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A C D E F G H I J L M P R S U V
| as.matrix.classres | as.matrix method for classification results |
| as.matrix.ldecomp | as.matrix method for ldecomp object |
| as.matrix.plsdares | as.matrix method for PLS-DA results |
| as.matrix.plsres | as.matrix method for PLS results |
| as.matrix.regcoeffs | as.matrix method for regression coefficients class |
| as.matrix.regres | as.matrix method for regression results |
| as.matrix.simcamres | as.matrix method for SIMCAM results |
| as.matrix.simcares | as.matrix method for SIMCA classification results |
| capitalize | Capitalize text or vector with text values |
| carbs | Raman spectra of carbonhydrates |
| categorize | Categorize PCA results |
| categorize.pca | Categorize PCA results based on orthogonal and score distances. |
| categorize.pls | Categorize data rows based on PLS results and critical limits for total distance. |
| chisq.crit | Calculates critical limits for distance values using Chi-square distribution |
| chisq.prob | Calculate probabilities for distance values using Chi-square distribution |
| classify.plsda | PLS-DA classification |
| classify.simca | SIMCA classification |
| classmodel.processRefValues | Check reference class values and convert it to a factor if necessary |
| classres | Results of classification |
| classres.getPerformance | Calculation of classification performance parameters |
| confint.regcoeffs | Confidence intervals for regression coefficients |
| constraint | Class for MCR-ALS constraint |
| constraintAngle | Method for angle constraint |
| constraintClosure | Method for closure constraint |
| constraintNonNegativity | Method for non-negativity constraint |
| constraintNorm | Method for normalization constraint |
| constraints.list | Shows information about all implemented constraints |
| constraintUnimod | Method for unimodality constraint |
| crossval | Generate sequence of indices for cross-validation |
| crossval.getParams | Define parameters based on 'cv' value |
| crossval.regmodel | Cross-validation of a regression model |
| crossval.simca | Cross-validation of a SIMCA model |
| crossval.str | String with description of cross-validation method |
| dd.crit | Calculates critical limits for distance values using Data Driven moments approach |
| ddmoments.param | Calculates critical limits for distance values using Data Driven moments approach |
| ddrobust.param | Calculates critical limits for distance values using Data Driven robust approach |
| ellipse | Create ellipse on the current plot |
| employ.constraint | Applies constraint to a dataset |
| employ.prep | Applies a list with preprocessing methods to a dataset |
| fprintf | Imitation of fprinf() function |
| getCalibrationData | Calibration data |
| getCalibrationData.pca | Returns matrix with original calibration data |
| getCalibrationData.simcam | Get calibration data |
| getConfidenceEllipse | Compute confidence ellipse for a set of points |
| getConfusionMatrix | Confusion matrix for classification results |
| getConfusionMatrix.classres | Confusion matrix for classification results |
| getConvexHull | Compute coordinates of a closed convex hull for data points |
| getDataLabels | Create a vector with labels for plot series |
| getImplementedConstraints | Shows a list with implemented constraints |
| getImplementedPrepMethods | Shows a list with implemented preprocessing methods |
| getLabelsAsIndices | Create labels as column or row indices |
| getLabelsAsValues | Create labels from data values |
| getMainTitle | Get main title |
| getPlotColors | Define colors for plot series |
| getProbabilities | Get class belonging probability |
| getProbabilities.pca | Probabilities for residual distances |
| getProbabilities.simca | Probabilities of class belonging for PCA/SIMCA results |
| getPureVariables | Identifies pure variables |
| getRegcoeffs | Get regression coefficients |
| getRegcoeffs.regmodel | Regression coefficients for PLS model' |
| getRes | Return list with valid results |
| getSelectedComponents | Get selected components |
| getSelectivityRatio | Selectivity ratio |
| getSelectivityRatio.pls | Selectivity ratio for PLS model |
| getVariance.mcr | Compute explained variance for MCR case |
| getVIPScores | VIP scores |
| getVIPScores.pls | VIP scores for PLS model |
| hotelling.crit | Calculate critical limits for distance values using Hotelling T2 distribution |
