Multivariate Data Analysis for Chemometrics
General class for Multivariate Curve Resolution model
Correlation plot for randomization test results
Correlation plot
Cumulative explained variance plot
Show plot with cumulative explained variance
Cumulative explained variance plot for PCA model
Variance plot
Show plot series as density plot (using hex binning)
Discrimination power plot
Explained variance plot
Show plot with explained variance
PLS model calibration
Compute coordinates of lines or curves with critical limits
Compute predictions for response values
Compute object with decomposition of x-values
Compute matrix with X-scores
Calculates critical limits for distance values using Data Driven robus...
Create ellipse on the current plot
Applies constraint to a dataset
Applies a list with preprocessing methods to a dataset
Imitation of fprinf() function
Returns matrix with original calibration data
Calibration data
Get calibration data
Compute confidence ellipse for a set of points
Confusion matrix for classification results
Confusion matrix for classification results
Compute coordinates of a closed convex hull for data points
Create a vector with labels for plot series
Shows a list with implemented constraints
Shows a list with implemented preprocessing methods
Create labels as column or row indices
Create labels from data values
Get main title
Define colors for plot series
Probabilities for residual distances
Get class belonging probability
Probabilities of class belonging for PCA/SIMCA results
Identifies pure variables
Get regression coefficients
Regression coefficients for PLS model'
Return list with valid results
Get selected components
Selectivity ratio for PLS model
Selectivity ratio
Compute explained variance for MCR case
Prepare colors based on palette and opacity value
Plotting function for a single set of objects
Plot colorbar
Plot lines
Create and return vector with legend values
Compute x-axis limits for mdaplotg
Compute y-axis limits for mdaplotg
Prepare data for mdaplotg
Check mdaplotg parameters and replicate them if necessary
Plotting function for several plot series
Show legend for mdaplotg
Create line plot with double y-axis
VIP scores for PLS model
VIP scores
Plot for randomization test results
Regression coefficients plot
Plot method for regression results
Model overview plot for SIMCA
Model overview plot for SIMCAM
Model overview plot for SIMCAM results
Show plot series as set of lines
Loadings plot for PCA model
Loadings plot
Misclassified ratio plot for classification model
Misclassified ratio plot for classification results
Misclassification ratio plot
Model distance plot
iPLS performance plot
Selected intervals plot
Predictions plot
Predictions plot for regression model
Predictions plot for regression results
Predictions plot for SIMCAM model
Prediction plot for SIMCAM results
Plot for class belonging probability
Plot for class belonging probability
Purity values plot
Plot purity values
Purity spectra plot
Plot purity spectra
Degrees of freedom plot for orthogonal distance (Nh)
Regression coefficients plot
Regression coefficient plot for regression model
Add regression line for data points
Residual distance plot
Residuals distance plot for PCA model
Residuals plot
Residuals plot for regression results
RMSE development plot
RMSE plot
RMSE plot for regression model
RMSE plot for regression results
Plot for ratio RMSEC/RMSECV vs RMSECV
RMSECV/RMSEC ratio plot for regression model
Show plot series as set of points
Scores plot
Scores plot for PCA model
Scores plot
Selectivity ratio plot for PLS model
Selectivity ratio plot
Sensitivity plot for classification model
Sensitivity plot for classification results
Sensitivity plot
Create plot series object based on data, plot type and parameters
Specificity plot for classification model
Specificity plot for classification results
Specificity plot
Show plot with resolved spectra
Plot resolved spectra
Degrees of freedom plot for score distance (Nh)
X loadings plot
Residual distance plot for decomposition of X data
Y variance plot
PCA predictions
PLS predictions
PLS-DA predictions
SIMCA predictions
SIMCA multiple classes predictions
Kubelka-Munk transformation
Savytzky-Golay filter
Standard Normal Variate transformation
Transformation
Variable selection
Take dataset and prepare them for plot
Prepares calibration data
Print information about classification result object
Print method for iPLS
Print method for linear decomposition
Print method for mcrpure object
Print method for mcrpure object
Print method for PCA model object
Print method for PCA results object
Print method for PLS model object
Print method for PLS-DA model object
Print method for PLS-DA results object
print method for PLS results object
Print method for randtest object
print method for regression coefficients class
Print method for PLS model object
print method for regression results object
Print method for SIMCA model object
Print method for SIMCAM model object
Print method for SIMCAM results object
Print method for SIMCA results object
Randomization test for PLS regression
Distribution statistics for regression coeffificents
Selectivity ratio calculation
Compute and set statistical limits for Q and T2 residual distances.
