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/*****************************************************************************/
/* lfitinfo.c */
/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
/* Symbolic fitting & arithmetic evaluating utility. */
/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
/* (c) 1996, 2002, 20042005, 2006, 20072008, 2009; */
/* Pal, A. (apal@szofi.net) */
/*****************************************************************************/
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include "fitsh.h"
#include "longhelp.h"
#include <lfit/lfit.h>
#include "lfitinfo.h"
#ifdef HAVE_NO_CC_EXTENSION
#define __extension__
#endif
/*****************************************************************************/
int fprint_lfit_usage(FILE *fw)
{
fprintf(fw,
"Usage:\tlfit [hhelplonghelpwikihelp] [version[short]]\n"
"\t[examples] [functionlist]\n"
"\t[Vverbose] [quiet] [ooutput <output>] \n");
fprintf(fw,
"Common parameters and arguments for fitting and/or regression analysis:\n"
"\tv <fitvariable>[[:]=<initial>[:<error>][[<min>]:[<max>]]] [,...]\n"
"\tg <derivedvariable>=<expression>[,...]\n"
"\t[Fformat <fitvariable>=<numericprintflikeformat>[,...]]\n"
"\t[Ccorrelationformat <numericprintflikeformat>\n"
"\t[qdifference <fitvariable>=<difference>[,...]]\n"
"\t[tconstraint <linear constraint>=<value>[,...]]\n"
"\t[xdefinemacro <function>(v1,v2,...)=<definition> [x ...]]\n");
fprintf(fw,
"Parameters for simple fitting (single data block):\n"
"\t[<file>] c <column names> f <function>[=<dependent>]\n"
"\t[y <dependent>] [e <error>w <weight>]\n");
fprintf(fw,
"Parameters for fitting more than one data blocks:\n"
"\ti<key1> <file> c<key1> <colum names 1> f<key1> <funct1>[=<dep1>]\n"
"\t[y<key1> <dependent1>] [e<key1> <error1>w<key1> <weight1>]\n"
"\ti<key2> <file> c<key2> <colum names 2> f<key2> <funct2>[=<dep2>]\n"
"\t[y<key2> <dependent2>] [e<key2> <error2>w<key2> <weight2>]\n"
"\t[...]\n");
fprintf(fw,
"Fit methods:\tderivatives & linearity\n"
"\t[Lclls]\tyes\tyes\t" "# Classic linear least squares\n"
"\t[Nnllm]\tyes\tno\t" "# Nonlinear LevenbergMarquardt\n"
"\t[Eemce]\topt.\topt.\t" "# Refitting to synthetic data sets\n"
"\t[Mmcmc]\tno\tno\t" "# Markov Chain MonteCarlo\n"
"\t[Kmchi]\tno\tno\t" "# Grid mapping of Chi^2\n"
"\t[Xxmmc]\tyes\tno\t" "# Extended Markov Chain MonteCarlo\n"
"\t[Ulmnd]\tno\tno\t" "# LevenbergMarquardt + num. derivatives\n"
"\t[Ddhsx]\topt.\tno\t" "# Downhill simplex\n"
"\t[Afima]\tyes\tno\t" "# Fisher Information Matrix Analysis\n");
fprintf(fw,
"Finetune parameters [Pparams]:\n"
"\tN P\t[default],[lambda=<l>],[multiply=<m>],[iterations=<i>]\n"
"\tU P\t[default],[lambda=<l>],[multiply=<m>],[iterations=<i>] q <...>\n"
"\tD P\t[default],[fisher]\n"
"\tE P\t[default],[skip],{linearnonlinearlmnddhsx[fisher]mc}\n"
"\tM P\t[default],[[non]accepted],[gibbs]\n"
"\tX P\t[default],[[non]accepted],[skip],\n"
"\t\t[adaptive],[iterations=<N>],[window=<W>]\n"
"\tA P\t[default],[[no]orig],[[no]errors],[[no]corr],[mcmontecarlo]\n"
"\t[UE [q <...>]]\n");
fprintf(fw,
"\t[i{emcemcmcxmmcfima}iterations <n>] [sseed <seed>1]\n"
"\t[errorserrorlineerrorcolumns] [residual]\n"
"\t[perturbations <key1>=<noise1>[,<key2>=<noise2>[,...]]]\n"
"\t[r <rejectionlevel> n <numofrejections> [weightedsigma]]\n"
"\t[kseparate [@],[!]<fitvariable>,...]\n");
#ifdef LFIT_ENABLE_DYNAMIC_EXTENSIONS
fprintf(fw,
"Dynamically loaded external libraries and functions:\n"
"\t[ddynamic <library.so>:<array>[,<array2>,...] [d <...>]]\n");
#endif
fprintf(fw,
"General output specification:\n"
"\t[ooutput <outputfilename] [zcolumnsoutput <>]\n");
fprintf(fw,
"More outputs:\n"
"\t[u <dumpusedto>] [j <dumprejectedto>]\n"
"\t[a <dumpallto> [deltadeltacomment]]\n"
"\t[p <dumpexpressionto>] [l <dumpfittedvariablesto>]\n");
return(0);
}
longhelp_entry lfit_long_help[]=
{
LONGHELP_OPTIONS,
{ "General options:", NULL },
{ "h, help",
"Gives general summary about the command line options." },
{ "longhelp, helplong",
"Gives a detailed list of command line options." },
{ "wikihelp, helpwiki, mediawikihelp, helpmediawiki",
"Gives a detailed list of command line options in Mediawiki format." },
