From 07ad8ff9816f659c0f0c717fa4de094a7fabdf81 Mon Sep 17 00:00:00 2001 From: Chitu Okoli Date: Tue, 9 Jan 2024 13:55:57 +0100 Subject: [PATCH] Full check for dev version 0.2.20240109 --- DESCRIPTION | 4 +- NAMESPACE | 2 + R/ale_core.R | 3 + R/calc_ale.R | 5 +- R/model_bootstrap.R | 16 +- R/stats.R | 32 +- inst/WORDLIST | 3 + man/ale.Rd | 41 +- man/ale_ixn.Rd | 37 +- man/create_p_funs.Rd | 192 + man/model_bootstrap.Rd | 68 +- tests/testthat/_snaps/ale.md | 6752 +++++++++++----------- tests/testthat/_snaps/model_bootstrap.md | 4230 +++++++------- 13 files changed, 5827 insertions(+), 5558 deletions(-) create mode 100644 man/create_p_funs.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 6601134..93c377a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: ale Title: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE) -Version: 0.2.20240105 +Version: 0.2.20240109 Authors@R: c( person("Chitu", "Okoli", , "Chitu.Okoli@skema.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-5574-7572")), @@ -30,12 +30,14 @@ Imports: ellipsis, glue, grDevices, + insight, labeling, methods, purrr, stats, stringr, tidyr, + univariateML, yaImpute Depends: R (>= 3.5.0), diff --git a/NAMESPACE b/NAMESPACE index 6532de2..1791bec 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,11 +2,13 @@ export(ale) export(ale_ixn) +export(create_p_funs) export(model_bootstrap) import(assertthat) import(dplyr) import(ggplot2) import(purrr) +importFrom(glue,glue) importFrom(stats,median) importFrom(stats,quantile) importFrom(stats,sd) diff --git a/R/ale_core.R b/R/ale_core.R index c2bebe5..f04f17c 100644 --- a/R/ale_core.R +++ b/R/ale_core.R @@ -200,6 +200,9 @@ #' #' } #' +#' +#' @importFrom glue glue +#' ale <- function ( data, model, diff --git a/R/calc_ale.R b/R/calc_ale.R index bcebc6d..5fd4ce5 100644 --- a/R/calc_ale.R +++ b/R/calc_ale.R @@ -60,10 +60,11 @@ calc_ale <- function( # local variables. `:=` <- NULL ale_y <- NULL - Var1 <- NULL + estimate <- NULL Freq <- NULL + p.value <- NULL statistic <- NULL - estimate <- NULL + Var1 <- NULL diff --git a/R/model_bootstrap.R b/R/model_bootstrap.R index 3ea8831..0069ad2 100644 --- a/R/model_bootstrap.R +++ b/R/model_bootstrap.R @@ -50,7 +50,8 @@ #' @param model_call_string character string. If NULL, [model_bootstrap()] tries to #' automatically detect and construct the call for bootstrapped datasets. If it cannot, the #' function will fail early. In that case, a character string of the full call -#' for the model must be provided that includes `boot_data` as the data argument for the call. See examples +#' for the model must be provided that includes `boot_data` as the data argument for the call. +#' See examples. # Future arguments: # * y_col: name of y column in data. This would allow SD and MAD to be calculated. # * pred_fun,pred_type: allows the prediction function to be called; this would @@ -127,7 +128,7 @@ #' attitude, #' model_call_string = 'mgcv::gam( #' rating ~ complaints + privileges + s(learning) + -#' raises + s(critical) + advance), +#' raises + s(critical) + advance, #' data = boot_data #' )', #' boot_it = 3 @@ -255,14 +256,15 @@ model_bootstrap <- function ( ale_y <- NULL ale_y_mean <- NULL ale_y_median <- NULL + aled <- NULL + estimate <- NULL it <- NULL + naler_max <- NULL name <- NULL - value <- NULL - term <- NULL - estimate <- NULL + p.value <- NULL statistic <- NULL - aled <- NULL - naler_max <- NULL + term <- NULL + value <- NULL # Create bootstrap tbl diff --git a/R/stats.R b/R/stats.R index 1397921..da2a201 100644 --- a/R/stats.R +++ b/R/stats.R @@ -164,6 +164,10 @@ #' # Plot the ALE data. The horizontal bands in the plots use the p-values. #' gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2) #' } +#' +#' +#' @importFrom glue glue +#' create_p_funs <- function( training_data, test_data, @@ -178,6 +182,7 @@ create_p_funs <- function( silent = FALSE, .testing_mode = FALSE ) { + # Validate arguments assert_that(training_data |> inherits('data.frame')) @@ -245,6 +250,17 @@ create_p_funs <- function( assert_that(is.flag(silent)) + # Hack to prevent devtools::check from thinking that masked variables are global: + # Make them null local variables within the function with the issues. So, + # when masking applies, the masked variables will be prioritized over these null + # local variables. + + # This super-assignment might be problematic. See this tip from ChatGPT to resolve it: + # https://chat.openai.com/c/08b68562-c339-4c37-baab-3c71d2e9fb73 + .rand_model <<- NULL + .rand_test <<- NULL + .rand_train <<- NULL + # Determine the closest distribution of the residuals @@ -294,7 +310,7 @@ create_p_funs <- function( else { # use the automatically detected model call # Update the model to call to add .random_variable and to train on .rand_train model_call$formula <- model_call$formula |> - update.formula(~ . + .random_variable) + stats::update.formula(~ . + .random_variable) model_call$data <- .rand_train .rand_model <<- eval(model_call) @@ -314,11 +330,11 @@ create_p_funs <- function( .rand_ale }) - ale_y_norm <- ale:::create_ale_y_norm_function(test_data[[y_col]]) + ale_y_norm <- create_ale_y_norm_function(test_data[[y_col]]) rand_stats <- map(rand_ales, \(.rand) { - ale:::ale_stats( + ale_stats( ale_y = .rand$data$.random_variable$ale_y, ale_n = .rand$data$.random_variable$ale_n, ale_y_norm_fun = ale_y_norm, @@ -336,11 +352,11 @@ create_p_funs <- function( # For aler_min and naler_min, the p-value is the simple ECDF if (stringr::str_sub(.name_stat, -4, -1) == '_min') { - ecdf(.stat_vals)(x) + stats::ecdf(.stat_vals)(x) } # For other statistics, the p-value is 1 - ECDF. else { - 1 - ecdf(.stat_vals)(x) + 1 - stats::ecdf(.stat_vals)(x) } } } @@ -361,13 +377,13 @@ create_p_funs <- function( if (stringr::str_sub(.name_stat, -4, -1) == '_min') { .stat_vals |> quantile(probs = p) |> - setNames(p) + stats::setNames(p) } # For other statistics, the value is the quantile of 1 - p else { .stat_vals |> quantile(probs = 1 - p) |> - setNames(p) + stats::setNames(p) } } } @@ -383,7 +399,7 @@ create_p_funs <- function( # Set S3 class information for the p_funs object class(p_funs) <- c('p_funs', 'ale', 'list') - attr(p_funs, 'ale_version') <- packageVersion('ale') + attr(p_funs, 'ale_version') <- utils::packageVersion('ale') return(p_funs) diff --git a/inst/WORDLIST b/inst/WORDLIST index d19307c..7fc3882 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -30,6 +30,7 @@ interpretable Interpretable Jingyu MMDD +Mmmm mtcars NALED NALER @@ -37,6 +38,7 @@ Noam noncrucial overfit PDP +pkgdown preprocesses pretrained programmatically @@ -56,6 +58,7 @@ www x1 x2 x3 +yyyy YYYY Zhu diff --git a/man/ale.Rd b/man/ale.Rd index 9f77ff4..d9e9792 100644 --- a/man/ale.Rd +++ b/man/ale.Rd @@ -13,11 +13,12 @@ ale( y_col = NULL, ..., output = c("plots", "data", "stats"), - pred_fun = function(object, newdata) { - stats::predict(object = object, newdata = - newdata, type = pred_type) + pred_fun = function(object, newdata, type = pred_type) { + stats::predict(object = + object, newdata = newdata, type = type) }, pred_type = "response", + p_values = NULL, x_intervals = 100, boot_it = 0, seed = 0, @@ -25,7 +26,7 @@ ale( boot_centre = "mean", relative_y = "median", y_type = NULL, - median_band = 0.05, + median_band = c(0.05, 0.5), rug_sample_size = 500, min_rug_per_interval = 1, ale_xs = NULL, @@ -44,7 +45,7 @@ one-way ALE data is to be calculated (that is, simple ALE without interactions). If not provided, ALE will be created for all columns in \code{data} except \code{y_col}.} \item{y_col}{character length 1. Name of the outcome target label (y) variable. -If not provided, \code{ale} will try to detect it automatically. For non-standard +If not provided, \code{ale()} will try to detect it automatically. For non-standard models, \code{y_col} should be provided. For survival models, set \code{y_col} to the name of the binary event column; in that case, \code{pred_type} should also be specified.} @@ -59,6 +60,16 @@ specified component. By default, all are returned.} returns a vector of predicted values of type \code{pred_type} from \code{model} on \code{data}. See details.} +\item{p_values}{instructions for calculating p-values. If \code{NULL} (default), no +p-values are calculated. To calculate p-values, an object generated by the +\code{\link[=create_p_funs]{create_p_funs()}} function must be provided here. If \code{p_values} is set to 'auto', +this \code{ale()} function will try to automatically create the p-values function; +this only works with standard R model types. Any error message will be given +if p-values cannot be generated. Any other input provided to this argument +will result in an error. For more details about creating p-values, +see documentation for \code{\link[=create_p_funs]{create_p_funs()}}. Note that p-values will not be +generated if 'stats' are not included as an option in the \code{output} argument.} + \item{x_intervals}{positive integer length 1. Maximum number of intervals on the x-axis for the ALE data for each column in \code{x_cols}. The number of intervals that the algorithm generates might eventually be fewer than what the user specifies if the data values for @@ -90,9 +101,10 @@ Must be one of c('binary', 'numeric', 'multinomial', 'ordinal'). Normally determined automatically; only provide for complex non-standard models that require it.