| hotelling.prob | Calculate probabilities for distance values and given parameters using Hotelling T2 distribution |
| imshow | show image data as an image |
| ipls | Variable selection with interval PLS |
| ipls.backward | Runs the backward iPLS algorithm |
| ipls.forward | Runs the forward iPLS algorithm |
| jm.crit | Calculate critical limits for distance values using Jackson-Mudholkar approach |
| jm.prob | Calculate probabilities for distance values and given parameters using Hotelling T2 distribution |
| ldecomp | Class for storing and visualising linear decomposition of dataset (X = TP' + E) |
| ldecomp.getDistances | Compute score and residual distances |
| ldecomp.getLimitsCoordinates | Compute coordinates of lines or curves with critical limits |
| ldecomp.getLimParams | Compute parameters for critical limits based on calibration results |
| ldecomp.getQLimits | Compute critical limits for orthogonal distances (Q) |
| ldecomp.getT2Limits | Compute critical limits for score distances (T2) |
| ldecomp.getVariances | Compute explained variance |
| ldecomp.plotResiduals | Residuals distance plot for a set of ldecomp objects |
| mcr | General class for Multivariate Curve Resolution model |
| mcrals | Multivariate curve resolution using Alternating Least Squares |
| mcrals.cal | Identifies pure variables |
| mcrals.fcnnls | Fast combinatorial non-negative least squares |
| mcrals.nnls | Non-negative least squares |
| mcrals.ols | Ordinary least squares |
| mcrpure | Multivariate curve resolution based on pure variables |
| mda.cbind | A wrapper for cbind() method with proper set of attributes |
| mda.data2im | Convert data matrix to an image |
| mda.df2mat | Convert data frame to a matrix |
| mda.exclcols | Exclude/hide columns in a dataset |
| mda.exclrows | Exclude/hide rows in a dataset |
| mda.getattr | Get data attributes |
| mda.getexclind | Get indices of excluded rows or columns |
| mda.im2data | Convert image to data matrix |
| mda.inclcols | Include/unhide the excluded columns |
| mda.inclrows | include/unhide the excluded rows |
| mda.purge | Removes excluded (hidden) rows and colmns from data |
| mda.purgeCols | Removes excluded (hidden) colmns from data |
| mda.purgeRows | Removes excluded (hidden) rows from data |
| mda.rbind | A wrapper for rbind() method with proper set of attributes |
| mda.setattr | Set data attributes |
| mda.setimbg | Remove background pixels from image data |
| mda.show | Wrapper for show() method |
| mda.subset | A wrapper for subset() method with proper set of attributed |
| mda.t | A wrapper for t() method with proper set of attributes |
| mdaplot | Plotting function for a single set of objects |
| mdaplot.areColors | Check color values |
| mdaplot.formatValues | Format vector with numeric values |
| mdaplot.getColors | Color values for plot elements |
| mdaplot.getXAxisLim | Calculate limits for x-axis. |
| mdaplot.getXTickLabels | Prepare xticklabels for plot |
| mdaplot.getXTicks | Prepare xticks for plot |
| mdaplot.getYAxisLim | Calculate limits for y-axis. |
| mdaplot.getYTickLabels | Prepare yticklabels for plot |
| mdaplot.getYTicks | Prepare yticks for plot |
| mdaplot.plotAxes | Create axes plane |
| mdaplot.prepareColors | Prepare colors based on palette and opacity value |
| mdaplot.showColorbar | Plot colorbar |
| mdaplot.showLines | Plot lines |
| mdaplotg | Plotting function for several plot series |
| mdaplotg.getLegend | Create and return vector with legend values |
| mdaplotg.getXLim | Compute x-axis limits for mdaplotg |
| mdaplotg.getYLim | Compute y-axis limits for mdaplotg |
| mdaplotg.prepareData | Prepare data for mdaplotg |
| mdaplotg.processParam | Check mdaplotg parameters and replicate them if necessary |
| mdaplotg.showLegend | Show legend for mdaplotg |
| mdaplotyy | Create line plot with double y-axis |
| mdatools | Package for Multivariate Data Analysis (Chemometrics) |
| pca | Principal Component Analysis |
| pca.cal | PCA model calibration |
| pca.getB | Low-dimensional approximation of data matrix X |
| pca.mvreplace | Replace missing values in data |
| pca.nipals | NIPALS based PCA algorithm |
| pca.run | Runs one of the selected PCA methods |
| pca.svd | Singular Values Decomposition based PCA algorithm |
| pcares | Results of PCA decomposition |
| pellets | Image data |
| people | People data |
| pinv | Pseudo-inverse matrix |
| plot.classres | Plot function for classification results |
