ME.RdThe Moran eigenvector filtering function is intended to remove spatial autocorrelation from the residuals of generalised linear models. It uses brute force eigenvector selection to reach a subset of such vectors to be added to the RHS of the GLM model to reduce residual autocorrelation to below the specified alpha value. Since eigenvector selection only works on symmetric weights, the weights are made symmetric before the eigenvectors are found (from spdep 0.5-50).
ME(formula, data=list(), family = gaussian, weights, offset,
na.action=na.fail,listw=NULL, alpha=0.05, nsim=99, verbose=NULL,
stdev=FALSE, zero.policy=NULL)a symbolic description of the model to be fit
an optional data frame containing the variables in the model
a description of the error distribution and link function to be used in the model
an optional vector of weights to be used in the fitting process
this can be used to specify an a priori known component to be included in the linear predictor during fitting
a function (default options("na.action")), can also be na.omit or na.exclude with consequences for residuals and fitted values - in these cases the spatial weights list will be subsetted to remove NAs in the data. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations. Note that only weights lists created without using the glist argument to nb2listw may be subsetted.
a listw object created for example by nb2listw
used as a stopping rule to choose all eigenvectors up to and including the one with a p-value exceeding alpha
number of permutations for permutation bootstrap for finding p-values
default NULL, use global option value; if TRUE report eigenvectors selected
if TRUE, p-value calculated from bootstrap permutation standard deviate using pnorm with alternative="greater", if FALSE the Hope-type p-value
default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors
The eigenvectors for inclusion are chosen by calculating the empirical Moran's I values for the initial model plus each of the doubly centred symmetric spatial weights matrix eigenvectors in turn. Then the first eigenvector is chosen as that with the lowest Moran's I value. The procedure is repeated until the lowest remaining Moran's I value has a permutation-based probability value above alpha. The probability value is either Hope-type or based on using the mean and standard deviation of the permutations to calculate ZI based on the stdev argument.
An object of class Me_res:
a matrix summarising the selection of eigenvectors for inclusion, with columns:
number of selected eigenvector
permutation-based standardized deviate of Moran's I if stdev=TRUE
probability value: if stdev=TRUE of the permutation-based standardized deviate, if FALSE the Hope-type probability value, in both cases on-sided
The first row is the value at the start of the search
a matrix of the selected eigenvectors in order of selection
Dray S, Legendre P and Peres-Neto PR (2005) Spatial modeling: a comprehensive framework for principle coordinate analysis of neigbbor matrices (PCNM), Ecological Modelling; Griffith DA and Peres-Neto PR (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.
#require("spdep", quietly=TRUE)
data(hopkins, package="spData")
hopkins_part <- hopkins[21:36,36:21]