} -\item{median_band}{numeric length 1 from 0 to 1. Alpha for "confidence interval" range +\item{median_band}{numeric length 2 from 0 to 1. Alpha for "confidence interval" ranges for printing bands around the median for single-variable plots. -The band range will be the median value of y ± \code{median_band}.} +The inner band range will be the median value of y ± \code{median_band[1]}. +For plots with a second outer band, its range will be the median ± \code{median_band[2]}.} \item{rug_sample_size, min_rug_per_interval}{single non-negative integer length 1. Rug plots are normally @@ -107,7 +119,7 @@ set the intervals of the ALE x axis for each variable. By default (NULL), the function automatically calculates the ale_x intervals. \code{ale_xs} is normally used in advanced analyses where the ale_x intervals from a previous analysis are reused for subsequent analyses (for example, for full model bootstrapping; -see the \code{model_bootstrap} function).} +see the \code{\link[=model_bootstrap]{model_bootstrap()}} function).} \item{silent}{logical length 1, default FALSE. If TRUE, do not display any non-essential messages during execution (such as progress bars). @@ -116,21 +128,22 @@ Regardless, any warnings and errors will always display.} \value{ list with elements \code{data}, \code{plots}, and \code{stats} as requested in the \code{output} argument. Each of these is a list named by the x variables with -the respective values for each variable. In addition, the return object +the respective values for each variable. If any of these was not requested in +the \code{output} argument, its return value will be NULL. In addition, the return object recapitulates several elements that were passed as arguments that apply to all the x variables for the ALE calculation. } \description{ -\code{ale} is the central function that manages the creation of ALE data and plots -for one-way ALE. For two-way interactions, see \code{ale_ixn}. This function calls +\code{ale()} is the central function that manages the creation of ALE data and plots +for one-way ALE. For two-way interactions, see \code{\link[=ale_ixn]{ale_ixn()}}. This function calls \code{ale_core} (a non-exported function) that manages the ALE data and plot creation in detail. For details, see the introductory vignette for this package or the details and examples below. \strong{Custom predict function} The calculation of ALE requires modifying several values of the original -\code{data}. Thus, \code{ale} needs direct access to a \code{predict} function that work on -\code{model}. By default, \code{ale} uses a generic default \code{predict} function of the form +\code{data}. Thus, \code{ale()} needs direct access to a \code{predict} function that work on +\code{model}. By default, \code{ale()} uses a generic default \code{predict} function of the form \code{predict(object, newdata, type)} with the default prediction type of 'response'. If, however, the desired prediction values are not generated with that format, the user must specify what they want. Most of the time, the only modification needed is @@ -244,6 +257,7 @@ gridExtra::grid.arrange(grobs = ale_gam_diamonds_custom$plots, ncol = 2) } + } \references{ Okoli, Chitu. 2023. @@ -254,6 +268,7 @@ on Accumulated Local Effects (ALE).” arXiv. \url{https://arxiv.org/abs/2310.09 Useful links: \itemize{ \item \url{https://github.com/Tripartio/ale} + \item \url{https://tripartio.github.io/ale/} \item Report bugs at \url{https://github.com/Tripartio/ale/issues} } diff --git a/man/ale_ixn.Rd b/man/ale_ixn.Rd index e54107d..a02ec92 100644 --- a/man/ale_ixn.Rd +++ b/man/ale_ixn.Rd @@ -12,15 +12,15 @@ ale_ixn( y_col = NULL, ..., output = c("plots", "data"), - pred_fun = function(object, newdata) { - stats::predict(object = object, newdata = - newdata, type = pred_type) + pred_fun = function(object, newdata, type = pred_type) { + stats::predict(object = + object, newdata = newdata, type = type) }, pred_type = "response", x_intervals = 100, relative_y = "median", y_type = NULL, - median_band = 0.05, + median_band = c(0.05, 0.5), rug_sample_size = 500, min_rug_per_interval = 1, ale_xs = NULL, @@ -31,9 +31,9 @@ ale_ixn( ) } \arguments{ -\item{data}{See documentation for \code{ale}} +\item{data}{See documentation for \code{\link[=ale]{ale()}}} -\item{model}{See documentation for \code{ale}} +\item{model}{See documentation for \code{\link[=ale]{ale()}}} \item{x1_cols, x2_cols}{character. Vectors of column names from \code{data} for which two-way interaction ALE data is to be calculated. ALE data will be calculated @@ -41,25 +41,25 @@ for each x1 column interacting with each x2 column. x1_cols can be of any standa datatype (logical, factor, or numeric) but x2_cols can only be numeric. If \code{ixn} is TRUE, then both values must be provided.} -\item{y_col}{See documentation for \code{ale}} +\item{y_col}{See documentation for \code{\link[=ale]{ale()}}} \item{...}{not used. Inserted to require explicit naming of subsequent arguments.} -\item{output}{See documentation for \code{ale}} +\item{output}{See documentation for \code{\link[=ale]{ale()}}} -\item{pred_fun, pred_type}{See documentation for \code{ale}} +\item{pred_fun, pred_type}{See documentation for \code{\link[=ale]{ale()}}} -\item{x_intervals}{See documentation for \code{ale}} +\item{x_intervals}{See documentation for \code{\link[=ale]{ale()}}} -\item{relative_y}{See documentation for \code{ale}} +\item{relative_y}{See documentation for \code{\link[=ale]{ale()}}} -\item{y_type}{See documentation for \code{ale}} +\item{y_type}{See documentation for \code{\link[=ale]{ale()}}} -\item{median_band}{See documentation for \code{ale}} +\item{median_band}{See documentation for \code{\link[=ale]{ale()}}} -\item{rug_sample_size, min_rug_per_interval}{See documentation for \code{ale}} +\item{rug_sample_size, min_rug_per_interval}{See documentation for \code{\link[=ale]{ale()}}} -\item{ale_xs}{See documentation for \code{ale}} +\item{ale_xs}{See documentation for \code{\link[=ale]{ale()}}} \item{n_x1_int, n_x2_int}{positive scalar integer. Number of intervals for the x1 or x2 axes respectively for interaction plot. These values are @@ -68,7 +68,7 @@ ignored if x1 or x2 are not numeric (i.e, if they are logical or factors).} \item{n_y_quant}{positive scalar integer. Number of intervals over which the range of y values is divided for the colour bands of the interaction plot. See details.} -\item{silent}{See documentation for \code{ale}} +\item{silent}{See documentation for \code{\link[=ale]{ale()}}} } \value{ list of ALE interaction data tibbles and plots. @@ -82,14 +82,15 @@ the \code{output} argument. } \description{ This is the central function that manages the creation of ALE data and plots -for two-way ALE interactions. For simple one-way ALE, see \code{ale}. +for two-way ALE interactions. For simple one-way ALE, see \code{\link[=ale]{ale()}}. See documentation there for functionality shared between both functions. For details, see the introductory vignette for this package or the details and examples below. For the plots, \code{n_y_quant} is the number of quantiles into which to divide the predicted variable (y). The middle quantiles are grouped specially: \itemize{ -\item The middle quantile is the \code{median_band} confidence interval around the median. +\item The middle quantile is the first confidence interval of \code{median_band} +(\code{median_band[1]}) around the median. This middle quantile is special because it generally represents no meaningful interaction. \item The quantiles above and below the middle are extended from the borders of diff --git a/man/create_p_funs.Rd b/man/create_p_funs.Rd new file mode 100644 index 0000000..51f5fb4 --- /dev/null +++ b/man/create_p_funs.Rd @@ -0,0 +1,192 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/stats.R +\name{create_p_funs} +\alias{create_p_funs} +\title{Create a p-value functions object that can be used to generate p-values} +\usage{ +create_p_funs( + training_data, + test_data, + model, + random_model_call_string = NULL, + y_col = NULL, + pred_fun = function(object, newdata, type = pred_type) { + stats::predict(object = + object, newdata = newdata, type = type) + }, + pred_type = "response", + rand_it = 1000, + silent = FALSE, + .testing_mode = FALSE +) +} +\arguments{ +\item{training_data}{dataframe. The dataset originally used to train \code{model}. +See details and also documentation for \code{\link[=ale]{ale()}}.} + +\item{test_data}{dataframe. The dataset that will be used to create ALE data. +See details.} + +\item{model}{model object. The model used to train the original \code{training_data}. +for which ALE should be calculated. See details and also documentation for \code{\link[=ale]{ale()}}.} + +\item{random_model_call_string}{character string. If NULL, \code{create_p_funs()} tries to +automatically detect and construct the call for p-values. If it cannot, the +function will fail early. In that case, a character string of the full call +for the model must be provided that includes the random variable. See details.} + +\item{y_col}{See documentation for \code{\link[=ale]{ale()}}} + +\item{pred_fun, pred_type}{See documentation for \code{\link[=ale]{ale()}}.} + +\item{rand_it}{non-negative integer length 1. Number of times that the model +should be retrained with a new random variable. The default of 1000 should +give reasonably stable p-values. It can be reduced as low as 100 for faster +test runs.