NaN Toolbox
- NaN Toolbox
- nan_contents — NaN Toolbox
- Data Correlation and Covariance
- nan_conv — Convolve two vectors.
- nan_conv2 — performs 2D convolution of matrices a and b
- nan_conv2nan — calculates 2-dim convolution between X and Y
- nan_cor — calculates the correlation matrix
- nan_corrcoef — calculates the correlation matrix from pairwise correlations.
- nan_corrcov — Compute correlation matrix from covariance matrix.
- nan_cov — calculates covariance matrix
- nan_covm — generates covariance matrix
- nan_decovm — decomposes extended covariance matrix
- nan_ecovm — produces an extended Covariance matrix,
- nan_partcorrcoef — calculates the partial correlation between X and Y after removing the influence of Z.
- nan_rankcorr — calculated the rank correlation coefficient.
- nan_tiedrank — compute rank of samples, the mean value is used in case of ties
- nan_xcorr — Compute correlation R_xy of X and Y for various lags k:
- nan_xcorr2 — Compute the 2D correlation
- nan_xcov — Compute covariance at various lags[=correlation(x-mean(x),y-mean(y))].
- nan_xcovf — generates cross-covariance function.
- Classification
- nan_cat2bin — converts categorial into binary data
- nan_classify — classifies sample data into categories
- nan_confusionmat — Confusion matrix for classification algorithms.
- nan_fss — feature subset selection and feature ranking
- nan_kappa — estimates Cohen's kappa coefficient
- nan_mahal — return the Mahalanobis' D-square distance
- nan_partest — This function calculate the performance, based on Bayes theorem, of a
- nan_rocplot — plot a Receiver Operating Characteristic (ROC) curve
- nan_row_col_deletion — selects the rows and columns for removing any missing values.
- nan_svmrocplot — plotroc draws the recevier operating characteristic(ROC) curve for an svm-model
- nan_test_sc — apply statistical and SVM classifier to test data
- nan_train_lda_sparse — Linear Discriminant Analysis for the Small Sample Size Problem as described in
- nan_train_sc — Train a (statistical) classifier
- nan_xval — is used for crossvalidation
- predict — Does prediction for a calculated svm model
- svmpredict — Does prediction for a calculated svm model
- svmtrain — trains a svm model
- train — trains a linear model
- Cluster Analysis
- nan_kmeans — K-means clustering algorithm.
- Descriptive Statistics
- nan_center — removes the mean
- nan_coef_of_variation — returns STD(X)/MEAN(X)
- nan_cumsum — Cumulative sum while skiping NaN's.
- nan_detrend — removes the trend from data, NaN's are considered as missing values
- nan_ecdf — empirical cumulative function
- nan_filter — is able to filter data with missing values encoded as NaN.
- nan_geomean — calculates the geomentric mean of data elements.
- nan_grpstats — Summary statistics by group.
- nan_harmmean — calculates the harmonic mean of data elements.
- nan_hist2res — Evaluates Histogram data
- nan_histc — Produce histogram counts.
- nan_histo — calculates histogram for each column
- nan_histo2 — calculates histogram for multiple columns with separate bin values for each data column.
- nan_histo3 — calculates histogram for multiple columns with common bin values among all data columns, and can be useful for data compression.
- nan_histo4 — calculates calculates histogram of multidimensional data samples and supports data compression
- nan_iqr — calculates the interquartile range
- nan_kurtosis — estimates the kurtosis
- nan_mad — estimates the Mean Absolute deviation
- nan_mean — calculates the mean of data elements.
- nan_meanAbsDev — estimates the Mean Absolute deviation
- nan_meandev — estimates the Mean deviation
- nan_meansq — calculates the mean of the squares
- nan_medAbsDev — calculates the median absolute deviation
- nan_median — median data elements,
- nan_moment — estimates the p-th moment
- nan_percentile — calculates the percentiles of histograms and sample arrays.
- nan_prctile — calculates the percentiles of histograms and sample arrays.
- nan_quantile — calculates the quantiles of histograms and sample arrays.
- nan_range — Range of values
- nan_ranks — gives the rank of each element in a vector.
- nan_rms — calculates the root mean square
- nan_sem — calculates the standard error of the mean
- nan_skewness — estimates the skewness
- nan_spearman — Spearman's rank correlation coefficient.
- nan_statistic — estimates various statistics at once.
- nan_std — calculates the standard deviation.
- nan_sumsq — calculates the sum of squares.
- nan_trimean — evaluates basic statistics of a data series
- nan_trimmean — calculates the trimmed mean by removing the upper and lower
- nan_var — calculates the variance.
- nan_y2res — evaluates basic statistics of a data series
- nan_zScoreMedian — removes the median and standardizes by the 1.483*median absolute deviation
- nan_zscore — removes the mean and normalizes the data to a variance of 1.
- File I/O
- readsparse — reads files in LIBSVM format
- writesparse — writes sparse matrix to a file in LIBSVM format
- xptopen — Read and write in stata fileformat
- Hypothesis Tests
- nan_ttest — (paired) t-test
- nan_ttest2 — (unpaired) t-test
- utility functions
- flag_accuracy_level — sets and gets accuracy level
- flag_impl_significance — sets and gets default alpha (level) of any significance test
- flag_impl_skip_nan — sets and gets default mode for handling NaNs
- flag_nans_occured — checks whether the last call(s) to sumskipnan or covm
- nan_accumarray — Create an array by accumulating the elements of a vector into the positions defined by their subscripts.
- nan_fft — matlab compatible fft
- nan_flix — floating point index - interpolates data in case of non-integer indices
- nan_grp2idx — Create index vector from a grouping variable.
- nan_ifft — matlab compatible ifft
- nan_ismember — Checks which elements of one matrix are member of an other matrix
- nan_mgrp2idx — Convert multiple grouping variables to index vector
- nan_postpad — append the scalar
- nan_prepad — prepend the scalar
- nan_unique — Return the unique elements of x, sorted in ascending order.
- str2array — C-MEX implementation of STR2ARRAY - this function is part of the NaN-toolbox.
- sumskipnan — adds all non-NaN values.
- Statistical Visualization
- nan_andrewsplot — Andrews plot for multivariate data.
- nan_boxplot — Draw a box-and-whiskers plot for data provided as column vectors.
- nan_cdfplot — plots empirical commulative distribution function
- nan_ecdfhist — Create histogram from ecdf output.
- nan_errorb — plot nice healthy error bars
- nan_errorbar — This function put an errobar range onto plot
- nan_fscatter3 — Plots point cloud data
- nan_gplotmatrix — Scatter plot matrix with grouping variable.
- nan_gscatter — scatter plot of groups
- nan_hist — Histogram.
- nan_nhist — Histogram
- nan_normplot — Produce a normal probability plot for each column of X.
- nan_parallelcoords — Parallel coordinates plot for multivariate data.
- nan_plotmatrix — function [h]=nan_plotmatrix(x,y,param1,param2)