Merge pull request #7 from USEPA/develop

CRAN 0.3.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: spmodel
 Title: Spatial Statistical Modeling and Prediction
-Version: 0.2.0
+Version: 0.3.0
 Authors@R: c(
     person(given = "Michael",
            family = "Dumelle",
@@ -19,9 +19,9 @@ Authors@R: c(
 Description: Fit, summarize, and predict for a variety of spatial statistical models
     applied to point-referenced and areal (lattice) data. Parameters
     are estimated using various methods. Additional modeling features include anisotropy,
-    random effects, partition factors, big data approaches, and more. Model-fit statistics are
+    non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are
     used to summarize, visualize, and compare models. Predictions at unobserved locations are
-    readily obtainable.
+    readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true

NAMESPACE

@@ -1,13 +1,21 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(AIC,spmod)
-S3method(AICc,spmod)
-S3method(anova,spmod)
-S3method(augment,spmod)
-S3method(coef,spmod)
-S3method(coefficients,spmod)
-S3method(confint,spmod)
-S3method(cooks.distance,spmod)
+S3method(AIC,spautor)
+S3method(AIC,splm)
+S3method(AICc,spautor)
+S3method(AICc,splm)
+S3method(anova,spautor)
+S3method(anova,splm)
+S3method(augment,spautor)
+S3method(augment,splm)
+S3method(coef,spautor)
+S3method(coef,splm)
+S3method(coefficients,spautor)
+S3method(coefficients,splm)
+S3method(confint,spautor)
+S3method(confint,splm)
+S3method(cooks.distance,spautor)
+S3method(cooks.distance,splm)
 S3method(cov_initial_search,car)
 S3method(cov_initial_search,cauchy)
 S3method(cov_initial_search,circular)
@@ -27,11 +35,16 @@ S3method(cov_initial_search,sar)
 S3method(cov_initial_search,spherical)
 S3method(cov_initial_search,triangular)
 S3method(cov_initial_search,wave)
-S3method(covmatrix,spmod)
-S3method(deviance,spmod)
-S3method(fitted,spmod)
-S3method(fitted.values,spmod)
-S3method(formula,spmod)
+S3method(covmatrix,spautor)
+S3method(covmatrix,splm)
+S3method(deviance,spautor)
+S3method(deviance,splm)
+S3method(fitted,spautor)
+S3method(fitted,splm)
+S3method(fitted.values,spautor)
+S3method(fitted.values,splm)
+S3method(formula,spautor)
+S3method(formula,splm)
 S3method(get_initial_range,cauchy)
 S3method(get_initial_range,circular)
 S3method(get_initial_range,cosine)
@@ -49,9 +62,12 @@ S3method(get_initial_range,rquad)
 S3method(get_initial_range,spherical)
 S3method(get_initial_range,triangular)
 S3method(get_initial_range,wave)
-S3method(glance,spmod)
-S3method(glances,default)
-S3method(glances,spmod_list)
+S3method(glance,spautor)
+S3method(glance,splm)
+S3method(glances,spautor)
+S3method(glances,spautor_list)
+S3method(glances,splm)
+S3method(glances,splm_list)
 S3method(gloglik_products,car)
 S3method(gloglik_products,cauchy)
 S3method(gloglik_products,circular)
@@ -71,25 +87,44 @@ S3method(gloglik_products,sar)
 S3method(gloglik_products,spherical)
 S3method(gloglik_products,triangular)
 S3method(gloglik_products,wave)
-S3method(hatvalues,spmod)
-S3method(influence,spmod)
-S3method(labels,spmod)
-S3method(logLik,spmod)
-S3method(loocv,spmod)
-S3method(model.frame,spmod)
-S3method(model.matrix,spmod)
-S3method(plot,spmod)
-S3method(predict,spmod)
-S3method(predict,spmodRF)
-S3method(predict,spmodRF_list)
-S3method(predict,spmod_list)
-S3method(print,anova.spmod)
-S3method(print,spmod)
-S3method(print,summary.spmod)
-S3method(pseudoR2,spmod)
-S3method(resid,spmod)
-S3method(residuals,spmod)
-S3method(rstandard,spmod)
+S3method(hatvalues,spautor)
+S3method(hatvalues,splm)
+S3method(influence,spautor)
+S3method(influence,splm)
+S3method(labels,spautor)
+S3method(labels,splm)
+S3method(logLik,spautor)
+S3method(logLik,splm)
+S3method(loocv,spautor)
+S3method(loocv,splm)
+S3method(model.frame,spautor)
+S3method(model.frame,splm)
+S3method(model.matrix,spautor)
+S3method(model.matrix,splm)
+S3method(plot,spautor)
+S3method(plot,splm)
+S3method(predict,spautor)
+S3method(predict,spautorRF)
+S3method(predict,spautorRF_list)
+S3method(predict,spautor_list)
+S3method(predict,splm)
+S3method(predict,splmRF)
+S3method(predict,splmRF_list)
+S3method(predict,splm_list)
+S3method(print,anova.spautor)
+S3method(print,anova.splm)
+S3method(print,spautor)
+S3method(print,splm)
+S3method(print,summary.spautor)
+S3method(print,summary.splm)
+S3method(pseudoR2,spautor)
+S3method(pseudoR2,splm)
+S3method(resid,spautor)
+S3method(resid,splm)
+S3method(residuals,spautor)
+S3method(residuals,splm)
+S3method(rstandard,spautor)
+S3method(rstandard,splm)
 S3method(spcov_matrix,car)
 S3method(spcov_matrix,cauchy)
 S3method(spcov_matrix,circular)
@@ -185,10 +220,16 @@ S3method(sprnorm,sar)
 S3method(sprnorm,spherical)
 S3method(sprnorm,triangular)
 S3method(sprnorm,wave)
-S3method(summary,spmod)
-S3method(tidy,anova.spmod)
-S3method(tidy,spmod)
-S3method(vcov,spmod)
+S3method(summary,spautor)
+S3method(summary,splm)
+S3method(tidy,anova.spautor)
+S3method(tidy,anova.splm)
+S3method(tidy,spautor)
+S3method(tidy,splm)
+S3method(varcomp,spautor)
+S3method(varcomp,splm)
+S3method(vcov,spautor)
+S3method(vcov,splm)
 export(AICc)
 export(augment)
 export(covmatrix)
@@ -207,6 +248,7 @@ export(splm)
 export(splmRF)
 export(sprnorm)
 export(tidy)
+export(varcomp)
 import(Matrix)
 importFrom(generics,augment)
 importFrom(generics,glance)