{ "version, versionshort, shortversion",
"Gives some version information about the program." },
{ "functions, listfunctions, functionlist",
"Lists the available arithmetic operations and builtin functions "
"supported by the program." },
{ "examples",
"Prints some very basic examples for the program invocation." },
{ "Common options for regression analysis:", NULL },
{ "v, variable, variables <listofvariables>",
"Commaseparated list of regression variables. In case of nonlinear "
"regression analysis, all of these fit variables are expected to have "
"some initial values (specified as <name>=<value>), otherwise the "
"initial values are set to be zero. Note that in the case of some of "
"the regression/analysis methods, additional parameters should be "
"assigned to these fit/regression variables. See the section "
"\"Regression analysis methods\" for additional details." },
{ "c, column, columns <independent>[:<column index>],...",
__extension__
"Commaseparated list of independet variable names as read from the "
"subsequent columns of the primary input data file. If the independent "
"variables are not in sequential order in the input file, the optional "
"column indices should be defined for each variable, by separating the "
"column index with a colon after the name of the variable. "
"In the case of multiple input files and data blocks, the user "
"should assign the individual independent variables and the "
"respective column names and definitions for each file "
"(see later, Sec. \"Multiple data blocks\")." },
{ "f, function <model function>",
__extension__
"Model function of the analysis in a symbolic form. This expression "
"for the model function "
"should contain builtin arithmetic operators, builtin functions, "
"userdefined macros (see x, define) or functions provided by the "
"dynamically loaded external modules (see d, dynamic). The model "
"function can depend on both the fit/regression variables "
"(see v, variables) and the independent variables read from the "
"input file (see c, columns). In the case of multiple input files "
"and data blocks, the user should assign the respective model functions "
"for each data block (see later). Note that some of the analysis methods "
"expects the model function to be either differentiable or linear in "
"the fit/regression variables. See \"Regression analysis methods\" later "
"on about more details." },
{ "y, dependent <dependent expression>",
__extension__
"The dependent variable of the regression analysis, in a form of an "
"arithmetic expression. This expression for the dependent variable "
"can depend only on the variables read from the input file "
"(see c, columns). In the case of multiple input files "
"and data blocks, the user should assign the respective dependent "
"expressions for each data block (see later)." },
{ "o, output <output file>",
"Name of the output file into which the fit results (the values for the "
"fit/regression variables) are written." },
{ "Common options for function evaluation:", NULL },
{ "f, function <function to evaluate>[...]",
"List of functions to be evaluated. More expressions can be specified by "
"either separating the subsequent expressions by a comma or by "
"specifying more f, function options in the command line." },
{ "Note that the two basic modes of `lfit` are distinguished only by the "
"presence or the absence of the y, dependent command line argument. "
"In other words, there isn't any explicit command line argument which "
"specify the mode of `lfit`. If the y, dependent command line argument "
"is omitted, `lfit` runs in function evaluation mode, otherwise "
"the program runs in regression analysis mode.", NULL }, { "",NULL },
{ "o, output <output file>",
"Name of the output file in which the results of the function "
"evaluation are written." },
{ "Regression analysis methods:", NULL },
{ "L, clls, linear",
__extension__
"The default mode of `lfit`, the classical linear least squares (CLLS) method. "
"The model functions specified after f, function are expected to be "
"both differentiable and linear with respect to the fit/regression "
