Prepare nonparametric temporal trend curve data for plotting

Validates pre-computed curve data (and optional CI bounds and binned data summary points) and returns an hv_nonparametric object. Call plot.hv_nonparametric to obtain a bare ggplot2 curve plot that you can decorate with colour/fill scales, axis limits, and hv_theme.

Usage

hv_nonparametric(
  curve_data,
  x_col = "time",
  estimate_col = "estimate",
  lower_col = NULL,
  upper_col = NULL,
  group_col = NULL,
  data_points = NULL
)

Arguments

curve_data: Data frame; one row per (time, group) combination.
x_col: Name of the x-axis column. Default "time".
estimate_col: Name of the predicted value column. Default "estimate".
lower_col: Name of the lower CI bound column, or NULL. Default NULL.
upper_col: Name of the upper CI bound column, or NULL. Default NULL.
group_col: Name of the stratification column, or NULL. Default NULL.
data_points: Optional data frame of binned data summary points. Must have columns matching x_col and "value", plus group_col when stratified. Default NULL.

Value

An object of class c("hv_nonparametric", "hv_data"); call plot() on the result to render the figure — see plot.hv_nonparametric. The list contains:

$data: The curve_data data frame.
$meta: Named list: x_col, estimate_col, lower_col, upper_col, group_col, has_ci, has_data_points, n_obs.
$tables: List; contains data_points when supplied.

Details

Covers the full range of tp.np.* SAS templates:

SAS template pattern	R usage
Single average curve (`avrg_curv`, `u.trend`)	`hv_nonparametric(dat)`
Curve + 68\ Curve + CI + data points	`+ data_points = ...`
Two-group comparison (`double`, `ozak`)	`+ group_col = "group"`
Multi-scenario / covariate-adjusted (`mult`)	`+ group_col = "group"`
Phase decomposition (`phases`, `independence`)	`+ group_col = "phase"`

SAS column mapping:

estimate_col ← prev, mnprev, _p_, est_fev, est_z0d
lower_col ← cll_p68 or cll_p95
upper_col ← clu_p68 or clu_p95
group_col ← indicator added after wide-to-long reshape

Examples

dat     <- sample_nonparametric_curve_data(n = 500, time_max = 12)
dat_pts <- sample_nonparametric_curve_points(n = 500, time_max = 12)

# 1. Build data object
np <- hv_nonparametric(dat, lower_col = "lower", upper_col = "upper",
                          data_points = dat_pts)
np  # prints CI / data-point flags
#> <hv_nonparametric>
#>   N curve pts : 500
#>   x / estimate: time / estimate
#>   CI ribbon   : lower / upper
#>   Data points : yes

# 2. Bare plot -- undecorated ggplot returned by plot.hv_nonparametric
p <- plot(np)

# 3. Decorate: colour/fill palettes, axis scales, labels, theme
p +
  ggplot2::scale_colour_manual(values = c("steelblue"), guide = "none") +
  ggplot2::scale_fill_manual(values   = c("steelblue"), guide = "none") +
  ggplot2::scale_x_continuous(limits = c(0, 12), breaks = 0:12) +
  ggplot2::scale_y_continuous(limits = c(0, 0.40),
                              breaks = seq(0, 0.40, 0.10),
                              labels = scales::percent) +
  ggplot2::labs(x = "Months", y = "Prevalence of AF") +
  hv_theme("poster")
#> Warning: Removed 187 rows containing missing values or values outside the scale range
#> (`geom_ribbon()`).
#> Warning: Removed 53 rows containing missing values or values outside the scale range
#> (`geom_line()`).
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# --- Global theme (set once per session) ----------------------------------
if (FALSE) { # \dontrun{
old <- ggplot2::theme_set(hv_theme_manuscript())
plot(np) +
  ggplot2::scale_colour_manual(values = c("steelblue"), guide = "none") +
  ggplot2::scale_fill_manual(values   = c("steelblue"), guide = "none") +
  ggplot2::labs(x = "Months", y = "Prevalence of AF")
# For multi-group curves swap scale_colour_manual with:
#   ggplot2::scale_colour_brewer(palette = "Set1", name = NULL)
#   ggplot2::scale_fill_brewer(palette = "Set1", guide = "none")
ggplot2::theme_set(old)
} # }

# See vignette("plot-decorators", package = "hvtiPlotR") for theming,
# colour scales, annotation labels, and saving plots.