Nothing
# Copyright 2009-2013 by Roger Bivand
is.formula <- function(x){
inherits(x,"formula")
}
# Copyright 1998-2011 by Roger Bivand (Wald test suggested by Rein Halbersma,
# output of correlations suggested by Michael Tiefelsdorf)
#
print.Sarlm <- function(x, ...)
{
#FIXME
if (x$type == "error") if (isTRUE(all.equal(x$lambda, x$interval[1])) ||
isTRUE(all.equal(x$lambda, x$interval[2])))
warning("lambda on interval bound - results should not be used")
if (x$type == "lag" || x$type == "mixed")
if (isTRUE(all.equal(x$rho, x$interval[1])) ||
isTRUE(all.equal(x$rho, x$interval[2])))
warning("rho on interval bound - results should not be used")
cat("\nCall:\n")
print(x$call)
cat("Type:", x$type, "\n")
cat("\nCoefficients:\n")
print(coef(x))
cat("\nLog likelihood:", logLik(x), "\n")
invisible(x)
}
summary.Sarlm <- function(object, correlation = FALSE, Nagelkerke=FALSE,
Hausman=FALSE, adj.se=FALSE, ...)
{
#FIXME
if (Hausman && object$type == "error" && !is.null(object$Hcov)) {
object$Haus <- Hausman.test(object)
}
if (object$type == "error") {
object$Wald1 <- Wald1.Sarlm(object)
if (correlation) {
oresvar <- object$resvar
ctext <- "Correlation of coefficients"
if (is.null(oresvar) || is.logical(oresvar) ||
inherits(oresvar, "try-error")) {
oresvar <- object$fdHess
ctext <- ifelse(object$insert,
"Approximate correlation of coefficients",
"** Guesswork correlation of coefficients **")
}
object$correlation <- diag((diag(oresvar))
^(-1/2)) %*% oresvar %*%
diag((diag(oresvar))^(-1/2))
dimnames(object$correlation) <- dimnames(oresvar)
object$correltext <- ctext
}
} else if (object$type != "error") {
object$Wald1 <- Wald1.Sarlm(object)
if (correlation) {
oresvar <- object$resvar
ctext <- "Correlation of coefficients"
if (is.null(oresvar) || is.logical(oresvar) ||
inherits(oresvar, "try-error")) {
oresvar <- object$fdHess
ctext <- "Approximate correlation of coefficients"
}
object$correlation <- diag((diag(oresvar))
^(-1/2)) %*% oresvar %*%
diag((diag(oresvar))^(-1/2))
dimnames(object$correlation) <- dimnames(oresvar)
object$correltext <- ctext
}
}
object$LR1 <- LR1.Sarlm(object)
adj <- NULL
if (object$type == "error" || ((object$type == "lag" ||
object$type == "mixed" || object$type == "sac" ||
object$type == "sacmixed") && object$ase)) {
object$coeftitle <- "(asymptotic standard errors)"
SE <- object$rest.se
if (adj.se) {
N <- length(residuals(object))
adj <- N/(N-(length(object$coefficients)))
SE <- sqrt((SE^2) * adj)
}
# varnames <- names(object$coefficients)
object$Coef <- cbind(object$coefficients, SE,
object$coefficients/SE,
2*(1-pnorm(abs(object$coefficients/SE))))
colnames(object$Coef) <- c("Estimate", "Std. Error",
ifelse(adj.se, "t value", "z value"), "Pr(>|z|)")
rownames(object$Coef) <- names(object$coefficients)
} else {
# intercept-only bug fix Larry Layne 20060404
if (!is.null(object$rest.se)) {
object$coeftitle <- "(numerical Hessian approximate standard errors)"
SE <- object$rest.se
if (adj.se) {
N <- length(residuals(object))
adj <- N/(N-(length(object$coefficients)))
SE <- sqrt((SE^2) * adj)
}
# varnames <- names(object$coefficients)
object$Coef <- cbind(object$coefficients, SE,
object$coefficients/SE,
2*(1-pnorm(abs(object$coefficients/SE))))
colnames(object$Coef) <- c("Estimate", "Std. Error",
ifelse(adj.se, "t value", "z value"), "Pr(>|z|)")
rownames(object$Coef) <- names(object$coefficients)
}
}
# temporary fix for broom 210312
# object$Coef <- object$coefficients
object$adj.se <- adj
if (Nagelkerke) {
nk <- NK.Sarlm(object)
if (!is.null(nk)) object$NK <- nk
}
structure(object, class=c("summary.Sarlm", class(object)))
}
print.summary.Sarlm <- function(x, digits = max(5, .Options$digits - 3),
signif.stars = FALSE, ...)