"variables. Otherwise, `lfit` detects the nondifferentiable and "
"nonlinear property of the model function(s) and refuses the analysis. "
"In this case, other types of regression analysis methods can be applied "
"depending our needs, for instance the LevenbergMarquardtalgorithm "
"(NLLM, see N, nllm) or the downhill simplex minimization "
"(DHSX, see D, dhsx)." },
{ "N, nllm, nonlinear",
__extension__
"This option implies a regression involving the nonlinear "
"LevenbergMarquardt (NLLM) minimization "
"algorithm. The model function(s) specified after f, function are "
"expected to be differentiable with respect to the fit/regression "
"variables. Otherwise, `lfit` detects the nondifferentiable property "
"and refuses the analysis. There some finetune parameters of the "
"LevenbergMarquardt algorithm, see also the secion "
"\"Finetuning of regression analysis methods\" for more details how "
"these additional regression parameters can be set. Note that all of "
"the fit/regression variables should have a proper initial value, "
"defined in the command line argument v, variable (see also there)." } ,
{ "U, lmnd",
__extension__
"LevenbergMarquardt minimization with numerical partial "
"derivatives (LMND). Same as the NLLM method, with the exception of "
"that the partial "
"derivatives of the model function(s) are calculated numerically. "
"Therefore, the model function(s) may contain functions of which "
"partial derivatives are not known in an analytic form. "
"The differences used in the computations of the partial derivatives "
"should be declared by the user, see also the command line option "
"q, differences. " },
{ "D, dhsx, downhill",
__extension__
"This option implies a regression involving the nonlinear "
"downhill simplex (DHSX) minimization algorithm. "
"The user should specify the proper inital values "
"and their uncertainties as <name>=<initial>:<uncertainty>, unless "
"the \"fisher\" option is passed to the P, parameters command line "
"argument (see later in the section \"Finetuning of regression "
"analysis methods\"). In the first case, the initial size of the "
"simplex is based on the uncertainties provided by the user while "
"in the second case, the initial simplex is derived from the "
"eigenvalues and eigenvectors of the Fisher covariance matrix. "
"Note that the model functions must be differentiable in the "
"latter case. " },
{ "M, mcmc",
__extension__
"This option implies the method of Markov Chain MonteCarlo (MCMC). "
"The model function(s) can be arbitrary in the point of "
"differentiability. However, each of the fit/regression variables "
"must have an initial assumption for their uncertainties which must "
"be specified via the command line argument v, variable. "
"The user should specify the proper inital values "
"and uncertainties of these as <name>=<initial>:<uncertainty>. "
"In the actual implementation of `lfit`, each variable has an "
"uncorrelated Gaussian a priori distribution with the specified "
"uncertainty. The MCMC algorithm has some finetune parameters, "
"see the section \"Finetuning of regression analysis methods\" "
"for more details." },
{ "K, mchi, chi2",
__extension__
"With this option one can perform a \"brute force\" Chi^2 minimization "
"by evaluating the value of the merit function of Chi^2 on a grid of "
"the fit/regression variables. In this case the grid size and resolution "
"must be specified in a specific form "
"after the v, variable command line argument. Namely each of the "
"fit/regression variables intended to be varied on a grid must "
"have a format of <name>=[<min>:<step>:<max>] while the other ones "
"specified as <name>=<value> are kept fixed. The output of this "
"analysis will be a series of lines with N+1 columns, where the "
"values of fit/regression variables are followed by the value "
"of the merit function. Note that all of the declared fit/regression "
"variables are written to the output, including the ones which are fixed "
"(therefore the output is somewhat redundant)." },
{ "E, emce",
__extension__
"This option implies the method of \"refitting to synthetic data sets\", "