| plot.ipls | Overview plot for iPLS results |
| plot.mcr | Plot summary for MCR model |
| plot.pca | Model overview plot for PCA |
| plot.pcares | Plot method for PCA results object |
| plot.pls | Model overview plot for PLS |
| plot.plsda | Model overview plot for PLS-DA |
| plot.plsdares | Overview plot for PLS-DA results |
| plot.plsres | Overview plot for PLS results |
| plot.randtest | Plot for randomization test results |
| plot.regcoeffs | Regression coefficients plot |
| plot.regres | Plot method for regression results |
| plot.simca | Model overview plot for SIMCA |
| plot.simcam | Model overview plot for SIMCAM |
| plot.simcamres | Model overview plot for SIMCAM results |
| plotBars | Show plot series as bars |
| plotBiplot | Biplot |
| plotBiplot.pca | PCA biplot |
| plotConfidenceEllipse | Add confidence ellipse for groups of points on scatter plot |
| plotContributions | Plot resolved contributions |
| plotContributions.mcr | Show plot with resolved contributions |
| plotConvexHull | Add convex hull for groups of points on scatter plot |
| plotCooman | Cooman's plot |
| plotCooman.simcam | Cooman's plot for SIMCAM model |
| plotCooman.simcamres | Cooman's plot for SIMCAM results |
| plotCorr | Correlation plot |
| plotCorr.randtest | Correlation plot for randomization test results |
| plotCumVariance | Variance plot |
| plotCumVariance.ldecomp | Cumulative explained variance plot |
| plotCumVariance.mcr | Show plot with cumulative explained variance |
| plotCumVariance.pca | Cumulative explained variance plot for PCA model |
| plotDensity | Show plot series as density plot (using hex binning) |
| plotDiscriminationPower | Discrimination power plot |
| plotDiscriminationPower.simcam | Discrimination power plot for SIMCAM model |
| plotDistDoF | Degrees of freedom plot for both distances |
| plotErrorbars | Show plot series as error bars |
| plotExtreme | Shows extreme plot for SIMCA model |
| plotExtreme.pca | Extreme plot |
| plotHist | Statistic histogram |
| plotHist.randtest | Histogram plot for randomization test results |
| plotHotellingEllipse | Hotelling ellipse |
| plotLines | Show plot series as set of lines |
| plotLoadings | Loadings plot |
| plotLoadings.pca | Loadings plot for PCA model |
| plotMisclassified | Misclassification ratio plot |
| plotMisclassified.classmodel | Misclassified ratio plot for classification model |
| plotMisclassified.classres | Misclassified ratio plot for classification results |
| plotModelDistance | Model distance plot |
| plotModelDistance.simcam | Model distance plot for SIMCAM model |
| plotModellingPower | Modelling power plot |
| plotPerformance | Classification performance plot |
| plotPerformance.classmodel | Performance plot for classification model |
| plotPerformance.classres | Performance plot for classification results |
| plotPointsShape | Add confidence ellipse or convex hull for group of points |
| plotPredictions | Predictions plot |
| plotPredictions.classmodel | Predictions plot for classification model |
| plotPredictions.classres | Prediction plot for classification results |
| plotPredictions.regmodel | Predictions plot for regression model |
| plotPredictions.regres | Predictions plot for regression results |
| plotPredictions.simcam | Predictions plot for SIMCAM model |
| plotPredictions.simcamres | Prediction plot for SIMCAM results |
| plotProbabilities | Plot for class belonging probability |
| plotProbabilities.classres | Plot for class belonging probability |
| plotPurity | Plot purity values |
| plotPurity.mcrpure | Purity values plot |
| plotPuritySpectra | Plot purity spectra |
| plotPuritySpectra.mcrpure | Purity spectra plot |
| plotQDoF | Degrees of freedom plot for orthogonal distance (Nh) |
| plotRegcoeffs | Regression coefficients plot |
| plotRegcoeffs.regmodel | Regression coefficient plot for regression model |
| plotRegressionLine | Add regression line for data points |
| plotResiduals | Residuals plot |
| plotResiduals.ldecomp | Residual distance plot |
| plotResiduals.pca | Residuals distance plot for PCA model |
| plotResiduals.regres | Residuals plot for regression results |
| plotRMSE | RMSE plot |
| plotRMSE.ipls | RMSE development plot |
| plotRMSE.regmodel | RMSE plot for regression model |
| plotRMSE.regres | RMSE plot for regression results |
| plotRMSERatio | Plot for ratio RMSEC/RMSECV vs RMSECV |
| plotRMSERatio.regmodel | RMSECV/RMSEC ratio plot for regression model |
