Predict a Sigmoidal Function.

Predict a Sigmoidal Function.

Usage

predict_sigmoidal(fit, x_range, model_type)

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.

Value

The function returns a table of results containing the time and predicted values.

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")

# Plot the results.
p <- ggplot2::ggplot(data, ggplot2::aes_string(x = "time", y = "completion")) +
  ggplot2::geom_point() +
  ggplot2::geom_line(data = predictions, ggplot2::aes(x = x, y = pred), color = "red") +
  ggplot2::labs(title = "Fitted Logistic Model", x = "time", y = "completion %") +
  ggplot2::theme_minimal()
p