"or \"error MonteCarlo estimation\" (EMCE). This method must have a "
"primarily assigned minimization algorithm (that can be any of the "
"CLLS, NLLM or DHSX methods). First, the program searches the best fit "
"values for the fit/regression variables involving the assigned primary "
"minimization algorithm and reports these best fit variables. "
"Then, additional synthetic data sets are generated around this set "
"of best fit variables and the minimization is repeated involving the same "
"primary method. The synthetic data sets are generated independently "
"for each input data block, taking into account the fit residuals. "
"The noise added to the best fit data is generated from the power "
"spectrum of the residuals." },
{ "X, xmmc",
__extension__
"This option implies an improved/extended version of the Markov Chain "
"MonteCarlo analysis (XMMC). The major differences between the classic MCMC "
"and XMMC methods are the following. 1/ The transition distribution "
"is derived from the Fisher covariance matrix. 2/ The program performs "
"an initial minimization of the merit function involving the method "
"of downhill simplex. 3/ Various sanity checks are performed in order "
"to verify the convergence of the Markov chains (including the "
"comparison of the actual and theoretical transition probabilities, "
"the computation of the autocorrelation lengths of each "
"fit/regression variable series and the comparison of the statistical "
"and Fisher covariance). " },
{ "A, fima",
__extension__
"Fisher information matrix analysis (FIMA). With this analysis method "
"one can estimate the uncertainties and correlations of the "
"fit/regression variables involving the method of Fisher matrix "
"analysis. This method does not minimize the merit functions by "
"adjusting the fit/regression variables, instead, the initial values "
"(specified after the v, variables option) are expected to be the "
"\"best fit\" ones." },
{ "Finetuning of regression analysis methods:", NULL },
{ "e, error <error expression>",
"Expression for the uncertainties. Note that zero or negative "
"uncertainty is equivalent to zero weight, i.e. input lines with zero "
"or negative errors are discarded from the fit." },
{ "w, weight <weight expression>",
"Expression for the weights. The weight is simply the reciprocal of "
"the uncertainty. The default error/uncertainty (and therefore "
"the weight) is unity. Note that most of the analysis/regression "
"methods are rather sensitive to the uncertainties since the merit "
"function also depends on these." },
{ "P, parameters <regression parameters>",
"This option is followed by a set of optional finetune parameters, "
"that is different for each primary regression analysis method:" },
{ "default, defaults",
"Use the default finetune parameters for the given regression method." },
{ "clls, linear",
"Use the classic linear least squares method as the primary minimization "
"algorithm of the EMCE method. Like in the case of the CLLS regression "
"analysis (see L, clls), the model function(s) must be both differentiable and linear "
"with respect to the fit/regression variables. " },
{ "nllm, nonlinear",
"Use the nonlinear LevenbergMarquardt minimization algorithm as "
"the primary minimization algorithm of the EMCE method. Like in the case of the NLLM regression "
"analysis (see N, nllm), the model function(s) must be differentiable "
"with respect to the fit/regression variables. " },
{ "lmnd",
"Use the nonlinear LevenbergMarquardt minimization algorithm as "
"the primary minimization algorithm of the EMCE method. "
"Like in the case of U, lmnd regression method, the parametric "
"derivatives of the model function(s) are calculated by a "
"numerical approximation (see also U, lmnd and q, differences for "
"additional details)." },
{ "dhsx, downhill",
"Use the downhill simplex (DHSX) minimization as the primary "
"minimization algorithm of the EMCE method. Unless the additional "
"'fisher' option is specified directly, like in the default case of "
"the DHSX regression method, the user should specify the uncertainties "
