Function reference • spHelper

All functions
`aes_sp()` `aes_sp_string()` `aes_sp_()`	Default aesthetics for plotting a hyperSpec object with ggplot2
`alr()`	[!] Additive logratio transformation
`apt()`	[!]Additive planar transform
`binning()`	[!] Signal binning
`boot_ci_mean()` `boot_ci_fun()` `boot_ci_corr()`	Bootstrapped mean and its confidence interval
`calculate_performance()`	Calculate best berformance measure
`center_subtracted_centers()`	[!] Calculate common center subtracted group centers of spectroscopic data
`class_add()`	Add an additional S3 class label to an object
`clr()`	[!] Centered logratio transformation
`colRatios()`	[!] Calculate ratios between values of every column pair.
`count_spectra()`	Summary statistics of factor variable in a hypecSpec object by ID
`cpt()`	[!] Centered planar transform
`createFolds2()` `stratifiedFolds()` `createFoldsBS()`	DEPRECATED!
`cvo_n_folds()`	DEPRECATED!
`DataSet1`	[+] Dataset: a data frame for illustrations
`Loadings2` `Loadings3` `Loadings4` `Scores2` `Scores3` `Scores4` `Spectra2` `Spectra3` `Spectra4`	[+] Datasets of simulated spectroscopic data
`deprecated`	Deprecated functions
`doALS2()`	[!.] Wrapper function for alternating least squares multivariate curve resolution (MCR-ALS)
`expr2text()`	[.] Convert `expression` and `call` to text and remove quotes
`file`	[!] Read header lines of OOI Base32 file
`gapDer()`	[!] Gap-Segment Derivative
`GaussAmp()`	[+] Generate Gaussian curves (GaussAmp).
`getScores()`	[+] Calculate amplitudes of spectroscopic components (a.k.a. scores) and label the resulting object
`get_LOF_by_class()`	Calculate lack_of_fit (LOF) for spectroscopic data by class
`gg_crosstab2()`	[!] Plot crosstab for 2 variables in hyperSpec object
`has_enough_IDs()`	Get names of groups that has enough unique cases.
`has_too_few_IDs()`	Get names of groups that has to few unique cases.
`hy2mat()`	Extract matrix from either `hyperSpec` object or a matrix
`hyAdd()`	[!] Add a variable to `hyperSpec` object
`hyRm_palette()` `hyAdd_palette()` `hyAdd_color()`	[+!] Add a variable with color names to `hyperSpec` object
`hyAdd_Labels_PAP_PD_2014()`	[+] Add labels to "PAP_PD_2014" and transform the dataset
`hyAdd_Labels_TD2009()`	[+] Add labels to "TD_2009" dataset
`hyAdd_Label_wl()` `label_wl()`	[!] Add a standardised label for x-axis in `hyperSpec` object
`hyDrop_NA()`	[+] Remove variables of `hyperSpec` object that contain only `NA` values
`hyGet_palette()` `hyGet_palette0()`	[+] Get color palette used to create variable'.color'
`hyperSpec()`	Initialize new hyperSpec object
`hy_spc2df()`	Return transposed matrix of spectra in hyperSpec object
`ilr()`	[!] Isometric logratio transformation
`ilrBase()`	[!] `ilrBase` method for `hyperSpec` object
`infoDim()`	Calculate information dimension of a matix
`ipt()`	[!] Isometric planar transformation
`IQR_outliers()`	Outlier detection with IQR based distances (limits)
`ldf()` `as.ldf()`	[!] As long data frame
`mad_outliers()`	Outlier detection with MAD based distances (limits)
`make_table()`	[Internal] Make table (from prediction object)
`mean_Nsd()`	[!] Find spectra that represent mean +/- n standard deiations
`measure_bac()`	Balanced accuracy (for 2 or more groups)
`measure_bac_2gr()`	Balanced accuracy (for binary classification)
`measure_kappa_tmp()`	[Internal] measure_kappa_tmp
`measure_tn()`	Calculate number of true negatives
`measure_tp()`	Calculate number of true positives
`measure_tpr()`	Rate of correctly identified values in a certain group
`median_CI()`	Confidence interval for median
`median_Nmad()`	[!] Find spectra that represent median +/- n median absolute deviations (MAD)
`merge(<hyperSpec>,<data.frame>)`	[!!!] Merge a hyperSpec object and a data frame
`Mode()`	[.] Compute a mode (most frequent value)
`na.replace()`	Mask NA values with other symbol
`nID_nSp()` `nID_nObs()`	[+!] Calculate number and percentage of unique IDs and observations
`normal_var_CI()`	Confidence interval for variance (parameter sigma squared) of Noral distribution
`palette_PAP`	[+] Color palette
`parser_1()`	[!] Parse a filename
`parser_TD2015()`	[!] Parse a filename
`parse_string()`	Parse a string and extract information to a dataframe.