} + +\item{silent}{See See documentation for \code{\link[=ale]{ale()}}} + +\item{.testing_mode}{logical. Internal use only.} +} +\value{ +The return value is a list of class \verb{c('p_funs', 'ale', 'list'}) with an +\code{ale_version} attribute whose value is the version of the \code{ale} package used to +create the object. See examples for an illustration of how to inspect this list. +Its elements are: +\itemize{ +\item \code{value_to_p}: a list of functions named for each each available ALE statistic. +Each function signature is \verb{function(x)} where x is a numeric. The function returns +the p-value (minimum 0; maximum 1) for the respective statistic based on the random variable analysis. +For an input x that returns p, its interpretation is that p\% of random variables +obtained the same or higher statistic value. For example, to get the p-value +of a NALED of 4.2, enter \code{p_funs$value_to_p(4.2)}. A return value of 0.03 means +that only 3\% of random variables obtained a NALED greater than or equal to 4.2. +\item \code{p_to_random_value}: a list of functions named for each each available ALE statistic. +These are the inverse functions of \code{value_to_p}. The signature is \verb{function(p)} +where p is a numeric from 0 to 1. The function returns the numeric value of the +random variable statistic that would yield the provided p-value. +For an input p that returns x, its interpretation is that p\% of random variables +obtained the same or higher statistic value. For example, to get the random +variable ALED for the 0.05 p-value, enter \code{p_funs$p_to_random_value(0.05)}. +A return value of 102 means that only 5\% of random variables obtained an ALED +greater than or equal to 102. +\item \code{rand_stats}: a tibble whose rows are each of the \code{rand_it} iterations of the +random variable analysis and whose columns are the ALE statistics obtained for +each random variable. +\item \code{residuals}: the actual \code{y_col} values from \code{training_data} minus the predicted +values from the \code{model} (without random variables) on the \code{training_data}. +\code{residual_distribution}: the closest estimated distribution for the \code{residuals} +as determined by \code{\link[univariateML:MaximumLikelihoodDistribution]{univariateML::rml()}}. This is the distribution used to generate +all the random variables. +} +} +\description{ +Calculating p-values is not trivial for ALE statistics because ALE is +non-parametric and model-agnostic. Because ALE is non-parametric (that is, +it does not assume any particular distribution of data), the \code{ale} package +generates p-values by calculating ALE for many random variables; this makes the +procedure somewhat slow. For this reason, they are not calculated by default; +they must be explicitly requested. Because the \code{ale} package is model-agnostic (that is, it +works with any kind of R model), the \code{\link[=ale]{ale()}} function cannot always automatically +manipulate the model object to create the p-values. It can only do so for +models that follow the standard R statistical modelling conventions, which +includes almost all built-in R algorithms (like \code{\link[stats:lm]{stats::lm()}} and \code{\link[stats:glm]{stats::glm()}}) and many widely +used statistics packages (like \code{mgcv} and \code{survival}), but which excludes most +machine learning algorithms (like \code{tidymodels} and \code{caret}). For non-standard +algorithms, the user needs to do a little work to help the ale function +correctly manipulate its model object. + +\strong{Approach to calculating p-values} +The \code{ale} package takes a literal frequentist approach to the calculation of +p-values. That is, it literally retrains the model 1000 times, each time +modifying it by adding a distinct random variable to the model. +(The number of iterations is customizable +with the \code{rand_it} argument.) The ALEs and ALE statistics are calculated for +each random variable. The percentiles of the distribution of these +random-variable ALEs are then used to determine p-values for non-random variables. +Thus, p-values are interpreted as the frequency of random variable ALE statistics +that exceed the value of ALE statistic of the actual variable in question. +The specific steps are as follows: +\itemize{ +\item The residuals of the original model trained on the training data are calculated +(residuals are the actual y target value minus the predicted values). +\item The closest distribution of the residuals is detected with +\code{univariateML::model_select()}. +\item 1000 new models are trained by generating a random variable each time with +\code{univariateML::rml()} and then training a new model with that random variable +added. +\item The ALEs and ALE statistics are calculated for each random variable. +\item For each ALE statistic, the empirical cumulative distribution function +(from \code{stats::ecdf()}) is used to create a function to determine p-values +according to the distribution of the random variables' ALE statistics. +} + +\strong{Datasets} +Because the \code{ale} package takes a literal frequentist approach to the +calculation of p-values, the precision of the p-values depends on correctly +representing the modelling workflow. Specifically, models should be trained +on a training subset and ALE statistics should be calculated on a test subset. +Thus, the random variables should be trained on the same training subset and +their ALE statistics should be calculated on the same test subset. Thus the +model takes both \code{training_data} and \code{test_data} as distinct arguments. If, +for whatever reason, the ALE is calculated on the same dataset as is used to +train the model, then the same dataset should be entered for both +\code{training_data} and \code{test_data}. +} +\examples{ +\donttest{ +# Sample 1000 rows from the diamonds dataset (for a simple example)diamonds +set.seed(0) +diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ] + +# Split the dataset into training and test sets +# https://stackoverflow.com/a/54892459/2449926 +set.seed(0) +train_test_split <- sample( + c(TRUE, FALSE), nrow(diamonds_sample), replace = TRUE, prob = c(0.8, 0.2) +) +diamonds_train <- diamonds_sample[train_test_split, ] +diamonds_test <- diamonds_sample[!train_test_split, ] + + +# Create a GAM model with flexible curves to predict diamond price +# Smooth all numeric variables and include all other variables +# Build model on training data, not on the full dataset. +gam_diamonds <- mgcv::gam( + price ~ s(carat) + s(depth) + s(table) + s(x) + s(y) + s(z) + + cut + color + clarity, + data = diamonds_train +) +summary(gam_diamonds) + +# Create p-value functions +pf_diamonds <- create_p_funs( + diamonds_train, + diamonds_test, + gam_diamonds, + # only 100 iterations for a quick demo; but usually should remain at 1000 + rand_it = 100 +) + +# Examine the structure of the returned object +str(pf_diamonds) +# In RStudio: View(pf_diamonds) + +# Calculate ALEs with p-values +ale_gam_diamonds <- ale( + diamonds_test, + gam_diamonds, + p_values = pf_diamonds +) + +# Plot the ALE data. The horizontal bands in the plots use the p-values. +gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2) +} + + +} +\references{ +Okoli, Chitu. 2023. +“Statistical Inference Using Machine Learning and Classical Techniques Based +on Accumulated Local Effects (ALE).” arXiv. \url{https://arxiv.org/abs/2310.09877}. +} diff --git a/man/model_bootstrap.Rd b/man/model_bootstrap.Rd index fb97570..b6e92c6 100644 --- a/man/model_bootstrap.Rd +++ b/man/model_bootstrap.Rd @@ -6,8 +6,9 @@ \usage{ model_bootstrap( data, - model_call_string, + model = NULL, ..., + model_call_string = NULL, boot_it = 100, seed = 0, boot_alpha = 0.05, @@ -22,14 +23,19 @@ model_bootstrap( \arguments{ \item{data}{dataframe. Dataset that will be bootstrapped.} -\item{model_call_string}{character. Character string of the full call for the model, -except that the data option must be left out. The data option will be replaced with the \code{data} argument.} +\item{model}{See documentation for \code{\link[=ale]{ale()}}} \item{...}{not used. Inserted to require explicit naming of subsequent arguments.} +\item{model_call_string}{character string. If NULL, \code{\link[=model_bootstrap]{model_bootstrap()}} tries to +automatically detect and construct the call for bootstrapped datasets. If it cannot, the +function will fail early. In that case, a character string of the full call +for the model must be provided that includes \code{boot_data} as the data argument for the call. +See examples.} + \item{boot_it}{integer from 0 to Inf. Number of bootstrap iterations. If boot_it = 0, then the model is run as normal once on the full \code{data} with -no bootstrap.} +no bootstrapping.} \item{seed}{integer. Random seed. Supply this between runs to assure identical bootstrap samples are generated each time on the same data.} @@ -38,7 +44,7 @@ bootstrap samples are generated each time on the same data.} 1 - boot_alpha. For example, the default 0.05 will give a 95\% confidence interval, that is, from the 2.5\% to the 97.5\% percentile.} -\item{boot_centre}{See documentation for \code{ale}} +\item{boot_centre}{See See documentation for \code{\link[=ale]{ale()}}} \item{output}{character vector. Which types of bootstraps to calculate and return: \itemize{ @@ -51,34 +57,35 @@ except if included in this \code{output} argument. }} \item{ale_options, tidy_options, glance_options}{list of named arguments. -Arguments to pass to the \code{ale}, \code{broom::tidy}, or \code{broom::glance} functions, respectively, -beyond (or overriding) the defaults.