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.
References
Damnjanovic, Ivan, and Kenneth Reinschmidt. Data analytics for engineering and construction project risk management. No. 172534. Cham, Switzerland: Springer, 2020.
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)