DESCRIPTION

@@ -1,7 +1,8 @@
 Package: tidymv
 Type: Package
 Title: Tidy Model Visualisation
-Version: 1.3.1
+Version: 1.4.0
+Date: 2018-07-03
 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

NEWS.md

@@ -1,5 +1,16 @@
 # Change Log
 
+## [1.4.0] - 2018-07-03
+### Added
+- `split` argument for separating columns (useful for interactions)
+- how to plot interactions in vignette
+- `exclude_terms` argument to exclude terms when predicting
+- exclude `tensor.smooths` when plotting smooths
+- experimental `ylim` argument to `plot_gamsd()`
+
+### Fixed
+- message in `plot_gamsd()` which said "will be deprecated"
+
 ## [1.3.1] - 2018-02-26
 ### Fixed
 - warning in `get_gam_predictions()`
@@ -52,6 +63,7 @@
 - `create_start_event` function
 - `plot_gamsd` function
 
+[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
 [1.2.0]: https://github.com/stefanocoretta/tidymv/compare/v1.1.0...v1.2.0

R/functions.R

@@ -28,9 +28,12 @@ create_event_start <- function(tibble, event_col) {
 #' @param series_length An integer indicating how many values along the time series to use for predicting the outcome term.
 #' @param conditions A list of quosures with \link[rlang]{quos} specifying the levels to plot from the model terms.
 #' @param exclude_random Whether to exclude random smooths (the default is \code{TRUE}).
+#' @param exclude_terms Terms to be excluded from the prediction. Term names should be given as they appear in the model summary (for example, \code{"s(x0,x1)"}).
+#' @param split Columns to separate as a named list.
+#' @param sep Separator between columns (default is \code{"\\."}, which is the default with \code{}). If character, it is interpreted as a regular expression.
 #'
 #' @export
-get_gam_predictions <- function(model, time_series, series_length = 25, conditions = NULL, exclude_random = TRUE) {
+get_gam_predictions <- function(model, time_series, series_length = 25, conditions = NULL, exclude_random = TRUE, exclude_terms = NULL, split = NULL, sep = "\\.") {
     time_series_q <- dplyr::enquo(time_series)
     time_series_name <- dplyr::quo_name(time_series_q)
     outcome_q <- model$formula[[2]]
@@ -88,19 +91,42 @@ get_gam_predictions <- function(model, time_series, series_length = 25, conditio
         exclude_random_effects <- as.null()
     }
 
-    # Exclude smooth terms which are not the time series to be plotted
+    # Exclude smooth terms which are not the time series to be plotted or tensor smooths
     exclude_smooths <- as.null()
-    exclude_terms <- as.null()
+    excluded_terms <- as.null()
     for (smooth in 1:length(model[["smooth"]])) {
         smooth_term <- model[["smooth"]][[smooth]][["term"]][[1]]
         if (smooth_term != time_series_name) {
-            exclude_terms <- c(exclude_terms, smooth_term)
+            excluded_terms <- c(excluded_terms, smooth_term)
             smooth_label <- model[["smooth"]][[smooth]][["label"]]
             exclude_smooths <- c(exclude_smooths, smooth_label)
         }
+        smooth_class <- attr(model$smooth[[smooth]],"class")[1]
+        if (smooth_class == "tensor.smooth") {
+            smooth_term <- model[["smooth"]][[smooth]][["term"]][[2]]
+            excluded_terms <- c(excluded_terms, smooth_term)
+            smooth_label <- model[["smooth"]][[smooth]][["label"]]
+            exclude_smooths <- c(exclude_smooths, smooth_label)
+        }
+    }
+
+    excluded <- as.null()
+    if (!is.null(exclude_terms)) {
+        for (term in 1:length(exclude_terms)) {
+            for (label in 1:length(model[["smooth"]])) {
+                smooth_label <- model[["smooth"]][[label]][["label"]]
+                if (smooth_label == exclude_terms[term]) {
+                    smooth_term <- model[["smooth"]][[label]][["term"]]
+                    if (length(smooth_term) > 1) {
+                        smooth_term_2 <- model[["smooth"]][[label]][["term"]][[2]]
+                        excluded <- c(excluded, smooth_term_2)
+                    }
+                }
+            }
+        }
     }
 
-    exclude_these <- c(exclude_random_effects, exclude_smooths)
+    exclude_these <- c(exclude_random_effects, exclude_smooths, exclude_terms)
 
     predicted <- stats::predict(
         model,
@@ -121,16 +147,33 @@ get_gam_predictions <- function(model, time_series, series_length = 25, conditio
 
     if (!is.null(exclude_random_effects)) {
         predicted_tbl <- predicted_tbl %>%
-            dplyr::select(-(!!!rlang::syms(random_effects_terms))) %>%
+            dplyr::select(-dplyr::one_of(random_effects_terms)) %>%
             unique()
     }
 
     if (!is.null(exclude_smooths)) {
         predicted_tbl <- predicted_tbl %>%
-            dplyr::select(-(!!!rlang::syms(exclude_terms))) %>%
+            dplyr::select(-dplyr::one_of(excluded_terms)) %>%
             unique()
     }
 
