Assumes linear fit if no expression given:

Fit a cubic polynomial:

Fit a gaussian:

Fit a linear function:

Fit a linear-log expression 10^(a+b*x):

Fit a log-linear expression a+b*Log[x]:

Fit a log-log expression 10^(a+b*Log[x]):

Fit error function sigmoid:

Fit logistic function sigmoid:

Fit a 2-D Gaussian:

Fit a linear function of two variables:

Fit a LinearLog function of two variables:

Fit a quadratic function of two variables:

Specify multivariate function to fit to:

Fit a LogLinear standard curve relating copy number to quantification cycle:

Fit a standard curve relating peak position to fragment size by pulling all x-values from first object and all y-values from second object:

Fit a standard curve to fluorescence intensities data:

Fit a standard curve to fluorescence kinetics data:

Fit function of two variables to fluorescence objects:

Fit function of two variables to peak positions:

Fit Volume vs LiquidLevel using ObjectFields to address multiple fields in the same objects:

One data point per object:

Pull peak positions from object and specify sizes:

Pull sizes from object and specify positions:

Data with x-uncertainty only:

Data with y-uncertainty only:

Fitting algorithm takes into account uncertainty in data points:

Fit to numerical distributions mixed with quantities:

Fit to quantity distributions mixed with quantities:

The given data can be a QuantityArray:

Distribution points are filtered out based on their means:

Points outside Domain will be excluded from fitting:

Use first order approximation to derive best fit parameters distribution:

Use monto carlo fitting to obtain best fit parameters distribution:

If there are known outliers in the data set, they can be excluded from fitting by listing them in the Exclude list:

Specify index of points to exclude:

Excluding outliers from a fit often results in new outliers due to the resulting fit being more accurate. ExcludeOutliers->Repeatedly re-fits excluding the newly found outliers up to 3 times or until no more outliers are found:

If ExcludeOutliers->True, any points detected as outliers will be excluded from fitting:

Specify data field for both x and y objects:

Specify data field for z objects only:

Specify field in data objects from which x-coordinates are pulled:

Whether or not to scale the fitting equation to help in finding the fit:

Fit log expression using base 2:

Applied when input fitType is LogisticFourParam, True when the input x axis is log-transformed, False otherwise:

Method used for fitting:

Name to be used as the name of Object[Analysis, Fit] generated by the analysis:

Specify number of samples used in monte carlo sampling:

Control the distance at which points are labeled as outliers:

Return the uploaded object:

Fit a third degree polynomial to data:

Any points outside range are excluded from the fit calcuation:

Specify reference field:

Whether or not to scale the data to the unit box before fitting:

Some problems require good starting values in order to obatin a good fit:

Specify initial guesses for some of the parameter values:

Explicitly specify PolynomialDegree option, and pull remaining options from previous fit analysis:

Use options from previous fit analysis to fit a cubic:

Transform Absorbance spectrum into x-values by computing its absorbance at a 550 Nanometer:

Transform EmissionTrajectories into x-values by taking the Max fluorescence across all time in the trajectory:

Set to True to force a linear fit through the origin:

Unable to find a satisfactory fit to the data:

Size of x and y data lists must match:

Not every object has fields that can be fit to:

Unable to find a real valued set of parameters:

Warn the user if any of the inputs contain temporal links:

Number of variables in function must match dimenions of input data:

In the Domain option, the left boundary should be less than the right boundary:

Given known expression type with incompatible data:

Given unknown expression type:

FitField option must be a field in the input objects:

Pure function input must contain symbolic parameters only:

Polynomial degree must not exceed number of data points:

The fit function results in numbers that are either too large or too small:

The input data results in numbers that are either too large or too small:

Negative x values will be excluded when fitting to a log function:

There must be unknown parameters in the expression in order to fit:

Cannot fit if all data points are filtered out by option restrictions:

Overfitting message thrown when you have fewer data points than parameters in the fitted function:

If too many of the points are excluded as outliers, the resulting fit should not be trusted:

The ZeroIntercept option can only be set to True if the fit type is Linear: