DESCRIPTION

@@ -1,16 +1,16 @@
 Package: tidymv
 Type: Package
 Title: Tidy Model Visualisation
-Version: 1.4.0
-Date: 2018-07-03
+Version: 1.5.0
+Date: 2018-08-11
 Authors@R: person("Stefano", "Coretta", email = paste0("stefano.coretta", "@", "gmail.com"),
                   role = c("aut", "cre"))
 Description: This package provides functions for model visualisation using tidy
     tools from the tidyverse.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.0
 Imports: cowplot,
     dplyr,
     ggplot2,

NAMESPACE

@@ -2,6 +2,7 @@
 
 export(create_event_start)
 export(get_gam_predictions)
+export(plot_difference)
 export(plot_gamsd)
 export(plot_smooths)
 importFrom(magrittr,"%>%")

NEWS.md

@@ -1,5 +1,16 @@
 # Change Log
 
+## [1.5.0] - 2018-08-11
+### Added
+- `plot_difference()` to plot difference smooths
+- support for plotting single smooth
+
+### Changed
+- deprecated message with `plot_gamsd()` now mentions `plot_difference()`
+
+### Fixed
+- vignettes titles and plots
+
 ## [1.4.0] - 2018-07-03
 ### Added
 - `split` argument for separating columns (useful for interactions)
@@ -63,6 +74,7 @@
 - `create_start_event` function
 - `plot_gamsd` function
 
+[1.5.0]: https://github.com/stefanocoretta/tidymv/compare/v1.4.0...v1.5.0
 [1.4.0]: https://github.com/stefanocoretta/tidymv/compare/v1.3.1...v1.4.0
 [1.3.1]: https://github.com/stefanocoretta/tidymv/compare/v1.3.0...v1.3.1
 [1.3.0]: https://github.com/stefanocoretta/tidymv/compare/v1.2.0...v1.3.0

R/functions.R

@@ -69,8 +69,12 @@ get_gam_predictions <- function(model, time_series, series_length = 25, conditio
         fitted$`(AR.start)` <- NULL
     }
 
-    fitted_series <- fitted %>%
-        unique()
+    if (ncol(fitted) > 0) {
+        fitted_series <- fitted %>%
+            unique()
+    } else {
+        fitted_series <- fitted
+    }
 
     fitted_series <- fitted_series %>%
         dplyr::mutate(
@@ -81,6 +85,11 @@ get_gam_predictions <- function(model, time_series, series_length = 25, conditio
         ) %>%
         tidyr::unnest(!!time_series_q)
 