hopkins_part[which(hopkins_part > 0, arr.ind=TRUE)] <- 1
hopkins.rook.nb <- spdep::cell2nb(16, 16, type="rook")
glmbase <- glm(c(hopkins_part) ~ 1, family="binomial")
lw <- spdep::nb2listw(hopkins.rook.nb, style="B")
set.seed(123)
system.time(MEbinom1 <- ME(c(hopkins_part) ~ 1, family="binomial",
listw=lw, alpha=0.05, verbose=TRUE, nsim=49))
#> eV[,2], I: 0.07978424 ZI: NA, pr(ZI): 0.04
#> eV[,54], I: 0.05704227 ZI: NA, pr(ZI): 0.12
#> user system elapsed
#> 1.295 0.003 1.304
glmME <- glm(c(hopkins_part) ~ 1 + fitted(MEbinom1), family="binomial")
#anova(glmME, test="Chisq")
coef(summary(glmME))
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.194892 0.1600429 -7.466073 8.262357e-14
#> fitted(MEbinom1)vec2 9.030818 2.5381594 3.558019 3.736629e-04
#> fitted(MEbinom1)vec54 -8.759610 2.5205844 -3.475230 5.104161e-04
anova(glmbase, glmME, test="Chisq")
#> Analysis of Deviance Table
#>
#> Model 1: c(hopkins_part) ~ 1
#> Model 2: c(hopkins_part) ~ 1 + fitted(MEbinom1)
#> Resid. Df Resid. Dev Df Deviance Pr(>Chi)
#> 1 255 292.23
#> 2 253 266.27 2 25.956 2.311e-06 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# \dontrun{
require("sf", quietly=TRUE)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
#require("spdep", quietly=TRUE)
col.gal.nb <- spdep::read.gal(system.file("weights/columbus.gal", package="spData")[1])
lw <- spdep::nb2listw(col.gal.nb)
lmbase <- lm(CRIME ~ INC + HOVAL, data=columbus)
lagcol <- SpatialFiltering(CRIME ~ 1, ~ INC + HOVAL, data=columbus,
nb=col.gal.nb, style="W", alpha=0.1, verbose=TRUE)
#> Step 0 SelEvec 0 MinMi 0.2123742 ZMinMi 2.681 Pr(ZI) 0.007340246
#> Step 1 SelEvec 6 MinMi 0.1178225 ZMinMi 1.84512 Pr(ZI) 0.06502014
#> Step 2 SelEvec 4 MinMi 0.06242664 ZMinMi 1.494821 Pr(ZI) 0.1349611
lagcol
#> Step SelEvec Eval MinMi ZMinMi Pr(ZI) R2 gamma
#> 0 0 0 0.0000000 0.21237415 2.681000 0.007340246 0.5524040 0.00000
#> 1 1 6 0.7161123 0.11782248 1.845120 0.065020139 0.6038801 25.46181
#> 2 2 4 0.8682938 0.06242664 1.494821 0.134961136 0.6531288 26.68319
lmlag <- lm(CRIME ~ INC + HOVAL + fitted(lagcol), data=columbus)
anova(lmbase, lmlag)
#> Analysis of Variance Table
#>
#> Model 1: CRIME ~ INC + HOVAL
#> Model 2: CRIME ~ INC + HOVAL + fitted(lagcol)
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 46 6014.9
#> 2 44 4661.3 2 1353.6 6.3884 0.003666 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
set.seed(123)
system.time(lagcol1 <- ME(CRIME ~ INC + HOVAL, data=columbus, family="gaussian",
listw=lw, alpha=0.1, verbose=TRUE))
#> eV[,6], I: 0.1178225 ZI: NA, pr(ZI): 0.08
#> eV[,4], I: 0.06242664 ZI: NA, pr(ZI): 0.27
#> user system elapsed
#> 0.783 0.000 0.786
lagcol1
#> Eigenvector ZI pr(ZI)
#> 0 NA NA 0.01
#> 1 6 NA 0.08
#> 2 4 NA 0.27
lmlag1 <- lm(CRIME ~ INC + HOVAL + fitted(lagcol1), data=columbus)
anova(lmbase, lmlag1)
#> Analysis of Variance Table
#>
#> Model 1: CRIME ~ INC + HOVAL
#> Model 2: CRIME ~ INC + HOVAL + fitted(lagcol1)
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 46 6014.9
#> 2 44 4661.3 2 1353.6 6.3884 0.003666 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
set.seed(123)
lagcol2 <- ME(CRIME ~ INC + HOVAL, data=columbus, family="gaussian",
listw=lw, alpha=0.1, stdev=TRUE, verbose=TRUE)
#> eV[,6], I: 0.1178225 ZI: 1.5509, pr(ZI): 0.06046283
