lmkknn.metrics

The goal of lmkknn.metrics is to build and record metrics derived from linear and KKNN regression prediction models.

Installation

You can install the development version of lmkknn.metrics from GitHub with:

# install.packages("devtools")
devtools::install_github("DSCI-310/dsci-310-group-16-pkg")

Functions:

The package provides the following functions:

target_df(): Selects the target variable and additional columns from a dataframe.

create_recipe(): Creates a recipe object for modeling based on the specified target variable and predictors.

create_spec_kmin(): This function creates a model specification for either linear regression or k-nearest neighbor regression, with an optional kmin tuning for the latter. If kmin is not specified, the function performs a grid search to find the optimal k value that gives the minimum root mean squared error (RMSE) on a 5-fold cross-validation of the training data.

get_list_item(): Retrieves the nth element of a given list.

create_fit(): Creates a fitted model using a model recipe based on predictors and target variable, model specification, and dataframe.

create_model_prediction(): Creates a prediction model using a trained model fit and a test dataset.

get_metric(): Extracts a specified metric from a prediction model.

str_collapse(): Collapses a string of arguments and joins them by ‘+’.

create_metric_df(): This function takes as input two data frames: train_df and test_df that contain the training and test data for a prediction model, respectively. It also takes a character string specifying the performance metric to calculate “metric”, a character string specifying the prediction model to use “method”, an integer specifying the minimum number of neighbors to consider when using the “kknn” method “kmin”, a character string specifying the name of the target variable in the data frames target_variable, and a character vector specifying the names of the predictor variables in the data frames “predictors_vector”. The function returns a data frame containing the specified performance metric, the predictor variables, the prediction model method, and the value of kmin “if applicable”.

metric_bind(): This function binds together data frames created from the create_metric_df() function using the rbindlist() function from the data.table package. The data frames are created by fitting linear models to the training data using the predictor variables provided in model_list, and then calculating the specified metric on the test data.

For more information on how to use each function, see the package documentation.

Example Use Cases:

target_df: Selecting the target variable and additional columns from a dataframe.

library(lmkknn.metrics)

# Selecting only the target variable
target_df(mtcars, "mpg")

# Selecting the target variable and additional columns
target_df(mtcars, "mpg", cyl, disp)

create_recipe: Creating a recipe object for modeling based on the specified target variable and predictors.

# Load data
data("mtcars")

# Select target variable and predictors
target_df <- target_df(mtcars, "gear", mpg, cyl, disp)

# Create recipe object for modeling
recipe <- create_recipe(target_df, "gear")

get_list_item: Retrieving the nth element of a given list.

# Create a list
my_list <- list(a = 1, b = 2, c = 3)

# Retrieve second element from list
get_list_item(my_list, 2)

create_model_prediction: Creating a prediction model using a trained model fit and a test dataset.

# Load data
data("mtcars")

# Split data into training and testing datasets
train_df <- target_df(mtcars[1:16, ], 'gear', c("am", "vs"))
test_df <- target_df(mtcars[17:32, ], 'gear', c("am", "vs"))

# Create recipe object for modeling
x_recipe <- create_recipe(train_df, target_variable="gear")

# Create specification for k-nn model
x_spec_list <- create_spec_kmin(train_df, model_recipe=x_recipe, method="lm", target_variable="gear")
x_spec <- get_list_item(x_spec_list, n=1)

# Fit the model to training data
x_fit <- create_fit(x_recipe, x_spec, train_df)

# Generate predictions for testing data
create_model_prediction(test_df, x_fit )

str_collapse: Collapsing a string of arguments and joining them by ‘+’.

str_collapse("my name")
str_collapse(c("my name", "is", "jake"))
str_collapse(c("my name", "is", 2))
str_collapse(c(3, 2, "boy"))

Contributing

We appreciate contributions to the lmkknn.metrics package. Please refer to our Contributing document and Code of Conduct for more information.

License

The software provided in this project is offered under the MIT open source license. Refer to the license file for more information.