`percent2probs()`	Converts percentage to perobabilities
`performance0()`	[.] Cusstomizable function `performance0`: function to create performance objects
`get_performance()` `get_max()` `AUC()` `tp()` `fp()` `fn()` `tn()` `cutoff()` `Se()` `Sp()` `J()` `Bac()` `Acc()` `Kappa()` `Wkappa()` `kappa_helper()` `make_conf_matrix()`	Performance measures for ROCR::prediction object
`plot_3D()` `mds2mat()` `SMACOF2mat()` `tSNE2mat()` `make_3D()`	[.] Plot 3D scatterplot
`plot_colors()`	[!] Vizualize colors
`plot_hyPalette()`	[!] Plot the color palette used in a hyperSpec object
`plot_LOF_hist()`	Plot histogram of LOF values
`plot_LOF_MDS()`	Plot MDS of LOF_obj data
`plot_LOF_sp()`	Plot spectra colored by LOF values
`plot_roc()`	[.] Plot ROC - sample code
`plot_spCompare()`	[!] Compare 2 spectroscopic signals
`plot_spDiff()`	[+] Plot difference between original and reconstructed spectra
`plot_spDistribution()`	[!] Distribution of spectroscopoc data as percentiles
`plot_stacked()`	Plot stacked spectra
`poisson_lambda_CI()`	Confidence interval for parameter lambda of Poisson distribution
`prediction0()`	[!] Function to create prediction objects
`prepare_PAP_RK_2014__MATLAB_failui()`	[+] Transform dataset "PAP_RK_2014" and add labels
`qplot_confusion()`	[!+] Plot a confusion matrix (a.k.a. classification table)
`qplot_crosstab()` `qplot_crosstab_sort()` `qplot_crosstab0()` `qplot_crosstab0s()`	[!.] Plot a cross-tabulation (classification table)
`qplot_infoDim()` `qplot_screeplot()` `qplot_scree()`	[+] Scree plot with indicated information dimension (ggplot2)
`qplot_kAmp()` `qplot_scores()`	[!+] Plot amplitudes (a.k.a scores) of spectroscopic components
`qplot_kSp()` `qplot_kSpFacets()` `qplot_sp()`	[!+] Plot spectroscopic curves and spectral components (a.k.a. loadings)
`qplot_prediction()` `qplot_proximity()`	[!!!] Proximity plot created using multi-dimensiolal scaling (MDS)
`qplot_resamples()`	ggplot Function for Visualizing Resampling Results of Models With 1 Tuned Parameter
`qplot_spc()` `gg_spc()`	[!!!] Plot spectroscopic curves
`qplot_spDistrib()`	[!] Plot distributions of spectroscopic data
`check_palette()` `qplot_spRange()` `layer_spRange()` `gg_spRange()`	[!] Plot range of spectroscopic data intensities
`qplot_spRangeCenter()`	[!] Plot mean and range of spectroscopic data intensities
`layer_spRangeMean()` `qplot_spRangeMean()`	[!] Plot mean and range of spectroscopic data intensities
`qplot_spRangeMedian()`	[!] Plot Median and range of spectroscopic data intensities
`qplot_spStat()` `layer_spStat()`	[+] Plot a summary statistic of spectroscopic data by group
`quality_of_fit()` `spFitEval()`	[!] Calculate quality of fit parameters (for spectroscopic data)
`quartiles()`	Median and quantiles
`read.OceanView.header()`	[!] Read header lines of OceanView file
