[GPF Home Page]

M_datapac man(3) pages

grouping page description
M_datapac:ANALYSISfourie perform a Fourier analysis of a data set
M_datapac:ANALYSISnorout Performs a normal outlier analysis on the data in the input vector X.
M_datapac:ANALYSISruns perform a runs test
M_datapac:ANALYSIStail performs a symmetric distribution tail length analysis
M_datapac:ANALYSIStime perform a time series analysis (autocorrelation plot, a test for white noise, a "pilot" spectrum, and 4 other estimated spectra based on differing bandwidth)
M_datapac:ANALYSISweib perform a Weibull distribution analysis (Weibull PPCC analysis)
M_datapac:CUMULATIVE_DISTRIBUTIONbincdf compute the binomial cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONcaucdf compute the Cauchy cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONchscdf compute the chi-square cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONdexcdf compute the double exponential cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONev1cdf compute the extreme value type 1 (Gumbel) cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONev2cdf compute the extreme value type 2 (Frechet) cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONexpcdf compute the exponential cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONfcdf compute the F cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONgamcdf compute the gamma cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONgeocdf compute the geometric cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONhfncdf compute the half-normal cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONlamcdf compute the Tukey-Lambda cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONlgncdf compute the lognormal cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONlogcdf compute the logistic cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONnbcdf compute the negative binomial cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONnorcdf compute the normal cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONparcdf compute the Pareto cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONpoicdf compute the Poisson cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONtcdf computes the cumulative distribution function value for student's t distribution with integer degrees of freedom NU.
M_datapac:CUMULATIVE_DISTRIBUTIONunicdf trivially compute the Uniform cumulative distribution function
M_datapac:CUMULATIVE_DISTRIBUTIONweicdf compute the Weibull cumulative distribution function
M_datapac:GENERIC_LINE_PLOTplot yields a one-page printer plot of Y(I) versus X(I)
M_datapac:GENERIC_LINE_PLOTplot10 generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplot6 generate a line printer plot
M_datapac:GENERIC_LINE_PLOTplot7 generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplot8 generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplot9 generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplotc generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplotco generate a line printer autocorrelation plot
M_datapac:GENERIC_LINE_PLOTplotct generate a line printer plot for the terminal (71 characters wide)
M_datapac:GENERIC_LINE_PLOTplots generate a line printer plot of Y vs X
M_datapac:GENERIC_LINE_PLOTplotsc generate a line printer plot with special plot characters
M_datapac:GENERIC_LINE_PLOTplotst generate a line printer plot of Y vs X for the terminal (71 characters wide)
M_datapac:GENERIC_LINE_PLOTplott generate a line printer plot of Y vs X for the terminal (71 characters wide)
M_datapac:GENERIC_LINE_PLOTplotu generate a line printer 4-plot
M_datapac:GENERIC_LINE_PLOTplotx generate a line printer run sequence plot
M_datapac:GENERIC_LINE_PLOTpltsct generate a line printer plot with special plot characters for the terminal (71 characters wide)
M_datapac:LINE_PLOTcauplt generate a Cauchy probability plot
M_datapac:LINE_PLOTchsplt generate a Chi-square probability plot
M_datapac:LINE_PLOTdexplt generate a double exponential probability plot
M_datapac:LINE_PLOTev1plt generate a extreme value type 1 (Gumbel) probability plot
M_datapac:LINE_PLOTev2plt generate a extreme value type 2 (Frechet) probability plot
M_datapac:LINE_PLOTexpplt generate a exponential probability plot
M_datapac:LINE_PLOTgamplt generate a gamma probability plot
M_datapac:LINE_PLOTgeoplt generate a geometric probability plot
M_datapac:LINE_PLOThfnplt generate a half-normal probability plot
M_datapac:LINE_PLOTlamplt generate a Tukey-Lambda probability plot
M_datapac:LINE_PLOTlgnplt generates a lognormal probability plot
M_datapac:LINE_PLOTlogplt generate a logistic probability plot
M_datapac:LINE_PLOTnorplt generate a normal probability plot
M_datapac:LINE_PLOTparplt generate a Pareto probability plot
M_datapac:LINE_PLOTplotsp generate a line printer spectrum plot
M_datapac:LINE_PLOTplotxt generate a line printer run sequence plot for the terminal (71 characters wide)
M_datapac:LINE_PLOTplotxx generate a line printer lag plot
M_datapac:LINE_PLOTpltxxt generate a line printer lag plot for the terminal (71 characters wide)
M_datapac:LINE_PLOTpoiplt generate a Poisson probability plot (line printer graph)
M_datapac:LINE_PLOTtplt generates a Student's T probability plot (with integer degrees of freedom parameter value NU).