+    if (ncol(fitted_series) > 0) {
+        fitted_series <- fitted_series %>%
+            unique()
+    }
+
     if (exclude_random) {
         if (rlang::is_empty(random_effects)) {
             exclude_random_effects <- as.null()
@@ -195,10 +204,13 @@ get_gam_predictions <- function(model, time_series, series_length = 25, conditio
 #' @importFrom rlang ":="
 #' @importFrom stats "predict"
 #' @export
-plot_smooths <- function(model, time_series, comparison, facet_terms = NULL, conditions = NULL, exclude_random = TRUE, exclude_terms = NULL, series_length = 25, split = NULL, sep = "\\.") {
+plot_smooths <- function(model, time_series, comparison = NULL, facet_terms = NULL, conditions = NULL, exclude_random = TRUE, exclude_terms = NULL, series_length = 25, split = NULL, sep = "\\.") {
     time_series_q <- dplyr::enquo(time_series)
     comparison_q <- dplyr::enquo(comparison)
     facet_terms_q <- dplyr::enquo(facet_terms)
+    if (comparison_q == dplyr::quo(NULL)) {
+        comparison_q <- NULL
+    }
     if (facet_terms_q == dplyr::quo(NULL)) {
         facet_terms_q <- NULL
     }
@@ -212,20 +224,38 @@ plot_smooths <- function(model, time_series, comparison, facet_terms = NULL, con
                 dplyr::quo_name(time_series_q), dplyr::quo_name(outcome_q)
             )
         ) +
-        ggplot2::geom_ribbon(
-            ggplot2::aes_string(
-                ymin = "CI_lower",
-                ymax = "CI_upper",
-                fill = dplyr::quo_name(comparison_q)
-            ),
-            alpha = 0.2
-        ) +
-        ggplot2::geom_path(
-            ggplot2::aes_string(
-                colour = dplyr::quo_name(comparison_q),
-                linetype = dplyr::quo_name(comparison_q)
+        {if (!is.null(comparison_q)) {
+            ggplot2::geom_ribbon(
+                ggplot2::aes_string(
+                    ymin = "CI_lower",
+                    ymax = "CI_upper",
+                    fill = dplyr::quo_name(comparison_q)
+                ),
+                alpha = 0.2
             )
-        ) +
+        }} +
+        {if (is.null(comparison_q)) {
+            ggplot2::geom_ribbon(
+                ggplot2::aes_string(
+                    ymin = "CI_lower",
+                    ymax = "CI_upper"
+                ),
+                alpha = 0.2
+            )
+        }} +
+        {if (!is.null(comparison_q)) {
+            ggplot2::geom_path(
+                ggplot2::aes_string(
+                    colour = dplyr::quo_name(comparison_q),
+                    linetype = dplyr::quo_name(comparison_q)
+                )
+            )
+        }} +
+        {if (is.null(comparison_q)) {
+            ggplot2::geom_path(
+                ggplot2::aes_string()
+            )
+        }} +
         {if (!is.null(facet_terms_q)) {
             ggplot2::facet_wrap(facet_terms_q)
         }}
@@ -250,7 +280,7 @@ plot_smooths <- function(model, time_series, comparison, facet_terms = NULL, con
 #' @importFrom magrittr "%>%"
 #' @export
 plot_gamsd <- function(model, view, comparison, conditions = NULL, rm_re = FALSE, bw = FALSE, ylim = NULL) {
-    .Deprecated("plot_smooth", msg = "'plot_gamsd' is deprecated and will be removed, use 'plot_smooths()'. (Plotting with the difference smooth is not supported yet.)\n")
+    .Deprecated("plot_smooth", msg = "'plot_gamsd' is deprecated and will be removed, use 'plot_smooths()' and 'plot_difference'.\n")
 
     diff.df <- itsadug::plot_diff(
         model,
@@ -362,3 +392,65 @@ plot_gamsd <- function(model, view, comparison, conditions = NULL, rm_re = FALSE
 
     cowplot::plot_grid(smooth.plot, diff.plot, align = "v", nrow = 2, rel_heights = c(2/3, 1/3))
 }
+
+#' Plot difference smooth from a GAM.
+#'
+#' It plots the difference smooth from a \link[mgcv]{gam} or \link[mgcv]{bam}.
+#' Significant differences are marked with red areas.
+#'
+#' @inheritParams get_gam_predictions
+#' @param time_series An unquoted expression indicating the model term that defines the time series.
+#' @param difference A named list with the levels to compute the difference of.
+#' @param conditions A named list specifying the levels to plot from the model terms not among \code{time_series} or \code{difference}. Notice the difference with \link[tidymv]{plot_smooths}, which uses \link[rlang]{quos}.
+#'
+#' @export
+plot_difference <- function(model, time_series, difference, conditions = NULL, series_length = 25) {
+    time_series_q <- dplyr::enquo(time_series)
+    time_series_chr <- quo_name(time_series_q)
+
+    fitted <- model$model
+
+    time_series_min <- dplyr::select(fitted, !!time_series_q) %>% min()
+    time_series_max <- dplyr::select(fitted, !!time_series_q) %>% max()
+
+    conditions <- c(conditions, rlang::ll(!!time_series_chr := seq(time_series_min, time_series_max, length.out = series_length)))
+
+    diff <- itsadug::get_difference(model, difference, cond = conditions, print.summary = FALSE) %>%
+        dplyr::mutate(
+            CI_upper = difference + CI,
+            CI_lower = difference - CI
+        )
+
+    sig_diff <- itsadug::find_difference(
+        diff$difference, diff$CI, diff[[time_series_chr]]
+    )
+
+    annotate <- ggplot2::annotate(
+        "rect",
+        xmin = sig_diff$start, xmax = sig_diff$end,
+        ymin = -Inf, ymax = Inf, alpha = 0.1,
+        fill = "red"
+    )
+
+    is_sig <- is.null(sig_diff) == FALSE
+
+    diff_plot <- diff %>%
+        ggplot2::ggplot(
+            ggplot2::aes_string(
+                dplyr::quo_name(time_series_q), "difference"
+            )
+        ) +
+        {if (is_sig) {annotate}} +
+        ggplot2::geom_ribbon(
+            ggplot2::aes_string(
+                ymin = "CI_lower",
+                ymax = "CI_upper"
+            ),
+            alpha = 0.2
+        ) +
+        ggplot2::geom_path(
+        ) +
+        geom_hline(yintercept = 0)
+
+    return(diff_plot)
+}