} +Arguments to pass to the \code{\link[=ale]{ale()}}, \code{\link[broom:reexports]{broom::tidy()}}, or \code{\link[broom:reexports]{broom::glance()}} functions, respectively, +beyond (or overriding) the defaults. In particular, to obtain p-values for ALE +statistics, see the details.} -\item{silent}{See documentation for \code{ale}} +\item{silent}{See documentation for \code{\link[=ale]{ale()}}} } \value{ list with tibbles of the following elements (depending on values requested in the \code{output} argument: \itemize{ -\item model_stats: bootstrapped results from \code{broom::glance} -\item model_coefs: bootstrapped results from \code{broom::tidy} +\item model_stats: bootstrapped results from \code{\link[broom:reexports]{broom::glance()}} +\item model_coefs: bootstrapped results from \code{\link[broom:reexports]{broom::tidy()}} \item ale: bootstrapped ALE results \itemize{ -\item data: ALE data (see \code{ale} for details about the format) +\item data: ALE data (see \code{\link[=ale]{ale()}} for details about the format) \item stats: ALE statistics. The same data is duplicated with different views that might be variously useful. The column \itemize{ \item by_term: statistic, estimate, conf.low, median, mean, conf.high. ("term" means variable name.) The column names are compatible with the \code{broom} package. The confidence intervals -are based on the \code{ale} function defaults; they can be changed with the +are based on the \code{\link[=ale]{ale()}} function defaults; they can be changed with the \code{ale_options} argument. The estimate is the median or the mean, depending on the \code{boot_centre} argument. \item by_statistic: term, estimate, conf.low, median, mean, conf.high. \item estimate: term, then one column per statistic Provided with the default estimate. This view does not present confidence intervals. } -\item plots: ALE plots (see \code{ale} for details about the format) +\item plots: ALE plots (see \code{\link[=ale]{ale()}} for details about the format) } \item boot_data: full bootstrap data (not returned by default) \item other values: the \code{boot_it}, \code{seed}, \code{boot_alpha}, and \code{boot_centre} arguments that @@ -92,14 +99,14 @@ Execute full model bootstrapping with ALE calculation on each bootstrap run No modelling results, with or without ALE, should be considered reliable without being bootstrapped. For large datasets with clear separation between training and testing samples, -\code{ale} bootstraps the ALE results of the test data. However, when a dataset +\code{\link[=ale]{ale()}} bootstraps the ALE results of the test data. However, when a dataset is too small to be subdivided into training and test sets, then the entire model should be bootstrapped. That is, multiple models should be trained, one on each bootstrap sample. The reliable results are the average results of all the bootstrap models, however many there are. For details, see the vignette on small datasets or the details and examples below. -\code{model_bootstrap} automatically carries out full-model bootstrapping suitable +\code{\link[=model_bootstrap]{model_bootstrap()}} automatically carries out full-model bootstrapping suitable for small datasets. Specifically, it: \itemize{ \item Creates multiple bootstrap samples (default 100; the user can specify any number); @@ -109,6 +116,20 @@ for each model on each bootstrap sample; \item Calculates the mean, median, and lower and upper confidence intervals for each of those values across all bootstrap samples. } + +\strong{P-values} +The \code{\link[broom:reexports]{broom::tidy()}} summary statistics will provide p-values as normal, but the +situation is somewhat complicated with p-values for ALE statistics. The challenge +is that the procedure for obtaining their p-values is very slow: it involves +retraining the model 1000 times. Thus, it is not efficient to calculate p-values +on every execution of \code{model_bootstrap()}. Although the \code{\link[=ale]{ale()}} function provides +an 'auto' option for creating p-values, +that option is disabled in \code{model_bootstrap()} because it would be far too slow: +it would involve retraining the model 1000 times the number of bootstrap iterations. +Rather, you must first create a p-values function object using the procedure +described in \code{help(create_p_funs)}. If the name of your p-values object is +\code{p_funs}, you can then request p-values each time you run \code{model_bootstrap()} +by passing it the argument \code{ale_options = list(p_values = p_funs)}. } \examples{ @@ -127,8 +148,20 @@ summary(gam_attitude) # Increase value of boot_it for more realistic results mb_gam <- model_bootstrap( attitude, - 'mgcv::gam(rating ~ complaints + privileges + s(learning) + - raises + s(critical) + advance)', + gam_attitude, + boot_it = 3 +) + +\donttest{ +# If the model is not standard, supply model_call_string with +# 'data = boot_data' in the string (not as a direct argument to [model_bootstrap()]) +mb_gam <- model_bootstrap( + attitude, + model_call_string = 'mgcv::gam( + rating ~ complaints + privileges + s(learning) + + raises + s(critical) + advance, + data = boot_data + )', boot_it = 3 ) @@ -136,7 +169,6 @@ mb_gam <- model_bootstrap( mb_gam$model_stats mb_gam$model_coefs -\donttest{ # Plot ALE gridExtra::grid.arrange(grobs = mb_gam$ale$plots, 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19.04095 19.28336 - 43 Inf 19.04095 19.28336 - 44 Inf 19.04095 19.28336 - 45 Inf 19.04095 19.28336 - 46 Inf 19.04095 19.28336 - 47 Inf 19.04095 19.28336 - 48 Inf 19.04095 19.28336 - 49 Inf 19.04095 19.28336 - 50 Inf 19.04095 19.28336 - 51 Inf 19.04095 19.28336 - 52 Inf 19.04095 19.28336 - 53 Inf 19.04095 19.28336 - 54 Inf 19.04095 19.28336 - 55 Inf 19.04095 19.28336 - 56 Inf 19.04095 19.28336 - 57 Inf 19.04095 19.28336 - 58 Inf 19.04095 19.28336 - 59 Inf 19.04095 19.28336 - 60 Inf 19.04095 19.28336 - 61 Inf 19.04095 19.28336 - 62 Inf 19.04095 19.28336 - $plots$qsec[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -2590,6 +2590,14 @@ $plots$vs $plots$vs[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 17.32625 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 21.60911 1 2 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + + $plots$vs[[2]] x y PANEL group flipped_aes ymin ymax xmin 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@@ -2632,6 +2632,14 @@ $plots$am $plots$am[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 26.905445 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 7.938195 1 2 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + + $plots$am[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 26.905445 1 1 FALSE 0 26.905445 0.55 1.45 NA gray 2 2 7.938195 1 2 FALSE 0 7.938195 1.55 2.45 NA gray @@ -2639,7 +2647,7 @@ 1 0.5 1 NA 2 0.5 1 NA - $plots$am[[2]] + $plots$am[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 26.905445 26.905445 1 26.905445 1 1 FALSE 0.975 1.025 black 2 7.938195 7.938195 2 7.938195 1 2 FALSE 1.975 2.025 black @@ -2647,7 +2655,7 @@ 1 0.5 1 0.05 NA 2 0.5 1 0.05 NA - $plots$am[[3]] + $plots$am[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 59% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 41% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -2655,14 +2663,6 @@ 1 1 1.2 2 1 1.2 - $plots$am[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 26.905445 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 7.938195 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - $plots$am[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -2674,6 +2674,16 @@ $plots$gear $plots$gear[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 9.876782 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 23.999836 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 35.650050 1 3 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + + $plots$gear[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 9.876782 1 1 FALSE 0 9.876782 0.55 1.45 NA gray 2 2 23.999836 1 2 FALSE 0 23.999836 1.55 2.45 NA gray @@ -2683,7 +2693,7 @@ 2 0.5 1 NA 3 0.5 1 NA - $plots$gear[[2]] + $plots$gear[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 9.876782 9.876782 1 9.876782 1 1 FALSE 0.975 1.025 black 2 23.999836 23.999836 2 23.999836 1 2 FALSE 1.975 2.025 black @@ -2693,7 +2703,7 @@ 2 0.5 1 0.05 NA 3 0.5 1 0.05 NA - $plots$gear[[3]] + $plots$gear[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 47% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 38% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -2703,16 +2713,6 @@ 2 1 1.2 3 1 1.2 - $plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 9.876782 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 23.999836 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 35.650050 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - $plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -2724,6 +2724,26 @@ $plots$carb $plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 19.12721 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 19.17474 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 3 19.22228 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 4 19.26982 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 