Package 'stressor'

Boston Housing Data

Description

A subset of data from the Housing data for 506 census tracts of Boston from the 1970 Census. Original data set can be found in the mlbench package.

Usage

data(boston)

Format

A data.frame with 506 rows and 13 columns:

cmedv

corrected median value of owner-occupied homes in USD 1000's

crim

per capita crime rate by town

zn

proportion of residential land zoned for lots over 25,000 sq.ft

indus

proportion of non-retail business acres per town

nox

nitric oxides concentration (parts per 10 million)

rm

average number of rooms per dwelling

age

proportion of owner-occupied units built prior to 1940

dis

weighted distances to five Boston employment centres

rad

index of accessibility to radial highways

tax

full-value property-tax rate per USD 10,000

ptratio

pupil-teacher ratio by town

chas

Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

lstat

percentage of lower status of the population

Source

mlbench package

---

Create Groups for CV

Description

Create groups for the data by separating them either into 10 fold cross-validation, LOO cross-validation, or k-means grouping.

Usage

create_groups(
  formula,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

Arguments

formula	A formula object that specifies the model to be fit.
data	The data that will be separated into each group.
n_folds	An integer value defaulted to 10 fold cross-validation. If NULL uses Leave One Out(LOO) instead.
k_mult	When specified, this is passed onto the cv_cluster to fit the data into k_groups.
repl	A Boolean value defaulted to 'FALSE', change to 'TRUE' when replicates need to be included in the same group.
grouping_formula	A formula object that specifies how the groups will be gathered.

Details

If 'k_mult' is specified as an integer, the formula object will be used to help determine the features specified by the user. This will be passed to the cv_cluster function, which takes a scaled matrix of features.

This function is called by the cv methods as it forms the groups necessary to perform the cross-validation. If you want to use this, it is a nice function that separates the 'data' into groups for training and testing.

Value

A vector of the length equal to number of rows of data.frame from the data argument.

Examples

# data generation
 lm_data <- data_gen_lm(1000)

 # 10 Fold CV group
 create_groups(Y ~ ., lm_data)

 # Spatial CV
 create_groups(Y ~ ., lm_data, n_folds = 10, k_mult = 5)

 # LOO CV group
 create_groups(Y ~ ., lm_data, n_folds = NULL)

---

Create 'Python' Virtual Environment

Description

Allows the user to create a stressor 'python' environment with 'PyCaret' installed in the environment. This function assumes that you have properly installed 'python'. We recommend version 3.8.10. It uses existing stressor environments.

Usage

create_virtualenv(python = Sys.which("python"), delete_env = FALSE)

Arguments

python	Defaults to your install of 'python'. We prefer version 3.8.10. This is assuming that you installed python from python.org. Currently 'Anaconda' installations of python are not implemented.
delete_env	Boolean value to indicate if the environments need to be deleted.

Details

To install 'python', it is recommended using 'python' version 3.8.10 from python.org. This is the same version recommended by 'PyCaret', as it is the most stable. Users have reported troubles using the 'Anaconda' distribution of 'python'.

For MacOS and Linux Users note that in order to run this package, 'LightGBM' package on python requires the install of an additional compiler 'cmake' and the 'libomp' (Open Multi-Processing interface). Troubleshoot link from the 'LightGBM'documentation here.

Value

A message indicating which environment is being used.

Troubleshoot

If 'python' is not being found properly, trying setting the 'RETICULATE_PYTHON' to blank string. Also ensure that you do not have other 'python' objects in your environment.

Also note that on some instances that a warning message may be displayed as to which version of 'python' is being used.

Examples

create_virtualenv()

---

Cross Validation

Description

This is the core of cross-validation- both standard and using k-mean groups. This method is called by other cv methods of classes.