M_datapac:LINE_PLOTuniplt generate a Uniform probability plot (line printer graph)
M_datapac:LINE_PLOTweiplt generate a Weibull probability plot (line printer graph)
M_datapac:PERCENT_POINTbinppf compute the binomial percent point function
M_datapac:PERCENT_POINTcauppf compute the Cauchy percent point function
M_datapac:PERCENT_POINTchsppf compute the chi-square percent point function
M_datapac:PERCENT_POINTdexppf compute the double exponential percent point function
M_datapac:PERCENT_POINTev1ppf compute the extreme value type 1 (Gumbel) percent point function
M_datapac:PERCENT_POINTev2ppf compute the extreme value type 2 (Frechet) percent point function
M_datapac:PERCENT_POINTexpppf compute the exponential percent point function
M_datapac:PERCENT_POINTgamppf compute the gamma percent point function
M_datapac:PERCENT_POINTgeoppf compute the geometric percent point function
M_datapac:PERCENT_POINThfnppf compute the half-normal percent point function
M_datapac:PERCENT_POINTlamppf compute the Tukey-Lambda percent point function
M_datapac:PERCENT_POINTlgnppf compute the lognormal percent point function
M_datapac:PERCENT_POINTlogppf compute the logistic percent point function
M_datapac:PERCENT_POINTnbppf compute the negative binomial percent point function
M_datapac:PERCENT_POINTnorppf compute the normal percent point function
M_datapac:PERCENT_POINTparppf compute the Pareto percent point function
M_datapac:PERCENT_POINTpoippf compute the Poisson percent point function
M_datapac:PERCENT_POINTsampp compute the sample 100P percent point (i.e., percentile)
M_datapac:PERCENT_POINTtppf computes the percent point function value for the student's T distribution
M_datapac:PERCENT_POINTunippf compute the Uniform percent point function
M_datapac:PERCENT_POINTweippf compute the Weibull percent point function
M_datapac:PROBABILITY_DENSITYcaupdf compute the Cauchy probability density function
M_datapac:PROBABILITY_DENSITYdexpdf compute the double exponential probability density function
M_datapac:PROBABILITY_DENSITYexppdf compute the exponential probability density function
M_datapac:PROBABILITY_DENSITYlampdf compute the Tukey-Lambda probability density function
M_datapac:PROBABILITY_DENSITYlogpdf compute the logistic probability density function
M_datapac:PROBABILITY_DENSITYnorpdf compute the normal probability density function
M_datapac:PROBABILITY_DENSITYunipdf trivially compute the Uniform probability density function
M_datapac:RANDOMbetran generate beta random numbers
M_datapac:RANDOMbinran generate binomial random numbers
M_datapac:RANDOMcauran generate Cauchy random numbers
M_datapac:RANDOMchsran generate chi-square random numbers
M_datapac:RANDOMdexran generate double exponential random numbers
M_datapac:RANDOMev1ran generate extreme value type 1 (Gumbel) random numbers
M_datapac:RANDOMev2ran generate extreme value type 2 (Frechet) random numbers
M_datapac:RANDOMexpran generate exponential random numbers
M_datapac:RANDOMfran generate F random numbers
M_datapac:RANDOMgamran generate gamma random numbers
M_datapac:RANDOMgeoran generate geometric random numbers
M_datapac:RANDOMhfnran generate half-normal random numbers
M_datapac:RANDOMlamran generate Tukey-Lambda random numbers
M_datapac:RANDOMlgnran generate lognormal random numbers
M_datapac:RANDOMlogran generate logistic random numbers
M_datapac:RANDOMnbran generate negative binomial random numbers
M_datapac:RANDOMnorran generate normal random numbers
M_datapac:RANDOMparran generate Pareto random numbers
M_datapac:RANDOMpoiran generate Poisson random numbers
M_datapac:RANDOMranper generates a random permutation
M_datapac:RANDOMtran a random sample of size n from the Student's t distribution with integer degrees of freedom parameter NU.
