The Statistics package for GNU Octave.
Select Category:
cluster
|
Define clusters from an agglomerative hierarchical cluster tree. |
clusterdata
|
Wrapper function for 'linkage' and 'cluster'. |
cmdscale
|
Classical multidimensional scaling of a matrix. |
confusionmat
|
Compute a confusion matrix for classification problems |
ConfusionMatrixChart
|
Create object P, a Confusion Matrix Chart object. |
cophenet
|
Compute the cophenetic correlation coefficient. |
evalclusters
|
Create a clustering evaluation object to find the optimal number of clusters. |
inconsistent
|
Compute the inconsistency coefficient for each link of a hierarchical cluster tree. |
kmeans
|
Perform a K-means clustering of the NxD matrix DATA. |
linkage
|
Produce a hierarchical clustering dendrogram. |
mahal
|
Mahalanobis' D-square distance. |
mhsample
|
Draws NSAMPLES samples from a target stationary distribution PDF using Metropolis-Hastings algorithm. |
optimalleaforder
|
Compute the optimal leaf ordering of a hierarchical binary cluster tree. |
pdist
|
Return the distance between any two rows in X. |
pdist2
|
Compute pairwise distance between two sets of vectors. |
procrustes
|
Procrustes Analysis. |
slicesample
|
Draws NSAMPLES samples from a target stationary distribution PDF using slice sampling of Radford M. |
squareform
|
Interchange between distance matrix and distance vector formats. |
@cvpartition/cvpartition
|
Create a partition object for cross validation. |
@cvpartition/display
|
Display a 'cvpartition' object, C. |
@cvpartition/get
|
Get a field, F, from a 'cvpartition' object, C. |
@cvpartition/repartition
|
Return a new cvpartition object. |
@cvpartition/set
|
Set FIELD, in a 'cvpartition' object, C. |
@cvpartition/test
|
Return logical vector for testing-subset indices from a 'cvpartition' object, C. |
@cvpartition/training
|
Return logical vector for training-subset indices from a 'cvpartition' object, C. |
combnk
|
Return all combinations of K elements in DATA. |
crosstab
|
Create a cross-tabulation (contingency table) T from data vectors. |
datasample
|
Randomly sample data. |
fillmissing
|
Replace missing entries of array A either with values in V or as determined by other specified methods. 'missing' values are determined by the data type of A as identified by the function ismissing, curently defined as: |
grp2idx
|
Get index for group variables. |
ismissing
|
Find missing data in a numeric or string array. |
normalise_distribution
|
Transform a set of data so as to be N(0,1) distributed according to an idea by van Albada and Robinson. |
rmmissing
|
Remove missing or incomplete data from an array. |
standardizeMissing
|
Replace data values specified by INDICATOR in A by the standard 'missing' data value for that data type. |
tabulate
|
Compute a frequency table. |
cl_multinom
|
Confidence level of multinomial portions. |
geomean
|
Compute the geometric mean of X. |
grpstats
|
Summary statistics by group. 'grpstats' computes groupwise summary statistics, for data in a matrix X. 'grpstats' treats NaNs as missing values, and removes them. |
harmmean
|
Compute the harmonic mean of X. |
jackknife
|
Compute jackknife estimates of a parameter taking one or more given samples as parameters. |
mean
|
Compute the mean of the elements of X. |
median
|
Compute the median value of the elements of X. |
nanmax
|
Find the maximal element while ignoring NaN values. |
nanmin
|
Find the minimal element while ignoring NaN values. |
nansum
|
Compute the sum while ignoring NaN values. |
std
|
Compute the standard deviation of the elements of X. |
trimmean
|
Compute the trimmed mean. |
var
|
Compute the variance of the elements of X. |
bbscdf
|
Birnbaum-Saunders cumulative distribution function (CDF). |
bbsinv
|
Inverse of the Birnbaum-Saunders cumulative distribution function (iCDF). |
bbspdf
