ModelaciĆ³n-Ca/Mg
Sindy Sandoval
25 7 2021
library(rworldmap)## Loading required package: sp## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')library(rworldxtra)
library(ggmap)## Loading required package: ggplot2## Google's Terms of Service: https://cloud.google.com/maps-platform/terms/.## Please cite ggmap if you use it! See citation("ggmap") for details.library(sf)## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1library(spdep)## Loading required package: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`library(ape)## Registered S3 method overwritten by 'ape':
## method from
## plot.mst spdeplibrary(sp)
library(MVA)## Loading required package: HSAUR2## Loading required package: toolslibrary(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following object is masked from 'package:ape':
##
## zoom## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(corrplot)## corrplot 0.90 loadedlibrary(psych)##
## Attaching package: 'psych'## The following object is masked from 'package:Hmisc':
##
## describe## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(crayon)##
## Attaching package: 'crayon'## The following object is masked from 'package:psych':
##
## %+%## The following object is masked from 'package:ggplot2':
##
## %+%library(pastecs)
library(readxl)
library(clhs)
library(spatialreg)## Loading required package: Matrix## Registered S3 methods overwritten by 'spatialreg':
## method from
## residuals.stsls spdep
## deviance.stsls spdep
## coef.stsls spdep
## print.stsls spdep
## summary.stsls spdep
## print.summary.stsls spdep
## residuals.gmsar spdep
## deviance.gmsar spdep
## coef.gmsar spdep
## fitted.gmsar spdep
## print.gmsar spdep
## summary.gmsar spdep
## print.summary.gmsar spdep
## print.lagmess spdep
## summary.lagmess spdep
## print.summary.lagmess spdep
## residuals.lagmess spdep
## deviance.lagmess spdep
## coef.lagmess spdep
## fitted.lagmess spdep
## logLik.lagmess spdep
## fitted.SFResult spdep
## print.SFResult spdep
## fitted.ME_res spdep
## print.ME_res spdep
## print.lagImpact spdep
## plot.lagImpact spdep
## summary.lagImpact spdep
## HPDinterval.lagImpact spdep
## print.summary.lagImpact spdep
## print.sarlm spdep
## summary.sarlm spdep
## residuals.sarlm spdep
## deviance.sarlm spdep
## coef.sarlm spdep
## vcov.sarlm spdep
## fitted.sarlm spdep
## logLik.sarlm spdep
## anova.sarlm spdep
## predict.sarlm spdep
## print.summary.sarlm spdep
## print.sarlm.pred spdep
## as.data.frame.sarlm.pred spdep
## residuals.spautolm spdep
## deviance.spautolm spdep
## coef.spautolm spdep
## fitted.spautolm spdep
## print.spautolm spdep
## summary.spautolm spdep
## logLik.spautolm spdep
## print.summary.spautolm spdep
## print.WXImpact spdep
## summary.WXImpact spdep
## print.summary.WXImpact spdep
## predict.SLX spdep##
## Attaching package: 'spatialreg'## The following objects are masked from 'package:spdep':
##
## anova.sarlm, as_dgRMatrix_listw, as_dsCMatrix_I, as_dsCMatrix_IrW,
## as_dsTMatrix_listw, as.spam.listw, bptest.sarlm, can.be.simmed,
## cheb_setup, coef.gmsar, coef.sarlm, coef.spautolm, coef.stsls,
## create_WX, deviance.gmsar, deviance.sarlm, deviance.spautolm,
## deviance.stsls, do_ldet, eigen_pre_setup, eigen_setup, eigenw,
## errorsarlm, fitted.gmsar, fitted.ME_res, fitted.sarlm,
## fitted.SFResult, fitted.spautolm, get.ClusterOption,
## get.coresOption, get.mcOption, get.VerboseOption,
## get.ZeroPolicyOption, GMargminImage, GMerrorsar, griffith_sone,
## gstsls, Hausman.test, HPDinterval.lagImpact, impacts, intImpacts,
## Jacobian_W, jacobianSetup, l_max, lagmess, lagsarlm, lextrB,
## lextrS, lextrW, lmSLX, logLik.sarlm, logLik.spautolm, LR.sarlm,