README.md

@@ -4,6 +4,6 @@ This is the repository of the `R` package `tidymv`. This package provides functi
 
 ## Installation
 
-To install the package, use `devtools::install_github("stefanocoretta/tidymv@v1.4.0", build_vignettes = TRUE)`. To learn how to use the package, do `vignette("plot-smooths", package = "tidymv")` after the installation.
+To install the package, use `devtools::install_github("stefanocoretta/tidymv@v1.5.0", build_vignettes = TRUE)`. To learn how to use the package, do `vignette("plot-smooths", package = "tidymv")` after the installation.
 
 If you wish to install the development version, use `devtools::install_github("stefanocoretta/tidymv", build_vignettes = TRUE)`.

vignettes/plot-gamms.Rmd

@@ -1,10 +1,10 @@
 ---
-title: "Plot GAM(M)s reference and difference smooths"
+title: "Plot GAM(M)s reference and difference smooths (deprecated)"
 author: "Stefano Coretta"
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Vignette Title}
+  %\VignetteIndexEntry{Plot GAM(M)s reference and difference smooths (deprecated)}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -20,6 +20,7 @@ library(tidymv)
 The estimated smooths and the difference smooth of factor predictors from a `gam` model can be plotted with `plot_gamsd()`.
 This function has now been deprecated and will be removed in future versions.
 This vignette is kept for historical reasons.
+Please check `vignette("plot-smooths", package = "tidymv")`.
 
 To illustrate how to use `plot_gamsd()`, let's first prepare some dummy data with a factor variable and run `gam()` on this data. The `gam` model includes a reference smooth `s(x2)`, a by-factor difference smooth `s(x2, by = fac)`, and a smooth `s(x0)`.
 

vignettes/plot-smooths.Rmd

@@ -4,7 +4,7 @@ author: "Stefano Coretta"
 date: "`r Sys.Date()`"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Vignette Title}
+  %\VignetteIndexEntry{Plotting GAMs}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -23,6 +23,8 @@ library(tidymv)
 data(simdat)
 ```
 
+## Plotting smooths
+
 To illustrate how to use `plot_smooths()`, let's first prepare some dummy data with a factor variable and run `gam()` on this data. The `gam` model includes a reference smooth `s(x2)`, a by-factor difference smooth `s(x2, by = fac)`, and a smooth `s(x0)`.
 
 ```{r gam}
@@ -50,6 +52,23 @@ plot_smooths(
     theme(legend.position = "top")
 ```
 
+## Plotting a single smooth
+
+It is also possible to plot a single smooth.
+
+```{r gam-2}
+model_2 <- gam(
+    y ~
+        s(x0),
+    data = data
+)
+
+plot_smooths(
+    model = model_2,
+    time_series = x0
+)
+```
+
 ## Plotting interactions
 
 It is very likely that the model will contain interactions.
@@ -66,9 +85,7 @@ simdata <- simdat %>%
 
 model_inter <- bam(
     Y ~
-        Group +
-#        s(Time, by = Group) +
-#        s(Time, by = Condition) +
+        GroupCondition +
         s(Time, by = GroupCondition),
     data = simdata
 )
@@ -81,7 +98,8 @@ plot_smooths(
     comparison = Group,
     facet_terms = Condition,
     split = list(GroupCondition = c("Group", "Condition"))
-)
+) +
+    theme(legend.position = "top")
 ```
 
 To plot just one or some of the facets, you should use the `conditions` argument.
@@ -95,7 +113,8 @@ plot_smooths(
     facet_terms = Condition,
     conditions = quos(Condition == -1),
     split = list(GroupCondition = c("Group", "Condition"))
-)
+) +
+    theme(legend.position = "top")
 ```
 
 ```{r plot-interactions-3}
@@ -106,5 +125,6 @@ plot_smooths(
     facet_terms = Condition,
     conditions = quos(Condition %in% c(-1, 3)),
     split = list(GroupCondition = c("Group", "Condition"))
-)
+) +
+    theme(legend.position = "top")
 ```