Package 'snazzieR'

Generate a Summary Table for ANOVA Results

Description

This function creates a summary table for ANOVA results, including degrees of freedom, sum of squares, mean squares, F-values, and p-values. The table can be output as either LaTeX (for PDF reports) or plain text (for console viewing).

Usage

ANOVA.summary.table(model, caption, latex = TRUE)

Arguments

model	A model object for which ANOVA results are computed (e.g., output from 'lm()' or 'aov()').
caption	A character string to be used as the caption for the table.
latex	Logical; if 'TRUE', returns a LaTeX-formatted table using 'kableExtra'. If 'FALSE', prints a plain-text version to the console.

Value

If 'latex = TRUE', a LaTeX-formatted table object. If 'latex = FALSE', prints the summary table and returns it (invisibly).

Examples

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Generate a plain-text ANOVA summary table
ANOVA.summary.table(model, caption = "ANOVA Summary", latex = FALSE)

---

Display a Color Reference Palette

Description

This function generates a plot displaying a predefined color palette with color codes for easy reference. The palette includes shades of Red, Orange, Yellow, Green, Blue, Purple, and Grey.

Usage

color.ref()

Details

Value

A plot displaying the color palette.

Examples

color.ref()

---

SnazzieR Color Palette

Description

A collection of named hex colors grouped by hue and tone. Each color is available as an exported object (e.g., Red, Dark.Red).

Usage

color.list

Format

Each color is a character string representing a hex code.

An object of class character of length 1.

An object of class list of length 35.

Details

Name	Hex	Swatch		Name	Hex	Swatch
Deep.Red	#590D21			Deep.Green	#304011
Dark.Red	#9F193D			Dark.Green	#54711E
Red	#C31E4A			Green	#83B02F
Light.Red	#E66084			Light.Green	#ABD45E
Pale.Red	#F1A7BB			Pale.Green	#C4E18E
Deep.Orange	#6F4B0B			Deep.Blue	#002429
Dark.Orange	#A77011			Dark.Blue	#004852
Orange	#E99F1F			Blue	#008C9E
Light.Orange	#F0BF6A			Light.Blue	#1FE5FF
Pale.Orange	#F4CF90			Pale.Blue	#85F1FF
Deep.Yellow	#9D7F06			Deep.Purple	#271041
Dark.Yellow	#CEA708			Dark.Purple	#4E2183
Yellow	#E8D206			Purple	#743496
Light.Yellow	#FFE373			Light.Purple	#A06CDA
Pale.Yellow	#FFF8DC			Pale.Purple	#CAADEB
Deep.Grey	#151315
Dark.Grey	#403A3F
Grey	#6F646C
Light.Grey	#9E949B
Pale.Grey	#CFC9CD

See Also

color.ref, snazzieR.theme

---

Create K-Fold Cross Validation Splits

Description

Create K-Fold Cross Validation Splits

Usage

create_kfold_splits(data, k = 5, seed = NULL)

Arguments

data	A data frame containing the dataset
k	Number of folds (default: 5)
seed	Random seed for reproducibility (optional)

Value

A list containing fold assignments for each observation

---

Summarize Eigenvalues and Eigenvectors of a Covariance Matrix

Description

This function computes the eigenvalues and eigenvectors of a given covariance matrix, ensures sign consistency in the eigenvectors, and outputs either a LaTeX table or plaintext summary displaying the results.

Usage

eigen.summary(
  cov.matrix,
  caption = "Eigenvectors of Covariance Matrix",
  latex = TRUE
)

Arguments

cov.matrix	A square numeric matrix representing the covariance matrix.
caption	A character string specifying the table caption (default: "Eigenvectors of Covariance Matrix").
latex	A logical indicating whether to output LaTeX table (default: TRUE). If FALSE, prints as plain text.

Value

A LaTeX formatted table (if latex = TRUE) or plaintext console output (if latex = FALSE).

Examples

cov_matrix <- matrix(c(4, 2, 2, 3), nrow = 2)
eigen.summary(cov_matrix, caption = "Eigenvalues and Eigenvectors", latex = FALSE)

---

Format PLS Model Output as LaTeX or Console Tables

Description

Formats and displays Partial Least Squares (PLS) model output from pls.regression() as either LaTeX tables (for PDF rendering) or console-friendly output.

