Type: Package
Title: Chic and Sleek Functions for Beautiful Statisticians
Version: 0.1.1
Maintainer: Aidan J. Wagner <JesusButForGayPeople@proton.me>
Description: Because your linear models deserve better than console output. A sleek color palette and kable styling to make your regression results look sharper than they are. Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms, along with a unified interface for model fitting and fabulous LaTeX and console output formatting. See the package manual at https://github.com/JesusButForGayPeople/snazzieR/releases/download/v0.1.1/snazzieR_0.1.1.pdf.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Imports: ggplot2, knitr, kableExtra, dplyr, stats
RoxygenNote: 7.3.2
Depends: R (≥ 4.1.0)
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-05-11 21:36:09 UTC; shared-psychosis
Author: Aidan J. Wagner [aut, cre]
Repository: CRAN
Date/Publication: 2025-05-11 22:10:02 UTC

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 is formatted for LaTeX output using the 'kableExtra' package.

Usage

ANOVA.summary.table(model, caption)

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.

Value

A LaTeX-formatted table generated by 'kableExtra::kable()'.

Examples

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

# Generate the ANOVA summary table
ANOVA.summary.table(model, caption = "ANOVA Summary")

Partial Least Squares Regression via NIPALS (Internal)

Description

This function is called internally by pls.regression and is not intended to be used directly. Use pls.regression(..., calc.method = "NIPALS") instead.

Performs Partial Least Squares (PLS) regression using the NIPALS (Nonlinear Iterative Partial Least Squares) algorithm. This method estimates the latent components (scores, loadings, weights) by iteratively updating the X and Y score directions until convergence. It is suitable for cases where the number of predictors is large or predictors are highly collinear.

Usage

NIPALS.pls(x, y, n.components = NULL)

Arguments

x

A numeric matrix or data frame of predictors (X). Should have dimensions n × p.

y

A numeric matrix or data frame of response variables (Y). Should have dimensions n × q.

n.components

Integer specifying the number of PLS components to extract. If NULL, it defaults to qr(x)$rank.

Details

The algorithm standardizes both x and y using z-score normalization. It then performs the following for each of the n.components latent variables:

  1. Initializes a random response score vector u.

  2. Iteratively:

    • Updates the X weight vector w = E^\top u, normalized.

    • Computes the X score t = E w, normalized.

    • Updates the Y loading q = F^\top t, normalized.

    • Updates the response score u = F q.

    • Repeats until t converges below a tolerance threshold.

  3. Computes scalar regression coefficient b = t^\top u.

  4. Deflates residual matrices E and F to remove current component contribution.

After component extraction, the final regression coefficient matrix B_{original} is computed and rescaled to the original data units. Explained variance is also computed component-wise and cumulatively.

Value

A list with the following elements:

model.type

Character string indicating the model type ("PLS Regression").

T

Matrix of X scores (n × H).

U

Matrix of Y scores (n × H).

W

Matrix of X weights (p × H).

C

Matrix of normalized Y weights (q × H).

P_loadings

Matrix of X loadings (p × H).

Q_loadings

Matrix of Y loadings (q × H).

B_vector

Vector of regression scalars (length H), one for each component.

coefficients

Matrix of regression coefficients in original data scale (p × q).

intercept

Vector of intercepts (length q). Always zero here due to centering.

X_explained

Percent of total X variance explained by each component.

Y_explained

Percent of total Y variance explained by each component.

X_cum_explained

Cumulative X variance explained.

Y_cum_explained

Cumulative Y variance explained.

References

Wold, H., & Lyttkens, E. (1969). Nonlinear iterative partial least squares (NIPALS) estimation procedures. Bulletin of the International Statistical Institute, 43, 29–51.

Examples

## Not run: 
X <- matrix(rnorm(100 * 10), 100, 10)
Y <- matrix(rnorm(100 * 2), 100, 2)
model <- pls.regression(X, Y, n.components = 3, calc.method = "NIPALS")
model$coefficients

## End(Not run)

Partial Least Squares Regression via SVD (Internal)

Description

This function is called internally by pls.regression and is not intended to be used directly. Use pls.regression(..., calc.method = "SVD") instead.

Performs Partial Least Squares (PLS) regression using the Singular Value Decomposition (SVD) of the cross-covariance matrix. This method estimates the latent components by identifying directions in the predictor and response spaces that maximize their covariance, using the leading singular vectors of the matrix R = X^\top Y.

Usage

SVD.pls(x, y, n.components = NULL)

Arguments

x

A numeric matrix or data frame of predictors (X). Should have dimensions n × p.

y

A numeric matrix or data frame of response variables (Y). Should have dimensions n × q.

n.components

Integer specifying the number of PLS components to extract. If NULL, defaults to qr(x)$rank.

Details

The algorithm begins by z-scoring both x and y (centering and scaling to unit variance). The initial residual matrices are set to the scaled values: E = X_scaled, F = Y_scaled.