Usage

cv(
  object,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

## S3 method for class 'lm'
cv(
  object,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

## S3 method for class 'mlm_stressor'
cv(
  object,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

## S3 method for class 'reg_asym'
cv(
  object,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

## S3 method for class 'reg_sine'
cv(
  object,
  data,
  n_folds = 10,
  k_mult = NULL,
  repl = FALSE,
  grouping_formula = NULL
)

Arguments

object	One of the four objects that is accepted: mlm_stressor, reg_sine, reg_asym, or lm.
data	A data.frame object that contains all the entries to be cross-validated on.
n_folds	An integer value for the number of folds defaulted to 10. If NULL, it will run LOO cross-validation.
k_mult	Used to specify if k-means clustering is to be used, defaulted to NULL.
repl	A Boolean value defaulted to 'FALSE', change to 'TRUE' when replicates need to be included in the same group.
grouping_formula	A formula object that specifies how the groups will be gathered.

Value

If the object is of class mlm_stressor, then a data.frame will be returned. Otherwise, a vector of the predictions will be returned.

Methods (by class)

• cv(lm): Cross-Validation for lm

• cv(mlm_stressor): Cross-Validation for mlm_stressor

• cv(reg_asym): Cross-Validation for reg_asym

• cv(reg_sine): Cross-Validation for reg_sine

Examples

# lm example
 lm_test <- data_gen_lm(20)
 lm <- lm(Y ~ ., lm_test)
 cv(lm, lm_test, n_folds = 2)


 lm_test <- data_gen_lm(20)
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_test)
 cv(mlm_lm, lm_test, n_folds = 2)

 # Asymptotic example
 asym_data <- data_gen_asym(10)
 asym_fit <- reg_asym(Y ~ ., asym_data)
 cv(asym_fit, asym_data, n_folds = 2)

 # Sine example
 sine_data <- data_gen_sine(10)
 sine_fit <- reg_sine(Y ~ ., sine_data)
 cv(sine_fit, sine_data, n_folds = 2)

---

Spatial Cluster-Based Partitions for Cross-Validation

Description

This function creates cluster-based partitions of a sample space based on k-means clustering. Included in the function are algorithms that attempt to produce clusters of roughly equal size.

Usage

cv_cluster(features, k, k_mult = 5, ...)

Arguments

features	A scaled matrix of features to be used in the clustering. Scaling usually done with scale and should not include the predictor variable.
k	The number of partitions for k-fold cross-validation.
k_mult	k*k_mult determines the number of subgroups that will be created as part of the balancing algorithm.
...	Additional arguments passed to kmeans as needed.

Details

More information regarding spatial cross-validation can be found in Robin Lovelace's explanation of spatial cross-validation in his textbook.

Value

An integer vector that is number of rows of features with indices of each group.

Examples

# Creating a matrix of predictor variables
 x_data <- base::scale(data_gen_lm(30)[, -1])
 groups <- cv_cluster(x_data, 5, k_mult = 5)
 groups

---

Cross Validation Function

Description

This is the machinery to run cross validation. It subsets the test and train set based on the groups it receives.

Usage

cv_core(object, data, t_groups, ...)

Arguments

object	Currently '"reg_sine", "reg_asym", "lm", "mlm_stressor"' objects are accepted.
data	A data.frame object that has the same formula that was fitted on the data.
t_groups	The groups for cross validation: standard cross validation, LOO cross_validation, or spatial cross validation.
...	Additional arguments that are passed to the predict function.

Value

Either a vector of predictions for '"reg_sine", "reg_asym", "lm"' and a data frame for '"mlm_stressor"'.

Examples

# lm example
 lm_test <- data_gen_lm(20)
 lm <- lm(Y ~ ., lm_test)
 cv(lm, lm_test, n_folds = 2)


 lm_test <- data_gen_lm(20)
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_test)
 cv(mlm_lm, lm_test, n_folds = 2)

 # Asymptotic example
 asym_data <- data_gen_asym(10)
 asym_fit <- reg_asym(Y ~ ., asym_data)
 cv(asym_fit, asym_data, n_folds = 2)

 # Sine example
 sine_data <- data_gen_sine(10)
 sine_fit <- reg_sine(Y ~ ., sine_data)
 cv(sine_fit, sine_data, n_folds = 2)

---

Data Generation Asymptotic

Description

Creates a synthetic data set for an additive asymptotic model. See the details section for clarification.