|
Birnbaum-Saunders probability density function (PDF). |
bbsrnd
|
Random arrays from the Birnbaum-Saunders distribution. |
betacdf
|
Beta cumulative distribution function (CDF). |
betainv
|
Inverse of the Beta distribution (iCDF). |
betapdf
|
Beta probability density function (PDF). |
betarnd
|
Random arrays from the Beta distribution. |
binocdf
|
Binomial cumulative distribution function (CDF). |
binoinv
|
Inverse of the Binomial cumulative distribution function (iCDF). |
binopdf
|
Binomial probability density function (PDF). |
binornd
|
Random arrays from the Binomial distribution |
burrcdf
|
Burr type XII cumulative distribution function (CDF). |
burrinv
|
Inverse of the Burr type XII cumulative distribution function (iCDF). |
burrpdf
|
Burr type XII probability density function (PDF). |
burrrnd
|
Random arrays from the Burr type XII distribution. |
bvncdf
|
Bivariate normal cumulative distribution function (CDF). |
bvtcdf
|
Bivariate Student's t cumulative distribution function (CDF). |
cauchy_cdf
|
Cauchy cumulative distribution function (CDF). |
cauchy_inv
|
Inverse of the Cauchy cumulative distribution function (iCDF). |
cauchy_pdf
|
Cauchy probability density function (PDF). |
cauchy_rnd
|
Random arrays from the Cauchy distribution. |
chi2cdf
|
Chi-square cumulative distribution function. |
chi2inv
|
Inverse of the Chi-square cumulative distribution function (iCDF). |
chi2pdf
|
Chi-square probability density function (PDF). |
chi2rnd
|
Random arrays from the Chi-square distribution. |
copulacdf
|
Copula family cumulative distribution functions (CDF). |
copulapdf
|
Copula family probability density functions (PDF). |
copularnd
|
Random arrays from the copula family distributions. |
evcdf
|
Extreme value cumulative distribution function (CDF). |
evinv
|
Inverse of the extreme value cumulative distribution function (iCDF). |
evpdf
|
Extreme value probability density function (PDF). |
evrnd
|
Random arrays from the extreme value distribution. |
expcdf
|
Exponential cumulative distribution function (CDF). |
expinv
|
Inverse of the exponential cumulative distribution function (iCDF). |
exppdf
|
Exponential probability density function (PDF). |
exprnd
|
Random arrays from the exponential distribution. |
fcdf
|
F cumulative distribution function (CDF). |
finv
|
Inverse of the F cumulative distribution function (iCDF). |
fpdf
|
F probability density function (PDF). |
frnd
|
Random arrays from the F distribution. |
gamcdf
|
Gamma cumulative distribution function (CDF). |
gaminv
|
Inverse of the Gamma cumulative distribution function (iCDF). |
gampdf
|
Gamma probability density function (PDF). |
gamrnd
|
Random arrays from the Gamma distribution. |
geocdf
|
Geometric cumulative distribution function (CDF). |
geoinv
|
Inverse of the geometric cumulative distribution function (iCDF). |
geopdf
|
Geometric probability density function (PDF). |
geornd
|
Random arrays from the geometric distribution. |
gevcdf
|
Generalized extreme value (GEV) cumulative distribution function (CDF). |
gevinv
|
Inverse of the generalized extreme value (GEV) cumulative distribution function (iCDF). |
gevpdf
|
Generalized extreme value (GEV) probability density function (PDF). |
gevrnd
|
Random arrays from the generalized extreme value (GEV) distribution. |
gpcdf
|
Generalized Pareto cumulative distribution function (cdf). |
gpinv
|
Inverse of the generalized Pareto cumulative distribution function (iCDF). |
gppdf
|
Generalized Pareto probability density function (PDF). |
gprnd
|
Random arrays from the generalized Pareto distribution. |
hygecdf
|
Hypergeometric cumulative distribution function (CDF). |
hygeinv
|
Inverse of the hypergeometric cumulative distribution function (iCDF). |
hygepdf
|
Hypergeometric probability density function (PDF). |
hygernd
|
Random arrays from the hypergeometric distribution. |
