add zero.policy pass-through, start zero.policy attribute changes

NEWS.md

@@ -1,5 +1,7 @@
 # Version 1.2-10 (development)
 
+* added `zero.policy` pass-through to `spdep::mat2listw` calls in `predict.Sarlm` and to `spdep::sn2listw` in `sids_models.Rmd`
+
 * corrected #19 because the fitted model weights component is never NULL, but may have a single unique value
 
 # Version 1.2-9 (2023-05-25)

R/kpgm_new.R

@@ -21,7 +21,7 @@
 
 GMerrorsar <- function(#W, y, X, 
 	formula, data = list(), listw, na.action=na.fail, 
-	zero.policy=NULL, method="nlminb", arnoldWied=FALSE, 
+	zero.policy=attr(listw, "zero.policy"), method="nlminb", arnoldWied=FALSE, 
         control=list(), pars=NULL, scaleU=FALSE, verbose=NULL, legacy=FALSE,
         se.lambda=TRUE, returnHcov=FALSE, pWOrder=250, tol.Hcov=1.0e-10) {
 #	ols <- lm(I(y) ~ I(X) - 1)
@@ -518,7 +518,7 @@ impacts.Gmsar <- function(obj, ..., n=NULL, tr=NULL, R=NULL, listw=NULL,
 ####SARAR model
 
 gstsls<-function (formula, data = list(), listw, listw2=NULL,
- na.action = na.fail, zero.policy = NULL, pars=NULL, scaleU=FALSE,
+ na.action = na.fail, zero.policy = attr(listw, "zero.policy"), pars=NULL, scaleU=FALSE,
  control = list(), verbose = NULL, method = "nlminb", robust = FALSE,
  legacy = FALSE, W2X = TRUE ) 
 {

R/predict.sarlm.R

@@ -193,7 +193,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
           W <- as(listw, "CsparseMatrix")
           W <- W[region.id, region.id]
           style <- listw$style
-          listw <- mat2listw(W, row.names = region.id, style = style) # re-normalize to keep the style
+          listw <- mat2listw(W, row.names = region.id, style = style, zero.policy=zero.policy) # re-normalize to keep the style
           rm(W) # avoid the use of a wrong W
         }
       }
@@ -247,7 +247,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
           W <- as(listw.mixed, "CsparseMatrix")
           W <- W[region.id.mixed, region.id.mixed]
           style <- listw.mixed$style
-          listw.mixed <- mat2listw(W, row.names = region.id.mixed, style = style) # re-normalize to keep the style
+          listw.mixed <- mat2listw(W, row.names = region.id.mixed, style = style, zero.policy=zero.policy) # re-normalize to keep the style
           rm(W) # avoid the use of a wrong W
         }
       }
@@ -540,7 +540,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
         for (i in 1:nrow(newdata)) {
           region.id.temp <- c(region.id.data, region.id.newdata[i])
           Wi <- W[region.id.temp, region.id.temp]
-          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style) # re-normalize
+          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style, zero.policy=zero.policy) # re-normalize
           if (power)
             Wi <- as(listwi, "CsparseMatrix")
           Xi <- rbind(Xs, Xo[i,])
@@ -564,7 +564,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
         for (i in 1:nrow(newdata)) {
           region.id.temp <- c(region.id.data, region.id.newdata[i])
           Wi <- W[region.id.temp, region.id.temp]
-          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style) # re-normalize
+          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style, zero.policy=zero.policy) # re-normalize
           Wi <- as(listwi, "CsparseMatrix")
           Xi <- rbind(Xs, Xo[i,])
           # compute TC1 for S and o units
@@ -599,7 +599,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
         for (i in 1:nrow(newdata)) {
           region.id.temp <- c(region.id.data, region.id.newdata[i])
           Wi <- W[region.id.temp, region.id.temp]
-          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style) # re-normalize
+          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style, zero.policy=zero.policy) # re-normalize
           Wi <- as(listwi, "CsparseMatrix")
           Xi <- rbind(Xs, Xo[i,])
           is.data <- 1:length(ys)
@@ -633,7 +633,7 @@ predict.Sarlm <- function(object, newdata=NULL, listw=NULL, pred.type="TS", all.
         for (i in 1:nrow(newdata)) {
           region.id.temp <- c(region.id.data, region.id.newdata[i])
           Wi <- W[region.id.temp, region.id.temp]
-          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style) # re-normalize
+          listwi <- mat2listw(Wi, row.names = region.id.temp, style = style, zero.policy=zero.policy) # re-normalize
           Wi <- as(listwi, "CsparseMatrix")
           Xi <- rbind(Xs, Xo[i,])
           # compute TC1 for S and o units

man/GMerrorsar.Rd

@@ -17,7 +17,7 @@
 }
 \usage{
 GMerrorsar(formula, data = list(), listw, na.action = na.fail,
- zero.policy = NULL, method="nlminb", arnoldWied=FALSE, 
+ zero.policy = attr(listw, "zero.policy"), method="nlminb", arnoldWied=FALSE, 
  control = list(), pars, scaleU=FALSE, verbose=NULL, legacy=FALSE,
  se.lambda=TRUE, returnHcov=FALSE, pWOrder=250, tol.Hcov=1.0e-10)
 \method{summary}{Gmsar}(object, correlation = FALSE, Hausman=FALSE, ...)

man/gstsls.Rd

@@ -8,7 +8,7 @@
 }
 \usage{
 gstsls(formula, data = list(), listw, listw2 = NULL, na.action = na.fail, 
-    zero.policy = NULL, pars=NULL, scaleU=FALSE, control = list(), 
+    zero.policy = attr(listw, "zero.policy"), pars=NULL, scaleU=FALSE, control = list(), 
     verbose=NULL, method="nlminb", robust=FALSE, legacy=FALSE, W2X=TRUE) 
 \method{impacts}{Gmsar}(obj, \dots, n = NULL, tr = NULL, R = NULL,
  listw = NULL, evalues=NULL, tol = 1e-06, empirical = FALSE, Q=NULL)

vignettes/sids_models.Rmd

@@ -97,7 +97,7 @@ n <- nc$BIR74
 el1 <- min(dij)/dij
 el2 <- sqrt(n[sids.nhbr$to]/n[sids.nhbr$from])
 sids.nhbr$weights <- el1*el2
-sids.nhbr.listw <- sn2listw(sids.nhbr)
+sids.nhbr.listw <- sn2listw(sids.nhbr, style="B", zero.policy=TRUE)
 ```
 
 The first model (I) is a null model with just an intercept, the second