Usage

data_gen_asym(
  n,
  weight_mat = matrix(rlnorm(10), nrow = 2, ncol = 5),
  y_int = 0,
  resp_sd = 1,
  window = 1e-05,
  ...
)

Arguments

n	The number of observations for each parameter.
weight_mat	The parameter coefficients, where each column represents the coefficients and is two rows as each additive equation contains two parameters. Defaulted to be 10 random numbers from the log-normal distribution. The second row of the matrix needs to be positive.
y_int	The y-intercept term of the additive model.
resp_sd	The standard deviation of the epsilon term to be added for noise.
window	Used to determine for any given X variable to get you within distance to capture the asymptotic behavior.
...	Additional arguments that are not currently implemented.

Details

Observations are generated from the following model:

$y = \sum_{i = 1}^n -\alpha_ie^{-\beta_i \cdot x_i} + y_{int}$

Where 'n' is the number of parameters to be used, $\alpha_i$'s are the scaling parameter and the $\beta_i$'s are the weights associated with each $x_i$. With the $y_{int}$ being where it crosses the y-axis.

Value

A data.frame object with the n rows and the response variable with the number of parameters being equal to the number of columns from the weight matrix.

Examples

# Generates 10 observations
 asym_data <- data_gen_asym(10)
 asym_data

---

Data Generation for Linear Regression

Description

Creates a synthetic data set for an additive linear model. See details for clarification.

Usage

data_gen_lm(n, weight_vec = rep(1, 5), y_int = 0, resp_sd = 1, ...)

Arguments

n	The number of observations for each parameter.
weight_vec	The parameter coefficients where each entry represents the coefficients for the additive linear model.
y_int	The y-intercept term of the additive model.
resp_sd	The standard deviation of the epsilon term to be added for noise.
...	Additional arguments that are not currently implemented.

Details

Observations are generated from the following model:

$y = \sum_{i = 1}^n \alpha_i\cdot x_i + y_{int}$

Where 'n' is the number of parameters to be used and the $\alpha_i$'s are the weights associated with each $x_i$. With the $y_{int}$ being where it crosses the y-axis.

Value

A data.frame object with the n rows and the response variable with the number of parameters being equal to the number of columns from the weight matrix.

Examples

# Generates 10 observations
 lm_data <- data_gen_lm(10)
 lm_data

---

Data Generation for Sinusoidal Regression

Description

Creates a synthetic data set for an additive sinusoidal regression model. See the details section for clarification.

Usage

data_gen_sine(
  n,
  weight_mat = matrix(rnorm(15), nrow = 3, ncol = 5),
  y_int = 0,
  resp_sd = 1,
  ...
)

Arguments

n	The number of observations for each parameter.
weight_mat	The parameter coefficients, where each column represents the coefficients and is three rows as each additive equation contains three parameters. Defaulted to be 15 random numbers from the normal distribution.
y_int	The y-intercept term of the additive model.
resp_sd	The standard deviation of the epsilon term to be added for noise.
...	Additional arguments that are not currently implemented.

Details

Observations are generated from the following model:

$y = \sum_{i = 1}^n \alpha_i \ \sin{(\beta_i(x_i - \gamma_i)))} + y_{int}$

Where 'n' is the number of parameters to be used, $\alpha_i$'s are the amplitude of each sine wave, $\beta_i$'s are the periods for each sine wave and indirectly the weight on each $x_i$, and the $\gamma_i$'s are the phase shift associated with each sine wave. With the $y_{int}$ being where it crosses the y-axis.

Value

A data.frame object with the n rows and the response variable with the number of parameters being equal to the number of columns from the weight matrix.

Examples

# Generates 10 observations
 sine_data <- data_gen_sine(10)
 sine_data

---

Distance to Center

Description

Calculates the distance from center of the matrix of predictor variables using a euclidean distance, or the average of all x-dimensions.

Usage

dist_cent(formula, data)

Arguments

formula	A formula object.
data	A data.frame object.

