interPerfMeasures: Interactive Model Performance Evaluation & Comparison
In anup50695/IMPPackage: Interactive Model Performance Evaluation

Description Usage Arguments Value Examples

Interactive version of the staticPerfMeasures function

1 2	interPerfMeasures(list_models, sample_size_concord = 5000, model_function = NULL, data = NULL, y = NULL)

`list_models`	A list of one (or more) dataframes for each model whose performance is to be evaluated. Each dataframe should comprise of 2 columns with the first column indicating the class labels (0 or 1) and the second column providing the raw predicted probabilities
`sample_size_concord`	For computing concordance-discordance measures (and c-statistic) a random sample is drawn from each dataset (if nrow(dataset) > 5000). Default sample size of 5000 can be adjusted by changing the value of this argument
`model_function`	Models can be created interactively, if required. For this option to work, a model function should be passed as an argument. The model function should take a formula as an argument, and return a a dataframe as output (dataframe should comprise of 2 columns with the first column indicating the class labels (0 or 1) and the second column provding the raw predicted probabilities) Refer to the example section for more details
`data`	The name of the data-set. The Independent Variable (IV) names, for interactive model building, is picked up from this data set
`y`	The column name of the Dependent Variable (DV), for interactive model building

This function will launch a ShinyApp.Input parameters (such as the number of bins, the "g" argument in the static version of this function) can be adjusted through app widgets. The 'Run-Analysis' button in the app, will generate model performance output basis selected input parameters

For interactive Model building, a model function, data set & the dependent variable name should be passed as arguments. Interactive model building option creates additional input widgets in the app. This includes -

A drop down to select independent variables (the names of the variables will be picked up from the data argument)

An input slider to include additional models (upto 4 additional models can be created). Each additional model updates the original model created. For e.g. consider the dataset has 10 IVs: x1-x10. Original model was created by selecting x1-x4 from the drop down list. If we need to create a second model, by including x5 and excluding x3 simply type, "+ x5 - x3" in the input text box

# Without interactive model development
model_1 <- glm(Species ~ Sepal.Length,data=iris,family=binomial)
model_2 <- glm(Species ~ Sepal.Width, data=iris, family = binomial)
df1 <- data.frame(model_1$y,fitted(model_1))
df2 <- data.frame(model_2$y,fitted(model_2))

## Not run: 
#This will launch a Shiny App
interPerfMeasures(list_models = list(df1,df2))
## End(Not run)

# With interactive model development
glm_model <- function(formula) {
   glm_model <- glm(formula, data = iris, family = "binomial")
   out <- data.frame(glm_model$y, fitted(glm_model))
   out }
 ## Not run: 
 #This will launch a Shiny App
 interPerfMeasures (model_function = glm_model,data=iris,y="Species")
## End(Not run)