iwishpdf
|
Compute the probability density function of the inverse Wishart distribution. |
iwishrnd
|
Return a random matrix sampled from the inverse Wishart distribution with given parameters. |
jsucdf
|
Johnson SU cumulative distribution function (CDF). |
jsupdf
|
Johnson SU probability density function (PDF). |
laplace_cdf
|
Laplace cumulative distribution function (CDF). |
laplace_inv
|
Inverse of the Laplace cumulative distribution function (iCDF). |
laplace_pdf
|
Laplace probability density function (PDF). |
laplace_rnd
|
Random arrays from the Laplace distribution. |
logistic_cdf
|
Logistic cumulative distribution function (CDF). |
logistic_inv
|
Inverse of the logistic cumulative distribution function (iCDF). |
logistic_pdf
|
Logistic probability density function (PDF). |
logistic_rnd
|
Random arrays from the logistic distribution. |
logncdf
|
Lognormal cumulative distribution function (CDF). |
logninv
|
Inverse of the lognormal cumulative distribution function (iCDF). |
lognpdf
|
Lognormal probability density function (PDF). |
lognrnd
|
Random arrays from the lognormal distribution. |
mnpdf
|
Multinomial probability density function (PDF). |
mnrnd
|
Random arrays from the multinomial distribution. |
mvncdf
|
Multivariate normal cumulative distribution function (CDF). |
mvnpdf
|
Multivariate normal probability density function (PDF). |
mvnrnd
|
Random vectors from the multivariate normal distribution. |
mvtcdf
|
Multivariate Student's t cumulative distribution function (CDF). |
mvtpdf
|
Multivariate Student's t probability density function (PDF). |
mvtrnd
|
Random vectors from the multivariate Student's t distribution. |
mvtcdfqmc
|
Quasi-Monte-Carlo computation of the multivariate Student's T CDF. |
nakacdf
|
Nakagami cumulative distribution function (CDF). |
nakainv
|
Inverse of the Nakagami cumulative distribution function (iCDF). |
nakapdf
|
Nakagami probability density function (PDF). |
nakarnd
|
Random arrays from the Nakagami distribution. |
nbincdf
|
Negative binomial cumulative distribution function (CDF). |
nbininv
|
Inverse of the negative binomial cumulative distribution function (iCDF). |
nbinpdf
|
Negative binomial probability density function (PDF). |
nbinrnd
|
Random arrays from the negative binomial distribution. |
ncfcdf
|
Noncentral F cumulative distribution function (CDF). |
ncfinv
|
Inverse of the non-central F cumulative distribution function (iCDF). |
ncfpdf
|
Noncentral F probability density function (PDF). |
ncfrnd
|
Random arrays from the noncentral F distribution. |
nctcdf
|
Noncentral T cumulative distribution function (CDF). |
nctinv
|
Inverse of the non-central T cumulative distribution function (iCDF). |
nctpdf
|
Noncentral Τ probability density function (pdf). |
nctrnd
|
Random arrays from the noncentral T distribution. |
ncx2cdf
|
Noncentral Chi-Square cumulative distribution function (CDF). |
ncx2inv
|
Inverse of the non-central chi-square cumulative distribution function (iCDF). |
ncx2pdf
|
Noncentral Chi-Square probability distribution function (PDF). |
ncx2rnd
|
Random arrays from the non-central chi-square distribution. |
normcdf
|
Normal cumulative distribution function (CDF). |
norminv
|
Inverse of the normal cumulative distribution function (iCDF). |
normpdf
|
Normal probability density function (PDF). |
normrnd
|
Random arrays from the normal distribution. |
poisscdf
|
Poisson cumulative distribution function (CDF). |
poissinv
|
Inverse of the Poisson cumulative distribution function (iCDF). |
poisspdf
|
Poisson probability density function (PDF). |
poissrnd
|
Random arrays from the Poisson distribution. |
raylcdf
|
Rayleigh cumulative distribution function (CDF). |
raylinv
|
Inverse of the Rayleigh cumulative distribution function (iCDF). |
raylpdf
|
Rayleigh probability density function (PDF). |