"of the fit/regression variables that are used as an initial size "
"of the simplex." },
{ "mc, montecarlo",
"Use a primitive MonteCarlo diffusion minimization technique as the "
"primary minimization algorithm of the EMCE method. The user should "
"specify the uncertainties of the fit/regression variables which are "
"then used to generate the MonteCarlo transitions. This primary "
"minimization technique is rather nasty (very slow), "
"so its usage is not recommended. " },
{ "fisher",
__extension__
"In the case of the DHSX regression method or in the case of the EMCE "
"method when the primary minimization is the downhill simplex algorithm, "
"the initial size of the simplex is derived from the Fisher covariance "
"approximation evaluated at the point represented by the initial "
"values of the fit/regression variables. Since the derivation of the "
"Fisher covariance requires the knowledge of the partial derivatives "
"of the model function(s) with respect to the fit/regression variables, "
"the(se) model function(s) must be differentiable. On the other hand, "
"the user do not have to specify the initial uncertainties after the "
"v, variables option since these uncertainties derived automatically "
"from the Fisher covariance." },
{ "skip",
"In the case of EMCE and XMMC method, the initial minimization "
"is skipped. " },
{ "lambda=<value>",
"Initial value for the \"lambda\" parameter of the LevenbergMarquardt "
"algorithm. " },
{ "multiply=<value>",
"Value of the \"lambda multiplicator\" parameter of the "
"LevenbergMarquardt algorithm. " },
{ "iterations=<max.iterations>",
"Number of iterations during the LevenbergMarquardt algorithm. " },
{ "accepted",
"Count the accepted transitions in the MCMC and XMMC methods (default)." },
{ "nonaccepted",
"Count the total (accepted plus nonaccepted) transitions in the MCMC "
"and XMMC methods." },
{ "gibbs",
"Use the Gibbs sampler in the MCMC method. " },
{ "adaptive",
"Use the adaptive XMMC algorithm (i.e. the Fisher covariance is "
"recomputed after each accepted transition). " },
{ "window=<window size>",
"Window size for calculating the autocorrelation lengths for the "
"Markov chains (these autocorrelation lengths are reported only "
"in the case of XMMC method). The default value is 20, which is "
"fine in the most cases since the typical autocorrelation lengths "
"are between 1 and 2 for nice convergent chains." },
{ "q, difference <variablename>=<difference>[,...]",
"The analysis method of LMND (LevenbergMarquardt minimization using "
"numerical derivatives, see U, lmnd) requires the differences that "
"are used during the computations of the partial derivatives of the "
"model function(s). With this option, one can specify these differences." },
{ "k, separate <variablename>[,...]",
__extension__
"In the case of nonlinear regression methods (for instance, DHSX or XMMC) "
"the fit/regression variables in which the model functions are linear "
"can be separated from the nonlinear part and therefore make the "
"minimization process more robust and reliable. Since the "
"set of variables in which the model functions are linear is "
"ambiguous, the user should explicitly specify this supposedly "
"linear subset of regression variables. "
"(For instance, the model function \"a*b*x+a*cos(x)+b*sin(x)+c*x^2\" "
"is linear in both \"(a,c)\" and \"(b,c)\" parameter vectors but it "
"is nonlinear in \"(a,b,c)\".) "
"The program checks whether the specified subset of "
"regression variables is a linear subset and reports "
"a warning if not. "
"Note that the subset of separated linear variables (defined here) "
"and the subset of the fit/regression variables affected by "
"linear constraints (see also section \"Constraints\") "
"must be disjoint." },
{ "perturbations <noise level>, perturbations <key>=<noise level>[,...]",
"Additional white noise to be added to each EMCE synthetic data sets. "
"Each data block (referred here by the approprate data block keys, "
"see also section \"Multiple data blocks\") may have different white "
"noise levels. If there is only one data block, this command line "