Usage

## S3 method for class 'pls'
format(x, ..., include.scores = TRUE, latex = FALSE)

Arguments

x	A list returned by pls.regression() (class "pls") containing PLS model components.
...	Further arguments passed to or from methods (unused).
include.scores	Logical. Whether to include score matrices (T and U). Default is TRUE.
latex	Logical. If TRUE, produces LaTeX output (for PDF rendering). If FALSE, prints to console. Default is FALSE.

Value

When latex = TRUE, returns a knitr::asis_output object (LaTeX code). When FALSE, prints formatted tables to console.

---

Perform K-Fold Cross Validation

Description

Perform K-Fold Cross Validation

Usage

kfold_cross_validation(
  data,
  formula,
  model_function,
  predict_function = predict,
  metric_function = NULL,
  k = 5,
  seed = NULL,
  ...
)

Arguments

data	A data frame containing the dataset
formula	A formula specifying the model (e.g., y ~ x1 + x2)
model_function	Function to fit the model (e.g., lm, glm)
predict_function	Function to make predictions (default: predict)
metric_function	Function to calculate performance metric
k	Number of folds (default: 5)
seed	Random seed for reproducibility (optional)
...	Additional arguments passed to model_function

Value

A list containing fold results and summary statistics

---

Generate a Model Equation from a Linear Model

Description

This function extracts and formats the equation from a linear model object. It includes an option to return the equation as a LaTeX-formatted string or print it to the console.

Usage

model.equation(model, latex = TRUE)

Arguments

model	A linear model object (e.g., output from 'lm()').
latex	A logical value indicating whether to return a LaTeX-formatted equation (default: TRUE). If FALSE, the equation is printed to the console.

Value

If 'latex' is TRUE, the equation is returned as LaTeX code using 'knitr::asis_output()'. If FALSE, the equation is printed to the console.

Examples

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Get LaTeX equation
model.equation(model)

# Print equation to console
model.equation(model, latex = FALSE)

---

Generate a Summary Table for a Linear Model

Description

This function creates a summary table for a linear model, including estimated coefficients, standard errors, p-values with significance codes, and model statistics such as MSE and R-squared. The table can be output as either LaTeX (for PDF reports) or plain text (for console viewing).

Usage

model.summary.table(model, caption, latex = TRUE)

Arguments

model	A linear model object (typically the result of 'lm()').
caption	A character string to be used as the caption for the table.
latex	Logical; if 'TRUE' (default), returns a LaTeX-formatted table using 'kableExtra'. If 'FALSE', prints plain-text summary tables to the console.

Value

If 'latex = TRUE', returns a LaTeX-formatted 'kableExtra' table object. If 'latex = FALSE', prints formatted summary tables to the console and returns the underlying data frame.

Examples

# Fit a linear model
model <- lm(mpg ~ wt + hp, data = mtcars)

# Print a plain-text version to the console
model.summary.table(model, caption = "Linear Model Summary", latex = FALSE)

---

Partial Least Squares (PLS) Regression Interface

Description

Performs Partial Least Squares (PLS) regression using either the NIPALS or SVD algorithm for component extraction. This is the main user-facing function for computing PLS models. Internally, it delegates to either NIPALS.pls() or SVD.pls().

Usage

pls.regression(x, y, n.components = NULL, calc.method = c("SVD", "NIPALS"))

Arguments

x	A numeric matrix or data frame of predictor variables (X), with dimensions n × p.
y	A numeric matrix or data frame of response variables (Y), with dimensions n × q.
n.components	Integer specifying the number of latent components (H) to extract. If NULL, defaults to the rank of x.
calc.method	Character string indicating the algorithm to use. Must be either "SVD" (default) or "NIPALS".

Details

This function provides a unified interface for Partial Least Squares regression. Based on the value of calc.method, it computes latent variables using either:

• "SVD" — A direct method using the singular value decomposition of the cross-covariance matrix ($X^\top Y$).

• "NIPALS" — An iterative method that alternately estimates predictor and response scores until convergence.

The outputs from both methods include scores, weights, loadings, regression coefficients, and explained variance.

Value

A list (from either SVD.pls() or NIPALS.pls()) containing:

model.type

Character string ("PLS Regression").

T, U

Score matrices for X and Y.