M_datapac:RANDOMuniran generate Uniform random numbers
M_datapac:RANDOMweiran generate Weibull random numbers
M_datapac:SORTrank rank a vector of sample observations
M_datapac:SORTsort sort a vector of sample observations, also return the positions in the original vector
M_datapac:SORTsortc sort a vector of sample observations and "carry" a second vector
M_datapac:SORTsortp sorts and ranks a numeric vector X
M_datapac:SPARSITYcausf compute the Cauchy sparsity function
M_datapac:SPARSITYdexsf compute the double exponential sparsity function
M_datapac:SPARSITYexpsf compute the exponential sparsity function
M_datapac:SPARSITYlamsf compute the Tukey-Lambda sparsity function
M_datapac:SPARSITYlogsf compute the logistic sparsity function
M_datapac:SPARSITYnorsf compute the normal sparsity function
M_datapac:SPARSITYunisf compute the Uniform sparsity function
M_datapac:STATISTICSautoco compute the sample autocorrelation coefficient
M_datapac:STATISTICScorr compute the sample correlation coefficient
M_datapac:STATISTICScount compute the number of observations between a minimum and a maximum value
M_datapac:STATISTICSdecomp decomposes a weighted data matrix (utility routine used by other routines)
M_datapac:STATISTICSdemod perform a complex demodulation
M_datapac:STATISTICSdiscr2 bin the elements of a vector (output vector contains class midpoints)
M_datapac:STATISTICSdiscr3 bin the elements of a vector (output vector contains 1's, 2's, 3's, and so on)
M_datapac:STATISTICSdiscre bin the elements of a vector (like DISCR2, but allows specification of min and max class limits)
M_datapac:STATISTICSextrem determine whether a type 1 or type 2 extreme value distribution better fits a given data set
M_datapac:STATISTICSfreq compute the sample frequency and cumulative sample frequency of a vector
M_datapac:STATISTICShist generates histograms based on two different class widths
M_datapac:STATISTICSloc print the sample mean, midrange, midmean, and median
M_datapac:STATISTICSmean compute the sample mean of a data vector
M_datapac:STATISTICSmedian compute the median of a data vector
M_datapac:STATISTICSmidm compute the midmean of a data vector
M_datapac:STATISTICSmidr compute the midrange of a data vector
M_datapac:STATISTICSmin compute the minimum of a data vector
M_datapac:STATISTICSpropor compute the sample proportion
M_datapac:STATISTICSrange compute the sample range
M_datapac:STATISTICSrelsd compute the relative standard deviation of a vector of observations
M_datapac:STATISTICSscale compute the sample range, sample standard deviation, sample relative standard deviation, and sample variance
M_datapac:STATISTICSsd compute the standard deviation of a vector of observations
M_datapac:STATISTICSspcorr compute the sample Spearman rank correlation coefficient between two vectors of observations
M_datapac:STATISTICSstmom3 compute the third central moment (i.e., the skewness) of a vector of observations
M_datapac:STATISTICSstmom4 compute the fourth central moment (i.e., the kurtosis) of a vector of observations
M_datapac:STATISTICStol compute normal and distribution-free tolerance limits
M_datapac:STATISTICStrim computes the sample trimmed mean of the data in the input vector X.
M_datapac:STATISTICSunimed generates the N order statistic medians from the uniform (rectangular) distribution on the unit interval (0, 1).
M_datapac:STATISTICSvar compute the sample variance of a vector of observations
M_datapac:STATISTICSwind compute the sample Winsorized mean of a vector of observations
M_datapac:VECTOR_OPERATIONScode code the elements of a vector (1 for the minimum, 2 for the next larger value, and so on)
M_datapac:VECTOR_OPERATIONcopy copy the elements of one vector into another vector
M_datapac:VECTOR_OPERATIONdefine set all elements of a vector equal to a specified constant
M_datapac:VECTOR_OPERATIONdelete delete all elements of a vector within some specified interval
M_datapac:VECTOR_OPERATIONdot compute a dot product of two vectors
M_datapac:VECTOR_OPERATIONmax MAX compute the maximum of a data vector
M_datapac:VECTOR_OPERATIONmove move selected elements of one vector into another vector
M_datapac:VECTOR_OPERATIONreplac replace all observations in a vector within a given interval with a user-specified constant
M_datapac:VECTOR_OPERATIONretain retain all observations in a vector within a user-specified interval
M_datapac:VECTOR_OPERATIONsubse1 extract the elements of a vector which fall into a user-specified subset (one subset variable)
M_datapac:VECTOR_OPERATIONsubse2 extract the elements of a vector which fall into a user-specified subset (two subset variables)
M_datapac:VECTOR_OPERATIONsubset extract the elements of a vector which fall into a user-specified subset (one subset variable)