Merge branch 'master' into 2023_12_fmesher

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: FRK
 Type: Package
 Title: Fixed Rank Kriging
-Version: 2.2.1
-Date: 2023-12-08
+Version: 2.2.3
+Date: 2024-04-03
 Authors@R: c(
     person("Andrew", "Zammit-Mangion", , "[email protected]", c("aut", "cre")),
     person("Matthew", "Sainsbury-Dale", , "[email protected]", c("aut")))
 Maintainer: Andrew Zammit-Mangion <[email protected]>
 VignetteBuilder: knitr
-Description: A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework.  Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale et al. <arXiv:2110.02507> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
+Description: A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.
 URL: https://andrewzm.github.io/FRK/, https://github.com/andrewzm/FRK/
 BugReports: https://github.com/andrewzm/FRK/issues/
 Depends:

R/SRE.R

@@ -278,7 +278,7 @@
 #' @references
 #' Zammit-Mangion, A. and Cressie, N. (2021). FRK: An R package for spatial and spatio-temporal prediction with large datasets. Journal of Statistical Software, 98(4), 1-48. doi:10.18637/jss.v098.i04.
 #'
-#' Sainsbury-Dale, M. and Zammit-Mangion, A. and Cressie, N. (2023) Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK, arXiv:2110.02507
+#' Sainsbury-Dale, M. and Zammit-Mangion, A. and Cressie, N. (2024) Modelling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data using FRK. Journal of Statistical Software, 108(10), 1--39. doi:10.18637/jss.v108.i10.
 #' @export
 #' @examples
 #' library("FRK")

inst/CITATION

@@ -1,30 +1,32 @@
-bibentry(
-  bibtype  = "Article",
-  title    = "FRK: an R package for spatial and spatio-temporal prediction with large datasets",
-  author   = c(person("Andrew Zammit-Mangion"), person("Noel Cressie")),
-  journal  = "Journal of Statistical Software",
-  year     = "2021",
-  volume   = "98",
-  number   = "4",
-  pages    = "1--48",
-  header   = "To cite FRK version 1 (with EM algorithm) in publications use:",
-  textVersion  = paste(
-        "Zammit-Mangion A, Cressie N (2021).",
-        "FRK: an R package for spatial and spatio-temporal prediction with large datasets.", 
-        "Journal of Statistical Software, 98(4), 1–48."
-        )
+bibentry(bibtype = "Article",
+  title        = "{FRK}: An {R} Package for Spatial and Spatio-Temporal Prediction with Large Datasets",
+  author       = c(person(given = "Andrew",
+                          family = "Zammit-Mangion"),
+                   person(given = "Noel",
+                          family = "Cressie")),
+  journal      = "Journal of Statistical Software",
+  year         = "2021",
+  volume       = "98",
+  number       = "4",
+  pages        = "1--48",
+  doi          = "10.18637/jss.v098.i04",
+  header       = "To cite FRK version 1 (with EM algorithm) in publications use:",
 )
 
-
-bibentry(
-  bibtype  = "Article",
-  title    = "Modelling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK",
-  author   = c(person("Matthew Sainsbury-Dale"), person("Andrew Zammit-Mangion"), person("Noel Cressie")),
-  journal  = "arXiv:2110.02507",
-  year     = "2022",
-  header   = "To cite FRK version 2 (non-Gaussian data) in publications use:",  textVersion  = paste(
-        "Sainsbury-Dale M, Zammit-Mangion A, Cressie N (2022).",
-        "Modelling big, heterogeneous, non-Gaussian spatial and spatio-temporal data using FRK", 
-        "arXiv:2110.02507"
-        )
+bibentry(bibtype = "Article",
+  title        = "Modeling Big, Heterogeneous, Non-{G}aussian Spatial and Spatio-Temporal Data Using {FRK}",
+  author       = c(person(given = "Matthew",
+                          family = "Sainsbury-Dale",
+                          email = "[email protected]"),
+                   person(given = "Andrew",
+                          family = "Zammit-Mangion"),
+                   person(given = "Noel",
+                          family = "Cressie")),
+  journal      = "Journal of Statistical Software",
+  year         = "2024",
+  volume       = "108",
+  number       = "10",
+  pages        = "1--39",
+  doi          = "10.18637/jss.v108.i10",
+  header       = "To cite FRK version 2 (non-Gaussian data) in publications use:"
 )