Details

Formula used to calculate the center point:

$\bar{x} = \frac{1}{N}\sum_{j = 1}^N x_{ij}$

Where $\bar{x}$ is a vector of the center of the x-dimensions, $N$ is the number of rows in the matrix, and $x_{ij}$ is the $i,j^{th}$ entry in the matrix.

Value

A vector of distances from the center.

Examples

data <- data_gen_lm(10)
  dist <- dist_cent(Y ~ ., data)
  dist

---

Kappa function

Description

A function to calculate the Kappa of binary classification.

Usage

kappa_class(confusion_matrix)

Arguments

confusion_matrix

A matrix or table that is the confusion matrix.

Value

A numeric value representing the kappa value.

---

Fit Machine Learning Classification Models

Description

Through the PyCaret module from 'python', this function fits many machine learning models simultaneously without requiring any 'python' programming on the part of the user. This function is specifically designed for the classification models fitted by 'PyCaret'.

Usage

mlm_classification(
  formula,
  train_data,
  fit_models = c("ada", "et", "lightgbm", "dummy", "lr", "rf", "ridge", "knn", "dt",
    "gbc", "svm", "lda", "nb", "qda"),
  sort_v = c("Accuracy", "AUC", "Recall", "Precision", "F1", "Kappa", "MCC"),
  n_models = 9999,
  seed = NULL,
  ...
)

Arguments

formula	The classification formula, as a formula object.
train_data	A data.frame object that includes data to be trained on.
fit_models	A character vector with all the possible Machine Learning classifiers that are currently being fit, the user may specify a subset of them using a character vector. ada AdaBoost Classifier dt Decision Tree Classifier dummy Dummy Classifier et Extra Trees Classifier gbc Gradient Boosting Classifier knn K Neighbors Classifier lda Linear Discriminant Analysis lightgbm Light Gradient Boosting Machine lr Logistic Regression nb Naive Bayes qda Quadratic Discriminant Analysis rf Random Forest Classifier ridge Ridge Classifier svm SVM - Linear Kernel
sort_v	A character vector indicating what to sort the tuned models on.
n_models	An integer value defaulted to a large integer value to return all possible models.
seed	An integer value to set the seed of the 'python' environment. Default value is set to 'NULL'.
...	Additional arguments passed onto mlm_init.

Details

'PyCaret' is a 'python' module where machine learning models can be fitted with little coding by the user. The pipeline that 'PyCaret' uses is a setup function to parameterize the data that is easy for all the models to fit on. Then the compare models function is executed, which fits all the models that are currently available. This process takes less than five minutes for data.frame objects that are less than 10,000 rows.

Value

A list object where the first entry is the models fitted and the second is the initial predictive accuracy on the random test data. Returns as two classes '"mlm_stressor"' and '"classifier"'.

Examples

lm_test <- data_gen_lm(20)
 binary_response <- sample(c(0, 1), 20, replace = TRUE)
 lm_test$Y <- binary_response
 mlm_class <- mlm_classification(Y ~ ., lm_test)

---

Compare Machine Learning Models

Description

Through the PyCaret module from 'python', this function fits many machine learning models simultaneously without requiring any 'python' programming on the part of the user. This is the core function to fitting the initial models. This function is the backbone to fitting all the models.

Usage

mlm_init(
  formula,
  train_data,
  fit_models,
  sort_v = NULL,
  n_models = 9999,
  classification = FALSE,
  seed = NULL,
  ...
)