NEWS.md

@@ -1,5 +1,38 @@
+# spmodel 0.3.0
+
+## Minor updates
+
+* Added the `varcomp` function to compare variance components.
+* Added an error message when there are `NA` values in predictors.
+* Added an error message when the design (model) matrix is not invertible (i.e., perfect collinearities are detected).
+* Added support for plotting anisotropic level curves of equal correlation when the `which` argument to `plot()` contains `8`.
+* Renamed `residuals()` type `raw` to `response` to match `stats::lm()`.
+* Changed class of `splm()` output to `"splm"` from `"spmod"` or `"splm_list"` from `"spmod_list"`.
+* Changed class of `spautor()` output to `"spautor"` from `"spmod"` or `"spautor_list"` from `"spautor_list"`.
+* Changed class of `splmRF()` output to `"splmRF"` from `"spmodRF"` or `"splmRF_list"` from `"spmodRF_list"`.
+* Changed class of `spautorRF()` output to `"spautorRF"` from `"spmodRF"` or `"spautorRF_list"` from `"spmodRF_list"`.
+* Methods corresponding to a generic function defined outside of `spmodel` are now all documented using
+    an `.spmodel` suffix, making it easier to find documentation of a particular
+    `spmodel` method for the generic function of interest.
+* Added an error when random effect grouping variables or partition factors are numeric.
+* Added an error when random effect or partition factor levels in `newdata` are not also in `data`.
+* Updated citation information.
+
+## Bug fixes
+
+* Fixed a bug that produced irregular spacing in an error message for `spcov_initial()`.
+* Fixed a bug that prevented proper display of row names when calling `predict()`
+    with `interval = "confidence"`.
+* Fixed a bug that sometimes caused miscalculations in model-fitting and prediction
+    when random effect or partition factor variables were improperly coerced to a different type.
+* Fixed bugs that sometimes caused miscalculations in certain model diagnostics.
+* Fixed inconsistencies in several non-exported generic functions.
+* Fixed a bug that prevented names from appearing with output from certain model diagnostics.
+
 # spmodel 0.2.0
 
+* `spmodel` v0.3.0 changed the names of `spmod`, `spmodRF`, `spmod_list`, and `spmodRF_list` objects.
+
 ## Minor updates
 
 * `splm()` and `spautor()` allow multiple models to be fit when the `spcov_type` argument is a vector of length greater than one or the `spcov_initial` argument is a list (with length greater than one) of `spcov_initial` objects.
@@ -13,7 +46,7 @@
 * Minor vignette updates.
 * Minor documentation updates.
 
-## Bug Fixes
+## Bug fixes
 
 * Fixed a bug that prevents display of spatial covariance type in summary of `spmod` objects.
 * Fixed a bug that prevented prediction of factor variables when all levels of all factor variables did not appear in `newdata`.