Summary method for PLS model object
Summary method for PLS-DA model object
Summary method for PLS-DA results object
summary method for PLS results object
Summary method for randtest object
Summary method for regcoeffs object
Summary method for regression model object
summary method for regression results object
Summary method for SIMCA model object
Summary method for SIMCAM model object
Summary method for SIMCAM results object
Summary method for SIMCA results object
Unmix spectral data using pure variables estimated before
Calculate critical limits for distance values using Hotelling T2 distr...
Calculate probabilities for distance values and given parameters using...
show image data as an image
Runs the backward iPLS algorithm
Runs the forward iPLS algorithm
Variable selection with interval PLS
Calculate critical limits for distance values using Jackson-Mudholkar ...
Calculate probabilities for distance values and given parameters using...
Include/unhide the excluded columns
include/unhide the excluded rows
Removes excluded (hidden) rows and colmns from data
Removes excluded (hidden) colmns from data
Removes excluded (hidden) rows from data
A wrapper for rbind() method with proper set of attributes
Set data attributes
Categorize PCA results based on orthogonal and score distances.
Confidence intervals for regression coefficients
as.matrix method for classification results
as.matrix method for ldecomp object
as.matrix method for PLS-DA results
as.matrix method for PLS results
as.matrix method for regression coefficients class
as.matrix method for regression results
as.matrix method for SIMCAM results
as.matrix method for SIMCA classification results
Capitalize text or vector with text values
Calculates critical limits for distance values using Data Driven momen...
Remove background pixels from image data
Categorize data rows based on PLS results and critical limits for tota...
Categorize PCA results
Calculates critical limits for distance values using Chi-square distri...
Calculate probabilities for distance values using Chi-square distribut...
PLS-DA classification
SIMCA classification
Check reference class values and convert it to a factor if necessary
Calculation of classification performance parameters
Results of classification
Wrapper for show() method
Class for MCR-ALS constraint
Method for angle constraint
Method for closure constraint
Method for non-negativity constraint
Method for normalization constraint
Shows information about all implemented constraints
Method for unimodality constraint
Define parameters based on 'cv' value
Generate sequence of indices for cross-validation
Cross-validation of a regression model
Cross-validation of a SIMCA model
String with description of cross-validation method
Calculates critical limits for distance values using Data Driven momen...
Compute score and residual distances
Compute coordinates of lines or curves with critical limits
Compute parameters for critical limits based on calibration results
Compute critical limits for orthogonal distances (Q)
Compute critical limits for score distances (T2)
Compute explained variance
Residuals distance plot for a set of ldecomp objects
Class for storing and visualising linear decomposition of dataset (X =...
Identifies pure variables
Fast combinatorial non-negative least squares
Non-negative least squares
Ordinary least squares
Multivariate curve resolution using Alternating Least Squares
Multivariate curve resolution based on pure variables
A wrapper for cbind() method with proper set of attributes
Convert data matrix to an image
Convert data frame to a matrix
Exclude/hide columns in a dataset
Exclude/hide rows in a dataset
Get data attributes
Get indices of excluded rows or columns
Convert image to data matrix
A wrapper for subset() method with proper set of attributed
A wrapper for t() method with proper set of attributes
Check color values
Format vector with numeric values
Color values for plot elements
Calculate limits for x-axis.
Prepare xticklabels for plot
Prepare xticks for plot
Calculate limits for y-axis.