W, C

Weight matrices for X and Y.

P_loadings, Q_loadings

Loading matrices.

B_vector

Component-wise regression weights.

coefficients

Final regression coefficient matrix (rescaled).

intercept

Intercept vector (typically zero due to centering).

X_explained, Y_explained

Variance explained by each component.

X_cum_explained, Y_cum_explained

Cumulative variance explained.

References

Abdi, H., & Williams, L. J. (2013). Partial least squares methods: Partial least squares correlation and partial least square regression. Methods in Molecular Biology (Clifton, N.J.), 930, 549–579. doi:10.1007/978-1-62703-059-5_23

de Jong, S. (1993). SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18(3), 251–263. doi:10.1016/0169-7439(93)85002-X

See Also

SVD.pls, NIPALS.pls

Examples

## Not run: 
X <- matrix(rnorm(100 * 10), 100, 10)
Y <- matrix(rnorm(100 * 2), 100, 2)

# Using SVD (default)
model1 <- pls.regression(X, Y, n.components = 3)

# Using NIPALS
model2 <- pls.regression(X, Y, n.components = 3, calc.method = "NIPALS")

## End(Not run)

---

Format PLS Model Output as LaTeX Tables

Description

Formats and displays Partial Least Squares (PLS) model output from pls.regression() as LaTeX tables for PDF rendering.

Usage

pls.summary(x, ..., include.scores = TRUE)

Arguments

x	A list returned by pls.regression() (class "pls") containing PLS model components.
...	Further arguments passed to or from methods (unused).
include.scores	Logical. Whether to include score matrices (T and U). Default is TRUE.

Value

Returns a knitr::asis_output object (LaTeX code) for PDF rendering.

Examples

# Load example data
data(mtcars)

# Prepare data for PLS regression
X <- mtcars[, c("wt", "hp", "disp")]
Y <- mtcars[, "mpg", drop = FALSE]

# Fit PLS model with 2 components
pls.fit <- pls.regression(X, Y, n.components = 2)

# Print a LaTeX-formatted summary
pls.summary(pls.fit, include.scores = FALSE)

---

Predict Method for Ridge Model Objects

Description

Predicts response values for new data using a fitted ridge model.

Usage

## S3 method for class 'ridge.model'
predict(object, newdata = NULL, ...)

Arguments

object	A 'ridge.model' object
newdata	A data frame or matrix containing new predictor values
...	Additional arguments (not used)

Value

A numeric vector of predicted values

---

Print Method for Ridge Model Objects

Description

Prints a summary of the ridge model fit.

Usage

## S3 method for class 'ridge.model'
print(x, ...)

Arguments

x	A 'ridge.model' object
...	Additional arguments (not used)

---

Ridge Regression with Automatic Lambda Selection

Description

Performs ridge regression with automatic selection of the optimal regularization parameter 'lambda' by minimizing k-fold cross-validated mean squared error (CV-MSE) using Brent's method. Supports both formula and matrix interfaces.

Usage

ridge.regression(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  lambda.range = c(1e-04, 100),
  folds = 5,
  ...
)

Arguments

formula	A model formula like 'y ~ x1 + x2'. Mutually exclusive with 'x'/'y'.
data	A data frame containing all variables used in the formula.
x	A numeric matrix of predictor variables (n × p). Used when formula is not provided.
y	A numeric vector of response variables (n × 1). Used when formula is not provided.
lambda.range	A numeric vector of length 2 specifying the interval for lambda optimization. Default: 'c(1e-4, 100)'.
folds	An integer specifying the number of cross-validation folds. Default: '5'.
...	Additional arguments passed to internal methods.

Details

This function implements ridge regression with automatic hyperparameter tuning. The algorithm:

• Standardizes predictor variables (centers and scales)

• Centers the response variable

• Uses k-fold cross-validation to find the optimal lambda

• Fits the final model with the optimal lambda

• Returns a structured object for prediction and analysis

The ridge regression solution is computed using the closed-form formula: $\hat{\beta} = (X^T X + \lambda I)^{-1} X^T y$

Value

A 'ridge.model' object containing:

coefficients

Final ridge coefficients (no intercept)

std_errors

Standard errors of the coefficients

intercept

Intercept term (from y centering)

optimal.lambda

Best lambda minimizing CV-MSE

cv.ms

Minimum CV-MSE achieved

cv.results

Data frame with lambda and CV-MSE pairs

x.scale

Standardization info: mean and sd for each predictor

y.center

Centering constant for y

fitted.values

Final model predictions on training data

residuals

Training residuals (y - fitted)

call

Matched call (for debugging)

method

Always "ridge"

folds

Number of CV folds used

formula

Stored if formula interface used

terms

Stored if formula interface used

References

Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.