| plotScatter | Show plot series as set of points |
| plotScores | Scores plot |
| plotScores.ldecomp | Scores plot |
| plotScores.pca | Scores plot for PCA model |
| plotSelection | Selected intervals plot |
| plotSelection.ipls | iPLS performance plot |
| plotSelectivityRatio | Selectivity ratio plot |
| plotSelectivityRatio.pls | Selectivity ratio plot for PLS model |
| plotSensitivity | Sensitivity plot |
| plotSensitivity.classmodel | Sensitivity plot for classification model |
| plotSensitivity.classres | Sensitivity plot for classification results |
| plotseries | Create plot series object based on data, plot type and parameters |
| plotSpecificity | Specificity plot |
| plotSpecificity.classmodel | Specificity plot for classification model |
| plotSpecificity.classres | Specificity plot for classification results |
| plotSpectra | Plot resolved spectra |
| plotSpectra.mcr | Show plot with resolved spectra |
| plotT2DoF | Degrees of freedom plot for score distance (Nh) |
| plotVariance | Variance plot |
| plotVariance.ldecomp | Explained variance plot |
| plotVariance.mcr | Show plot with explained variance |
| plotVariance.pca | Explained variance plot for PCA model |
| plotVariance.pls | Variance plot for PLS |
| plotVariance.plsres | Explained X variance plot for PLS results |
| plotVIPScores | VIP scores plot |
| plotVIPScores.pls | VIP scores plot for PLS model |
| plotWeights | Plot for PLS weights |
| plotWeights.pls | X loadings plot for PLS |
| plotXCumVariance | X cumulative variance plot |
| plotXCumVariance.pls | Cumulative explained X variance plot for PLS |
| plotXCumVariance.plsres | Explained cumulative X variance plot for PLS results |
| plotXLoadings | X loadings plot |
| plotXLoadings.pls | X loadings plot for PLS |
| plotXResiduals | X residuals plot |
| plotXResiduals.pls | Residual distance plot for decomposition of X data |
| plotXResiduals.plsres | X residuals plot for PLS results |
| plotXScores | X scores plot |
| plotXScores.pls | X scores plot for PLS |
| plotXScores.plsres | X scores plot for PLS results |
| plotXVariance | X variance plot |
| plotXVariance.pls | Explained X variance plot for PLS |
| plotXVariance.plsres | Explained X variance plot for PLS results |
| plotXYLoadings | X loadings plot |
| plotXYLoadings.pls | XY loadings plot for PLS |
| plotXYResiduals | Plot for XY-residuals |
| plotXYResiduals.pls | Residual XY-distance plot |
| plotXYResiduals.plsres | Residual distance plot |
| plotXYScores | XY scores plot |
| plotXYScores.pls | XY scores plot for PLS |
| plotXYScores.plsres | XY scores plot for PLS results |
| plotYCumVariance | Y cumulative variance plot |
| plotYCumVariance.pls | Cumulative explained Y variance plot for PLS |
| plotYCumVariance.plsres | Explained cumulative Y variance plot for PLS results |
| plotYResiduals | Y residuals plot |
| plotYResiduals.plsres | Y residuals plot for PLS results |
| plotYResiduals.regmodel | Y residuals plot for regression model |
| plotYVariance | Y variance plot |
| plotYVariance.pls | Explained Y variance plot for PLS |
| plotYVariance.plsres | Explained Y variance plot for PLS results |
| pls | Partial Least Squares regression |
| pls.cal | PLS model calibration |
| pls.getLimitsCoordinates | Compute coordinates of lines or curves with critical limits |
| pls.getpredictions | Compute predictions for response values |
| pls.getxdecomp | Compute object with decomposition of x-values |
| pls.getxscores | Compute matrix with X-scores |
| pls.getydecomp | Compute object with decomposition of y-values |
| pls.getyscores | Compute and orthogonalize matrix with Y-scores |
| pls.getZLimits | Compute critical limits for orthogonal distances (Q) |
| pls.run | Runs selected PLS algorithm |
| pls.simpls | SIMPLS algorithm |
| pls.simplsold | SIMPLS algorithm (old implementation) |
| plsda | Partial Least Squares Discriminant Analysis |
| plsdares | PLS-DA results |
| plsres | PLS results |
| predict.mcrals | MCR ALS predictions |
| predict.mcrpure | MCR predictions |
| predict.pca | PCA predictions |
| predict.pls | PLS predictions |
| predict.plsda | PLS-DA predictions |
| predict.simca | SIMCA predictions |
| predict.simcam | SIMCA multiple classes predictions |
| prep | Class for preprocessing object |
| prep.alsbasecorr | Baseline correction using asymetric least squares |
| prep.autoscale | Autoscale values |
| prep.generic | Generic function for preprocessing |