"argument is followed only by a single number specifying the "
"white noise level." },
{ "Additional parameters for MonteCarlo analysis:", NULL },
{ "s, seed <random seed>",
__extension__
"Seed for the random number generator. By default this seed is 0, thus "
"all of the MonteCarlo regression analyses (EMCE, MCMC, XMMC "
"and the optional generator for the FIMA method) generate "
"reproducible parameter distributions. A positive value after this "
"option yields alternative random seeds while all negative values "
"result in an automatic random seed (derived from various available "
"sources, such as /dev/[u]random, system time, hardware MAC address "
"and so), therefore distributions generated involving this kind of "
"automatic random seed are not reproducible." },
{ "i, [mcmc,emce,xmmc,fima]iterations <iterations>",
"The actual number of MonteCarlo iterations for the MCMC, EMCE, "
"XMMC methods. Additionally, the FIMA method is capable to generate "
"a mock Gaussian distribution of the parameter with the same covariance "
"as derived by the Fisher analysis. The number of points in this mock "
"distribution is also specified by this command line option. "},
{ "Clipping outlier data points:", NULL },
{ "r, sigma, rejectionlevel <level>",
"Rejection level in the units of standard deviations." },
{ "n, iterations <number of iterations>",
"Maximum number of iterations in the outlier clipping cycles. "
"The actual number of outlier points can be traced by increasing the "
"verbosity of the program (see V, verbose)." },
{ "[no]weightedsigma",
"During the derivation of the standard deviation, the contribution of "
"the data points data points can be weighted by the respective "
"weights/error bars (see also w, weight or e, error in the "
"section \"Finetuning of regression analysis methods\"). If no "
"weights/error bars are associated to the data points (i.e. both "
"w, weight or e, error options are omitted), this option will have "
"no practical effect." },
{ "Note that in the actual version of `lfit`, only the CLLS, NLLM and LMND "
"regression methods support the above discussed way of "
"outlier clipping.", NULL }, { "", NULL },
{ "Multiple data blocks:", NULL },
{ "i<key> <input file name>",
"Input file name for the data block named as <key>." },
{ "c<key> <independent>[:<column index>],...",
"Column definitions (see also c, columns) for the given data "
"block named as <key>." },
{ "f<key> <model function>",
"Expression for the model function assigned to the data block named as <key>." },
{ "y<key> <dependent expression>",
"Expression of the dependent variable for the data block named as <key>." },
{ "e<key> <errors>",
"Expression of the uncertainties for the data block named as <key>." },
{ "w<key> <weights>",
"Expression of the weights for the data block named as <key>. Note that "
"like in the case of e, errors and w, weights, only one of the "
"e<key>, w<key> arguments should be specified." },
{ "Constraints:", NULL },
{ "t, constraint, constraints <expression>{=<>}<expression>[,...]",
__extension__
"List of fit and domain constraints between the regression variables. "
"Each fit constraint expression must be linear in the fit/regression variables. "
"The program checks the linearity of the fit constraints and reports an "
"error if any of the constraints are nonlinear. "
" A domain constraint can be any "
"expression involving arbitrary binary arithmetic relation (such as "
"strict greater than: '>', strict less than: '<', "
"greater or equal to: '>=' and less or requal to: '<='). "
"Constraints can be "
"specified either by a commaseparated list after a single command "
"line argument of t, constraints or by multiple of these "
"command line arguments. "
},
{ "v, variable <name>:=<value>",
"Another form of specifying constraints. The variable specifications "
"after v, variable can also be used to define constraints by writing "
"\":=\" instead of \"=\" between the variable name and initial value. "
"Thus, v <name>:=<value> is equivalent to v <name>=<value> "
"t <name>=<value>." },
{ "Userdefined functions:", NULL },
