Supports idea of curve fitting is to find a mathematical model that fits your data. fitting to a subset of a waveform or XY pair. fitting to functions of any number of independent variables, either gridded data or multicolumn data. fitting to user-defined functions of any complexity. Automatic initial guesses for built-in functions. Fit by ordinary least squares, or by least orthogonal distance for errors-in-variables models.
#EXPONENTIAL CURVE FITTING IGOR PRO PRO#
235 curve fitting 236 Chapter III-8 Curve FittingIII-152 OverviewIgor Pro s curve fitting capability is one of its strongest analysis features.ħ Here are some of the highlights: Linear and general nonlinear curve fitting. 234 Errors Due to X Values with Large Offsets. 233 Special Considerations for Polynomial Fits. 231 Multivariate Structure Fit 232 curve fitting Using 232 Batch 232 curve fitting 233 Singularities in curve fitting. 228 Structure Fit 229 Basic Structure Fit Function 230 The WMFitInfoStruct Structure. 224 Multivariate All-At-Once fitting Functions. 223 Format of a Multivariate fitting Function. 221 Functions that the Fit Function Dialog Doesn t Handle Well. 221 The New Fit Function Dialog Adds Special Comments. 220 Intermediate Results for Very Long 220 Conditionals. 218 Constraints and ThreadSafe 218 User-Defined fitting 219 User-Defined fitting Function Formats.Ħ 219 Format of a Basic fitting Function. 217 curve fitting with Multiple 218 curve fitting with Automatic 218 curve fitting with Programmed Multithreading. 215 Constraints Applied to Sums of Fit Functions. 208 Error Estimates from ODR 209 Chapter III-8 Curve FittingIII-151 ODR fitting Examples. 208 ODR Fit 208 Constraints and ODR fitting. 207 ODR Initial 207 Holding Independent Variable Adjustments. 206S_Info.ĥ 206 Errors in Variables: Orthogonal Distance Regression. 204 Bit 0: Controls X Scaling of Auto-Trace 204 Bit 1: Robust fitting. 202 Special Variables for Curve 202V_FitOptions. 201 Constrained curve fitting 202 NaNs and INFs in Curve Fits. 199 Equality 199 Example Fit with 199 Constraint Matrix and Vector. 198 Complex Constraints Using a Constraints 199 Constraint Expressions. 198 Constraints Using the curve fitting Dialog. 195 Confidence Band 195 Some Statistics.Ĥ 195 Confidence Bands and Nonlinear 196 Covariance 196 Correlation Matrix. 193 Calculating Confidence Intervals After the Fit. 192 Estimates of 193 Confidence Bands and Coefficient Confidence Intervals. 192 Explicit Residual Wave Using New 192 Calculating Residuals After the Fit. 192 Residuals Using an Explicit Residual Wave. 186 Function and Data 186 Data Options Tab. 179 Inputs and Outputs for Built-In 184 curve fitting Dialog 185 Global Controls. 177 Problems with the curve fitting Dialog. 176 Chapter III-8 Curve FittingIII-150 Example One Remove Planar Trend Using Poly2D.ģ 176 Example Two User-Defined Simplified 2D Gaussian Fit. 175 Model Results for Multivariate fitting. 174 fitting a Subrange of the Data for a Multivariate Function. 174 Selecting Fit Data for a Multivariate Function. 170 Using a Mask 172 Proportional Weighting. 167 Removing a User-Defined fitting 167 User-Defined fitting Function Details.Ģ 167 fitting to an External Function (XFUNC). 166 Making a User-Defined Function Always Available. 163 Coefficients Tab for a User-Defined Function. 163 fitting to a User-Defined 163 Creating the Function. 156 Choosing the Function and 157 Two Useful Additions: Holding a Coefficient and Generating 158 Automatic Guesses Didn t 161 Fits with Constants. 155A Simple Case fitting to a Built-In Function: Line Fit. 154 curve fitting Using the Quick Fit Menu. 154 Termination 154 Errors in curve fitting. 152 Overview of Curve 153 Iterative 153 Initial Guesses. 1 ChapterIII-8 III-8 Curve FittingOverview.
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