`read.OceanView()` `read.OceanView2()` `read.OceanView0()` `read.OceanView.ascii()` `read.OceanView.ascii2()` `read.OceanView.ascii0()` `read.OceanView.ts()` `read.OceanView.ts2()` `read.OceanView.ts0()`	[!v0.3] Read spectroscopic OceanView file
`read.OOIBase32()` `read.OOIBase32_2()` `read.OOIBase32_0()`	[!] Read data from OceanOptics OOI Base32 ASCII file with header lines
`read.sp.csv2()`	[!v0.1] Read spectroscopic CSV (comma separated values) file
`read3csv2hy()`	[+] Read spectroscopic data from 3 CSV files ("data", "wavelengths" and "spectra") to `hyperSpec` object
`read_OOspectra()`	Read OceanOptics spectra
`reconstructSp()`	[+] Reconstruct spectra from loadings and scores (i.e. components and amplitudes)
`replace_spc()`	[!!!] Replace `object$spc` and `colnames(object$spc)`
`rmExpr()`	[.] Convert expressions in `ggplot2` object labels to strings
`rm_stripes()`	[!] Remove facet stripes from `ggplot2` plot
`ROC_()` `ROC_table()` `ROC_plots()`	[.] Do ROC analysis and plot it's results (ROC_)
`roc_extract_info()`	[!!!] roc_create_predictor
`save_rds_unique()`	[~!~] Save object as `.RDS` with unique name (i.e., without overwritting)
`sd_outliers()` `outside_mean_pm_Nsd()`	[!] Outlier detection based on standard deviations (sd)
`simSpectra()`	[!] Simulate spectroscopic data
`sortLoadings()`	[!+] Process (Sort, flip, name, etc.) spectra of components (a.k.a. loadings)
`spHelper`	Extension for `hyperSpec` and convenience functions
`spStat()`	[+] Calculate summary statistic of spectroscopic data for all spectra and by groups
`spStat_ci()` `spStat_ci_corr()` `ggplot_ci_rez()`	Calculate a statistic
`spStat_ldf()`	Calculate a summary statistic and return long-format data frame
`spZone()`	[!] Annotate x axis range in ggplot2 graphs
`sp_class_perform()` `sp_class_perform_cv()` `print.sp_class_perform_cv()` `print.sp_classif_performance()` `predict(<sp_classif_performance>)` `sp_classification_performance()` `sp_compare_gr_wl()` `class_perform_sp()`	[.!] Compare spectra of groups at each wavelength
`sp_data_labels()`	Lables for variables derived from spectroscopic data files
`sp_filter()`	Filter noise form a spectroscopic signal
`sp_normalize()`	Normalize spectra
`sp_normalize2()`	Normalize spectra
`sp_remove_offset()`	Remove offset
`stat_chull()`	[+] A convex hull layer for `ggplot2`
`subt()`	[+] Add bold title and subtitle to a plot
`summary_hyData()`	[!] Summary statistics of non-spectroscopic data in `hyperSpec` objecet
`test_folds_BS()` `foldTests()`	DEPRECATED!
`uncall()`	[.] Unwrap text from `call` object Assumption: only the second element in a `call` can be text first element is an object of class "name", that can be dropped. This algorithm can loose some text elements.
`unGroup()`	[!] Mix values of factor variable levels in equal proportions
`unipeak()`	[+] Find and keep only the highest positive part of a curve
`whichOutlier()`	[! DEPRECATED] Find indices of rows that contain outlier column-wise outliersscores
`x90()` `x30()`	Rotate x axis tick labels

Reference

All functions