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The function selects eigenvectors in a semi-parametric spatial filtering approach to removing spatial dependence from linear models. Selection is by brute force by finding the single eigenvector reducing the standard variate of Moran's I for regression residuals most, and continuing until no candidate eigenvector reduces the value by more than tol. It returns a summary table from the selection process and a matrix of selected eigenvectors for the specified model.
SpatialFiltering(formula, lagformula, data = list(), nb, glist = NULL, style = "C",
zero.policy = NULL, tol = 0.1, zerovalue = 1e-04, ExactEV = FALSE,
symmetric = TRUE, alpha=NULL, alternative="two.sided", verbose=NULL)a symbolic description of the model to be fit, assuming a spatial error representation; when lagformula is given, it should include only the response and the intercept term
An extra one-sided formula to be used when a spatial lag representation is desired; the intercept is excluded within the function if present because it is part of the formula argument, but excluding it explicitly in the lagformula argument in the presence of factors generates a collinear model matrix
an optional data frame containing the variables in the model
an object of class nb
list of general weights corresponding to neighbours
style can take values W, B, C, U, and S
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
tolerance value for convergence of spatial filtering
eigenvectors with eigenvalues of an absolute value smaller than zerovalue will be excluded in eigenvector search
Set ExactEV=TRUE to use exact expectations and variances rather than the expectation and variance of Moran's I from the previous iteration, default FALSE
Should the spatial weights matrix be forced to symmetry, default TRUE
if not NULL, used instead of the tol= argument as a stopping rule to choose all eigenvectors up to and including the one with a probability value exceeding alpha.
a character string specifying the alternative hypothesis, must be one of greater, less or two.sided (default).
default NULL, use global option value; if TRUE report eigenvectors selected
An SFResult object, with:
a matrix summarising the selection of eigenvectors for inclusion, with columns:
Step counter of the selection procedure
number of selected eigenvector (sorted descending)
its associated eigenvalue
value Moran's I for residual autocorrelation
standardized value of Moran's I assuming a normal approximation
probability value of the permutation-based standardized deviate for the given value of the alternative argument
R\^2 of the model including exogenous variables and eigenvectors
regression coefficient of selected eigenvector in fit
a matrix of the selected eigenvectors in order of selection
Tiefelsdorf M, Griffith DA. (2007) Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach. Environment and Planning A, 39 (5) 1193 - 1221. http://www.spatialfiltering.com
# NOT RUN {
example(columbus)
lmbase <- lm(CRIME ~ INC + HOVAL, data=columbus)
sarcol <- SpatialFiltering(CRIME ~ INC + HOVAL, data=columbus,
nb=col.gal.nb, style="W", ExactEV=TRUE)
sarcol
lmsar <- lm(CRIME ~ INC + HOVAL + fitted(sarcol), data=columbus)
lmsar
anova(lmbase, lmsar)
lm.morantest(lmsar, nb2listw(col.gal.nb))
lagcol <- SpatialFiltering(CRIME ~ 1, ~ INC + HOVAL - 1, data=columbus,
nb=col.gal.nb, style="W")
lagcol
lmlag <- lm(CRIME ~ INC + HOVAL + fitted(lagcol), data=columbus)
lmlag
anova(lmbase, lmlag)
lm.morantest(lmlag, nb2listw(col.gal.nb))
# }
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