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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# lmkknn.metrics
<!-- badges: start -->
[](https://github.com/DSCI-310/dsci-310-group-16-pkg/actions/workflows/R-CMD-check.yaml/badge.svg)
[](https://codecov.io/gh/DSCI-310/dsci-310-group-16-pkg/branch/master/graph/badge.svg?token=qCuceyp6lY)
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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:
``` r
# 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](https://dsci-310.github.io/dsci-310-group-16-pkg/reference/index.html).
## 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](https://github.com/DSCI-310/dsci-310-group-16-pkg/blob/master/CONTRIBUTING.md) document and [Code of Conduct](https://github.com/DSCI-310/dsci-310-group-16-pkg/blob/master/CODE_OF_CONDUCT.md) for more information.
## License
The software provided in this project is offered under the MIT open source license. Refer to the [license](https://github.com/DSCI-310/dsci-310-group-16-pkg/blob/master/LICENSE.md) file for more information.