vignettes/FRK_intro-concordance.tex

@@ -1,12 +1,11 @@
 \Sconcordance{concordance:FRK_intro.tex:FRK_intro.Rnw:%
-1 223 1 50 0 1 7 48 1 4 0 7 1 4 0 17 1 7 0 5 1 4 0 14 1 4 0 6 1 4 %
-0 8 1 7 0 12 1 4 0 10 1 15 0 8 1 4 0 12 1 6 0 15 1 16 0 8 1 4 0 6 %
-1 4 0 30 1 4 0 5 1 4 0 11 1 4 0 7 1 4 0 10 1 1 22 12 1 4 0 8 1 4 0 %
-8 1 4 0 14 1 4 0 12 1 4 0 13 1 8 0 12 1 3 0 9 1 1 5 19 1 13 0 8 1 %
-4 0 3 1 1 6 7 1 1 11 7 1 1 5 7 1 1 7 14 1 4 0 11 1 4 0 5 1 8 0 6 1 %
-4 0 8 1 4 0 6 1 4 0 15 1 4 0 12 1 4 0 10 1 15 0 7 1 4 0 6 1 4 0 6 %
-1 4 0 20 1 4 0 11 1 4 0 8 1 1 9 7 1 1 6 6 1 4 0 20 1 4 0 6 1 9 0 %
-12 1 4 0 8 1 4 0 6 1 4 0 10 1 4 0 25 1 4 0 10 1 1 12 22 1 4 0 21 1 %
-4 0 1 1 1 10 15 1 1 6 12 1 1 3 12 1 1 4 15 1 1 27 2 1 1 52 6 1 4 0 %
-19 1 4 0 8 1 1 12 10 1 4 0 7 1 4 0 12 1 4 0 10 1 3 0 3 1 1 42 5 1 %
-4 0 69 1}
+1 218 1 50 0 1 7 48 1 4 0 7 1 4 0 17 1 7 0 5 1 4 0 14 1 4 0 6 1 4 0 8 1 %
+6 0 12 1 4 0 10 1 12 0 8 1 4 0 12 1 4 0 15 1 12 0 8 1 4 0 6 1 4 0 30 1 %
+4 0 5 1 3 0 11 1 4 0 7 1 4 0 9 1 1 23 12 1 4 0 8 1 4 0 8 1 4 0 14 1 4 0 %
+12 1 4 0 13 1 4 0 12 1 3 0 8 1 1 6 19 1 4 0 8 1 4 0 3 1 1 6 6 1 1 17 6 %
+1 1 11 6 1 1 13 14 1 4 0 11 1 4 0 5 1 7 0 6 1 4 0 8 1 4 0 6 1 4 0 15 1 %
+4 0 12 1 4 0 10 1 15 0 7 1 4 0 6 1 3 0 6 1 4 0 20 1 4 0 11 1 4 0 7 1 1 %
+10 6 1 1 7 6 1 4 0 20 1 14 0 6 1 8 0 12 1 4 0 8 1 4 0 6 1 4 0 10 1 4 0 %
+25 1 4 0 9 1 1 13 22 1 8 0 21 1 4 0 1 1 1 10 14 1 1 12 11 1 1 9 11 1 1 %
+10 15 1 1 27 2 1 1 52 6 1 3 0 19 1 4 0 7 1 1 13 10 1 4 0 7 1 4 0 12 1 4 %
+0 10 1 2 0 3 1 1 42 5 1 3 0 69 1}