For each component h = 1, ..., H:

  1. Compute the cross-covariance matrix R = E^\top F.

  2. Perform SVD on R = U D V^\top.

  3. Extract the first singular vectors: w = U[,1], q = V[,1].

  4. Compute scores: t = E w (normalized), u = F q.

  5. Compute loadings: p = E^\top t, regression scalar b = t^\top u.

  6. Deflate residuals: E \gets E - t p^\top, F \gets F - b t q^\top.

After all components are extracted, a post-processing step removes components with zero regression weight. The scaled regression coefficients are computed using the Moore–Penrose pseudoinverse of the loading matrix P, and then rescaled to the original variable units.

Value

A list containing:

model.type

Character string indicating the model type ("PLS Regression").

T

Matrix of predictor scores (n × H).

U

Matrix of response scores (n × H).

W

Matrix of predictor weights (p × H).

C

Matrix of normalized response weights (q × H).

P_loadings

Matrix of predictor loadings (p × H).

Q_loadings

Matrix of response loadings (q × H).

B_vector

Vector of scalar regression weights (length H).

coefficients

Matrix of final regression coefficients in the original scale (p × q).

intercept

Vector of intercepts (length q). All zeros due to centering.

X_explained

Percent of total X variance explained by each component.

Y_explained

Percent of total Y variance explained by each component.

X_cum_explained

Cumulative X variance explained.

Y_cum_explained

Cumulative Y 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

Examples

## Not run: 
X <- matrix(rnorm(100 * 10), 100, 10)
Y <- matrix(rnorm(100 * 2), 100, 2)
model <- pls.regression(X, Y, n.components = 3, calc.method = "SVD")
model$coefficients

## End(Not run)

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

Light Grey color swatch

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

<table style='width:100 <tr><th align='left'>Name</th><th align='left'>Hex</th><th align='left'>Swatch</th></tr> <tr><td>Dark.Red</td><td>#9F193D</td><td><img src='figures/Dark.Red.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Red</td><td>#C31E4A</td><td><img src='figures/Red.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Red</td><td>#E66084</td><td><img src='figures/Light.Red.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Red</td><td>#F1A7BB</td><td><img src='figures/Pale.Red.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Dark.Orange</td><td>#A77011</td><td><img src='figures/Dark.Orange.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Orange</td><td>#E99F1F</td><td><img src='figures/Orange.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Orange</td><td>#F0BF6A</td><td><img src='figures/Light.Orange.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Orange</td><td>#F4CF90</td><td><img src='figures/Pale.Orange.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Yellow</td><td>#E8D206</td><td><img src='figures/Yellow.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Yellow</td><td>#FFE373</td><td><img src='figures/Light.Yellow.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Yellow</td><td>#FFF8DC</td><td><img src='figures/Pale.Yellow.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Dark.Green</td><td>#54711E</td><td><img src='figures/Dark.Green.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Green</td><td>#83B02F</td><td><img src='figures/Green.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Green</td><td>#ABD45E</td><td><img src='figures/Light.Green.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Green</td><td>#C4E18E</td><td><img src='figures/Pale.Green.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Dark.Blue</td><td>#004852</td><td><img src='figures/Dark.Blue.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Blue</td><td>#008C9E</td><td><img src='figures/Blue.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Blue</td><td>#1FE5FF</td><td><img src='figures/Light.Blue.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Blue</td><td>#85F1FF</td><td><img src='figures/Pale.Blue.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Dark.Purple</td><td>#4E2183</td><td><img src='figures/Dark.Purple.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Purple</td><td>#743496</td><td><img src='figures/Purple.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Purple</td><td>#A06CDA</td><td><img src='figures/Light.Purple.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Purple</td><td>#CAADEB</td><td><img src='figures/Pale.Purple.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Dark.Grey</td><td>#403A3F</td><td><img src='figures/Dark.Grey.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Grey</td><td>#6F646C</td><td><img src='figures/Grey.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Light.Grey</td><td>#9E949B</td><td><img src='figures/Light.Grey.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> <tr><td>Pale.Grey</td><td>#CFC9CD</td><td><img src='figures/Pale.Grey.png' style='width:1em; height:1em; border:1px solid #000;'></td></tr> </table>

See Also

color.list, color.ref

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 a formatted LaTeX table displaying the results.

Usage

eigen.summary(
  cov.matrix,
  caption = "Eigenvectors of Covariance Matrix",
  space_after_caption = "5mm"
)

Arguments

cov.matrix

A square numeric matrix representing the covariance matrix.

caption

A character string specifying the table caption (default: "Eigenvectors of Covariance Matrix").

space_after_caption

A character string specifying the space after the caption in LaTeX (default: "5mm").

Value

A LaTeX formatted table displaying the eigenvectors and eigenvalues.

Examples

cov_matrix <- matrix(c(4, 2, 2, 3), nrow = 2)
eigen.summary(cov_matrix)

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.

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 coefficients, standard errors, p-values, and model statistics (e.g., MSE, R-squared). The table is formatted for LaTeX output using the 'kableExtra' package.

Usage

model.summary.table(model, caption)

Arguments

model

A linear model object (e.g., output from 'lm()').

caption

A character string to be used as the caption for the table.

Value

A LaTeX-formatted table generated by 'kableExtra::kable()'.

Examples

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

# Generate the summary table
model.summary.table(model, caption = "Linear Model Summary")

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:

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)

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.

Examples

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  snazzieR.theme()