clean docs

.Rbuildignore

@@ -18,3 +18,4 @@ tests/testthat/test-rstan-conformity-results
 index.html
 cran-comments.md
 ^CRAN-RELEASE$
+LICENSE.md

R/convenience-functions.R

@@ -203,7 +203,7 @@ sp_diag.geostan_fit <- function(y,
                             w = shape2mat(shape, match.arg(style)),
                             binwidth = function(x) 0.5 * stats::sd(x, na.rm = TRUE),
                             rates = TRUE,
-                            size = 0.15,
+                            size = 0.05,
                             ...) {
     mc_style <- match.arg(mc_style, c("scatter", "hist"))
     if (!inherits(shape, "sf")) shape <- sf::st_as_sf(shape)
@@ -231,7 +231,7 @@ sp_diag.geostan_fit <- function(y,
     marginal_residual <- apply(residuals(y, summary = FALSE, ...), 2, mean, na.rm = TRUE)
     map.y <- ggplot(shape) +
         geom_sf(aes(fill = marginal_residual),
-                lwd = 0.05,
+                lwd =  .01,
                 col = "gray20") +
         scale_fill_gradient2(name = name,
                              label = signs::signs) +
@@ -979,7 +979,14 @@ prep_icar_data <- function(C, scale_factor = NULL) {
 #' @md
 #' @importFrom rstan extract_sparse_parts
 #' @importFrom Matrix isSymmetric Matrix rowSums summary
-prep_car_data <- function(A, style = c("WCAR", "ACAR", "DCAR"), k = 1, gamma = 0, lambda = TRUE, cmat = TRUE, stan_fn = ifelse(style == "WCAR", "wcar_normal_lpdf", "car_normal_lpdf")) {
+prep_car_data <- function(A,
+                          style = c("WCAR", "ACAR", "DCAR"),
+                          k = 1,
+                          gamma = 0,
+                          lambda = TRUE,
+                          cmat = TRUE,
+                          stan_fn = ifelse(style == "WCAR", "wcar_normal_lpdf", "car_normal_lpdf")
+                          ) {
     style = match.arg(style)
     if (style != "WCAR" & stan_fn == "wcar_normal_lpdf") stop("wcar_normal_lpdf only works with style = 'WCAR'.")
     stopifnot(inherits(A, "matrix") | inherits(A, "Matrix"))

R/moran.R

@@ -40,7 +40,7 @@ mc <- function(x, w, digits = 3, warn = TRUE, na.rm = FALSE) {
     check_sa_data(x, w) 
     na_idx <- which(is.na(x))   
     if (na.rm == TRUE && length(na_idx) > 0) {   
-        if (warn) message(length(na_idx), " NA values found in x. They will be removed from the data before calculating the Moran coefficient. If matrix w was row-standardized, it may not longer be. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.")
+        if (warn) message(length(na_idx), " NA values found in x; they will be removed from the data before calculating the Moran coefficient. If matrix w was row-standardized, it may not longer be. You may want to use a binary connectivity matrix by using style = 'B' in shape2mat.")
         x <- x[-na_idx]
         w <- w[-na_idx, -na_idx]
     }
@@ -101,7 +101,7 @@ moran_plot <- function(x, w, xlab = "x (centered)", ylab = "Spatial Lag", pch =
     na_idx <- which(is.na(x))
     if (length(na_idx) > 0) {
         if (na.rm == TRUE) {   
-            message(length(na_idx), " NA values found in x. They will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.")
+            message(length(na_idx), " NA values found in x; they will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. You may want to use a binary connectivity matrix using style = 'B' in shape2mat.")
             x <- x[-na_idx]
             w <- w[-na_idx, -na_idx]
         } else {

README.Rmd

@@ -69,13 +69,13 @@ data(georgia)
 The `sp_diag` function provides visual summaries of spatial data, including a histogram, Moran scatter plot, and map:
 
 ```{r fig.width = 8}
-sp_diag(georgia$rate.female, georgia)
+A <- shape2mat(georgia, style = "B")
+sp_diag(georgia$rate.female, georgia, w = A)
 ```
 
