update to SlX object call and summary/print methods

NAMESPACE

@@ -127,6 +127,9 @@ export(lmSLX, create_WX)
 
 S3method(impacts, SlX)
 S3method(predict, SlX)
+S3method(print, SlX)
+S3method(summary, SlX)
+S3method(print, summary.SlX)
 
 # SF/ME
 export(SpatialFiltering, ME)

NEWS.md

@@ -1,5 +1,7 @@
 # Version 1.3-2 (development)
 
+* pass through SlX formula in call
+
 * re-corrected #19 because the fitted model weights component may be NULL
 
 * suppress warning from `multcomp::glht` as the test which throws the warning is discarded

R/SLX_WX.R

@@ -25,8 +25,12 @@ lmSLX <- function(formula, data = list(), listw, na.action, weights=NULL, Durbin
 	    subset <- !(1:length(listw$neighbours) %in% na.act)
 	    listw <- subset(listw, subset, zero.policy=zero.policy)
 	}
-
+
+        d_name <- deparse(substitute(data))
+	listw_name <- deparse(substitute(listw))
+
 	y <- model.response(mf, "numeric")
+        y_name <- as.character(mt)[2]
 	if (any(is.na(y))) stop("NAs in dependent variable")
 	x <- model.matrix(mt, mf)
 	if (any(is.na(x))) stop("NAs in independent variable")
@@ -198,11 +202,40 @@ lmSLX <- function(formula, data = list(), listw, na.action, weights=NULL, Durbin
         attr(lm.model, "mixedImps") <- mixedImps
         attr(lm.model, "dvars") <- dvars
         if (is.formula(Durbin)) attr(lm.model, "Durbin") <- deparse(Durbin)
+        if (is.null(weights)) attr(lm.model, "weights") <- weights
+        tms <- as.character(lm.model$terms)
+        attr(lm.model, "SLX_call") <- paste(y_name, " ", tms[1], " ", tms[3],
+            ", data = ", d_name, ", listw = ", listw_name, sep="")
         class(lm.model) <- c("SlX", class(lm.model))
         lm.model
 }
 
 
+print.SlX <- function(x, digits = max(3L, getOption("digits") - 3L), ...) {
+    x$ocall <- x$call
+    if (!is.null(attr(x, "SLX_call"))) 
+        x$call <- str2lang(paste("lm(", attr(x, "SLX_call"), ")", sep=""))
+    class(x) <- "lm"
+    invisible(print(x, digits=digits, ...))
+}
+
+summary.SlX <- function(object, correlation = FALSE, symbolic.cor = FALSE, ...) {
+    class(object) <- "lm"
+    sum_obj <- summary(object, correlation=correlation, symbolic.cor=symbolic.cor, ...)
+    class(sum_obj) <- "summary.SlX"
+    sum_obj
+}
+
+
+print.summary.SlX <- function(x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...) {
+    x$ocall <- x$call
+    if (!is.null(attr(x, "SLX_call")))
+        x$call <- str2lang(paste("lm(", attr(x, "SLX_call"), ")", sep=""))
+    class(x) <- "lm"
+    invisible(print(x, digits=digits, symbolic.cor=symbolic.cor, signif.stars=signif.stars, ...))
+}
+
+
 predict.SlX <- function(object, newdata, listw, zero.policy=NULL, ...) {
     if (is.null(zero.policy))
         zero.policy <- get("zeroPolicy", envir = .spatialregOptions)

man/SLX.Rd

@@ -5,6 +5,9 @@
 \alias{print.WXimpact}
 \alias{summary.WXimpact}
 \alias{print.summary.WXimpact}
+\alias{print.SlX}
+\alias{summary.SlX}
+\alias{print.summary.SlX}
 \alias{predict.SlX}
 
 \title{Spatial Durbin linear (SLX, spatially lagged X) model}
@@ -14,12 +17,14 @@
 \usage{
 lmSLX(formula, data = list(), listw, na.action, weights=NULL, Durbin=TRUE,
  zero.policy=NULL)
-create_WX(x, listw, zero.policy=NULL, prefix="")
+\method{print}{SlX}(x, digits = max(3L, getOption("digits") - 3L), ...)
+\method{summary}{SlX}(object, correlation = FALSE, symbolic.cor = FALSE, ...)
+\method{print}{summary.SlX}(x, digits = max(3L, getOption("digits") - 3L), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)
 \method{impacts}{SlX}(obj, ...)
 \method{print}{WXimpact}(x, ...)
 \method{summary}{WXimpact}(object, ..., adjust_k=(attr(object, "type") == "SDEM"))
 \method{predict}{SlX}(object, newdata, listw, zero.policy=NULL, ...)
-
+create_WX(x, listw, zero.policy=NULL, prefix="")
 }
 
 \arguments{
@@ -33,6 +38,10 @@ is called.}
   \item{weights}{an optional vector of weights to be used in the fitting process. Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely proportional to the variances); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations (including the case that there are w_i observations equal to y_i and the data have been summarized) - \code{\link{lm}}}
   \item{Durbin}{default TRUE for \code{lmSLX} (Durbin model including WX); if TRUE, full spatial Durbin model; if a formula object, the subset of explanatory variables to lag}
   \item{zero.policy}{default NULL, use global option value; if TRUE assign zero to the lagged value of zones without neighbours, if FALSE assign NA}
+  \item{digits}{the number of significant digits to use when printing}
+  \item{correlation}{logical; if \code{TRUE}, the correlation matrix of the estimated parameters is returned and printed}
+  \item{symbolic.cor}{logical. If \code{TRUE}, print the correlations in a symbolic form (see 'symnum') rather than as numbers}
+  \item{signif.stars}{logical. If \code{TRUE}, 'significance stars' are printed for each coefficient}
   \item{obj}{A spatial regression object created by \code{lmSLX}}
   \item{\dots}{Arguments passed through}
   \item{prefix}{default empty string, may be \dQuote{lag} in some cases}