{
cat("\nCall:", deparse(x$call), sep = "", fill=TRUE)
if (x$type == "error") if (isTRUE(all.equal(x$lambda, x$interval[1])) ||
isTRUE(all.equal(x$lambda, x$interval[2])))
warning("lambda on interval bound - results should not be used")
if (x$type == "lag" || x$type == "mixed")
if (isTRUE(all.equal(x$rho, x$interval[1])) ||
isTRUE(all.equal(x$rho, x$interval[2])))
warning("rho on interval bound - results should not be used")
cat("\nResiduals:\n")
resid <- residuals(x)
nam <- c("Min", "1Q", "Median", "3Q", "Max")
rq <- if (length(dim(resid)) == 2L)
structure(apply(t(resid), 1, quantile), dimnames = list(nam,
dimnames(resid)[[2]]))
else structure(quantile(resid), names = nam)
print(rq, digits = digits, ...)
cat("\nType:", x$type, "\n")
if (x$zero.policy) {
zero.regs <- attr(x, "zero.regs")
if (!is.null(zero.regs))
cat("Regions with no neighbours included:\n",
zero.regs, "\n")
}
if (!is.null(x$coeftitle)) {
cat("Coefficients:", x$coeftitle, "\n")
coefs <- x$Coef
if (!is.null(aliased <- x$aliased) && any(x$aliased)){
cat(" (", table(aliased)["TRUE"],
" not defined because of singularities)\n", sep = "")
cn <- names(aliased)
coefs <- matrix(NA, length(aliased), 4, dimnames = list(cn,
colnames(x$Coef)))
coefs[!aliased, ] <- x$Coef
}
printCoefmat(coefs, signif.stars=signif.stars, digits=digits,
na.print="NA")
}
res <- x$LR1
pref <- ifelse(x$ase, "Asymptotic", "Approximate (numerical Hessian)")
if (x$type == "error") {
cat("\nLambda: ", format(signif(x$lambda, digits)),
", LR test value: ", format(signif(res$statistic,
digits)), ", p-value: ", format.pval(res$p.value,
digits), "\n", sep="")
if (!is.null(x$lambda.se)) {
if (!is.null(x$adj.se)) {
x$lambda.se <- sqrt((x$lambda.se^2)*x$adj.se)
}
cat(pref, " standard error: ",
format(signif(x$lambda.se, digits)),
ifelse(is.null(x$adj.se), "\n z-value: ",
"\n t-value: "), format(signif((x$lambda/
x$lambda.se), digits)),
", p-value: ", format.pval(2*(1-pnorm(abs(x$lambda/
x$lambda.se))), digits), "\n", sep="")
cat("Wald statistic: ", format(signif(x$Wald1$statistic,
digits)), ", p-value: ", format.pval(x$Wald1$p.value,
digits), "\n", sep="")
}
} else if (x$type == "sac" || x$type == "sacmixed") {
cat("\nRho: ", format(signif(x$rho, digits)), "\n",
sep="")
if (!is.null(x$rho.se)) {
if (!is.null(x$adj.se)) {
x$rho.se <- sqrt((x$rho.se^2)*x$adj.se)
}
cat(pref, " standard error: ",
format(signif(x$rho.se, digits)),
ifelse(is.null(x$adj.se), "\n z-value: ",
"\n t-value: "),
format(signif((x$rho/x$rho.se), digits)),
", p-value: ", format.pval(2 * (1 - pnorm(abs(x$rho/
x$rho.se))), digits), "\n", sep="")
}
cat("Lambda: ", format(signif(x$lambda, digits)), "\n", sep="")
if (!is.null(x$lambda.se)) {
pref <- ifelse(x$ase, "Asymptotic",
"Approximate (numerical Hessian)")
if (!is.null(x$adj.se)) {
x$lambda.se <- sqrt((x$lambda.se^2)*x$adj.se)
}
cat(pref, " standard error: ",
format(signif(x$lambda.se, digits)),
ifelse(is.null(x$adj.se), "\n z-value: ",