raylrnd
|
Random arrays from the Rayleigh distribution. |
stdnormal_cdf
|
Standard normal cumulative distribution function (CDF). |
stdnormal_inv
|
Inverse of the standard normal cumulative distribution function (iCDF). |
stdnormal_pdf
|
Standard normal probability density function (PDF). |
stdnormal_rnd
|
Random arrays from the standard normal distribution. |
tcdf
|
Student's T cumulative distribution function (CDF). |
tinv
|
Inverse of the Student's T cumulative distribution function (iCDF). |
tpdf
|
Student's T probability density function (PDF). |
trnd
|
Random arrays from the Student's T distribution. |
tricdf
|
Triangular cumulative distribution function (CDF). |
triinv
|
Inverse of the triangular cumulative distribution function (iCDF). |
tripdf
|
Triangular probability density function (PDF). |
trirnd
|
Random arrays from the triangular distribution. |
unidcdf
|
Discrete uniform cumulative distribution function (CDF). |
unidinv
|
Inverse of the discrete uniform cumulative distribution function (iCDF). |
unidpdf
|
Discrete uniform probability density function (PDF). |
unidrnd
|
Random arrays from the discrete uniform distribution. |
unifcdf
|
Uniform cumulative distribution function (CDF). |
unifinv
|
Inverse of the uniform cumulative distribution function (iCDF). |
unifpdf
|
Uniform probability density function (PDF). |
unifrnd
|
Random arrays from the uniform distribution. |
vmcdf
|
Von Mises probability density function (PDF). |
vmpdf
|
Von Mises probability density function (PDF). |
vmrnd
|
Random arrays from the von Mises distribution. |
wblcdf
|
Weibull cumulative distribution function (CDF). |
wblinv
|
Inverse of the Weibull cumulative distribution function (iCDF). |
wblpdf
|
Weibull probability density function (PDF). |
wblrnd
|
Random arrays from the Weibull distribution. |
wienrnd
|
Return a simulated realization of the D-dimensional Wiener Process on the interval [0, T]. |
wishpdf
|
Compute the probability density function of the Wishart distribution |
wishrnd
|
Return a random matrix sampled from the Wishart distribution with given parameters |
cdfcalc
|
Calculate an empirical cumulative distribution function. |
evfit
|
Estimate parameters and confidence intervals for extreme value data. |
evlike
|
Negative log-likelihood for the extreme value distribution. |
expfit
|
Estimate the mean of the exponential probability distribution function from which sample data S has been taken. |
explike
|
Compute the negative log-likelihood of data under the exponential distribution with given parameter value. |
gamfit
|
Calculate gamma distribution parameters. |
gamlike
|
Calculates the negative log-likelihood function for the Gamma distribution over vector R, with the given parameters A and B in a 2-element vector PARAMS. |
gevfit_lmom
|
Find an estimator (PARAMHAT) of the generalized extreme value (GEV) distribution fitting DATA using the method of L-moments. |
gevfit
|
Find the maximum likelihood estimator PARAMHAT of the generalized extreme value (GEV) distribution to fit DATA. |
gevlike
|
Compute the negative log-likelihood of data under the generalized extreme value (GEV) distribution with given parameter values. |
gpfit
|
Parameter estimates and confidence intervals for generalized Pareto data. |
gplike
|
Negative log-likelihood for the generalized Pareto distribution. |
normlike
|
Negative log-likelihood for the normal distribution. |
betastat
|
Compute mean and variance of the beta distribution. |
binostat
|
Compute mean and variance of the binomial distribution. |
chi2stat
|
Compute mean and variance of the chi-square distribution. |
evstat
|
Mean and variance of the extreme value distribution. |
expstat
|
Compute mean and variance of the exponential distribution. |
fstat
|
Compute mean and variance of the F distribution. |
gamstat
|
