.5332e+10
## Residual Sum of Squares: 2.1286e+10
## R-Squared: 0.15972
## Adj. R-Squared: 0.15888
## Chisq: 189.701 on 1 DF, p-value: < 2.22e-16phtest(walhus,within)##
## Hausman Test
##
## data: Ingreso ~ Satisfacción
## chisq = 64.632, df = 1, p-value = 9.03e-16
## alternative hypothesis: one model is inconsistent# Si el P-Value es <0.05, usamos efectos fijos(within)<span style “color: yellow”> Tema 2. Series de tiempo: mapas
<span style “color: yellow”> Instalar paquetes y llamar librerias
#install.packages("sf")
library(sf)## Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUE#install.packages("devtools")
#devtools::install_github("diegovalle/mxmaps")
#1
library(mxmaps)
library(forecast)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo<span style “color: yellow”> Generar el mapa
df2 <- df_mxstate_2020
df_mxstate_2020$value <- df2$pop #Remplazar aqui los valores
mxstate_choropleth(df_mxstate_2020)
# <span style “color: yellow”> 1. importar la base de datos
df3 <- read.csv("C:\\Users\\admin\\Downloads\\population.csv")<span style “color: yellow”> 2. Serie de tiempo: TX
df4 <- df3 %>% filter(state == "TX")
ts <- ts (df4$population, start = 1900, frequency=1) #Serie de tiempo anual
arima <- auto.arima(ts)
arima## Series: ts
## ARIMA(0,2,2)
##
## Coefficients:
## ma1 ma2
## -0.5950 -0.1798
## s.e. 0.0913 0.0951
##
## sigma^2 = 1.031e+10: log likelihood = -1527.14
## AIC=3060.28 AICc=3060.5 BIC=3068.6pronostico <- forecast(arima, level=c(95), h=31)
pronostico## Point Forecast Lo 95 Hi 95
## 2020 29398472 29199487 29597457
## 2021 29806827 29463665 30149990
## 2022 30215183 29742956 30687410
## 2023 30623538 30024100 31222977
## 2024 31031894 30303359 31760429
## 2025 31440249 30579246 32301253
## 2026 31848605 30851090 32846119
## 2027 32256960 31118581 33395339
## 2028 32665316 31381587 33949044
## 2029 33073671 31640070 34507272
## 2030 33482027 31894047 35070007
## 2031 33890382 32143561 35637204
## 2032 34298738 32388674 36208801
## 2033 34707093 32629456 36784730
## 2034 35115449 32865983 37364914
## 2035 35523804 33098330 37949278
## 2036 35932160 33326573 38537746
## 2037 36340515 33550788 39130242
## 2038 36748871 33771046 39726695
## 2039 37157226 33987418 40327034
## 2040 37565581 34199972 40931191
## 2041 37973937 34408774 41539100
## 2042 38382292 34613887 42150698
## 2043 38790648 34815371 42765925
## 2044 39199003 35013284 43384723
## 2045 39607359 35207682 44007036
## 2046 40015714 35398618 44632810
## 2047 40424070 35586145 45261995
## 2048 40832425 35770311 45894540
## 2049 41240781 35951163 46530399
## 2050 41649136 36128748 47169524plot(pronostico, main="Poblacion en Texas")
<span style “color: yellow”> Contexto
• Calidad del Suelo: o SPH: pH del Suelo o NC: Contenido de Nutrientes o OM: Materia Orgánica
• Calidad del Agua: o CL: Niveles de Contaminantes o DO: Oxígeno Disuelto o WPH: pH del Agua
• Salud del Ecosistema o SD: Diversidad de Especies o BM: Biomasa o EP: Productividad del Ecosistema
<span style “color: yellow”> Importar librerias
library(tidyverse)
library(gplots)
library(plm)
library(forecast)
library(lavaan)
library(lavaanPlot)
library(DataExplorer)
library(readxl)<span style “color: yellow”> importar cvs
datos <- read_csv("C:\\Users\\admin\\Downloads\\ecosistema.csv")## Rows: 200 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (9): SD, BM, EP, SPH, NC, OM, WPH, DO, CL
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.dfeco <- scale(datos)Tema 3. Modelos de Ecuaciones Estructurales
modelo_eco <- '
# Regresiones
# Variables latentes
CalidadSuelo =~ SPH + NC + OM
CalidadAgua =~ CL + DO + WPH
SaludEco =~ SD + BM + EP
# Varianza y covarianza
CalidadSuelo ~~ CalidadAgua
CalidadSuelo ~~ SaludEco
CalidadAgua ~~ SaludEco
# Intercepto
'Generar el Diagrama
dfeco <- cfa(modelo_eco, datos)## Warning: lavaan->lav_data_full():
## some observed variances are (at least) a factor 1000 times larger than
## others; use varTable(fit) to investigate## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.summary(dfeco)## lavaan 0.6-19 ended normally after 251 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 21
##
## Number of observations 200
##
## Model Test User Model:
##
## Test statistic 17.215
## Degrees of freedom 24
## P-value (Chi-square) 0.839
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## CalidadSuelo =~
## SPH 1.000
## NC 91.480 NA
## OM 0.207 NA
## CalidadAgua =~
## CL 1.000
## DO -1.429 NA
## WPH 0.197 NA
## SaludEco =~
## SD 1.000
## BM -282.583 NA
## EP -1181.602 NA
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## CalidadSuelo ~~
## CalidadAgua -0.020 NA
## SaludEco -0.001 NA
## CalidadAgua ~~
## SaludEco -0.002 NA
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .SPH 0.249 NA
## .NC 227.969 NA
## .OM 0.948 NA
## .CL 0.261 NA
## .DO 0.878 NA
## .WPH 0.066 NA
## .SD 110.749 NA
## .BM 1358.763 NA
## .EP 2358.864 NA
## CalidadSuelo 0.017 NA
## CalidadAgua 0.012 NA
## SaludEco 0.000 NAeco <- sem(modelo_eco, data= dfeco)## Warning: lavaan->lav_model_vcov():
## Could not compute standard errors! The information matrix could not be
## inverted. This may be a symptom that the model is not identified.
## Warning: lavaan->lav_object_post_check():
## covariance matrix of latent variables is not positive definite ; use
## lavInspect(fit, "cov.lv") to investigate.lavaanPlot(eco, coef=TRUE, cov=TRUE)