"\n t-value: "), format(signif((x$lambda/
x$lambda.se), digits)),
", p-value: ", format.pval(2*(1-pnorm(abs(x$lambda/
x$lambda.se))), digits), "\n", sep="")
}
cat("\nLR test value: ", format(signif(res$statistic, digits)),
", p-value: ", format.pval(res$p.value, digits), "\n",
sep="")
} else {
cat("\nRho: ", format(signif(x$rho, digits)),
", LR test value: ", format(signif(res$statistic, digits)),
", p-value: ", format.pval(res$p.value, digits), "\n",
sep="")
if (!is.null(x$rho.se)) {
if (!is.null(x$adj.se)) {
x$rho.se <- sqrt((x$rho.se^2)*x$adj.se)
}
cat(pref, " standard error: ",
format(signif(x$rho.se, digits)),
ifelse(is.null(x$adj.se), "\n z-value: ",
"\n t-value: "),
format(signif((x$rho/x$rho.se), digits)),
", p-value: ", format.pval(2 * (1 - pnorm(abs(x$rho/
x$rho.se))), digits), "\n", sep="")
}
if (!is.null(x$Wald1)) {
cat("Wald statistic: ", format(signif(x$Wald1$statistic,
digits)), ", p-value: ", format.pval(x$Wald1$p.value,
digits), "\n", sep="")
}
}
cat("\nLog likelihood:", logLik(x), "for", x$type, "model\n")
cat("ML residual variance (sigma squared): ",
format(signif(x$s2, digits)), ", (sigma: ",
format(signif(sqrt(x$s2), digits)), ")\n", sep="")
if (!is.null(x$NK)) cat("Nagelkerke pseudo-R-squared:",
format(signif(x$NK, digits)), "\n")
cat("Number of observations:", length(x$residuals), "\n")
cat("Number of parameters estimated:", x$parameters, "\n")
if ((is.null(x$weights) || (!is.null(x$weights) && length(unique(x$weights))) == 1L)) {
cat("AIC: ", format(signif(AIC(x), digits)), ", (AIC for lm: ",
format(signif(x$AIC_lm.model, digits)), ")\n", sep="")
} else {
cat("AIC: NA (not available for weighted model), (AIC for lm: ",
format(signif(x$AIC_lm.model, digits)), ")\n", sep="")
}
if (x$type == "error") {
if (!is.null(x$Haus)) {
cat("Hausman test: ", format(signif(x$Haus$statistic,
digits)), ", df: ", format(x$Haus$parameter),
", p-value: ", format.pval(x$Haus$p.value, digits),
"\n", sep="")
}
}
if ((x$type == "lag" || x$type == "mixed") && x$ase) {
cat("LM test for residual autocorrelation\n")
cat("test value: ", format(signif(x$LMtest, digits)),
", p-value: ", format.pval((1 - pchisq(x$LMtest, 1)),
digits), "\n", sep="")
}
if (x$type != "error" && !is.null(x$LLCoef)) {
cat("\nCoefficients: (log likelihood/likelihood ratio)\n")
printCoefmat(x$LLCoef, signif.stars=signif.stars,
digits=digits, na.print="NA")
}
correl <- x$correlation
if (!is.null(correl)) {
p <- NCOL(correl)
if (p > 1) {
cat("\n", x$correltext, "\n")
correl <- format(round(correl, 2), nsmall = 2,
digits = digits)
correl[!lower.tri(correl)] <- ""
print(correl[-1, -p, drop = FALSE], quote = FALSE)
}
}
cat("\n")
invisible(x)
}
coef.summary.Sarlm <- function(object, ...) object$Coef
getVmate <- function(coefs, env, s2, trs, tol.solve=1.0e-10, optim=FALSE,
optimM="optimHess") {
if (optim) {
if (optimM == "nlm") {
options(warn=-1)
opt <- nlm(f=f_errlm_hess_nlm, p=coefs, env=env, hessian=TRUE)
options(warn=0)
mat <- opt$hessian