## LR1.sarlm, LR1.spautolm, LU_prepermutate_setup, LU_setup,
## Matrix_J_setup, Matrix_setup, mcdet_setup, MCMCsamp, ME, mom_calc,
## mom_calc_int2, moments_setup, powerWeights, predict.sarlm,
## predict.SLX, print.gmsar, print.ME_res, print.sarlm,
## print.sarlm.pred, print.SFResult, print.spautolm, print.stsls,
## print.summary.gmsar, print.summary.sarlm, print.summary.spautolm,
## print.summary.stsls, residuals.gmsar, residuals.sarlm,
## residuals.spautolm, residuals.stsls, sacsarlm, SE_classic_setup,
## SE_interp_setup, SE_whichMin_setup, set.ClusterOption,
## set.coresOption, set.mcOption, set.VerboseOption,
## set.ZeroPolicyOption, similar.listw, spam_setup, spam_update_setup,
## SpatialFiltering, spautolm, spBreg_err, spBreg_lag, spBreg_sac,
## stsls, subgraph_eigenw, summary.gmsar, summary.sarlm,
## summary.spautolm, summary.stsls, trW, vcov.sarlm, Wald1.sarlmdatos<-read_excel("/Users/sindyluh/Downloads/datos.xlsx")
dfCaMg=data.frame(datos)
#Muestreo espacial
n=0.80*403
n=round(n,0)
df_sindy=data.frame(x=dfCaMg$Long,
y=dfCaMg$Lat,
Mg=dfCaMg$relacion
)
res<-clhs(df_sindy, size=n, iter=100, progress=FALSE,simple=TRUE)
df_sindy=dfCaMg[res,]
dfCaMg=df_sindy
#matriz de peso
X=as.matrix(data.frame(dfCaMg[,4:14 ]))#Tabla con todo menos coordenadas
XY=as.matrix(data.frame(dfCaMg[,2:3 ]))#Tabla con coordenadas
CaMg.d=as.matrix(dist(XY, diag=T, upper=T))#Matriz
CaMg.d.inv<-as.matrix(1/CaMg.d)#Inversa de la matriz
diag(CaMg.d.inv)<-0#Asignando 0 a la diag
W=as.matrix(CaMg.d.inv)#matriz de peso basado en distancia
SUMAS=apply(W,1,sum)
We=W/SUMAS#Matriz estandarizada
#Creando matriz de peso con funciones de librerĆa
contnb=dnearneigh(coordinates(XY),0,380000,longlat=F)
dlist<-nbdists(contnb,XY)
dlist<-lapply(dlist,function(x)1/x)
Wve=nb2listw(contnb,glist = dlist,style="W")Modelo 1: SAR
modelo.arp=spautolm(relacion~1,data=dfCaMg,listw=Wve)
summary(modelo.arp)##
## Call: spautolm(formula = relacion ~ 1, data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.92331 -1.09072 -0.17582 0.60749 24.19328
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.4427 1.9945 2.7289 0.006355
##
## Lambda: 0.9393 LR test value: 33.26 p-value: 8.0635e-09
## Numerical Hessian standard error of lambda: 0.059534
##
## Log likelihood: -709.4037
## ML residual variance (sigma squared): 4.7196, (sigma: 2.1725)
## Number of observations: 322
## Number of parameters estimated: 3
## AIC: 1424.8library(spatialreg)
res1=modelo.arp$fit$residuals #Tomar residuales de la salida de modelo.arp
Moran.I(res1,CaMg.d.inv)## $observed
## [1] 0.03622526
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004914095
##
## $p.value
## [1] 1.110223e-15#InterpretaciĆ³n: dependencia Modelo 2: SARAR
mod2 = sacsarlm(relacion~1,data=dfCaMg,listw=Wve)## Warning in sacsarlm(relacion ~ 1, data = dfCaMg, listw = Wve): inversion of asymptotic covariance matrix failed for tol.solve = 2.22044604925031e-16
## reciprocal condition number = 3.76734e-18 - using numerical Hessian.summary(mod2)##
## Call:sacsarlm(formula = relacion ~ 1, data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.19293 -0.99100 -0.18593 0.62419 24.49762
##
## Type: sac
## Coefficients: (numerical Hessian approximate standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.9194 1.1608 0.7921 0.4283
##
## Rho: 0.87651
## Approximate (numerical Hessian) standard error: 0.11779
## z-value: 7.4415, p-value: 9.9476e-14
## Lambda: 0.87651
## Approximate (numerical Hessian) standard error: 0.11791
## z-value: 7.4337, p-value: 1.0569e-13
##
## LR test value: 47.492, p-value: 4.8675e-11
##
## Log likelihood: -702.2877 for sac model
## ML residual variance (sigma squared): 4.4836, (sigma: 2.1174)
## Number of observations: 322
## Number of parameters estimated: 4
## AIC: 1412.6, (AIC for lm: 1456.1)res2=mod2$residuals #Tomar residuales de la salida de modelo.arp
Moran.I(res2, CaMg.d.inv)## $observed