Arguments

formula	The regression formula or classification formula. This formula should be linear.
train_data	A data.frame object that includes data to be trained on.
fit_models	A character vector with all the possible Machine Learning regressors that are currently being fit. The user may specify a subset of them using a character vector. ada AdaBoost Regressor br Bayesian Ridge dt Decision Tree Regressor dummy Dummy Regressor en Elastic Net et Extra Trees Regressor gbr Gradient Boosting Regressor huber Huber Regressor knn K Neighbors Regressor lar Least Angle Regression lasso Lasso Regression lightgbm Light Gradient Boosting Machine llar Lasso Least Angle Regression lr Linear Regression omp Orthogonal Matching Pursuit par Passive Aggressive Regressor rf Random Forest Regressor ridge Ridge Regression If classification is set to 'TRUE', these models can be used depending on user. These are the default values for classification: ada AdaBoost Classifier dt Decision Tree Classifier dummy Dummy Classifier et Extra Trees Classifier gbc Gradient Boosting Classifier knn K Neighbors Classifier lda Linear Discriminant Analysis lightgbm Light Gradient Boosting Machine lr Logistic Regression nb Naive Bayes qda Quadratic Discriminant Analysis rf Random Forest Classifier ridge Ridge Classifier svm SVM - Linear Kernel
sort_v	A character vector indicating what to sort the tuned models on. Default value is 'NULL'.
n_models	A defaulted integer to return the maximum number of models.
classification	A Boolean value tag to indicate if classification methods should be used.
seed	An integer value to set the seed of the python environment. Default value is set to 'NULL'.
...	Additional arguments passed to the setup function in 'PyCaret'.

Details

The formula should be linear. However, that does not imply a linear fit. The formula is a convenient way to separate predictor variables from explanatory variables.

'PyCaret' is a 'python' module where machine learning models can be fitted with little coding by the user. The pipeline that 'PyCaret' uses has a setup function to parameterize the data that is easy for all the models to fit on. Then compare models function is executed which fits all the models that are currently available. This process takes less than five minutes for data.frame objects that are less than 10,000 rows.

Value

A list object that contains all the fitted models and the CV predictive accuracy. With a class attribute of '"mlm_stressor"'.

Examples

lm_test <- data_gen_lm(20)
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_test)

---

Refit Machine Learning Models

Description

Refits models fitted in the mlm_init, and returns the predictions.

Usage

mlm_refit(mlm_object, train_data, test_data, classification = FALSE)

Arguments

mlm_object	A '"mlm_stressor"' object.
train_data	A data.frame object used for refitting excludes the test data. Can be 'NULL' to allow for predictions to be used on the current model.
test_data	A data.frame object used for predictions.
classification	A Boolean value used to represent if classification methods need to be used to refit the data.

Value

A matrix with the predictions of the various machine learning methods.

Examples

lm_train <- data_gen_lm(20)
 train_idx <- sample.int(20, 5)
 train <- lm_train[train_idx, ]
 test <- lm_train[-train_idx, ]
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_train)
 mlm_refit(mlm_lm, train, test, classification = FALSE)

---

Fit Machine Learning Regressor Models

Description

Through the PyCaret module from 'python', this function fits many machine learning models simultaneously with without requiring any 'python' programming on the part of the user. This function is specifically designed for the regression models.

Usage

mlm_regressor(
  formula,
  train_data,
  fit_models = c("ada", "et", "lightgbm", "gbr", "lr", "rf", "ridge", "knn", "dt",
    "dummy", "lar", "br", "huber", "omp", "lasso", "en", "llar", "par"),
  sort_v = c("MAE", "MSE", "RMSE", "R2", "RMSLE", "MAPE"),
  n_models = 9999,
  seed = NULL,
  ...
)

Arguments

formula	A linear formula object.
train_data	A data.frame object that includes data to be trained on.
fit_models	A character vector with all the possible Machine Learning regressors that are currently being fit. The user may specify a subset of them using a character vector. ada AdaBoost Regressor br Bayesian Ridge dt Decision Tree Regressor dummy Dummy Regressor en Elastic Net et Extra Trees Regressor gbr Gradient Boosting Regressor huber Huber Regressor knn K Neighbors Regressor lar Least Angle Regression lasso Lasso Regression lightgbm Light Gradient Boosting Machine llar Lasso Least Angle Regression lr Linear Regression omp Orthogonal Matching Pursuit par Passive Aggressive Regressor rf Random Forest Regressor ridge Ridge Regression
sort_v	A character vector indicating what to sort the tuned models on.
n_models	An integer value defaulted to a large integer value to return all possible models.
seed	An integer value to set the seed of the 'python' environment. Default value is set to 'NULL'.
...	Additional arguments passed onto mlm_init.

