ggcalibrate plots the stats::predicted events against the actual event rate
Usage
ggcalibrate(
x1,
x2 = NULL,
y = NULL,
n_knots = 5,
ci_level = 0.95,
smooth_method = "loess",
smooth_span = 0.75
)Arguments
- x1
Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the baseline model. Must be between 0 & 1
- x2
Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the new (alternative) model. Must be between 0 & 1
- y
Binary of outcome of interest. Must be 0 or 1 (if fitted models are provided this is extracted from the fit which for an rms fit must have x = TRUE, y = TRUE).
- n_knots
The curves are made by fitting a restricted cubic spline (rms package). The default 5-knots is usually enough.
- ci_level
Confidence interval of the curve (default = 0.95).
- smooth_method
Smoothing method for geom_smooth. Options: "loess", "lm", "glm", "gam". Default is "loess"
- smooth_span
Span parameter for loess smoothing, controls the degree of smoothing (default = 0.75). Lower values = less smooth
Examples
# Quick example with subset of data
data(data_risk)
data_subset <- data_risk[1:100, ] # Use first 100 rows for speed
complete_cases <- complete.cases(data_subset)
data_clean <- data_subset[complete_cases, ]
y <- data_clean$outcome
x1 <- data_clean$baseline
x2 <- data_clean$new
output <- ggcalibrate(x1, x2, y, n_knots = 3, ci_level = 0.95)
#> New names:
#> • `Model` -> `Model...1`
#> • `Model` -> `Model...3`
# \donttest{
# Full dataset example
data(data_risk)
complete_cases <- complete.cases(data_risk)
data_clean <- data_risk[complete_cases, ]
y <- data_clean$outcome
x1 <- data_clean$baseline
x2 <- data_clean$new
output <- ggcalibrate(x1, x2, y, n_knots = 5, ci_level = 0.95)
#> New names:
#> • `Model` -> `Model...1`
#> • `Model` -> `Model...3`
# }