See Also

predict.ridge.model

Examples

## Not run: 
# Formula interface
model1 <- ridge.regression(mpg ~ wt + hp + disp, data = mtcars)

# Matrix interface
X <- as.matrix(mtcars[, c("wt", "hp", "disp")])
y <- mtcars$mpg
model2 <- ridge.regression(x = X, y = y)

# Custom lambda range and folds
model3 <- ridge.regression(mpg ~ .,
    data = mtcars,
    lambda.range = c(0.1, 10), folds = 10
)

## End(Not run)

---

Format Ridge Model Output as LaTeX Tables

Description

Formats and displays ridge regression model output from ridge.regression() as LaTeX tables for PDF rendering or plain text for console viewing.

Usage

ridge.summary(x, ..., include.cv.trace = TRUE, latex = TRUE)

Arguments

x	A ridge model object returned by ridge.regression() (class "ridge.model").
...	Further arguments passed to or from methods (unused).
include.cv.trace	Logical. Whether to include cross-validation trace information. Default is TRUE.
latex	Logical; if TRUE (default), returns LaTeX-formatted tables using kableExtra. If FALSE, prints plain-text summary tables to the console.

Value

If latex = TRUE, returns a knitr::asis_output object (LaTeX code) for PDF rendering. If latex = FALSE, prints formatted summary tables to the console and returns the underlying data frames.

Examples

# Load example data
data(mtcars)

# Fit ridge regression model
ridge.fit <- ridge.regression(mpg ~ wt + hp + disp, data = mtcars)

# Print a LaTeX-formatted summary
ridge.summary(ridge.fit, include.cv.trace = FALSE)

# Print a plain-text summary
ridge.summary(ridge.fit, include.cv.trace = FALSE, latex = FALSE)

---

A Custom ggplot2 Theme for Publication-Ready Plots

Description

This theme provides a clean, polished look for ggplot2 plots, with a focus on readability and aesthetics. It includes a custom color palette and formatting for titles, axes, and legends.

Usage

snazzieR.theme()

Value

A ggplot2 theme object.

See Also

color.list, color.ref

Examples

library(ggplot2)
set.seed(123)
chains.df <- data.frame(
  Iteration = 1:500,
  alpha.1 = cumsum(rnorm(500, mean = 0.01, sd = 0.2)) + rnorm(1, 5, 0.2),
  alpha.2 = cumsum(rnorm(500, mean = 0.005, sd = 0.2)) + rnorm(1, 5, 0.2),
  alpha.3 = cumsum(rnorm(500, mean = 0.000, sd = 0.2)) + rnorm(1, 5, 0.2),
  alpha.4 = cumsum(rnorm(500, mean = -0.005, sd = 0.2)) + rnorm(1, 5, 0.2),
  alpha.5 = cumsum(rnorm(500, mean = -0.01, sd = 0.2)) + rnorm(1, 5, 0.2)
)
chain.colors <- c("Chain 1" = Red, "Chain 2" = Orange, "Chain 3" = Yellow,
                  "Chain 4" = Green, "Chain 5" = Blue)
ggplot(chains.df, aes(x = Iteration)) +
  geom_line(aes(y = alpha.1, color = "Chain 1"), linewidth = 1.2) +
  geom_line(aes(y = alpha.2, color = "Chain 2"), linewidth = 1.2) +
  geom_line(aes(y = alpha.3, color = "Chain 3"), linewidth = 1.2) +
  geom_line(aes(y = alpha.4, color = "Chain 4"), linewidth = 1.2) +
  geom_line(aes(y = alpha.5, color = "Chain 5"), linewidth = 1.2) +
  labs(x = "Iteration", y = expression(alpha),
       title = expression("Traceplot for " ~ alpha)) +
  scale_color_manual(values = chain.colors, name = "Chains") +
  snazzieR.theme()

---