R/predict_hill.R

In brentscott93/biophysr: Analysis tools for muscle biophysics

#' Make Hill Equation predictions for plotting
#'
#' @param drm_model
#'
#' @return a tibble of theoretetical pCa values and their estimated y-values using the parameters generated by the fit of the drm model
#' @export
#'
#' @examples predict_hill(my_drm_model)
predict_hill <- function(drm_model){

# Makes a list of a lot of random pCa values.
  theoretical_pCa_values <- expand.grid(pCa=exp(seq(log(4), log(10), length=100)))


# This will make y-predictions based off the theoretical "x" values provided in the "theorectical_pCa_values" list and will include confidence intervals
  predicted_model_values <- predict(drm_model, newdata = theoretical_pCa_values, interval="confidence", level = 0.95)
  predicted_model_values <- as.tibble(predicted_model_values)


# Take the predicted values tibble provided from "predicted_model_values" and add the theoretical_pCa values as a column

  final_prediction_tibble <- predicted_model_values %>%
    mutate(theoretical_pCa = theoretical_pCa_values$pCa)

  final_prediction_tibble %>% dplyr::rename(predicted_y = Prediction, lower_error = Lower, upper_error = Upper)

}