5 19.31736 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 6 19.36490 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 7 19.41244 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 8 8 19.45998 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + 7 19.04095 19.28336 + 8 19.04095 19.28336 + + $plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill linewidth 1 19.12721 19.12721 1 19.12721 1 -1 FALSE NA grey85 0.5 2 19.17474 19.17474 2 19.17474 1 -1 FALSE NA grey85 0.5 @@ -2743,7 +2763,7 @@ 7 1 0.5 8 1 0.5 - $plots$carb[[2]] + $plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 19.12721 1 -1 FALSE black 0.5 1 NA 2 2 19.17474 1 -1 FALSE black 0.5 1 NA @@ -2754,26 +2774,6 @@ 7 7 19.41244 1 -1 FALSE black 0.5 1 NA 8 8 19.45998 1 -1 FALSE black 0.5 1 NA - $plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 19.12721 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 19.17474 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 19.22228 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 19.26982 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 19.31736 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 19.36490 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 7 7 19.41244 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 8 8 19.45998 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - 7 19.04095 19.28336 - 8 19.04095 19.28336 - $plots$carb[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -2852,6 +2852,22 @@ $plots$country $plots$country[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 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19.28336 - 43 Inf 19.04095 19.28336 - 44 Inf 19.04095 19.28336 - 45 Inf 19.04095 19.28336 - 46 Inf 19.04095 19.28336 - 47 Inf 19.04095 19.28336 - 48 Inf 19.04095 19.28336 - 49 Inf 19.04095 19.28336 - 50 Inf 19.04095 19.28336 - 51 Inf 19.04095 19.28336 - 52 Inf 19.04095 19.28336 - 53 Inf 19.04095 19.28336 - 54 Inf 19.04095 19.28336 - 55 Inf 19.04095 19.28336 - 56 Inf 19.04095 19.28336 - 57 Inf 19.04095 19.28336 - 58 Inf 19.04095 19.28336 - 59 Inf 19.04095 19.28336 - 60 Inf 19.04095 19.28336 - 61 Inf 19.04095 19.28336 - 62 Inf 19.04095 19.28336 - $plots$qsec[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5650,6 +5650,14 @@ $plots$vs $plots$vs[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 17.32625 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 19.61490 1 2 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + + $plots$vs[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 17.32625 1 1 FALSE 0 17.32625 0.55 1.45 NA gray 2 2 19.61490 1 2 FALSE 0 19.61490 1.55 2.45 NA gray @@ -5657,7 +5665,7 @@ 1 0.5 1 NA 2 0.5 1 NA - $plots$vs[[2]] + $plots$vs[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 17.32625 17.32625 1 17.32625 1 1 FALSE 0.975 1.025 black 2 19.54129 19.66844 2 19.61490 1 2 FALSE 1.975 2.025 black @@ -5665,7 +5673,7 @@ 1 0.5 1 0.05 NA 2 0.5 1 0.05 NA - $plots$vs[[3]] + $plots$vs[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 56% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 44% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5673,14 +5681,6 @@ 1 1 1.2 2 1 1.2 - $plots$vs[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 17.32625 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 19.61490 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - $plots$vs[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5692,6 +5692,14 @@ $plots$am $plots$am[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 26.90545 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 17.48109 1 2 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + + $plots$am[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 26.90545 1 1 FALSE 0 26.90545 0.55 1.45 NA gray 2 2 17.48109 1 2 FALSE 0 17.48109 1.55 2.45 NA gray @@ -5699,7 +5707,7 @@ 1 0.5 1 NA 2 0.5 1 NA - $plots$am[[2]] + $plots$am[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 26.90545 26.90545 1 26.90545 1 1 FALSE 0.975 1.025 black 2 15.70291 19.14073 2 17.48109 1 2 FALSE 1.975 2.025 black @@ -5707,7 +5715,7 @@ 1 0.5 1 0.05 NA 2 0.5 1 0.05 NA - $plots$am[[3]] + $plots$am[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 59% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 41% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5715,14 +5723,6 @@ 1 1 1.2 2 1 1.2 - $plots$am[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 26.90545 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 17.48109 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - $plots$am[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5734,6 +5734,16 @@ $plots$gear $plots$gear[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 9.876782 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 16.347252 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 27.248136 1 3 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + + $plots$gear[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 9.876782 1 1 FALSE 0 9.876782 0.55 1.45 NA gray 2 2 16.347252 1 2 FALSE 0 16.347252 1.55 2.45 NA gray @@ -5743,7 +5753,7 @@ 2 0.5 1 NA 3 0.5 1 NA - $plots$gear[[2]] + $plots$gear[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 9.876782 9.876782 1 9.876782 1 1 FALSE 0.975 1.025 black 2 15.303744 17.300495 2 16.347252 1 2 FALSE 1.975 2.025 black @@ -5753,7 +5763,7 @@ 2 0.5 1 0.05 NA 3 0.5 1 0.05 NA - $plots$gear[[3]] + $plots$gear[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 47% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 38% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5763,16 +5773,6 @@ 2 1 1.2 3 1 1.2 - $plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 9.876782 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 16.347252 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 27.248136 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - $plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5784,6 +5784,26 @@ $plots$carb $plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 19.12721 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 19.17474 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 3 19.22228 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 4 19.26982 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 5 19.31736 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 6 19.35302 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 7 19.38075 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 8 8 19.42829 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + 7 19.04095 19.28336 + 8 19.04095 19.28336 + + $plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill linewidth 1 19.12721 19.12721 1 19.12721 1 -1 FALSE NA grey85 0.5 2 19.17474 19.17474 2 19.17474 1 -1 FALSE NA grey85 0.5 @@ -5803,7 +5823,7 @@ 7 1 0.5 8 1 0.5 - $plots$carb[[2]] + $plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 19.12721 1 -1 FALSE black 0.5 1 NA 2 2 19.17474 1 -1 FALSE black 0.5 1 NA @@ -5814,26 +5834,6 @@ 7 7 19.38075 1 -1 FALSE black 0.5 1 NA 8 8 19.42829 1 -1 FALSE black 0.5 1 NA - $plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 19.12721 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 19.17474 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 19.22228 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 19.26982 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 19.31736 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 19.35302 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 7 7 19.38075 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 8 8 19.42829 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - 7 19.04095 19.28336 - 8 19.04095 19.28336 - $plots$carb[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5912,6 +5912,22 @@ $plots$country $plots$country[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 19.337294 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 22.357086 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 12.132134 1 3 NA lightgray 0.5 1 NA -Inf Inf + 4 4 9.837536 1 4 NA lightgray 0.5 1 NA -Inf Inf + 5 5 7.431797 1 5 NA lightgray 0.5 1 NA -Inf Inf + 6 6 14.480652 1 6 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + + $plots$country[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 19.337294 1 1 FALSE 0 19.337294 0.55 1.45 NA gray 2 2 22.357086 1 2 FALSE 0 22.357086 1.55 2.45 NA gray @@ -5927,7 +5943,7 @@ 5 0.5 1 NA 6 0.5 1 NA - $plots$country[[2]] + $plots$country[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 19.3372937 19.33729 1 19.337294 1 1 FALSE 0.975 1.025 black 2 16.3347139 28.15914 2 22.357086 1 2 FALSE 1.975 2.025 black @@ -5943,7 +5959,7 @@ 5 0.5 1 0.05 NA 6 0.5 1 0.05 NA - $plots$country[[3]] + $plots$country[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 3% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 3% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5959,22 +5975,6 @@ 5 1 1.2 6 1 1.2 - $plots$country[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 19.337294 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 22.357086 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 12.132134 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 9.837536 1 4 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 7.431797 1 5 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 14.480652 1 6 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - $plots$country[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -6199,6 +6199,24 @@ $plots $plots$cyl $plots$cyl[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 3 -0.12716823 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 4 -0.08039371 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 5 -0.03361919 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 6 0.01315533 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 7 0.05992985 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 8 0.10670437 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 9 0.15347889 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 0 0 + 2 0 0 + 3 0 0 + 4 0 0 + 5 0 0 + 6 0 0 + 7 0 0 + + $plots$cyl[[2]] ymin ymax x y PANEL group flipped_aes colour fill 1 -0.12716823 -0.12716823 3 -0.12716823 1 -1 FALSE NA grey85 2 -0.08039371 -0.08039371 4 -0.08039371 1 -1 FALSE NA grey85 @@ -6216,7 +6234,7 @@ 6 0.5 1 0.5 7 0.5 1 0.5 - $plots$cyl[[2]] + $plots$cyl[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 3 -0.12716823 1 -1 FALSE black 0.5 1 NA 2 4 -0.08039371 1 -1 FALSE black 0.5 1 NA @@ -6226,24 +6244,6 @@ 6 8 0.10670437 1 -1 FALSE black 0.5 1 NA 7 9 0.15347889 1 -1 FALSE black 0.5 1 NA - $plots$cyl[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 3 -0.12716823 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 4 -0.08039371 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 5 -0.03361919 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 6 0.01315533 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 5 7 0.05992985 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 6 8 0.10670437 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 7 9 0.15347889 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 0 0 - 2 0 0 - 3 0 0 - 4 0 0 - 5 0 0 - 6 0 0 - 7 0 0 - $plots$cyl[[4]] yintercept PANEL group colour linewidth linetype alpha 1 -1.124298 1 -1 black 0.5 dashed NA @@ -6322,6 +6322,102 @@ $plots$disp $plots$disp[[1]] + x y PANEL group colour fill linewidth linetype alpha + 1 70.92941 18.3582048 1 -1 NA lightgray 0.5 1 NA + 2 71.10000 18.2922290 1 -1 NA lightgray 0.5 1 NA + 3 75.70000 NA 1 -1 NA lightgray 0.5 1 NA + 4 76.44475 18.0051725 1 -1 NA lightgray 0.5 1 NA + 5 78.70000 17.1381113 1 -1 NA lightgray 0.5 1 NA + 6 79.00000 17.0230873 1 -1 NA lightgray 0.5 1 NA + 7 79.04253 NA 1 -1 NA lightgray 0.5 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1.2 3 1 1.2 - $plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 -1.0995548 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 -0.2121510 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 0.5053857 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 0.003288333 0.006233712 - 2 0.003288333 0.006233712 - 3 0.003288333 0.006233712 - $plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 -0.0002450745 1 -1 black 0.5 dashed NA @@ -13904,6 +13904,26 @@ $plots$carb $plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 0.004668639 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 0.004189462 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 3 0.003710285 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 4 0.003231108 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 5 0.002751930 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 6 0.002432479 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 7 0.002113028 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 8 8 0.001553988 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 0.003288333 0.006233712 + 2 0.003288333 0.006233712 + 3 0.003288333 0.006233712 + 4 0.003288333 0.006233712 + 5 0.003288333 0.006233712 + 6 0.003288333 0.006233712 + 7 0.003288333 0.006233712 + 8 0.003288333 0.006233712 + + $plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill 1 0.004668639 0.004668639 1 0.004668639 1 -1 FALSE NA grey85 2 0.004189462 0.004189462 2 0.004189462 1 -1 FALSE NA grey85 @@ -13923,7 +13943,7 @@ 7 0.5 1 0.5 8 0.5 1 0.5 - $plots$carb[[2]] + $plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 0.004668639 1 -1 FALSE black 0.5 1 NA 2 2 0.004189462 1 -1 FALSE black 0.5 1 NA @@ -13934,26 +13954,6 @@ 7 7 0.002113028 1 -1 FALSE black 0.5 1 NA 8 8 0.001553988 1 -1 FALSE black 0.5 1 NA - $plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 0.004668639 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 0.004189462 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 0.003710285 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 0.003231108 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 0.002751930 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 0.002432479 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 7 7 0.002113028 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 8 8 0.001553988 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 0.003288333 0.006233712 - 2 0.003288333 0.006233712 - 3 0.003288333 0.006233712 - 4 0.003288333 0.006233712 - 5 0.003288333 0.006233712 - 6 0.003288333 0.006233712 - 7 0.003288333 0.006233712 - 8 0.003288333 0.006233712 - $plots$carb[[4]] yintercept PANEL group colour linewidth linetype alpha 1 -0.0002450745 1 -1 black 0.5 dashed NA @@ -14032,6 +14032,22 @@ $plots$country $plots$country[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 0.7671419 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 0.6181895 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 0.5650391 1 3 NA lightgray 0.5 1 NA -Inf Inf + 4 4 0.6905424 1 4 NA lightgray 0.5 1 NA -Inf Inf + 5 5 0.1506054 1 5 NA lightgray 0.5 1 NA -Inf Inf + 6 6 0.5835802 1 6 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 0.003288333 0.006233712 + 2 0.003288333 0.006233712 + 3 0.003288333 0.006233712 + 4 0.003288333 0.006233712 + 5 0.003288333 0.006233712 + 6 0.003288333 0.006233712 + + $plots$country[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 0.7671419 1 1 FALSE 0 0.7671419 0.55 1.45 NA gray 2 2 0.6181895 1 2 FALSE 0 0.6181895 1.55 2.45 NA gray @@ -14047,7 +14063,7 @@ 5 0.5 1 NA 6 0.5 1 NA - $plots$country[[2]] + $plots$country[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 0.76714191 0.7671419 1 0.7671419 1 1 FALSE 0.975 1.025 black 2 0.32759554 0.8690106 2 0.6181895 1 2 FALSE 1.975 2.025 black @@ -14063,7 +14079,7 @@ 5 0.5 1 0.05 NA 6 0.5 1 0.05 NA - $plots$country[[3]] + $plots$country[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 -0.00773904 3% 1 1 1 black 3 0 0.5 -0.2 0.5 2 -0.00773904 3% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -14079,22 +14095,6 @@ 5 1 1.2 6 1 1.2 - $plots$country[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 0.7671419 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 0.6181895 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 0.5650391 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 0.6905424 1 4 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 0.1506054 1 5 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 0.5835802 1 6 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 0.003288333 0.006233712 - 2 0.003288333 0.006233712 - 3 0.003288333 0.006233712 - 4 0.003288333 0.006233712 - 5 0.003288333 0.006233712 - 6 0.003288333 0.006233712 - $plots$country[[5]] yintercept PANEL group colour linewidth linetype alpha 1 -0.0002450745 1 -1 black 0.5 dashed NA @@ -14323,6 +14323,24 @@ $plots $plots$cyl $plots$cyl[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin + 1 3 -0.0057992492 1 -1 NA lightgray 0.5 1 NA -Inf + 2 4 -0.0036661920 1 -1 NA lightgray 0.5 1 NA -Inf + 3 5 -0.0015331349 1 -1 NA lightgray 0.5 1 NA -Inf + 4 6 0.0005999223 1 -1 NA lightgray 0.5 1 NA -Inf + 5 7 0.0027329795 1 -1 NA lightgray 0.5 1 NA -Inf + 6 8 0.0048660367 1 -1 NA lightgray 0.5 1 NA -Inf + 7 9 0.0069990939 1 -1 NA lightgray 0.5 1 NA -Inf + xmax ymin ymax + 1 Inf -0.0003454771 0.0003454771 + 2 Inf -0.0003454771 0.0003454771 + 3 Inf -0.0003454771 0.0003454771 + 4 Inf -0.0003454771 0.0003454771 + 5 Inf -0.0003454771 0.0003454771 + 6 Inf -0.0003454771 0.0003454771 + 7 Inf -0.0003454771 0.0003454771 + + $plots$cyl[[2]] ymin ymax x y PANEL group flipped_aes colour 1 -0.0057992492 -0.0057992492 3 -0.0057992492 1 -1 FALSE NA 2 -0.0036661920 -0.0036661920 4 -0.0036661920 1 -1 FALSE NA @@ -14340,7 +14358,7 @@ 6 grey85 0.5 1 0.5 7 grey85 0.5 1 0.5 - $plots$cyl[[2]] + $plots$cyl[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 3 -0.0057992492 1 -1 FALSE black 0.5 1 NA 2 4 -0.0036661920 1 -1 FALSE black 0.5 1 NA @@ -14350,24 +14368,6 @@ 6 8 0.0048660367 1 -1 FALSE black 0.5 1 NA 7 9 0.0069990939 1 -1 FALSE black 0.5 1 NA - $plots$cyl[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin - 1 3 -0.0057992492 1 -1 NA lightgray 0.5 1 0.1 -Inf - 2 4 -0.0036661920 1 -1 NA lightgray 0.5 1 0.1 -Inf - 3 5 -0.0015331349 1 -1 NA lightgray 0.5 1 0.1 -Inf - 4 6 0.0005999223 1 -1 NA lightgray 0.5 1 0.1 -Inf - 5 7 0.0027329795 1 -1 NA lightgray 0.5 1 0.1 -Inf - 6 8 0.0048660367 1 -1 NA lightgray 0.5 1 0.1 -Inf - 7 9 0.0069990939 1 -1 NA lightgray 0.5 1 0.1 -Inf - xmax ymin ymax - 1 Inf -0.0003454771 0.0003454771 - 2 Inf -0.0003454771 0.0003454771 - 3 Inf -0.0003454771 0.0003454771 - 4 Inf -0.0003454771 0.0003454771 - 5 Inf -0.0003454771 0.0003454771 - 6 Inf -0.0003454771 0.0003454771 - 7 Inf -0.0003454771 0.0003454771 - $plots$cyl[[4]] yintercept PANEL group colour linewidth linetype alpha 1 -0.002045382 1 -1 black 0.5 dashed NA @@ -14446,6 +14446,102 @@ $plots$disp $plots$disp[[1]] + x y PANEL group colour fill linewidth linetype alpha + 1 70.92941 0.52950091 1 -1 NA lightgray 0.5 1 