This function predicts values using a fitted sigmoidal model (Pearl, Gompertz, or Logistic) over a specified range of time values.
Arguments
- fit
A list containing the results of a sigmoidal model.
- x_range
A vector of time values for the prediction.
- model_type
The type of model (Pearl, Gompertz, or Logistic) for the prediction.
- conf_level
Optional confidence level for confidence bounds (e.g., 0.95 for 95 If NULL (default), no confidence bounds are computed.
Value
The function returns a data frame containing the time (x), predicted values (pred), and optionally lower (lwr) and upper (upr) confidence bounds.
Examples
# Set up a data frame of time and completion percentage data
data <- data.frame(time = 1:10, completion = c(5, 15, 40, 60, 70, 75, 80, 85, 90, 95))
# Fit a logistic model to the data.
fit <- fit_sigmoidal(data, "time", "completion", "logistic")
# Use the model to predict future completion times.
predictions <- predict_sigmoidal(fit, seq(min(data$time), max(data$time),
length.out = 100
), "logistic")
# Predict with 95% confidence bounds
predictions_ci <- predict_sigmoidal(fit, seq(min(data$time), max(data$time),
length.out = 100
), "logistic", conf_level = 0.95)