+    if (!is.null(excluded)) {
+        predicted_tbl <- predicted_tbl %>%
+            dplyr::select(-dplyr::one_of(excluded)) %>%
+            unique()
+    }
+
+    if (!is.null(split)) {
+        for (i in 1:length(split)) {
+            predicted_tbl <- tidyr::separate(
+                data = predicted_tbl,
+                col = names(split)[i],
+                into = split[[i]],
+                sep = sep
+            )
+        }
+    }
+
     if (!is.null(conditions)) {
         predicted_tbl <- predicted_tbl %>%
             dplyr::filter(!!!conditions)
@@ -152,7 +195,7 @@ 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, series_length = 25) {
+plot_smooths <- function(model, time_series, comparison, 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)
@@ -161,7 +204,7 @@ plot_smooths <- function(model, time_series, comparison, facet_terms = NULL, con
     }
     outcome_q <- model$formula[[2]]
 
-    predicted_tbl <- get_gam_predictions(model, !!time_series_q, conditions, exclude_random = exclude_random, series_length = series_length)
+    predicted_tbl <- get_gam_predictions(model, !!time_series_q, conditions, exclude_random = exclude_random, exclude_terms = exclude_terms, series_length = series_length, split = split, sep = sep)
 
     smooths_plot <- predicted_tbl %>%
         ggplot2::ggplot(
@@ -202,11 +245,12 @@ plot_smooths <- function(model, time_series, comparison, facet_terms = NULL, con
 #' @param conditions The values to use for other predictors as a named list.
 #' @param rm_re Whether to remove random effects (the default is \code{FALSE}).
 #' @param bw Whether to plot in black and white (the default is \code{FALSE}).
+#' @param ylim Limits of the y-axis of the smooths panel.
 #'
 #' @importFrom magrittr "%>%"
 #' @export
-plot_gamsd <- function(model, view, comparison, conditions = NULL, rm_re = FALSE, bw = FALSE) {
-    .Deprecated("plot_smooth", msg = "'plot_gamsd' will be deprecated, use 'plot_smooths'. (Plotting with the difference smooth is not supported yet.)\n")
+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")
 
     diff.df <- itsadug::plot_diff(
         model,
@@ -291,7 +335,8 @@ plot_gamsd <- function(model, view, comparison, conditions = NULL, rm_re = FALSE
                 ggplot2::scale_linetype_discrete(name = names(comparison))
                 }
         } +
-        {if (bw == FALSE) {ggplot2::scale_fill_discrete(name = names(comparison))}}
+        {if (bw == FALSE) {ggplot2::scale_fill_discrete(name = names(comparison))}} +
+        {if (!is.null(ylim)) {ggplot2::ylim(ylim[1], ylim[2])}}
 
     ymin.di <- diff.df$est - diff.df$CI
     ymax.di <- diff.df$est + diff.df$CI

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@v1.3.1", 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@v1.4.0", 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

@@ -45,7 +45,8 @@ Significance is based on the difference smooth: at any point, if the confidence
 plot_gamsd(
     model = model,
     view = "x2",
-    comparison = list(fac = c("1", "2"))
+    comparison = list(fac = c("1", "2")),
+    ylim = c(-5, 5)
 )
 ```
 

vignettes/plot-smooths.Rmd

@@ -17,8 +17,10 @@ knitr::opts_chunk$set(
 )
 library(ggplot2)
 theme_set(theme_bw())
+library(dplyr)
 library(itsadug)
 library(tidymv)
+data(simdat)
 ```
 
 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)`.
@@ -47,3 +49,62 @@ plot_smooths(
 ) +
     theme(legend.position = "top")
 ```
+
+## Plotting interactions
+
+It is very likely that the model will contain interactions.
+It is possible to plot models with interactions by specifying faceting with the `facet_terms` argument.
+
+```{r interaction-data}
+simdata <- simdat %>%
+    filter(
+    Subject %in% c("a01", "a08", "a15", "c01", "c08", "c15")
+) %>%
+    mutate(
+    GroupCondition = interaction(Group, Condition)
+)
+
+model_inter <- bam(
+    Y ~
+        Group +
+#        s(Time, by = Group) +
+#        s(Time, by = Condition) +
+        s(Time, by = GroupCondition),
+    data = simdata
+)
+```
+
+```{r plot-interactions}
+plot_smooths(
+    model = model_inter,
+    time_series = Time,
+    comparison = Group,
+    facet_terms = Condition,
+    split = list(GroupCondition = c("Group", "Condition"))
+)
+```
+
+To plot just one or some of the facets, you should use the `conditions` argument.
+This argument takes a list of quosures qith `quos()`.
+
+```{r plot-interactions-2}
+plot_smooths(
+    model = model_inter,
+    time_series = Time,
+    comparison = Group,
+    facet_terms = Condition,
+    conditions = quos(Condition == -1),
+    split = list(GroupCondition = c("Group", "Condition"))
+)
+```
+
+```{r plot-interactions-3}
+plot_smooths(
+    model = model_inter,
+    time_series = Time,
+    comparison = Group,
+    facet_terms = Condition,
+    conditions = quos(Condition %in% c(-1, 3)),
+    split = list(GroupCondition = c("Group", "Condition"))
+)
+```