diagnoseDataModel
diagnoseDataModel.RdThis function can be used to generate diagnostic QC plots for given model assumptions related to the input data, identify potential outlier observations and/or outlier experimental units
Arguments
- data
Input data frame with columns having all the necessary information regarding the dependent and independent variables of interest
- design
an object of class RMeDesign with the necessary design information about the data
- model
an object of class ProbabilityModel giving the error distribution of the data
Value
A list with four elements. 1) models: representing the names of the models evaluated based on differnt modifications of the response column. The models would include one called natural_scale, another model called natural_scale_wo_outliers if outliers had beeen identified, another model called log_scale if the respose column is continuous and the model on the log-transformed values of the responses are what was evaluated and finally log_scale_wo_outliers model if there were outliers identified in the log_scale model. 2) Data_updated representing the updated data frame with additional columns for the modified response column corresponding to each of the models evaluated. 3) cooks_result: cooks distance of each of the experimental columns for each of the models evaluated. For models based on the binomial probability distribution, cooks distance is only reported for the first experimental column on account the increased computation time for evaluating this metric for the other experimental columns. 4) plots_info: is a list with two elements plots and captions. plots is a named list and captions is a character vector, both of the same length as the number of models evaluated. Each element of the plots list is yet another list of QC/diagnostic plots related to the corresponding model fit, while the captions is a vector of captions for each of the QC plots output