## [1] 0.01838899
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004843046
##
## $p.value
## [1] 8.986129e-06#InterpretaciĆ³n:dependenciaModelo 3:SLM
mser1=errorsarlm(formula=relacion~z+cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mser1)##
## Call:errorsarlm(formula = relacion ~ z + cos + K + Na + CICE + Cu +
## Zn, data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.99031 -0.82857 -0.17828 0.66042 24.11000
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.7767357 1.2375011 3.0519 0.0022739
## z 0.0068642 0.0110232 0.6227 0.5334765
## cos 1.6362573 0.3246694 5.0398 4.661e-07
## K -4.6523455 0.9797090 -4.7487 2.047e-06
## Na -3.1730846 0.9212574 -3.4443 0.0005725
## CICE 0.1678637 0.0329975 5.0872 3.635e-07
## Cu -0.1780271 0.0830709 -2.1431 0.0321072
## Zn -0.1274413 0.0652698 -1.9525 0.0508753
##
## Lambda: 0.86799, LR test value: 11.584, p-value: 0.00066515
## Asymptotic standard error: 0.090642
## z-value: 9.5759, p-value: < 2.22e-16
## Wald statistic: 91.699, p-value: < 2.22e-16
##
## Log likelihood: -666.8816 for error model
## ML residual variance (sigma squared): 3.6431, (sigma: 1.9087)
## Number of observations: 322
## Number of parameters estimated: 10
## AIC: 1353.8, (AIC for lm: 1363.3)res3=mser1$residuals
Moran.I(res3, CaMg.d.inv)## $observed
## [1] 0.01506639
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004630904
##
## $p.value
## [1] 8.631406e-05#InterpretaciĆ³n: dependencia,lambda sign
#Sin z
mser2=errorsarlm(formula=relacion~cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mser2)##
## Call:errorsarlm(formula = relacion ~ cos + K + Na + CICE + Cu + Zn,
## data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.95790 -0.85811 -0.10341 0.65327 24.06531
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.282984 0.832249 5.1463 2.657e-07
## cos 1.641851 0.324728 5.0561 4.280e-07
## K -4.687857 0.979335 -4.7868 1.695e-06
## Na -3.172288 0.921910 -3.4410 0.0005796
## CICE 0.168385 0.033007 5.1015 3.371e-07
## Cu -0.184216 0.082679 -2.2281 0.0258744
## Zn -0.125480 0.065267 -1.9226 0.0545331
##
## Lambda: 0.85515, LR test value: 11.336, p-value: 0.00076022
## Asymptotic standard error: 0.099009
## z-value: 8.6371, p-value: < 2.22e-16
## Wald statistic: 74.599, p-value: < 2.22e-16
##
## Log likelihood: -667.0707 for error model
## ML residual variance (sigma squared): 3.6497, (sigma: 1.9104)
## Number of observations: 322
## Number of parameters estimated: 9
## AIC: 1352.1, (AIC for lm: 1361.5)res4=mser2$residuals
Moran.I(res4, CaMg.d.inv)## $observed
## [1] 0.01418225
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004639448
##
## $p.value
## [1] 0.000192732#InterpretaciĆ³n: dependencia,lambda sign
#Sin Zn
mser3=errorsarlm(formula=relacion~cos+K+Na+CICE+Cu,data=dfCaMg,listw=Wve)
summary(mser3)##
## Call:errorsarlm(formula = relacion ~ cos + K + Na + CICE + Cu, data = dfCaMg,
## listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.602866 -0.932206 -0.095637 0.645459 24.401218
##
## Type: error
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.135755 0.749276 5.5197 3.396e-08
## cos 1.582547 0.324812 4.8722 1.104e-06
## K -4.805469 0.983390 -4.8866 1.026e-06
## Na -3.070768 0.925687 -3.3173 0.000909
## CICE 0.183152 0.032338 5.6637 1.481e-08
## Cu -0.284781 0.064830 -4.3927 1.119e-05
##
## Lambda: 0.83243, LR test value: 9.6288, p-value: 0.0019155
## Asymptotic standard error: 0.11356
## z-value: 7.3305, p-value: 2.2937e-13
## Wald statistic: 53.736, p-value: 2.2926e-13
##
## Log likelihood: -668.8967 for error model
## ML residual variance (sigma squared): 3.695, (sigma: 1.9222)
## Number of observations: 322
## Number of parameters estimated: 8
## AIC: 1353.8, (AIC for lm: 1361.4)res5=mser3$residuals
Moran.I(res5, CaMg.d.inv)## $observed