 There are three censored observations in the `georgia` female mortality data, which means there were 9 or fewer deaths in those counties. The following code fits a spatial conditional autoregressive (CAR) model to female county mortality data. By using the `censor_point` argument we include our information on the censored observations to obtain results for all counties:
 
 ```{r}
-A <- shape2mat(georgia, style = "B")
 cars <- prep_car_data(A)
 fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
                 censor_point = 9,

README.md

@@ -70,22 +70,17 @@ Load the package and the `georgia` county mortality data set (ages
 
 ``` r
 library(geostan)
-#> This is geostan version 0.3.0
-#> 
-#> Attaching package: 'geostan'
-#> The following object is masked from 'package:base':
-#> 
-#>     gamma
 data(georgia)
 ```
 
 The `sp_diag` function provides visual summaries of spatial data,
 including a histogram, Moran scatter plot, and map:
 
 ``` r
-sp_diag(georgia$rate.female, georgia)
+A <- shape2mat(georgia, style = "B")
+sp_diag(georgia$rate.female, georgia, w = A)
 #> 3 NA values found in x. They will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.
-#> Warning: Removed 3 rows containing non-finite values (stat_bin).
+#> Warning: Removed 3 rows containing non-finite values (`stat_bin()`).
 ```
 
 <img src="man/figures/README-unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
@@ -98,9 +93,7 @@ include our information on the censored observations to obtain results
 for all counties:
 
 ``` r
-A <- shape2mat(georgia, style = "B")
 cars <- prep_car_data(A)
-#> as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") instead
 #> Range of permissible rho values:  -1.661134 1
 fit <- stan_car(deaths.female ~ offset(log(pop.at.risk.female)),
                 censor_point = 9,
@@ -132,7 +125,7 @@ diagnostics for spatial models:
 ``` r
 sp_diag(fit, georgia, w = A)
 #> 3 NA values found in x. They will be dropped from the data before creating the Moran plot. If matrix w was row-standardized, it no longer is. To address this, you can use a binary connectivity matrix, using style = 'B' in shape2mat.
-#> Warning: Removed 3 rows containing missing values (geom_pointrange).
+#> Warning: Removed 3 rows containing missing values (`geom_pointrange()`).
 ```
 
 <img src="man/figures/README-unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
@@ -150,20 +143,20 @@ print(fit)
 #> Spatial method (outcome):  CAR 
 #> Likelihood function:  poisson 
 #> Link function:  log 
-#> Residual Moran Coefficient:  0.0014105 
-#> WAIC:  1291.3 
+#> Residual Moran Coefficient:  0.00053825 
+#> WAIC:  1291.44 
 #> Observations:  159 
 #> Data models (ME): none
 #> Inference for Stan model: foundation.
 #> 4 chains, each with iter=2000; warmup=1000; thin=1; 
 #> post-warmup draws per chain=1000, total post-warmup draws=4000.
 #> 
 #>             mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff  Rhat
-#> intercept -4.671   0.004 0.107 -4.855 -4.718 -4.672 -4.625 -4.478   845 1.004
-#> car_rho    0.926   0.001 0.057  0.780  0.897  0.939  0.968  0.996  3130 0.999
-#> car_scale  0.456   0.001 0.035  0.392  0.431  0.455  0.479  0.532  3233 1.001
+#> intercept -4.674   0.002 0.083 -4.826 -4.717 -4.674 -4.633 -4.510  2199 1.002
+#> car_rho    0.921   0.001 0.057  0.781  0.891  0.932  0.963  0.994  3783 1.000
+#> car_scale  0.458   0.001 0.034  0.395  0.434  0.456  0.480  0.531  4130 1.000
 #> 
-#> Samples were drawn using NUTS(diag_e) at Tue Sep 13 08:59:48 2022.
+#> Samples were drawn using NUTS(diag_e) at Sat Sep 17 21:02:38 2022.
 #> For each parameter, n_eff is a crude measure of effective sample size,
 #> and Rhat is the potential scale reduction factor on split chains (at 
 #> convergence, Rhat=1).