#> eV[,4], I: 0.06242664 ZI: 0.681174, pr(ZI): 0.2478807
lagcol2
#> Eigenvector ZI pr(ZI)
#> 0 NA 2.351591 0.009346653
#> 1 6 1.550900 0.060462832
#> 2 4 0.681174 0.247880696
lmlag2 <- lm(CRIME ~ INC + HOVAL + fitted(lagcol2), data=columbus)
anova(lmbase, lmlag2)
#> Analysis of Variance Table
#>
#> Model 1: CRIME ~ INC + HOVAL
#> Model 2: CRIME ~ INC + HOVAL + fitted(lagcol2)
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 46 6014.9
#> 2 44 4661.3 2 1353.6 6.3884 0.003666 **
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
NA.columbus <- columbus
NA.columbus$CRIME[20:25] <- NA
COL.ME.NA <- ME(CRIME ~ INC + HOVAL, data=NA.columbus, family="gaussian",
listw=lw, alpha=0.1, stdev=TRUE, verbose=TRUE,
na.action=na.exclude)
#> eV[,8], I: 0.1426723 ZI: 1.483169, pr(ZI): 0.06901474
#> eV[,1], I: 0.09838877 ZI: 0.9862904, pr(ZI): 0.1619953
COL.ME.NA$na.action
#> 20 21 22 23 24 25
#> 20 21 22 23 24 25
#> attr(,"class")
#> [1] "exclude"
summary(lm(CRIME ~ INC + HOVAL + fitted(COL.ME.NA), data=NA.columbus,
na.action=na.exclude))
#>
#> Call:
#> lm(formula = CRIME ~ INC + HOVAL + fitted(COL.ME.NA), data = NA.columbus,
#> na.action = na.exclude)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -30.1382 -6.0105 0.4095 7.1504 19.9399
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 66.92248 5.28663 12.659 3.33e-15 ***
#> INC -1.40484 0.35678 -3.938 0.00034 ***
#> HOVAL -0.30446 0.09831 -3.097 0.00366 **
#> fitted(COL.ME.NA)1 29.69422 10.58481 2.805 0.00788 **
#> fitted(COL.ME.NA)2 26.61612 11.29187 2.357 0.02367 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 10.48 on 38 degrees of freedom
#> (6 observations deleted due to missingness)
#> Multiple R-squared: 0.6294, Adjusted R-squared: 0.5904
#> F-statistic: 16.13 on 4 and 38 DF, p-value: 8.353e-08
#>
nc.sids <- st_read(system.file("shapes/sids.shp", package="spData")[1], quiet=TRUE)
rn <- as.character(nc.sids$FIPS)
ncCC89_nb <- spdep::read.gal(system.file("weights/ncCC89.gal", package="spData")[1],
region.id=rn)
ncCR85_nb <- spdep::read.gal(system.file("weights/ncCR85.gal", package="spData")[1],
region.id=rn)
glmbase <- glm(SID74 ~ 1, data=nc.sids, offset=log(BIR74),
family="poisson")
set.seed(123)
MEpois1 <- ME(SID74 ~ 1, data=nc.sids, offset=log(BIR74),
family="poisson", listw=spdep::nb2listw(ncCR85_nb, style="B"), alpha=0.2, verbose=TRUE)
#> eV[,1], I: 0.1327384 ZI: NA, pr(ZI): 0.03
#> eV[,8], I: 0.06936385 ZI: NA, pr(ZI): 0.12
#> eV[,4], I: 0.03584503 ZI: NA, pr(ZI): 0.3
MEpois1
#> Eigenvector ZI pr(ZI)
#> 0 NA NA 0.01
#> 1 1 NA 0.03
#> 2 8 NA 0.12
#> 3 4 NA 0.30
glmME <- glm(SID74 ~ 1 + fitted(MEpois1), data=nc.sids, offset=log(BIR74),
family="poisson")
anova(glmME, test="Chisq")
#> Analysis of Deviance Table
#>
#> Model: poisson, link: log
#>
#> Response: SID74
#>
#> Terms added sequentially (first to last)
#>
#>
#> Df Deviance Resid. Df Resid. Dev Pr(>Chi)
#> NULL 99 203.34
#> fitted(MEpois1) 3 32.499 96 170.84 4.108e-07 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
anova(glmbase, glmME, test="Chisq")
#> Analysis of Deviance Table
#>
#> Model 1: SID74 ~ 1
#> Model 2: SID74 ~ 1 + fitted(MEpois1)
#> Resid. Df Resid. Dev Df Deviance Pr(>Chi)
#> 1 99 203.34
#> 2 96 170.84 3 32.499 4.108e-07 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# }