vignettes/FRK_intro.Rnw

@@ -613,45 +613,64 @@ mumax <- quantile(X$mu,0.99)
 @
 
 <<echo=FALSE,fig.width=7,fig.height=3.5,out.width="\\linewidth",fig.pos="t!",fig.cap="CO$_2$ mole-fraction readings in ppm from the \\tt{AIRS}.\\label{fig:AIRSresults1}",fig.align="centre",dev = 'png',dpi=70>>=
-g1 <- (EmptyTheme() +
-           geom_point(data=data.frame(AIRS_05_2003),
-                      aes(lon,lat,
-                          colour=pmin(pmax(
-                              co2avgret,mumin),
+df_to_plot <- data.frame(
+        lon = as.numeric(AIRS_05_2003$lon),
+        lat = as.numeric(AIRS_05_2003$lat),
+        co2avgret = as.numeric(AIRS_05_2003$co2avgret)
+)
+g1 <- (ggplot(df_to_plot) +   geom_point(data = df_to_plot,
+                      aes(lon, lat,
+                          colour = pmin(pmax(
+                              co2avgret, mumin),
                               mumax)),
-                      pch=46) +
-           scale_colour_distiller(palette="Spectral",
-                                  guide_legend(title="co2 (ppm)")
-           ) +
-           coord_map("mollweide")) %>%
-    draw_world(inc_border=TRUE) +
-          xlab("lon (deg)") +
-          ylab("lat (deg)")
+                      pch = 46) +
+        scale_colour_distiller(palette = "Spectral",
+                               name = "co2 (ppm)") +
+        coord_map("mollweide")) %>%
+        draw_world(inc_border=TRUE) +
+           xlab("lon (deg)") +
+           ylab("lat (deg)") +
+     theme_minimal() +
+     theme(axis.text.x=element_blank(),
+       axis.ticks.x=element_blank(),
+       axis.text.y=element_blank(),
+       axis.ticks.y=element_blank())
+
 print(g1)
 @
 
 <<echo=FALSE,fig.width=7,fig.height=3.5,out.width="\\linewidth",fig.pos="t!",fig.cap="Prediction of $\\Yvec_P$ in ppm following FRK on the \\tt{AIRS} data.\\label{fig:AIRSresults2}",fig.align="centre",dev = 'png',dpi=70>>=
-g2 <- (EmptyTheme() +
+g2 <- (ggplot() +
            geom_polygon(data=X,
                         aes(lon,lat,fill=pmin(pmax(mu,mumin),mumax),group=id))+
            scale_fill_distiller(palette="Spectral",name="pred. (ppm)",limits=c(mumin,mumax)) +
            coord_map("mollweide")) %>%
     draw_world(inc_border=TRUE)+
           xlab("lon (deg)") +
-          ylab("lat (deg)")
+          ylab("lat (deg)") +
+    theme_minimal() +
+    theme(axis.text.x=element_blank(),
+      axis.ticks.x=element_blank(),
+      axis.text.y=element_blank(),
+      axis.ticks.y=element_blank())
 print(g2)
 @
 
 <<echo=FALSE,fig.keep=TRUE,fig.width=7,fig.height=3.5,out.width="\\linewidth",fig.pos="t!",fig.cap="Prediction standard error of $\\Yvec_P$ in ppm following FRK on the \\tt{AIRS} data.\\label{fig:AIRSresults3}",fig.align="centre",dev = 'png',dpi=70>>=
 X$se <- pmax(sqrt(X$var),0.32)
-g3 <- (EmptyTheme() +
+g3 <- (ggplot() +
            geom_polygon(data=X,
                         aes(lon,lat,fill=se,group=id))+
            scale_fill_distiller(palette="BrBG",name="s.e. (ppm)") +
            coord_map("mollweide")) %>%
     draw_world(inc_border=TRUE)+
           xlab("lon (deg)") +
-          ylab("lat (deg)")
+          ylab("lat (deg)") +
+    theme_minimal() +
+    theme(axis.text.x=element_blank(),
+      axis.ticks.x=element_blank(),
+      axis.text.y=element_blank(),
+      axis.ticks.y=element_blank())
 
 print(g3)
 @
@@ -996,7 +1015,7 @@ mumax <- max(X$mu)
 