## [1] 0.01220543
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004616178
##
## $p.value
## [1] 0.0009036837#InterpretaciĆ³n: A pesar de eliminar variables no explicativas el modelo sigue presentando dependenciaModelo 4: SEM
mod4=lagsarlm(formula=relacion~z+cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mod4) ##
## Call:lagsarlm(formula = relacion ~ z + cos + K + Na + CICE + Cu +
## Zn, data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.10030 -0.85025 -0.13572 0.55634 24.17533
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3752954 0.7722050 0.4860 0.6269637
## z -0.0021859 0.0067749 -0.3227 0.7469587
## cos 1.5568370 0.3103088 5.0171 5.247e-07
## K -4.7249558 0.9473681 -4.9875 6.118e-07
## Na -3.0946650 0.8976060 -3.4477 0.0005654
## CICE 0.1705593 0.0312384 5.4599 4.763e-08
## Cu -0.1840667 0.0788317 -2.3349 0.0195469
## Zn -0.1147232 0.0632165 -1.8148 0.0695599
##
## Rho: 0.87759, LR test value: 18.425, p-value: 1.7671e-05
## Asymptotic standard error: 0.083207
## z-value: 10.547, p-value: < 2.22e-16
## Wald statistic: 111.24, p-value: < 2.22e-16
##
## Log likelihood: -663.4612 for lag model
## ML residual variance (sigma squared): 3.5647, (sigma: 1.888)
## Number of observations: 322
## Number of parameters estimated: 10
## AIC: 1346.9, (AIC for lm: 1363.3)
## LM test for residual autocorrelation
## test value: 3.0266, p-value: 0.081911res6 = mod4$residuals
Moran.I(res6, CaMg.d.inv)## $observed
## [1] 0.007684906
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004589105
##
## $p.value
## [1] 0.01860074#InterpretaciĆ³n: dependencia ,Rho sign
#Eliminando z
mod4.1=lagsarlm(formula=relacion~cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mod4.1) ##
## Call:lagsarlm(formula = relacion ~ cos + K + Na + CICE + Cu + Zn,
## data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.11911 -0.82777 -0.14894 0.56363 24.20793
##
## Type: lag
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.195351 0.556621 0.3510 0.7256190
## cos 1.546726 0.308918 5.0069 5.531e-07
## K -4.708386 0.946238 -4.9759 6.494e-07
## Na -3.096701 0.897712 -3.4495 0.0005615
## CICE 0.171088 0.031194 5.4847 4.141e-08
## Cu -0.179643 0.077708 -2.3118 0.0207903
## Zn -0.115452 0.063188 -1.8271 0.0676812
##
## Rho: 0.87778, LR test value: 18.451, p-value: 1.7436e-05
## Asymptotic standard error: 0.082888
## z-value: 10.59, p-value: < 2.22e-16
## Wald statistic: 112.15, p-value: < 2.22e-16
##
## Log likelihood: -663.5134 for lag model
## ML residual variance (sigma squared): 3.5658, (sigma: 1.8883)
## Number of observations: 322
## Number of parameters estimated: 9
## AIC: 1345, (AIC for lm: 1361.5)
## LM test for residual autocorrelation
## test value: 3.6976, p-value: 0.054491res7 = mod4.1$residuals
Moran.I(res7, CaMg.d.inv)## $observed
## [1] 0.008525429
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004585149
##
## $p.value
## [1] 0.01112391#InterpretaciĆ³n: modelo empeorĆ³Modelo 5:SAC
mod5=sacsarlm(formula=relacion~z+cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mod5)##
## Call:sacsarlm(formula = relacion ~ z + cos + K + Na + CICE + Cu +
## Zn, data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.09339 -0.81223 -0.13995 0.54761 24.26443
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3764893 1.9433438 0.1937 0.8463852