# opt <- optimHess(par=coefs, fn=f_errlm_hess, env=env)
# mat <- opt
} else if (optimM == "optimHess") {
mat <- optimHess(par=coefs, fn=f_errlm_hess, env=env)
} else {
opt <- optim(par=coefs, fn=f_errlm_hess, env=env, method=optimM,
hessian=TRUE)
mat <- opt$hessian
}
# opt <- optimHess(par=coefs, fn=f_errlm_hess, env=env)
# mat <- opt
} else {
fd <- fdHess(coefs, f_errlm_hess, env)
mat <- fd$Hessian
}
if (!is.null(trs)) {
mat <- insert_asye(coefs, env, s2, mat, trs)
}
res <- solve(-(mat), tol.solve=tol.solve)
res
}
sar_error_hess_sse <- function(lambda, beta, env) {
if (get("compiled_sse", envir=env)) {
ft <- get("first_time", envir=env)
SSE <- .Call("R_ml1_sse_env", env, lambda, beta, PACKAGE="spatialreg")
if (ft) assign("first_time", FALSE, envir=env)
} else {
yl <- get("y", envir=env) - lambda * get("wy", envir=env)
xl <- get("x", envir=env) - lambda * get("WX", envir=env)
res <- get("sw", envir=env) * (yl - (xl %*% beta))
SSE <- c(crossprod(res))
}
SSE
}
f_errlm_hess <- function(coefs, env) {
lambda <- coefs[1]
int <- get("interval", envir=env)
if (lambda <= int[1] || lambda >= int[2]) return(-Inf)
beta <- coefs[-1]
SSE <- sar_error_hess_sse(lambda, beta, env)
n <- get("n", envir=env)
s2 <- SSE/n
det <- do_ldet(lambda, env)
ret <- (det + (1/2)*get("sum_lw", envir=env) - ((n/2) * log(2 * pi)) -
(n/2) * log(s2) - (1/(2 * s2)) * SSE)
if (get("verbose", envir=env)) cat("lambda:", lambda, " function:", ret,
" Jacobian:", det, " SSE:", SSE, "\n")
assign("hf_calls", get("hf_calls", envir=env)+1L, envir=env)
if (!is.finite(ret)) return(-Inf)
ret
}
f_errlm_hess_nlm <- function(coefs, env) {
ret <- f_errlm_hess(coefs, env)
-ret
}
insert_asye <- function(coefs, env, s2, mat, trs) {
lambda <- coefs[1]
p <- length(coefs)-1L
p2 <- p+2
omat <- matrix(0, nrow=p2, ncol=p2)
LX <- get("sw", envir=env) * (get("x", envir=env) - lambda *
get("WX", envir=env))
# omat[3:p2, 3:p2] <- -crossprod(LX)*s2
# omat[3:p2, 3:p2] <- -crossprod(LX)
omat[3:p2, 3:p2] <- -crossprod(LX)/s2
omat[2, 2] <- mat[1, 1]
n <- get("n", envir=env)
omat[1, 1] <- -n/(2*(s2^2))
# omat[1, 1] <- -n/(2*(s2))
omat[1, 2] <- omat[2, 1] <- -trB(lambda, trs)/s2
# omat[1, 2] <- omat[2, 1] <- -trB(lambda, trs)
omat
}
getVmatl <- function(coefs, env, s2, trs, tol.solve=1.0e-10, optim=FALSE,
optimM="optimHess") {
if (optim) {
if (optimM == "nlm") {
options(warn=-1)
opt <- nlm(f=f_laglm_hess_nlm, p=coefs, env=env, hessian=TRUE)
options(warn=0)
mat <- opt$hessian
# opt <- optimHess(par=coefs, fn=f_laglm_hess, env=env)
# mat <- opt
} else if (optimM == "optimHess") {
mat <- optimHess(par=coefs, fn=f_laglm_hess, env=env)
} else {
opt <- optim(par=coefs, fn=f_laglm_hess, env=env, method=optimM,
hessian=TRUE)
mat <- opt$hessian
}
} else {
fd <- fdHess(coefs, f_laglm_hess, env)
mat <- fd$Hessian
}
if (!is.null(trs)) {
mat <- insert_asy(coefs, env, s2, mat, trs)
}
res <- solve(-(mat), tol.solve=tol.solve)
res
}
sar_lag_hess_sse <- function(rho, beta, env) {