Prepare yticklabels for plot
Prepare yticks for plot
Create axes plane
Package for Multivariate Data Analysis (Chemometrics)
PCA model calibration
Low-dimensional approximation of data matrix X
Replace missing values in data
NIPALS based PCA algorithm
Principal Component Analysis
Runs one of the selected PCA methods
Singular Values Decomposition based PCA algorithm
Show plot series as bars
Results of PCA decomposition
Pseudo-inverse matrix
Plot function for classification results
Overview plot for iPLS results
Plot summary for MCR model
Model overview plot for PCA
Plot method for PCA results object
Model overview plot for PLS
Model overview plot for PLS-DA
Overview plot for PLS-DA results
Overview plot for PLS results
PCA biplot
Biplot
Add confidence ellipse for groups of points on scatter plot
Show plot with resolved contributions
Plot resolved contributions
Add convex hull for groups of points on scatter plot
Cooman's plot
Cooman's plot for SIMCAM model
Cooman's plot for SIMCAM results
Discrimination power plot for SIMCAM model
Degrees of freedom plot for both distances
Show plot series as error bars
Extreme plot
Shows extreme plot for SIMCA model
Histogram plot for randomization test results
Statistic histogram
Hotelling ellipse
Model distance plot for SIMCAM model
Modelling power plot
Performance plot for classification model
Performance plot for classification results
Classification performance plot
Add confidence ellipse or convex hull for group of points
Predictions plot for classification model
Prediction plot for classification results
Explained variance plot for PCA model
Variance plot for PLS
Explained X variance plot for PLS results
Variance plot
VIP scores plot for PLS model
VIP scores plot
X loadings plot for PLS
Plot for PLS weights
Cumulative explained X variance plot for PLS
Explained cumulative X variance plot for PLS results
X cumulative variance plot
X loadings plot for PLS
X residuals plot for PLS results
X residuals plot
X scores plot for PLS
X scores plot for PLS results
X scores plot
Explained X variance plot for PLS
Explained X variance plot for PLS results
X variance plot
XY loadings plot for PLS
X loadings plot
Residual XY-distance plot
Residual distance plot
Plot for XY-residuals
XY scores plot for PLS
XY scores plot for PLS results
XY scores plot
Cumulative explained Y variance plot for PLS
Explained cumulative Y variance plot for PLS results
Y cumulative variance plot
Y residuals plot for PLS results
Y residuals plot
Y residuals plot for regression model
Explained Y variance plot for PLS
Explained Y variance plot for PLS results
Compute object with decomposition of y-values
Compute and orthogonalize matrix with Y-scores
Compute critical limits for orthogonal distances (Q)
Partial Least Squares regression
Runs selected PLS algorithm
SIMPLS algorithm
SIMPLS algorithm (old implementation)
Partial Least Squares Discriminant Analysis
PLS-DA results
PLS results
MCR ALS predictions
MCR predictions
Baseline correction using asymetric least squares
Autoscale values
Generic function for preprocessing
Shows information about all implemented preprocessing methods.
Multiplicative Scatter Correction transformation
Normalization
Class for preprocessing object
Regression coefficients
Prediction bias
Error of prediction
Determination coefficient
Regression results
RMSE
Slope
Add names and attributes to matrix with statistics
Replicate matric x
Select optimal number of components for PCA model
Select optimal number of components for PLS model
Select optimal number of components for a model
Compute and set statistical limits for residual distances.
Set residual distance limits
Show residual distance limits
Show labels on plot
Show predicted class values
Predictions
SIMCA one-class classification
Performance statistics for SIMCAM model
SIMCA multiclass classification
VIP scores for PLS model
Results of SIMCA multiclass classification
Results of SIMCA one-class classification
Split the excluded part of data
Split dataset to x and y values depending on plot type
Summary statistics about classification result object
Summary for iPLS results
Summary statistics for linear decomposition
Summary method for mcrals object
Summary method for mcrpure object
Summary method for PCA model object
Summary method for PCA results object
Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.