## z 0.0019981 0.0096171 0.2078 0.8354144
## cos 1.5448265 0.3213807 4.8068 1.533e-06
## K -4.4782471 0.9631628 -4.6495 3.327e-06
## Na -3.0909127 0.9057053 -3.4127 0.0006432
## CICE 0.1595294 0.0323275 4.9348 8.024e-07
## Cu -0.1544459 0.0817391 -1.8895 0.0588250
## Zn -0.1305425 0.0639727 -2.0406 0.0412907
##
## Rho: 0.81222
## Asymptotic standard error: 0.41701
## z-value: 1.9477, p-value: 0.051447
## Lambda: 0.59697
## Asymptotic standard error: 0.81771
## z-value: 0.73005, p-value: 0.46536
##
## LR test value: 20.179, p-value: 4.152e-05
##
## Log likelihood: -662.5844 for sac model
## ML residual variance (sigma squared): 3.5423, (sigma: 1.8821)
## Number of observations: 322
## Number of parameters estimated: 11
## AIC: 1347.2, (AIC for lm: 1363.3)res8 = mod5$residuals
Moran.I(res8, CaMg.d.inv)## $observed
## [1] 0.004269304
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004566282
##
## $p.value
## [1] 0.1058362#InterpretaciĆ³n: no dependencia espacial, Rho sig y lambda no sig
#Sin z
mod5.1=sacsarlm(formula=relacion~cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve)
summary(mod5.1)##
## Call:sacsarlm(formula = relacion ~ cos + K + Na + CICE + Cu + Zn,
## data = dfCaMg, listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.08319 -0.83397 -0.15160 0.55662 24.24496
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.510445 1.810850 0.2819 0.77803
## cos 1.548284 0.320347 4.8331 1.344e-06
## K -4.497118 0.962032 -4.6746 2.945e-06
## Na -3.090580 0.905251 -3.4141 0.00064
## CICE 0.159884 0.032252 4.9573 7.149e-07
## Cu -0.157604 0.081221 -1.9404 0.05233
## Zn -0.129450 0.063911 -2.0255 0.04282
##
## Rho: 0.81666
## Asymptotic standard error: 0.37091
## z-value: 2.2018, p-value: 0.027682
## Lambda: 0.57185
## Asymptotic standard error: 0.78132
## z-value: 0.73189, p-value: 0.46423
##
## LR test value: 20.266, p-value: 3.974e-05
##
## Log likelihood: -662.6056 for sac model
## ML residual variance (sigma squared): 3.5436, (sigma: 1.8824)
## Number of observations: 322
## Number of parameters estimated: 10
## AIC: 1345.2, (AIC for lm: 1361.5)res9 = mod5.1$residuals
Moran.I(res9, CaMg.d.inv)## $observed
## [1] 0.004001731
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004569621
##
## $p.value
## [1] 0.1193616#InterpretaciĆ³n: no dependencia espacial, Rho sig y lambda no sig. Aunque lambda disminuyĆ³ un poco
#Sin Zn
mod5.2=sacsarlm(formula=relacion~cos+K+Na+CICE+Cu,data=dfCaMg,listw=Wve)
summary(mod5.2)##
## Call:sacsarlm(formula = relacion ~ cos + K + Na + CICE + Cu, data = dfCaMg,
## listw = Wve)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.741320 -0.850639 -0.079101 0.540419 24.582036
##
## Type: sac
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.397308 1.543982 0.2573 0.796927
## cos 1.491022 0.318985 4.6743 2.950e-06
## K -4.633935 0.964956 -4.8022 1.569e-06
## Na -2.987160 0.908377 -3.2885 0.001007
## CICE 0.175995 0.031501 5.5869 2.312e-08
## Cu -0.261861 0.063615 -4.1163 3.849e-05
##
## Rho: 0.81292
## Asymptotic standard error: 0.31692
## z-value: 2.5651, p-value: 0.010315
## Lambda: 0.48074
## Asymptotic standard error: 0.75972
## z-value: 0.63278, p-value: 0.52688
##
## LR test value: 18.202, p-value: 0.00011157
##
## Log likelihood: -664.6103 for sac model
## ML residual variance (sigma squared): 3.5926, (sigma: 1.8954)
## Number of observations: 322
## Number of parameters estimated: 9
## AIC: 1347.2, (AIC for lm: 1361.4)res10 = mod5.2$residuals
Moran.I(res10, CaMg.d.inv)## $observed
## [1] 0.002954723
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004546264
##
## $p.value
## [1] 0.1818241#InterpretaciĆ³n: no dependencia espacial(mejoro), Rho sig y lambda no sig. (empeorĆ³)Modelo 6:GNS
mod6=sacsarlm(formula=relacion~z+cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve,type="mixed")