if (get("compiled_sse", envir=env)) {
ft <- get("first_time", envir=env)
SSE <- .Call("R_ml2_sse_env", env, rho, beta, PACKAGE="spatialreg")
if (ft) assign("first_time", FALSE, envir=env)
} else {
res <- (get("y", envir=env) - rho * get("wy", envir=env)) -
get("x", envir=env) %*% beta
SSE <- c(crossprod(res))
}
SSE
}
f_laglm_hess <- function(coefs, env) {
rho <- coefs[1]
int <- get("interval", envir=env)
if (rho <= int[1] || rho >= int[2]) return(-Inf)
beta <- coefs[-1]
SSE <- sar_lag_hess_sse(rho, beta, env)
n <- get("n", envir=env)
s2 <- SSE/n
det <- do_ldet(rho, env)
ret <- (det - ((n/2) * log(2 * pi)) - (n/2) * log(s2) -
(1/(2 * s2)) * SSE)
if (get("verbose", envir=env)) cat("Hessian: rho:\t", rho, "\tfunction value:\t", ret, "\n")
assign("hf_calls", get("hf_calls", envir=env)+1L, envir=env)
if (!is.finite(ret)) return(-Inf)
ret
}
f_laglm_hess_nlm <- function(coefs, env) {
ret <- f_laglm_hess(coefs, env)
-ret
}
trB <- function(rho, tr) sum(sapply(0:(length(tr)-1L),
function(i) rho^i * tr[i+1]))
insert_asy <- function(coefs, env, s2, mat, trs) {
p <- length(coefs)-1L
p2 <- p+2
n <- get("n", envir=env)
omat <- matrix(0, nrow=p2, ncol=p2)
omat[3:p2, 3:p2] <- -crossprod(get("x", envir=env))/s2
omat[2, 2] <- mat[1, 1]
omat[2, 3:p2] <- omat[3:p2, 2] <- -c(crossprod(get("wy", envir=env),
get("x", envir=env))/s2)
omat[1, 1] <- -n/(2*(s2^2))
omat[1, 2] <- omat[2, 1] <- -trB(coefs[1], trs)/s2
omat
}
sar_sac_hess_sse <- function(rho, lambda, beta, env) {
yl <- get("y", envir=env) - rho * get("wy", envir=env) -
lambda * get("w2y", envir=env) + rho * lambda * get("w2wy", envir=env)
xl <- get("x", envir=env) - lambda * get("WX", envir=env)
res <- yl - (xl %*% beta)
SSE <- c(crossprod(res))
SSE
}
getVmatsac <- function(coefs, env, tol.solve=1.0e-10) {
fd <- fdHess(coefs, f_sac_hess, env)
mat <- fd$Hessian
res <- solve(-(mat), tol.solve=tol.solve)
res
}
f_sac_hess <- function(coefs, env) {
rho <- coefs[1]
int <- get("interval1", envir=env)
if (rho <= int[1] || rho >= int[2]) return(-Inf)
lambda <- coefs[2]
int <- get("interval2", envir=env)
if (lambda <= int[1] || lambda >= int[2]) return(-Inf)
beta <- coefs[-(1:2)]
SSE <- sar_sac_hess_sse(rho, lambda, beta, env)
n <- get("n", envir=env)
s2 <- SSE/n
ldet1 <- do_ldet(rho, env, which=1)
ldet2 <- do_ldet(lambda, env, which=2)
ret <- (ldet1 + ldet2 - ((n/2) * log(2 * pi)) - (n/2) * log(s2) -
(1/(2 * s2)) * SSE)
if (get("verbose", envir=env)) cat("rho:", rho, "lambda:", lambda,
" function:", ret, " Jacobian1:", ldet1, " Jacobian2:",
ldet2, " SSE:", SSE, "\n")
if (!is.finite(ret)) return(-Inf)
ret
}
sar_sac_hess_sse <- function(rho, lambda, beta, env) {
yl <- get("y", envir=env) - rho * get("wy", envir=env) -
lambda * get("w2y", envir=env) + rho * lambda * get("w2wy", envir=env)
xl <- get("x", envir=env) - lambda * get("WX", envir=env)
res <- yl - (xl %*% beta)
SSE <- c(crossprod(res))
SSE
}
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