| prep.list | Shows information about all implemented preprocessing methods. |
| prep.msc | Multiplicative Scatter Correction transformation |
| prep.norm | Normalization |
| prep.ref2km | Kubelka-Munk transformation |
| prep.savgol | Savytzky-Golay filter |
| prep.snv | Standard Normal Variate transformation |
| prep.transform | Transformation |
| prep.varsel | Variable selection |
| preparePlotData | Take dataset and prepare them for plot |
| prepCalData | Prepares calibration data |
| print.classres | Print information about classification result object |
| print.ipls | Print method for iPLS |
| print.ldecomp | Print method for linear decomposition |
| print.mcrals | Print method for mcrpure object |
| print.mcrpure | Print method for mcrpure object |
| print.pca | Print method for PCA model object |
| print.pcares | Print method for PCA results object |
| print.pls | Print method for PLS model object |
| print.plsda | Print method for PLS-DA model object |
| print.plsdares | Print method for PLS-DA results object |
| print.plsres | print method for PLS results object |
| print.randtest | Print method for randtest object |
| print.regcoeffs | print method for regression coefficients class |
| print.regmodel | Print method for PLS model object |
| print.regres | print method for regression results object |
| print.simca | Print method for SIMCA model object |
| print.simcam | Print method for SIMCAM model object |
| print.simcamres | Print method for SIMCAM results object |
| print.simcares | Print method for SIMCA results object |
| randtest | Randomization test for PLS regression |
| regcoeffs | Regression coefficients |
| regcoeffs.getStats | Distribution statistics for regression coeffificents |
| regres | Regression results |
| regres.bias | Prediction bias |
| regres.err | Error of prediction |
| regres.r2 | Determination coefficient |
| regres.rmse | RMSE |
| regres.slope | Slope |
| regress.addattrs | Add names and attributes to matrix with statistics |
| repmat | Replicate matric x |
| selectCompNum | Select optimal number of components for a model |
| selectCompNum.pca | Select optimal number of components for PCA model |
| selectCompNum.pls | Select optimal number of components for PLS model |
| selratio | Selectivity ratio calculation |
| setDistanceLimits | Set residual distance limits |
| setDistanceLimits.pca | Compute and set statistical limits for Q and T2 residual distances. |
| setDistanceLimits.pls | Compute and set statistical limits for residual distances. |
| showDistanceLimits | Show residual distance limits |
| showLabels | Show labels on plot |
| showPredictions | Predictions |
| showPredictions.classres | Show predicted class values |
| simca | SIMCA one-class classification |
| simcam | SIMCA multiclass classification |
| simcam.getPerformanceStats | Performance statistics for SIMCAM model |
| simcamres | Results of SIMCA multiclass classification |
| simcares | Results of SIMCA one-class classification |
| simdata | Spectral data of polyaromatic hydrocarbons mixing |
| splitExcludedData | Split the excluded part of data |
| splitPlotData | Split dataset to x and y values depending on plot type |
| summary.classres | Summary statistics about classification result object |
| summary.ipls | Summary for iPLS results |
| summary.ldecomp | Summary statistics for linear decomposition |
| summary.mcrals | Summary method for mcrals object |
| summary.mcrpure | Summary method for mcrpure object |
| summary.pca | Summary method for PCA model object |
| summary.pcares | Summary method for PCA results object |
| summary.pls | Summary method for PLS model object |
| summary.plsda | Summary method for PLS-DA model object |
| summary.plsdares | Summary method for PLS-DA results object |
| summary.plsres | summary method for PLS results object |
| summary.randtest | Summary method for randtest object |
| summary.regcoeffs | Summary method for regcoeffs object |
| summary.regmodel | Summary method for regression model object |
| summary.regres | summary method for regression results object |
| summary.simca | Summary method for SIMCA model object |
| summary.simcam | Summary method for SIMCAM model object |
| summary.simcamres | Summary method for SIMCAM results object |
| summary.simcares | Summary method for SIMCA results object |
| unmix.mcrpure | Unmix spectral data using pure variables estimated before |
| vipscores | VIP scores for PLS model |