Details

'PyCaret' is a 'python' module where machine learning models can be fitted with little coding by the user. The pipeline that 'PyCaret' uses is a setup function to parameterize the data that is easy for all the models to fit on. Then the compare models function is executed, which fits all the models that are currently available. This process takes less than five minutes for data.frame objects that are less than 10,000 rows.

Value

A list object where the first entry is the models fitted and the second is the initial predictive accuracy on the random test data. Returns as two classes '"mlm_stressor"' and '"regressor"'.

Examples

lm_test <- data_gen_lm(20)
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_test)

---

Prediction Methods for Various Models

Description

Predict values on 'mlm_stressor', 'reg_asym', or 'reg_sine' objects. This expands the predict function.

Usage

## S3 method for class 'mlm_stressor'
predict(object, newdata, train_data = NULL, ...)

## S3 method for class 'reg_asym'
predict(object, newdata, ...)

## S3 method for class 'reg_sine'
predict(object, newdata, ...)

Arguments

object	A 'mlm_stressor', 'reg_asym', or 'reg_sine' object.
newdata	A data.frame object that is the data to be predicted on.
train_data	A data.frame object defaulted to 'NULL'. This is only used when an 'mlm_stressor' object needs to be refitted.
...	Extending the predict function default. In this case, it is ignored.

Value

A data.frame of predictions if 'mlm_stressor' object or vector of predicted values.

Examples

# mlm_stressor example
 lm_test <- data_gen_lm(20)
 create_virtualenv()
 mlm_lm <- mlm_regressor(Y ~ ., lm_test)
 predict(mlm_lm, lm_test)

 # Asymptotic Examples
 asym_data <- data_gen_asym(10)
 asym_fit <- reg_asym(Y ~ ., asym_data)
 predict(asym_fit, asym_data)
 # Sinusoidal Examples
 sine_data <- data_gen_sine(10)
 sine_fit <- reg_sine(Y ~ ., sine_data)
 predict(sine_fit, sine_data)

---

Check if 'Python' is Available

Description

A function that allows examples to run when appropriate.

Usage

python_avail()

Value

A Boolean value is returned.

Examples

python_avail()

---

Asymptotic Regression

Description

A simple example of asymptotic regression that is in the form of $y = -e^{-x}$ and is the sum of multiple of these exponential functions with a common intercept term.

Usage

reg_asym(
  formula,
  data,
  method = "BFGS",
  init_guess = rep(1, ncol(data) * 2 - 1),
  ...
)

Arguments

formula	A formula object to describe the relationship.
data	The response and predictor variables.
method	The method that is passed to the optim function. By default, it is the BFGS method which uses a gradient.
init_guess	The initial parameter guesses for the optim function. By default, it is all ones.
...	Additional arguments passed to the optim function.

Value

A "reg_asym" object is returned which contains the results from the optim function that was returned.

Examples

asym_data <- data_gen_asym(10)
 reg_asym(Y ~ ., asym_data)

---

Sinusoidal Regression

Description

A simple example of sinusoidal regression that is in the form of $y = asin(b(x - c))$ and is the sum of of multiple of these sine functions with a common intercept term.

Usage

reg_sine(
  formula,
  data,
  method = "BFGS",
  init_guess = rep(1, ncol(data) * 3 - 2),
  ...
)

Arguments

formula	A formula object to describe the relationship.
data	The response and predictor variables.
method	The method that is passed to the optim function. By default, it is the BFGS method which uses a gradient.
init_guess	The initial parameter guesses for the optim function. By default, it is all ones.
...	Additional arguments passed to the optim function.

Value

A "reg_sine" object is returned which contains the results from the optim function that was returned.

Examples

sine_data <- data_gen_sine(10)
 reg_sine(Y ~ ., sine_data)

---

Root Mean Squarred Error (RMSE)

Description

A function to calculate the RMSE.