summary(mod6)##
## Call:sacsarlm(formula = relacion ~ z + cos + K + Na + CICE + Cu +
## Zn, data = dfCaMg, listw = Wve, type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.82474 -0.81637 -0.13113 0.52154 23.90671
##
## Type: sacmixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.759321 9.608682 0.9116 0.3619767
## z 0.019108 0.021691 0.8809 0.3783581
## cos 1.481342 0.329212 4.4997 6.806e-06
## K -4.158869 0.973561 -4.2718 1.939e-05
## Na -3.052916 0.904044 -3.3770 0.0007329
## CICE 0.139139 0.034015 4.0905 4.304e-05
## Cu -0.102227 0.084915 -1.2039 0.2286373
## Zn -0.156419 0.065444 -2.3901 0.0168423
## lag.z -0.078898 0.055518 -1.4211 0.1552761
## lag.cos 1.789815 4.290850 0.4171 0.6765879
## lag.K -20.657963 17.933601 -1.1519 0.2493565
## lag.Na 3.987532 13.051514 0.3055 0.7599682
## lag.CICE 0.857792 0.614776 1.3953 0.1629276
## lag.Cu -2.256618 1.314093 -1.7172 0.0859346
## lag.Zn 0.904086 0.920630 0.9820 0.3260851
##
## Rho: -0.067288
## Asymptotic standard error: 1.213
## z-value: -0.055472, p-value: 0.95576
## Lambda: 0.42936
## Asymptotic standard error: 0.85617
## z-value: 0.5015, p-value: 0.61602
##
## LR test value: 35.974, p-value: 4.0071e-05
##
## Log likelihood: -654.6868 for sacmixed model
## ML residual variance (sigma squared): 3.4101, (sigma: 1.8466)
## Number of observations: 322
## Number of parameters estimated: 18
## AIC: 1345.4, (AIC for lm: 1363.3)res11 = mod6$residuals
Moran.I(res11, CaMg.d.inv)## $observed
## [1] 0.002594858
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004553396
##
## $p.value
## [1] 0.2098289#InterpretaciĆ³n:no dep,Rho y lambda no sigModelo 7:SDE
mod7=lagsarlm(formula=relacion~z+cos+K+Na+CICE+Cu+Zn,data=dfCaMg,listw=Wve,type="mixed")
summary(mod7)##
## Call:lagsarlm(formula = relacion ~ z + cos + K + Na + CICE + Cu +
## Zn, data = dfCaMg, listw = Wve, type = "mixed")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.74911 -0.81162 -0.12396 0.55325 23.87339
##
## Type: mixed
## Coefficients: (asymptotic standard errors)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.293905 6.170242 1.0200 0.3077086
## z 0.016601 0.021431 0.7746 0.4385610
## cos 1.494790 0.331000 4.5160 6.302e-06
## K -4.170348 0.974160 -4.2810 1.861e-05
## Na -3.105862 0.908196 -3.4198 0.0006266
## CICE 0.139410 0.034082 4.0904 4.306e-05
## Cu -0.102126 0.084900 -1.2029 0.2290180
## Zn -0.158448 0.065526 -2.4181 0.0156028
## lag.z -0.067546 0.047477 -1.4227 0.1548160
## lag.cos 1.353409 3.455807 0.3916 0.6953293
## lag.K -16.880843 11.787933 -1.4320 0.1521311
## lag.Na 7.172463 11.748477 0.6105 0.5415296
## lag.CICE 0.751621 0.335928 2.2374 0.0252573
## lag.Cu -2.223693 0.900195 -2.4702 0.0135025
## lag.Zn 1.046389 0.866881 1.2071 0.2274038
##
## Rho: 0.13527, LR test value: 0.069628, p-value: 0.79188
## Asymptotic standard error: 0.39696
## z-value: 0.34077, p-value: 0.73327
## Wald statistic: 0.11613, p-value: 0.73327
##
## Log likelihood: -654.9767 for mixed model
## ML residual variance (sigma squared): 3.4218, (sigma: 1.8498)
## Number of observations: 322
## Number of parameters estimated: 17
## AIC: 1344, (AIC for lm: 1342)
## LM test for residual autocorrelation
## test value: 3.8214, p-value: 0.050603res12 = mod7$residuals
Moran.I(res12, CaMg.d.inv)## $observed
## [1] 0.003721987
##
## $expected
## [1] -0.003115265
##
## $sd
## [1] 0.004563814
##
## $p.value
## [1] 0.1340958#InterpretaciĆ³n: hay dependencia, Rho no significativo#ConclusiĆ³n: al parecer ningĆŗn modelo ajusta para los datosR Markdown
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