 
 <<echo=FALSE, fig.keep=TRUE,fig.cap="CO$_2$ readings taken from the \\tt{AIRS} on the 04, 08 and 12 May 2003 in ppm. \\label{fig:FRK_AIRS_ST1}",fig.align="center",out.width="\\linewidth",fig.height=3,fig.width=16,fig.pos="t!",dev = 'png',dpi=70>>=
-g1 <- (EmptyTheme() +
+g1 <- (ggplot() +
            geom_point(data=dplyr::filter(AIRS_05_2003,day %in% c(4,8,12)),
                       aes(lon,lat,
                           colour=pmin(pmax(
@@ -1005,24 +1024,34 @@ g1 <- (EmptyTheme() +
                       size=0.5) +
            facet_grid(~day)+
            scale_colour_distiller(palette="Spectral",
-                                  guide_legend(title="co2 (ppm)")) +
+                                  name="co2 (ppm)") +
            coord_map("mollweide") +
             xlab("lon (deg)") +
-            ylab("lat (deg)")) %>%
+            ylab("lat (deg)") +
+           theme_minimal() +
+    theme(axis.text.x=element_blank(),
+      axis.ticks.x=element_blank(),
+      axis.text.y=element_blank(),
+      axis.ticks.y=element_blank()) ) %>%
     draw_world(inc_border=TRUE)
 print(g1)
 @
 
 <<echo=FALSE, fig.keep=TRUE,fig.cap="Prediction of $\\Yvec_P$ in ppm on 04, 08, and 12 May 2003 obtained with \\pkg{FRK} on the \\tt{AIRS} data. \\label{fig:FRK_AIRS_ST2}",fig.align="center",out.width="\\linewidth",fig.height=3,fig.width=16,fig.pos="t!",dev = 'png',dpi=70>>=
 
-g2 <-  (EmptyTheme() +
+g2 <-  (ggplot() +
             geom_polygon(data=filter(X,(t %in% c(4,8,12)) & abs(lon) < 175),
                          aes(lon,lat,fill=mu,group=id))+
            scale_fill_distiller(palette="Spectral",name="pred. (ppm)") +
            coord_map("mollweide") +
            facet_grid(~t) +
             xlab("lon (deg)") +
-            ylab("lat (deg)")) %>%
+            ylab("lat (deg)") +
+            theme_minimal() +
+    theme(axis.text.x=element_blank(),
+      axis.ticks.x=element_blank(),
+      axis.text.y=element_blank(),
+      axis.ticks.y=element_blank())) %>%
     draw_world(inc_border=FALSE)
 
 print(g2)
@@ -1031,14 +1060,19 @@ print(g2)
 <<echo=FALSE, fig.keep=TRUE,fig.cap="Prediction standard error of $\\Yvec_P$ in ppm on 04, 08 and 12 May 2003 obtained with \\pkg{FRK} on the \\tt{AIRS} data. \\label{fig:FRK_AIRS_ST3}",fig.align="center",out.width="\\linewidth",fig.height=3,fig.width=16,fig.pos="t!",dev = 'png',dpi=70>>=
 
 X$se <- sqrt(X$var)
-g3 <-  (EmptyTheme() +
+g3 <-  (ggplot() +
             geom_polygon(data=filter(X,(t %in% c(4,8,12)) & abs(lon) < 175),
                          aes(lon,lat,fill=se,group=id))+
            scale_fill_distiller(palette="BrBG",name="s.e. (ppm)") +
            coord_map("mollweide") +
            facet_grid(~t) +
             xlab("lon (deg)") +
-            ylab("lat (deg)")) %>%
+            ylab("lat (deg)") +
+            theme_minimal() +
+    theme(axis.text.x=element_blank(),
+      axis.ticks.x=element_blank(),
+      axis.text.y=element_blank(),
+      axis.ticks.y=element_blank()) ) %>%
     draw_world(inc_border=FALSE)
 
 print(g3)