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lightgray 0.5 1 NA + 22 166.13111 -0.44141495 1 -1 NA lightgray 0.5 1 NA + 23 167.60000 NA 1 -1 NA lightgray 0.5 1 NA + 24 168.03279 -0.43979489 1 -1 NA lightgray 0.5 1 NA + 25 225.00000 0.44017921 1 -1 NA lightgray 0.5 1 NA + 26 258.00000 0.57997114 1 -1 NA lightgray 0.5 1 NA + 27 258.37592 0.57892978 1 -1 NA lightgray 0.5 1 NA + 28 274.17816 0.50162687 1 -1 NA lightgray 0.5 1 NA + 29 275.80000 0.49115362 1 -1 NA lightgray 0.5 1 NA + 30 300.03890 0.31491922 1 -1 NA lightgray 0.5 1 NA + 31 301.00000 0.30765281 1 -1 NA lightgray 0.5 1 NA + 32 302.24983 NA 1 -1 NA lightgray 0.5 1 NA + 33 318.00000 0.19219299 1 -1 NA lightgray 0.5 1 NA + 34 320.76473 0.17280936 1 -1 NA lightgray 0.5 1 NA + 35 350.00000 NA 1 -1 NA lightgray 0.5 1 NA + 36 350.17436 NA 1 -1 NA lightgray 0.5 1 NA + 37 351.06172 0.16876308 1 -1 NA lightgray 0.5 1 NA + 38 360.00000 0.13278047 1 -1 NA lightgray 0.5 1 NA + 39 362.93910 0.12271327 1 -1 NA lightgray 0.5 1 NA + 40 397.00444 NA 1 -1 NA lightgray 0.5 1 NA + 41 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2 black 3 0 0.5 -0.2 0.5 @@ -2724,16 +2734,6 @@ 2 1 1.2 3 1 1.2 - $ale$plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 9.876782 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 23.999836 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 35.650050 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - $ale$plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -2745,6 +2745,26 @@ $ale$plots$carb $ale$plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 19.12721 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 19.17474 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 3 19.22228 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 4 19.26982 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 5 19.31736 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 6 19.36490 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 7 19.41244 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 8 8 19.45998 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + 7 19.04095 19.28336 + 8 19.04095 19.28336 + + $ale$plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill linewidth 1 19.12721 19.12721 1 19.12721 1 -1 FALSE NA grey85 0.5 2 19.17474 19.17474 2 19.17474 1 -1 FALSE NA grey85 0.5 @@ -2764,7 +2784,7 @@ 7 1 0.5 8 1 0.5 - $ale$plots$carb[[2]] + $ale$plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 19.12721 1 -1 FALSE black 0.5 1 NA 2 2 19.17474 1 -1 FALSE black 0.5 1 NA @@ -2775,26 +2795,6 @@ 7 7 19.41244 1 -1 FALSE black 0.5 1 NA 8 8 19.45998 1 -1 FALSE black 0.5 1 NA - $ale$plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 19.12721 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 19.17474 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 19.22228 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 19.26982 1 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19.28336 - 30 Inf 19.04095 19.28336 - 31 Inf 19.04095 19.28336 - 32 Inf 19.04095 19.28336 - 33 Inf 19.04095 19.28336 - 34 Inf 19.04095 19.28336 - 35 Inf 19.04095 19.28336 - 36 Inf 19.04095 19.28336 - 37 Inf 19.04095 19.28336 - 38 Inf 19.04095 19.28336 - 39 Inf 19.04095 19.28336 - 40 Inf 19.04095 19.28336 - 41 Inf 19.04095 19.28336 - 42 Inf 19.04095 19.28336 - 43 Inf 19.04095 19.28336 - 44 Inf 19.04095 19.28336 - 45 Inf 19.04095 19.28336 - 46 Inf 19.04095 19.28336 - 47 Inf 19.04095 19.28336 - 48 Inf 19.04095 19.28336 - 49 Inf 19.04095 19.28336 - 50 Inf 19.04095 19.28336 - 51 Inf 19.04095 19.28336 - 52 Inf 19.04095 19.28336 - 53 Inf 19.04095 19.28336 - 54 Inf 19.04095 19.28336 - 55 Inf 19.04095 19.28336 - 56 Inf 19.04095 19.28336 - 57 Inf 19.04095 19.28336 - 58 Inf 19.04095 19.28336 - 59 Inf 19.04095 19.28336 - 60 Inf 19.04095 19.28336 - 61 Inf 19.04095 19.28336 - 62 Inf 19.04095 19.28336 - $ale$plots$qsec[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 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1 -12.66558 15.87988 1 5.571952 1 1 FALSE 0.975 1.025 black 2 28.18418 68.17922 2 40.912453 1 2 FALSE 1.975 2.025 black @@ -5726,7 +5734,7 @@ 1 0.5 1 0.05 NA 2 0.5 1 0.05 NA - $ale$plots$am[[3]] + $ale$plots$am[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 59% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 41% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5734,14 +5742,6 @@ 1 1 1.2 2 1 1.2 - $ale$plots$am[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 5.571952 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 40.912453 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - $ale$plots$am[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5753,6 +5753,16 @@ $ale$plots$gear $ale$plots$gear[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 38.6327395 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 0.1823524 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 -3.0622712 1 3 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + + $ale$plots$gear[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour 1 1 38.6327395 1 1 FALSE 0.000000 38.6327395 0.55 1.45 NA 2 2 0.1823524 1 2 FALSE 0.000000 0.1823524 1.55 2.45 NA @@ -5762,7 +5772,7 @@ 2 gray 0.5 1 NA 3 gray 0.5 1 NA - $ale$plots$gear[[2]] + $ale$plots$gear[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 23.75298 75.60617 1 38.6327395 1 1 FALSE 0.975 1.025 black 2 -41.89342 15.37607 2 0.1823524 1 2 FALSE 1.975 2.025 black @@ -5772,7 +5782,7 @@ 2 0.5 1 0.05 NA 3 0.5 1 0.05 NA - $ale$plots$gear[[3]] + $ale$plots$gear[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 47% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 38% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5782,16 +5792,6 @@ 2 1 1.2 3 1 1.2 - $ale$plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 38.6327395 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 0.1823524 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 -3.0622712 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - $ale$plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5803,6 +5803,26 @@ $ale$plots$carb $ale$plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 21.517884 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 20.001720 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 3 3 18.485555 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 4 4 16.969390 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 5 5 14.726849 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 6 6 12.848364 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 7 7 -4.183389 1 -1 NA lightgray 0.5 1 NA -Inf Inf + 8 8 -3.279924 1 -1 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + 7 19.04095 19.28336 + 8 19.04095 19.28336 + + $ale$plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill 1 13.754432 38.61823 1 13.754432 1 -1 FALSE NA grey85 2 17.641677 25.09154 2 17.641677 1 -1 FALSE NA grey85 @@ -5822,7 +5842,7 @@ 7 0.5 1 0.5 8 0.5 1 0.5 - $ale$plots$carb[[2]] + $ale$plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 21.517884 1 -1 FALSE black 0.5 1 NA 2 2 20.001720 1 -1 FALSE black 0.5 1 NA @@ -5833,26 +5853,6 @@ 7 7 -4.183389 1 -1 FALSE black 0.5 1 NA 8 8 -3.279924 1 -1 FALSE black 0.5 1 NA - $ale$plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 21.517884 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 20.001720 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 18.485555 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 16.969390 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 14.726849 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 12.848364 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 7 7 -4.183389 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - 8 8 -3.279924 1 -1 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - 7 19.04095 19.28336 - 8 19.04095 19.28336 - $ale$plots$carb[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -5931,6 +5931,22 @@ $ale$plots$country $ale$plots$country[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 27.486342 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 51.981651 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 13.829451 1 3 NA lightgray 0.5 1 NA -Inf Inf + 4 4 24.089613 1 4 NA lightgray 0.5 1 NA -Inf Inf + 5 5 22.550216 1 5 NA lightgray 0.5 1 NA -Inf Inf + 6 6 6.283883 1 6 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + + $ale$plots$country[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 27.486342 1 1 FALSE 0 27.486342 0.55 1.45 NA gray 2 2 51.981651 1 2 FALSE 0 51.981651 1.55 2.45 NA gray @@ -5946,7 +5962,7 @@ 5 0.5 1 NA 6 0.5 1 NA - $ale$plots$country[[2]] + $ale$plots$country[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 14.0086673 50.65363 1 27.486342 1 1 FALSE 0.975 1.025 black 2 21.8969727 143.77942 2 51.981651 1 2 FALSE 1.975 2.025 black @@ -5962,7 +5978,7 @@ 5 0.5 1 0.05 NA 6 0.5 1 0.05 NA - $ale$plots$country[[3]] + $ale$plots$country[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 3% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 3% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -5978,22 +5994,6 @@ 5 1 1.2 6 1 1.2 - $ale$plots$country[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 27.486342 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 51.981651 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 13.829451 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 24.089613 1 4 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 22.550216 1 5 