ggxmean: Statistical Geoms

#' compute_group_ols_ind <- function(data, scales) {
#'
#'                                        model <- lm(y ~ x + indicator,
#'                                                    data = data)
#'
#'                                        data.frame(x = data$x,
#'                                                   y = model$fitted.values,#,
#'                                                   indicator = data$indicator
#'                                                   )
#'                                      }
#'
#' #### confint #####
#'
#'
#'
#' # For most applications the grouping is set implicitly by mapping one
#' # or more discrete variables to x, y, colour, fill, alpha, shape, size, and/or linetype
#'
#' StatOlsindicator1 <- ggplot2::ggproto("StatOlsindicator1",
#'                                    ggplot2::Stat,
#'                                    compute_panel = compute_group_ols_ind,
#'                                    required_aes = c("x", "y", "indicator"),
#'                                    default_aes = aes(group = after_stat(indicator))
#'
#' )
#'
#'
#'
#' geom_lm_indicator1 <- function(mapping = NULL, data = NULL,
#'                            position = "identity", na.rm = FALSE,
#'                            show.legend = NA,
#'                            inherit.aes = TRUE, ...) {
#'   ggplot2::layer(
#'     stat = StatOlsindicator1, geom = ggplot2::GeomLine, data = data, mapping = mapping,
#'     position = position, show.legend = show.legend, inherit.aes = inherit.aes,
#'     params = list(na.rm = na.rm, ...)
#'   )
#' }
#'
#' library(tidyverse)
#' starwars %>%
#' filter(mass < 1000) %>%
#' mutate(sex_numeric = sex)  %>%
#' ggplot() +
#' aes(x = height, y = mass, indicator = sex) +
#' geom_point(aes(color = sex)) +
#' geom_lm_indicator1(linetype = "dashed")
#' # geom_lm_indicator1 <- function(mapping = NULL, data = NULL,#
#' #' Drawing prediction interval for OLS linear model
#' #'
#' #' @param mapping
#' #' @param data
#' #' @param position
#' #' @param na.rm
#' #' @param show.legend
#' #' @param inherit.aes
#' #' @param ...
#' #'
#' #' @return
#' #' @export
#' #'
#' #' @examples
#' #' library(ggplot2)
#' #' library(dplyr)
#' #' library(magrittr)
#' #'
#' #' starwars %>%
#' #'    filter(mass < 1000) %>%
#' #'    tidyr::drop_na(mass, height, sex) ->
#' #'  my_data
#' #'
#' #'  my_data %>%
#' #'    lm(mass ~ height + sex, data = .) ->
#' #'  model
#' #'
#' #' data.frame(x = my_data$height,
#' #' y = my_data$mass,
#' #' yfit = model$fitted.values,
#' #' group = my_data$sex) %>%
#' #' ggplot() +
#' #' aes(x = x, y = y, group = group) +
#' #' geom_point(aes(color = group, alpha = group == "none")) +
#' #' geom_line(aes(y = yfit))
#' #'
#' #' starwars %>%
#' #' filter(mass < 1000) %>%
#' #' mutate(sex_numeric = sex)  %>%
#' #' ggplot() +
#' #' aes(x = height, y = mass) +
#' #' #geom_point(aes(color = sex)) +
#' #'  geom_lm_indicator1(linetype = "dashed")
#' # geom_lm_indicator1 <- function(mapping = NULL, data = NULL,#                              position = "identity", na.rm = FALSE, show.legend = NA,#                              inherit.aes = T, ...) {#   ggplot2::layer(#     stat = StatOlsindicator1, geom = ggplot2::GeomLine, data = data, mapping = mapping,#     position = position, show.legend = show.legend, inherit.aes = inherit.aes,#     params = list(na.rm = na.rm, ...)#   )# }
#'
#'