R/AIC.R

@@ -37,8 +37,8 @@
 #'   to the objects and columns representing the number of parameters estimated
 #'   (\code{df}) and the AIC or AICc.
 #'
-#'
-#' @method AIC spmod
+#' @name AIC.spmodel
+#' @method AIC splm
 #' @order 1
 #' @export
 #'
@@ -49,7 +49,7 @@
 #' )
 #' AIC(spmod)
 #' AICc(spmod)
-AIC.spmod <- function(object, ..., k = 2) {
+AIC.splm <- function(object, ..., k = 2) {
 
   # set k as 2
   k <- 2
@@ -126,17 +126,22 @@ AIC.spmod <- function(object, ..., k = 2) {
   AIC_val
 }
 
-#' @rdname AIC.spmod
+#' @rdname AIC.spmodel
+#' @method AIC spautor
+#' @export
+AIC.spautor <- AIC.splm
+
+#' @rdname AIC.spmodel
 #' @export
 AICc <- function(object, ..., k = 2) {
   # method dispatch
   UseMethod("AICc", object)
 }
 
-#' @rdname AIC.spmod
-#' @method AICc spmod
+#' @rdname AIC.spmodel
+#' @method AICc splm
 #' @export
-AICc.spmod <- function(object, ..., k = 2) {
+AICc.splm <- function(object, ..., k = 2) {
 
   # set k as 2
   k <- 2
@@ -211,3 +216,8 @@ AICc.spmod <- function(object, ..., k = 2) {
   # return AICc value
   AICc_val
 }
+
+#' @rdname AIC.spmodel
+#' @method AICc spautor
+#' @export
+AICc.spautor <- AICc.splm

R/PseudoR2.R

@@ -29,10 +29,11 @@
 pseudoR2 <- function(object, ...) {
   UseMethod("pseudoR2", object)
 }
+
 #' @rdname pseudoR2
-#' @method pseudoR2 spmod
+#' @method pseudoR2 splm
 #' @export
-pseudoR2.spmod <- function(object, adjust = FALSE, ...) {
+pseudoR2.splm <- function(object, adjust = FALSE, ...) {
   if (adjust) {
     has_intercept <- "(Intercept)" %in% tidy(object)$term
     pr2 <- object$pseudoR2
@@ -42,3 +43,8 @@ pseudoR2.spmod <- function(object, adjust = FALSE, ...) {
     return(object$pseudoR2)
   }
 }
+
+#' @rdname pseudoR2
+#' @method pseudoR2 spautor
+#' @export
+pseudoR2.spautor <- pseudoR2.splm

R/anova.R

@@ -29,7 +29,6 @@
 #'   Then separate hypothesis tests are conducted. The easiest
 #'   way to obtain all possible coefficients is to run \code{tidy(object)$term}.
 #'
-#'
 #' @details When one fitted model object is present, \code{anova()}
 #'   performs a general linear hypothesis test corresponding to some hypothesis
 #'   specified by a matrix of constraints. If \code{Terms} and \code{L} are not specified,
@@ -57,8 +56,8 @@
 #'   \code{tidy()} can be used
 #'   to obtain tidy tibbles of the \code{anova(object)} output.
 #'
-#'
-#' @method anova spmod
+#' @name anova.spmodel
+#' @method anova splm
 #' @order 1
 #' @export
 #'
@@ -81,7 +80,7 @@
 #'   spcov_type = "none"
 #' )
 #' tidy(anova(spmod, lmod))
-anova.spmod <- function(object, ..., test = TRUE, Terms, L) {
+anova.splm <- function(object, ..., test = TRUE, Terms, L) {
 
 
   # see if one or two models
@@ -174,6 +173,11 @@ anova.spmod <- function(object, ..., test = TRUE, Terms, L) {
   structure(anova_val, class = c(paste("anova", class(object), sep = "."), "data.frame"))
 }
 
+#' @rdname anova.spmodel
+#' @method anova spautor
+#' @export
+anova.spautor <- anova.splm
+
 get_marginal_Chi2 <- function(L, object) {
   # make matrix if a numeric vector
   if (!is.matrix(L)) {
@@ -198,12 +202,12 @@ get_marginal_Chi2 <- function(L, object) {
   Chi2_df
 }
 
-#' @rdname anova.spmod
+#' @rdname anova.spmodel
 #' @param x An object from \code{anova(object)}.
 #'
-#' @method tidy anova.spmod
+#' @method tidy anova.splm
 #' @export
-tidy.anova.spmod <- function(x, ...) {
+tidy.anova.splm <- function(x, ...) {
   if (!is.null(attr(x, "full")) && !is.null(attr(x, "reduced"))) {
     result <- tibble::tibble(full = attr(x, "full"), reduced = attr(x, "reduced"), df = x$Df, statistic = x$Chi2)
   } else {
@@ -214,3 +218,8 @@ tidy.anova.spmod <- function(x, ...) {
   }
   result
 }
+
+#' @rdname anova.spmodel
+#' @method tidy anova.spautor
+#' @export
+tidy.anova.spautor <- tidy.anova.splm