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 6.283883 1 6 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - $ale$plots$country[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -6759,6 +6759,24 @@ $ale$plots $ale$plots$cyl $ale$plots$cyl[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin + 1 3 -0.017269681 1 -1 NA lightgray 0.5 1 NA -Inf + 2 4 -0.010184683 1 -1 NA lightgray 0.5 1 NA -Inf + 3 5 -0.003099686 1 -1 NA lightgray 0.5 1 NA -Inf + 4 6 0.003985311 1 -1 NA lightgray 0.5 1 NA -Inf + 5 7 0.011070308 1 -1 NA lightgray 0.5 1 NA -Inf + 6 8 0.018155305 1 -1 NA lightgray 0.5 1 NA -Inf + 7 9 0.025240302 1 -1 NA lightgray 0.5 1 NA -Inf + xmax ymin ymax + 1 Inf 19.04095 19.28336 + 2 Inf 19.04095 19.28336 + 3 Inf 19.04095 19.28336 + 4 Inf 19.04095 19.28336 + 5 Inf 19.04095 19.28336 + 6 Inf 19.04095 19.28336 + 7 Inf 19.04095 19.28336 + + $ale$plots$cyl[[2]] ymin ymax x y PANEL group flipped_aes colour fill 1 -0.47223471 1.17290662 3 -0.47223471 1 -1 FALSE NA grey85 2 -0.29163111 0.75370516 4 -0.29163111 1 -1 FALSE NA grey85 @@ -6776,7 +6794,7 @@ 6 0.5 1 0.5 7 0.5 1 0.5 - $ale$plots$cyl[[2]] + $ale$plots$cyl[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 3 -0.017269681 1 -1 FALSE black 0.5 1 NA 2 4 -0.010184683 1 -1 FALSE black 0.5 1 NA @@ -6786,24 +6804,6 @@ 6 8 0.018155305 1 -1 FALSE black 0.5 1 NA 7 9 0.025240302 1 -1 FALSE black 0.5 1 NA - $ale$plots$cyl[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin - 1 3 -0.017269681 1 -1 NA lightgray 0.5 1 0.1 -Inf - 2 4 -0.010184683 1 -1 NA lightgray 0.5 1 0.1 -Inf - 3 5 -0.003099686 1 -1 NA lightgray 0.5 1 0.1 -Inf - 4 6 0.003985311 1 -1 NA lightgray 0.5 1 0.1 -Inf - 5 7 0.011070308 1 -1 NA lightgray 0.5 1 0.1 -Inf - 6 8 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-Inf Inf 19.04095 19.28336 - 21 -Inf Inf 19.04095 19.28336 - 22 -Inf Inf 19.04095 19.28336 - 23 -Inf Inf 19.04095 19.28336 - 24 -Inf Inf 19.04095 19.28336 - 25 -Inf Inf 19.04095 19.28336 - 26 -Inf Inf 19.04095 19.28336 - 27 -Inf Inf 19.04095 19.28336 - 28 -Inf Inf 19.04095 19.28336 - 29 -Inf Inf 19.04095 19.28336 - 30 -Inf Inf 19.04095 19.28336 - 31 -Inf Inf 19.04095 19.28336 - 32 -Inf Inf 19.04095 19.28336 - 33 -Inf Inf 19.04095 19.28336 - 34 -Inf Inf 19.04095 19.28336 - 35 -Inf Inf 19.04095 19.28336 - 36 -Inf Inf 19.04095 19.28336 - 37 -Inf Inf 19.04095 19.28336 - 38 -Inf Inf 19.04095 19.28336 - 39 -Inf Inf 19.04095 19.28336 - 40 -Inf Inf 19.04095 19.28336 - 41 -Inf Inf 19.04095 19.28336 - 42 -Inf Inf 19.04095 19.28336 - 43 -Inf Inf 19.04095 19.28336 - 44 -Inf Inf 19.04095 19.28336 - 45 -Inf Inf 19.04095 19.28336 - 46 -Inf Inf 19.04095 19.28336 - 47 -Inf Inf 19.04095 19.28336 - 48 -Inf Inf 19.04095 19.28336 - 49 -Inf Inf 19.04095 19.28336 - 50 -Inf Inf 19.04095 19.28336 - 51 -Inf Inf 19.04095 19.28336 - 52 -Inf Inf 19.04095 19.28336 - 53 -Inf Inf 19.04095 19.28336 - 54 -Inf Inf 19.04095 19.28336 - 55 -Inf Inf 19.04095 19.28336 - 56 -Inf Inf 19.04095 19.28336 - 57 -Inf Inf 19.04095 19.28336 - 58 -Inf Inf 19.04095 19.28336 - 59 -Inf Inf 19.04095 19.28336 - 60 -Inf Inf 19.04095 19.28336 - 61 -Inf Inf 19.04095 19.28336 - 62 -Inf Inf 19.04095 19.28336 - $ale$plots$qsec[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -9005,6 +9005,14 @@ $ale$plots$vs $ale$plots$vs[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 -4.920311 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 5.576353 1 2 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + + $ale$plots$vs[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 0.000000 1 1 FALSE -4.920311 0.000000 0.55 1.45 NA gray 2 2 5.576353 1 2 FALSE 0.000000 5.576353 1.55 2.45 NA gray @@ -9012,7 +9020,7 @@ 1 0.5 1 NA 2 0.5 1 NA - $ale$plots$vs[[2]] + $ale$plots$vs[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 -7.745490 -3.658689 1 -4.920311 1 1 FALSE 0.975 1.025 black 2 4.146514 7.010293 2 5.576353 1 2 FALSE 1.975 2.025 black @@ -9020,7 +9028,7 @@ 1 0.5 1 0.05 NA 2 0.5 1 0.05 NA - $ale$plots$vs[[3]] + $ale$plots$vs[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 56% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 44% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -9028,14 +9036,6 @@ 1 1 1.2 2 1 1.2 - $ale$plots$vs[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 -4.920311 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 5.576353 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - $ale$plots$vs[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -9047,6 +9047,14 @@ $ale$plots$am $ale$plots$am[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 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2.4533156 1 1 FALSE 0.975 1.025 black 2 -3.162360 1.957260 2 -0.6044931 1 2 FALSE 1.975 2.025 black @@ -9108,7 +9118,7 @@ 2 0.5 1 0.05 NA 3 0.5 1 0.05 NA - $ale$plots$gear[[3]] + $ale$plots$gear[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 47% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 38% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -9118,16 +9128,6 @@ 2 1 1.2 3 1 1.2 - $ale$plots$gear[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 2.4533156 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 -0.6044931 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 -0.9135960 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - $ale$plots$gear[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -9139,6 +9139,26 @@ $ale$plots$carb $ale$plots$carb[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin + 1 1 0.005501888 1 -1 NA lightgray 0.5 1 NA -Inf + 2 2 0.001674488 1 -1 NA lightgray 0.5 1 NA -Inf + 3 3 -0.002152913 1 -1 NA lightgray 0.5 1 NA -Inf + 4 4 -0.005980313 1 -1 NA lightgray 0.5 1 NA -Inf + 5 5 -0.009807713 1 -1 NA lightgray 0.5 1 NA -Inf + 6 6 -0.013635113 1 -1 NA lightgray 0.5 1 NA -Inf + 7 7 -0.017462513 1 -1 NA lightgray 0.5 1 NA -Inf + 8 8 -0.015654827 1 -1 NA lightgray 0.5 1 NA -Inf + xmax ymin ymax + 1 Inf 19.04095 19.28336 + 2 Inf 19.04095 19.28336 + 3 Inf 19.04095 19.28336 + 4 Inf 19.04095 19.28336 + 5 Inf 19.04095 19.28336 + 6 Inf 19.04095 19.28336 + 7 Inf 19.04095 19.28336 + 8 Inf 19.04095 19.28336 + + $ale$plots$carb[[2]] ymin ymax x y PANEL group flipped_aes colour fill 1 -0.26292344 0.24173470 1 -0.26292344 1 -1 FALSE NA grey85 2 -0.10576006 0.07864671 2 -0.10576006 1 -1 FALSE NA grey85 @@ -9158,7 +9178,7 @@ 7 0.5 1 0.5 8 0.5 1 0.5 - $ale$plots$carb[[2]] + $ale$plots$carb[[3]] x y PANEL group flipped_aes colour linewidth linetype alpha 1 1 0.005501888 1 -1 FALSE black 0.5 1 NA 2 2 0.001674488 1 -1 FALSE black 0.5 1 NA @@ -9169,26 +9189,6 @@ 7 7 -0.017462513 1 -1 FALSE black 0.5 1 NA 8 8 -0.015654827 1 -1 FALSE black 0.5 1 NA - $ale$plots$carb[[3]] - x y PANEL group colour fill linewidth linetype alpha xmin - 1 1 0.005501888 1 -1 NA lightgray 0.5 1 0.1 -Inf - 2 2 0.001674488 1 -1 NA lightgray 0.5 1 0.1 -Inf - 3 3 -0.002152913 1 -1 NA lightgray 0.5 1 0.1 -Inf - 4 4 -0.005980313 1 -1 NA lightgray 0.5 1 0.1 -Inf - 5 5 -0.009807713 1 -1 NA lightgray 0.5 1 0.1 -Inf - 6 6 -0.013635113 1 -1 NA lightgray 0.5 1 0.1 -Inf - 7 7 -0.017462513 1 -1 NA lightgray 0.5 1 0.1 -Inf - 8 8 -0.015654827 1 -1 NA lightgray 0.5 1 0.1 -Inf - xmax ymin ymax - 1 Inf 19.04095 19.28336 - 2 Inf 19.04095 19.28336 - 3 Inf 19.04095 19.28336 - 4 Inf 19.04095 19.28336 - 5 Inf 19.04095 19.28336 - 6 Inf 19.04095 19.28336 - 7 Inf 19.04095 19.28336 - 8 Inf 19.04095 19.28336 - $ale$plots$carb[[4]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA @@ -9267,6 +9267,22 @@ $ale$plots$country $ale$plots$country[[1]] + x y PANEL group colour fill linewidth linetype alpha xmin xmax + 1 1 1.5218725 1 1 NA lightgray 0.5 1 NA -Inf Inf + 2 2 5.6124284 1 2 NA lightgray 0.5 1 NA -Inf Inf + 3 3 1.7157562 1 3 NA lightgray 0.5 1 NA -Inf Inf + 4 4 3.7980967 1 4 NA lightgray 0.5 1 NA -Inf Inf + 5 5 5.3604063 1 5 NA lightgray 0.5 1 NA -Inf Inf + 6 6 0.6099854 1 6 NA lightgray 0.5 1 NA -Inf Inf + ymin ymax + 1 19.04095 19.28336 + 2 19.04095 19.28336 + 3 19.04095 19.28336 + 4 19.04095 19.28336 + 5 19.04095 19.28336 + 6 19.04095 19.28336 + + $ale$plots$country[[2]] x y PANEL group flipped_aes ymin ymax xmin xmax colour fill 1 1 1.5218725 1 1 FALSE 0 1.5218725 0.55 1.45 NA gray 2 2 5.6124284 1 2 FALSE 0 5.6124284 1.55 2.45 NA gray @@ -9282,7 +9298,7 @@ 5 0.5 1 NA 6 0.5 1 NA - $ale$plots$country[[2]] + $ale$plots$country[[3]] ymin ymax x y PANEL group flipped_aes xmin xmax colour 1 -8.779977 8.714856 1 1.5218725 1 1 FALSE 0.975 1.025 black 2 -6.825244 7.287860 2 5.6124284 1 2 FALSE 1.975 2.025 black @@ -9298,7 +9314,7 @@ 5 0.5 1 0.05 NA 6 0.5 1 0.05 NA - $ale$plots$country[[3]] + $ale$plots$country[[4]] y label x PANEL group colour size angle hjust vjust alpha family 1 10.39108 3% 1 1 1 black 3 0 0.5 -0.2 0.5 2 10.39108 3% 2 1 2 black 3 0 0.5 -0.2 0.5 @@ -9314,22 +9330,6 @@ 5 1 1.2 6 1 1.2 - $ale$plots$country[[4]] - x y PANEL group colour fill linewidth linetype alpha xmin xmax - 1 1 1.5218725 1 1 NA lightgray 0.5 1 0.1 -Inf Inf - 2 2 5.6124284 1 2 NA lightgray 0.5 1 0.1 -Inf Inf - 3 3 1.7157562 1 3 NA lightgray 0.5 1 0.1 -Inf Inf - 4 4 3.7980967 1 4 NA lightgray 0.5 1 0.1 -Inf Inf - 5 5 5.3604063 1 5 NA lightgray 0.5 1 0.1 -Inf Inf - 6 6 0.6099854 1 6 NA lightgray 0.5 1 0.1 -Inf Inf - ymin ymax - 1 19.04095 19.28336 - 2 19.04095 19.28336 - 3 19.04095 19.28336 - 4 19.04095 19.28336 - 5 19.04095 19.28336 - 6 19.04095 19.28336 - $ale$plots$country[[5]] yintercept PANEL group colour linewidth linetype alpha 1 15.43921 1 -1 black 0.5 dashed NA