Compute mean and variance of the gamma distribution. |
geostat
|
Compute mean and variance of the geometric distribution. |
gevstat
|
Compute the mean and variance of the generalized extreme value distribution. |
gpstat
|
Mean and variance of the generalized Pareto distribution. |
hygestat
|
Compute mean and variance of the hypergeometric distribution. |
lognstat
|
Compute mean and variance of the lognormal distribution. |
nbinstat
|
Compute mean and variance of the negative binomial distribution. |
ncfstat
|
Mean and variance for the noncentral F distribution. |
nctstat
|
Mean and variance for the noncentral T distribution. |
ncx2stat
|
Mean and variance for the noncentral chi-square distribution. |
normstat
|
Compute mean and variance of the normal distribution. |
poisstat
|
Compute mean and variance of the Poisson distribution. |
raylstat
|
Compute mean and variance of the Rayleigh distribution. |
tstat
|
Compute mean and variance of the t (Student) distribution. |
unidstat
|
Compute mean and variance of the discrete uniform distribution. |
unifstat
|
Compute mean and variance of the continuous uniform distribution. |
wblstat
|
Compute mean and variance of the Weibull distribution. |
fullfact
|
Full factorial design. |
ff2n
|
Two-level full factorial design. |
sigma_pts
|
Calculates 2*N+1 sigma points in N dimensions. |
x2fx
|
Convert predictors to design matrix. |
hmmestimate
|
Estimation of a hidden Markov model for a given sequence. |
hmmgenerate
|
Output sequence and hidden states of a hidden Markov model. |
hmmviterbi
|
Viterbi path of a hidden Markov model. |
svmpredict
|
This function predicts new labels from a testing instance matrtix based on an SVM MODEL created with 'svmtrain'. |
svmtrain
|
This function trains an SVM MODEL based on known LABELS and their corresponding DATA which comprise an instance matrtix. |
crossval
|
Perform cross validation on given data. |
fitgmdist
|
Fit a Gaussian mixture model with K components to DATA. |
fitlm
|
Regress the continuous outcome (i.e. dependent variable) Y on continuous or categorical predictors (i.e. independent variables) X by minimizing the sum-of-squared residuals. |
adtest
|
Anderson-Darling goodness-of-fit hypothesis test. |
anova1
|
Perform a one-way analysis of variance (ANOVA) for comparing the means of two or more groups of data under the null hypothesis that the groups are drawn from distributions with the same mean. |
anova2
|
Performs two-way factorial (crossed) or a nested analysis of variance (ANOVA) for balanced designs. |
anovan
|
Perform a multi (N)-way analysis of (co)variance (ANOVA or ANCOVA) to evaluate the effect of one or more categorical or continuous predictors (i.e. independent variables) on a continuous outcome (i.e. dependent variable). |
bartlett_test
|
Perform a Bartlett test for the homogeneity of variances. |
barttest
|
Bartlett's test of sphericity for correlation. |
binotest
|
Test for probability P of a binomial sample |
chi2gof
|
Chi-square goodness-of-fit test. |
chi2test
|
Perform a chi-squared test (for independence or homogeneity). |
correlation_test
|
Perform a correlation coefficient test whether two samples X and Y come from uncorrelated populations. |
fishertest
|
Fisher's exact test. |
friedman
|
Performs the nonparametric Friedman's test to compare column effects in a two-way layout. friedman tests the null hypothesis that the column effects are all the same against the alternative that they are not all the same. |
hotelling_t2test
|
Compute Hotelling's T^2 ("T-squared") test for a single sample or two dependent samples (paired-samples). |
hotelling_t2test2
|
Compute Hotelling's T^2 ("T-squared") test for two independent samples. |
kruskalwallis
|