Usage

rmse(predicted, observed)

Arguments

predicted	A data.frame or vector object that is the same number of rows or length as the length of observed values.
observed	A vector of the observed results.

---

Score Function for Metrics

Description

A score function takes the observed and predicted values and returns a vector or data.frame of the various metrics that are reported from 'PyCaret'. For regression, the following metrics are available: 'RMSE', 'MAE', 'MSE', 'R2', 'RMSLE', and 'MAPE'. For classification, the following metrics are available:'Accuracy', 'AUC', 'Recall', 'Prec.', 'F1', 'MCC', and 'Kappa'.

Usage

score(observed, predicted, ...)

Arguments

observed	A vector of the observed results.
predicted	A data.frame or vector object that is the same number of rows or length as the length of observed values.
...	Arguments passed on to score_classification, score_regression metrics A character vector of the metrics to be fitted. This is defaulted to be the metrics from 'PyCaret'.

Value

A matrix with the various metrics reported.

Examples

lm_data <- data_gen_lm(100)
indices <- split_data_prob(lm_data, .2)
train <- lm_data[!indices,]
test <- lm_data[indices,]
model <- lm(Y ~ ., train)
pred_lm <- predict(model, test)
score(test$Y, pred_lm)

---

Score Function for Binary Classification

Description

This function takes the observed and predicted values and computes metrics that are found in 'PyCaret' such as: 'Accuracy', 'AUC', 'Recall', 'Prec.', 'F1', 'MCC', and 'Kappa'.

Usage

score_classification(
  observed,
  predicted,
  metrics = c("Accuracy", "AUC", "Recall", "Prec.", "F1", "MCC", "Kappa")
)

Arguments

observed	A vector of the observed results.
predicted	A data.frame or vector object that is the same number of rows or length as the length of observed values.
metrics	A character vector of the metrics to be fitted. This is defaulted to be the metrics from 'PyCaret'.

Value

A vector or data.frame of the methods and metrics.

---

Score Function for Regression

Description

This function takes the observed and predicted values and computes metrics that are found in 'PyCaret' such as: 'RMSE', 'MAE', 'MSE', 'R2', 'RMSLE', and 'MAPE'.

Usage

score_regression(
  observed,
  predicted,
  metrics = c("RMSE", "MAE", "MSE", "R2", "RMSLE", "MAPE")
)

Arguments

observed	A vector of the observed results.
predicted	A data.frame or vector object that is the same number of rows or length as the length of observed values.
metrics	A character vector of the metrics to be fitted. This is defaulted to be the metrics from 'PyCaret'.

Value

A vector or data.frame of the methods and metrics.

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Create Train Index Set

Description

This function takes in a data.frame object and the training size and returns a logical vector indicating which entries to include.

Usage

split_data_prob(data, test_prop)

Arguments

data	A data.frame object used to determine the length of the vector.
test_prop	A numeric that is between zero and one that represents the proportion of observations to be included in the test data.

Value

A logical vector is returned that is the same length as the number of rows of the data.

Examples

lm_data <- data_gen_lm(10)
  indices <- split_data_prob(lm_data, .8)
  train <- lm_data[indices, ]
  test <- lm_data[!indices, ]

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Thinning Algorithm for Models with Predict Function

Description

Fits various train size and test sizes.

Usage

thinning(
  model,
  data,
  max = 0.95,
  min = 0.05,
  iter = 0.05,
  classification = FALSE
)

Arguments

model	A model that is currently of class type "reg_sine", "reg_asym", "lm", or "mlm_stressor".
data	A data frame with all the data.
max	A numeric value in (0, 1] and greater than 'min', defaulted to .95.
min	A numeric value in (0, 1) and less than 'max', defaulted to .05.
iter	A numeric value to indicate the step size, defaulted to .05.
classification	A Boolean value defaulted 'FALSE', used for 'mlm_classification'.

Value

A list of objects, where the first element is the RMSE values at each iteration and the second element is the predictions.

Examples

lm_data <- data_gen_lm(1000)
 lm_model <- lm(Y ~ ., lm_data)
 thin_results <- thinning(lm_model, lm_data)

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