{ "x, define, macro <name>(<parameters>)=<definition expression>",
__extension__
"With this option, the user can define additional functions "
"(also called macros) on the top of the builtin functions and operators, "
"dynamically loadaded functions and previously defined macros. "
"Note that each such userdefined function must be standalone, i.e. "
"external variables (such as fit/regression variables and independent "
"variables) cannot be part of the definition expression, only the "
"parameters of these functions." },
{ "Dynamically loaded extensions and functions:", NULL },
{ "d, dynamic <library>:<array>[,...]",
__extension__
"Load the dynamically linked library (shared object) named <library> "
"and import the global `lfit`compatible set of functions defined "
"in the arrays specified after the name of the library. The arrays "
"must have to be declared with the type of 'lfitfunction', as it is "
"defined in the file \"lfit.h\". Each record in this array contains "
"information about a certain imported function, namely "
"the actual name of this function, flags specifying whether the "
"function is differentiable and/or linear in its regression parameters, "
"the number of regression variables and independent variables "
"and the actual C subroutine that implements the evaulation of the "
"function (and the optional computation of the partial derivatives). "
"The module 'linear.c' and 'linear.so' provides a simple example "
"that implements the \"line(a,b,x)=a*x+b\" function. "
"This example function has "
"two regression variables (\"a\" and \"b\") and one independent "
"variable (\"x\") and the function itself is linear in the regression "
"variables." },
{ "More on outputs:", NULL },
{ "z, columnsoutput <column indices>",
__extension__
"Column indices where the results are written in evaluation mode. "
"If this option is omitted, the results of the function evaluation "
"are written sequentally. Otherwise, the input file is written to "
"the output and the appropriate columns (specified here) are replaced "
"by the respective results of the function evaluation. Thus, although "
"the default column order is sequential, there is a significant "
"difference between omitting this option and specifying \"z 1,2,...,N\". "
"In the first case, the output file contains only the results of the "
"function evaluations, while in the latter case, the first N columns "
"of the original file are replaced with the results. " },
{ "errors, errorline, errorcolumns",
"Print the uncertainties of the fit/regression variables." },
{ "F, format <variable name>=<format>[,...]",
"Format of the output in printfstyle for each fit/regression variable"
"(see printf(3)). The default "
"format is %12.6g (6 signifiant figures)." },
{ "F, format <format>[,...]",
"Format of the output in evaluation mode. The default "
"format is %12.6g (6 signifiant figures)." },
{ "C, correlationformat <format>",
"Format of the correlation matrix elements. The default format "
"is %6.3f (3 significant figures)." },
{ "g, derivedvariable[s] <variable name>=<expression>[,...]",
"Some of the regression and analysis methods are capable to "
"compute the uncertainties and correlations for derived regression "
"variables. These additional (and therefore not independent) "
"variables can be defined with this command line option. "
"In the definition expression one should use only the fit/regression "
"variables (as defined by the v, variables command line argument). "
"The output format of these variables can also be specified by the "
"F, format command line argument." },
{ "u, outputfitted <filename>",
"Neme of an output file into which those lines of the input are "
"written that were involved in the final regression. This option "
"is useful in the case of outlier clipping in order to see what "
"was the actual subset of input data that was used in the fit "
"(see also the n, iterations and r, sigma options)." },
{ "j, outputrejected <filename>",
"Neme of an output file into which those lines of the input are "
"written that were rejected from the final regression. This option "