Perform a Kruskal-Wallis test, the non-parametric alternative of a one-way analysis of variance (ANOVA), for comparing the means of two or more groups of data under the null hypothesis that the groups are drawn from the same population, ... |
kstest
|
Single sample Kolmogorov-Smirnov (K-S) goodness-of-fit hypothesis test. |
kstest2
|
Two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test. |
levene_test
|
Perform a Levene's test for the homogeneity of variances. |
manova1
|
One-way multivariate analysis of variance (MANOVA). |
multcompare
|
Perform posthoc multiple comparison tests or p-value adjustments to control the family-wise error rate (FWER) or false discovery rate (FDR). |
ranksum
|
Wilcoxon rank sum test for equal medians. |
regression_ftest
|
F-test for General Linear Regression Analysis |
regression_ttest
|
Perform a linear regression t-test. |
runstest
|
Runs test for detecting serial correlation in the vector X. |
sampsizepwr
|
Sample size and power calculation for hypothesis test. |
signtest
|
Test for median. |
ttest
|
Test for mean of a normal sample with unknown variance. |
ttest2
|
Perform a t-test to compare the means of two groups of data under the null hypothesis that the groups are drawn from distributions with the same mean. |
vartest
|
One-sample test of variance. |
vartest2
|
Two-sample F test for equal variances. |
vartestn
|
Test for equal variances across multiple groups. |
ztest
|
One-sample Z-test. |
ztest2
|
Two proportions Z-test. |
libsvmread
|
This function reads the labels and the corresponding instance_matrix from a LIBSVM data file and stores them in LABELS and DATA respectively. |
libsvmwrite
|
This function saves the labels and the corresponding instance_matrix in a file specified by FILENAME. |
boxplot
|
Produce a box plot. |
cdfplot
|
Display an empirical cumulative distribution function. |
confusionchart
|
Display a chart of a confusion matrix. |
dendrogram
|
Plot a dendrogram of a hierarchical binary cluster tree. |
ecdf
|
Empirical (Kaplan-Meier) cumulative distribution function. |
gscatter
|
Draw a scatter plot with grouped data. |
histfit
|
Plot histogram with superimposed fitted normal density. |
hist3
|
Produce bivariate (2D) histogram counts or plots. |
manovacluster
|
Cluster group means using manova1 output. |
normplot
|
Produce normal probability plot of the data in X. |
ppplot
|
Perform a PP-plot (probability plot). |
qqplot
|
Perform a QQ-plot (quantile plot). |
silhouette
|
Compute the silhouette values of clustered data and show them on a plot. |
violin
|
Produce a Violin plot of the data X. |
wblplot
|
Plot a column vector DATA on a Weibull probability plot using rank regression. |
canoncorr
|
Canonical correlation analysis. |
cholcov
|
Cholesky-like decomposition for covariance matrix. |
dcov
|
Distance correlation, covariance and correlation statistics. |
logistic_regression
|
Perform ordinal logistic regression. |
monotone_smooth
|
Produce a smooth monotone increasing approximation to a sampled functional dependence. |
pca
|
Performs a principal component analysis on a data matrix X. |
pcacov
|
Perform principal component analysis on the NxN covariance matrix X. |
pcares
|
Calculate residuals from principal component analysis. |
plsregress
|
Calculate partial least squares regression using SIMPLS algorithm. |
princomp
|
Performs a principal component analysis on a NxP data matrix X. |
regress
|
Multiple Linear Regression using Least Squares Fit of Y on X with the model 'y = X * beta + e'. |
regress_gp
|
Linear scalar regression using gaussian processes. |
stepwisefit
|
Linear regression with stepwise variable selection. |
cdf
|
Return the CDF of NAME distribution function for value X. |
icdf
|
Return the inverse CDF of NAME distribution function for value P. |
pdf
|
Return the PDF of NAME distribution function for value X. |
random
|
Random arrays from from a given one-, two-, or three-parameter distribution. |