- Home
- CRAN
**caret**: Classification and Regression Training**oil**: Fatty acid composition of commercial oils

# Fatty acid composition of commercial oils

### Description

Fatty acid concentrations of commercial oils were measured using gas chromatography. The data is used to predict the type of oil. Note that only the known oils are in the data set. Also, the authors state that there are 95 samples of known oils. However, we count 96 in Table 1 (pgs. 33-35).

### Value

`fattyAcids` |
data frame of fatty acid compositions: Palmitic, Stearic, Oleic, Linoleic, Linolenic, Eicosanoic and Eicosenoic. When values fell below the lower limit of the assay (denoted as <X in the paper), the limit was used. |

`oilType` |
factor of oil types: pumpkin (A), sunflower (B), peanut (C), olive (D), soybean (E), rapeseed (F) and corn (G). |

### Source

Brodnjak-Voncina et al. (2005). Multivariate data analysis in
classification of vegetable oils characterized by the content of fatty
acids, *Chemometrics and Intelligent Laboratory Systems*, Vol.
75:31-45.

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.

- as.matrix.confusionMatrix: Confusion matrix as a table
- avNNet: Neural Networks Using Model Averaging
- bag: A General Framework For Bagging
- bagEarth: Bagged Earth
- bagFDA: Bagged FDA
- BloodBrain: Blood Brain Barrier Data
- BoxCoxTrans: Box-Cox and Exponential Transformations
- calibration: Probability Calibration Plot
- caretFuncs: Backwards Feature Selection Helper Functions
- caret-internal: Internal Functions
- caretSBF: Selection By Filtering (SBF) Helper Functions
- cars: Kelly Blue Book resale data for 2005 model year GM cars
- classDist: Compute and predict the distances to class centroids
- confusionMatrix: Create a confusion matrix
- confusionMatrix.train: Estimate a Resampled Confusion Matrix
- cox2: COX-2 Activity Data
- createDataPartition: Data Splitting functions
- densityplot.rfe: Lattice functions for plotting resampling results of...
- dhfr: Dihydrofolate Reductase Inhibitors Data
- diff.resamples: Inferential Assessments About Model Performance
- dotPlot: Create a dotplot of variable importance values
- dotplot.diff.resamples: Lattice Functions for Visualizing Resampling Differences
- downSample: Down- and Up-Sampling Imbalanced Data
- dummyVars: Create A Full Set of Dummy Variables
- featurePlot: Wrapper for Lattice Plotting of Predictor Variables
- filterVarImp: Calculation of filter-based variable importance
- findCorrelation: Determine highly correlated variables
- findLinearCombos: Determine linear combinations in a matrix
- format.bagEarth: Format 'bagEarth' objects
- gafs.default: Genetic algorithm feature selection
- gafs_initial: Ancillary genetic algorithm functions
- GermanCredit: German Credit Data
- getSamplingInfo: Get sampling info from a train model
- histogram.train: Lattice functions for plotting resampling results
- icr.formula: Independent Component Regression
- index2vec: Convert indicies to a binary vector
- knn3: k-Nearest Neighbour Classification
- knnreg: k-Nearest Neighbour Regression
- learing_curve_dat: Create Data to Plot a Learning Curve
- lift: Lift Plot
- maxDissim: Maximum Dissimilarity Sampling
- mdrr: Multidrug Resistance Reversal (MDRR) Agent Data
- modelLookup: Tools for Models Available in 'train'
- models: A List of Available Models in train
- nearZeroVar: Identification of near zero variance predictors
- nullModel: Fit a simple, non-informative model
- oil: Fatty acid composition of commercial oils
- oneSE: Selecting tuning Parameters
- panel.lift2: Lattice Panel Functions for Lift Plots
- panel.needle: Needle Plot Lattice Panel
- pcaNNet: Neural Networks with a Principal Component Step
- plotClassProbs: Plot Predicted Probabilities in Classification Models
- plot.gafs: Plot Method for the gafs and safs Classes
- plotObsVsPred: Plot Observed versus Predicted Results in Regression and...
- plot.rfe: Plot RFE Performance Profiles
- plot.train: Plot Method for the train Class
- plot.varImp.train: Plotting variable importance measures
- plsda: Partial Least Squares and Sparse Partial Least Squares...
- postResample: Calculates performance across resamples
- pottery: Pottery from Pre-Classical Sites in Italy
- prcomp.resamples: Principal Components Analysis of Resampling Results
- predict.bagEarth: Predicted values based on bagged Earth and FDA models
- predict.gafs: Predict new samples
- predict.knn3: Predictions from k-Nearest Neighbors
- predict.knnreg: Predictions from k-Nearest Neighbors Regression Model
- predictors: List predictors used in the model
- predict.train: Extract predictions and class probabilities from train...
- preProcess: Pre-Processing of Predictors
- print.confusionMatrix: Print method for confusionMatrix
- print.train: Print Method for the train Class
- recall: Calculate recall, precision and F values
- resampleHist: Plot the resampling distribution of the model statistics
- resamples: Collation and Visualization of Resampling Results
- resampleSummary: Summary of resampled performance estimates
- rfe: Backwards Feature Selection
- rfeControl: Controlling the Feature Selection Algorithms
- Sacramento: Sacramento CA Home Prices
- safs: Simulated annealing feature selection
- safsControl: Control parameters for GA and SA feature selection
- safs_initial: Ancillary simulated annealing functions
- sbf: Selection By Filtering (SBF)
- sbfControl: Control Object for Selection By Filtering (SBF)
- scat: Morphometric Data on Scat
- segmentationData: Cell Body Segmentation
- sensitivity: Calculate sensitivity, specificity and predictive values
- spatialSign: Compute the multivariate spatial sign
- summary.bagEarth: Summarize a bagged earth or FDA fit
- tecator: Fat, Water and Protein Content of Meat Samples
- train: Fit Predictive Models over Different Tuning Parameters
- trainControl: Control parameters for train
- twoClassSim: Simulation Functions
- update.safs: Update or Re-fit a SA or GA Model
- update.train: Update or Re-fit a Model
- varImp: Calculation of variable importance for regression and...
- varImp.gafs: Variable importances for GAs and SAs
- var_seq: Sequences of Variables for Tuning
- xyplot.resamples: Lattice Functions for Visualizing Resampling Results