"is useful in the case of outlier clipping in order to see what "
"was the actual subset of input data where the dependent variable "
"represented outlier points "
"(see also the n, iterations and r, sigma options)." },
{ "a, outputall <filename>",
"File containing the lines of the input file that were involved "
"in the complete regression analysis. This file is simply the "
"original file, only the commented and empty lines are omitted. " },
{ "p, outputexpression <filename>",
"In this file the model function is written in which the "
"fit/regression variables are replaced by their bestfit values. " },
{ "l, outputvariables <filename>",
"List of the names and values of the fit/regression variables in the "
"same format as used after the v, variables command line argument. "
"The content of this file can therefore be passed to subsequent "
"invocations of `lfit`." },
{ "delta",
"Write the individual differences between the independent "
"variables and the evaluated best fit model function values for each "
"line in the output files specified by the u, outputfitted, "
"j, outputrejected and a, outputall command line options." },
{ "deltacomment",
"Same as delta, but the differences are written as a comment "
"(i.e. separated by a '##' from the original input lines)." },
{ "residual",
"Write the final fit residual to the output file (after the list of "
"the bestfit values for the fit/regression variables)." },
{ NULL, NULL }
};
int fprint_lfit_long_help(FILE *fw,int is_wiki)
{
char *synopsis=
"lfit [method of analysis] [options] <input> [o, output <output>]";
char *description=
__extension__
"The program `lfit` is a standalone command line driven tool designed for "
"both interactive and batch processed data analysis and regression. "
"In principle, the program may run in two modes. First, `lfit` supports "
"numerous regression analysis methods that can be used to search for "
"\"best fit\" parameters of model functions in order to model the input "
"data (which are read from one or more input files in tabulated form). "
"Second, `lfit` is capable to read input data and performs various "
"arithmetic operations as it is specified by the user. Basically this second "
"mode is used to evaluate the model functions with the parameters presumably "
"derived by the actual regression methods (and in order to complete this "
"evaluation, only slight changes are needed in the command line invocation "
"arguments).";
fprint_generic_long_help(fw,is_wiki,lfit_long_help,synopsis,description);
return(0);
}
int fprint_lfit_examples(FILE *fw)
{
fprintf(fw,
"Examples:\n"
"* linear regression in 1+1 dim:\n"
"\tlfit v a,b c x,y f \"a*x+b\" y y\n"
"* linear regression in 1+1 dim, taking into account errors (from 3d column):\n"
"\tlfit v a,b c x,y,yerr f \"a*x+b\" y y e yerr\n"
"* fit a circle in 2 dim (find the center and radius of a circle which \n"
" fits well to the points given in the first two columns of the input):\n"
"\tlfit v u,v,uvr c x,y f \"2*x*u+2*y*vuvr\" y \"x*x+y*y\"\n"
" The center of the circle is (u,v) and the raduis is r=sqrt(u*u+v*vuvr).\n");
fprintf(fw,
"Some notes: \n"
"  Avoid to put spaces or any nasty characters (such as wildchards: *, ?,\n"
" backslash, brackets  which are preinterpreted by the shell) directly\n"
" in the arguments of v, c, f, y and ew. Simply put such arguments\n"
" between quotation marks (or escape them), see the examples above.\n"
"  If the function 'function' is linear in the variables 'vars' are to be\n"
" fitted, the standard linear regression algorithm will be used. Otherwise,\n");
fprintf(fw,
" the LevenbergMarquardt method will be used (with the parameters optionally\n"
" defined by the switches l, m and i), if the switch N is specified in the\n"
" command line to force the nonlinear method. In this case, the initial\n"
" values of the variables 'vars' are important to be defined in the argument\n"
" of v. With the switch V (verbose) the evolution of the variables and\n"
" the lambda parameter can be traced ('max_iter' lines are written to stderr).\n");
return(0);
}
/*****************************************************************************/
