Built-in Mathematica Symbol

LinearModelFit

LinearModelFit[{y₁, y₂, ...}, {f₁, f₂, ...}, x]
constructs a linear model of the form

₀+

₁ f₁+

₂ f₂+

that fits the y_i for successive x values 1, 2, ....

LinearModelFit[{{x₁₁, x₁₂, ..., y₁}, {x₂₁, x₂₂, ..., y₂}, ...}, {f₁, f₂, ...}, {x₁, x₂, ...}]
constructs a linear model of the form

₀+

₁ f₁+

₂ f₂+

where the f_i depend on the variables x_k.

LinearModelFit[{m, v}]
constructs a linear model from the design matrix m and response vector v.

MORE INFORMATION

LinearModelFit returns a symbolic FittedModel object to represent the linear model it constructs. The properties and diagnostics of the model can be obtained from model["property"].

The value of the best-fit function from LinearModelFit at a particular point x₁, ... can be found from model[x₁, ...].

With data in the form ${{x_(11),x_(12),... ,y_(1)},{x_(21),x_(22),... ,y_(2)},...}$ , the number of coordinates x_i1, x_i2, ... should equal the number of variables x_i.

Data in the form {y₁, y₂, ...} is equivalent to data in the form {{1, y₁}, {2, y₂}, ...}.

LinearModelFit produces a linear model of the form under the assumption that the original y_i are independent normally distributed with mean and common standard deviation.

LinearModelFit takes the following options:

ConfidenceLevel	95/100	confidence level to use for parameters and predictions
IncludeConstantBasis	True	whether to include a constant basis function
LinearOffsetFunction	None	known offset in the linear predictor
NominalVariables	None	variables considered as nominal or categorical
VarianceEstimatorFunction	Automatic	function for estimating the error variance
Weights	Automatic	weights for data elements
WorkingPrecision	Automatic	precision used in internal computations

With the setting IncludeConstantBasis->False, a model of form ₁ f₁+₂ f₂+ is fitted.

With the setting LinearOffsetFunction->h, a model of the form h[x₁, x₂, ...]+₀+₁ f₁+₂ f₂+ is fitted.

With ConfidenceLevel->p, probability-p confidence intervals are computed for parameter and prediction intervals.

With the setting Weights->{w₁, w₂, ...}, the error variance for y_i is assumed to be ²/w_i. By default unit weights are used.

With the setting VarianceEstimatorFunction->f, the variance is estimated by f[res, w] where is the list of residuals and w={w₁, w₂, ...} is the list of weights for the measurements y_i.

Using VarianceEstimatorFunction->(1&) and Weights->{1/y₁², 1/y₂², ...}, y_i is treated as the known uncertainty of measurement y_i and parameter standard errors are effectively computed only from the weights.

Properties related to data and the fitted function obtained using model["property"] include:

	"BestFit"	fitted function
	"BestFitParameters"	parameter estimates
	"Data"	the input data or design matrix and response vector
	"DesignMatrix"	design matrix for the model
	"Function"	best fit pure function
	"Response"	response values in the input data

Types of residuals include:

	"FitResiduals"	difference between actual and predicted responses
	"StandardizedResiduals"	fit residuals divided by the standard error for each residual
	"StudentizedResiduals"	fit residuals divided by single deletion error estimates

Properties related to the sum of squared errors include:

	"ANOVATable"	analysis of variance table
	"ANOVATableDegreesOfFreedom"	degrees of freedom from the ANOVA table
	"ANOVATableEntries"	unformatted array of values from the table
	"ANOVATableFStatistics"	F statistics from the table
	"ANOVATableMeanSquares"	mean square errors from the table
	"ANOVATablePValues"	p-values from the table
	"ANOVATableSumsOfSquares"	sums of squares from the table
	"CoefficientOfVariation"	response mean divided by the estimated standard deviation
	"EstimatedVariance"	estimate of the error variance
	"PartialSumOfSquares"	changes in model sum of squares as nonconstant basis functions are removed
	"SequentialSumOfSquares"	the model sum of squares partitioned componentwise

Properties and diagnostics for parameter estimates include:

	"CorrelationMatrix"	parameter correlation matrix
	"CovarianceMatrix"	parameter covariance matrix
	"EigenstructureTable"	eigenstructure of the parameter correlation matrix
	"EigenstructureTableEigenvalues"	eigenvalues from the table
	"EigenstructureTableEntries"	unformatted array of values from the table
	"EigenstructureTableIndexes"	index values from the table
	"EigenstructureTablePartitions"	partitioning from the table
	"ParamaterTable"	table of fitted parameter information
	"ParameterConfidenceIntervals"	parameter confidence intervals
	"ParameterConfidenceIntervalTable"	table of confidence interval information for the fitted parameters
	"ParameterConfidenceIntervalTableEntries"	unformatted array of values from the table
	"ParameterConfidenceRegion"	ellipsoidal parameter confidence region
	"ParameterErrors"	standard errors for parameter estimates
	"ParameterPValues"	p-values for parameter t statistics
	"ParameterTableEntries"	unformatted array of values from the table
	"ParameterTStatistics"	t statistics for parameter estimates
	"VarianceInflationFactors"	list of inflation factors for the estimated parameters

Properties related to influence measures include:

	"BetaDifferences"	DFBETAS measures of influence on parameter values
	"CatcherMatrix"	catcher matrix
	"CookDistances"	list of Cook distances
	"CovarianceRatios"	COVRATIO measures of observation influence
	"DurbinWatsonD"	Durbin-Watson d statistic for autocorrelation
	"FitDifferences"	DFFITS measures of influence on predicted values
	"FVarianceRatios"	FVARATIO measures of observation influence
	"HatDiagonal"	diagonal elements of the hat matrix
	"SingleDeletionVariances"	list of variance estimates with the data point omitted

Properties of predicted values include:

	"MeanPredictionBands"	confidence bands for mean predictions
	"MeanPredictionConfidenceIntervals"	confidence intervals for the mean predictions
	"MeanPredictionConfidenceIntervalTable"	table of confidence intervals for the mean predictions
	"MeanPredictionConfidenceIntervalTableEntries"	unformatted array of values from the table
	"MeanPredictionErrors"	standard errors for mean predictions
	"PredictedResponse"	fitted values for the data
	"SinglePredictionBands"	confidence bands based on single observations
	"SinglePredictionConfidenceIntervals"	confidence intervals for the predicted response of single observations
	"SinglePredictionConfidenceIntervalTable"	table of confidence intervals for the predicted response of single observations
	"SinglePredictionConfidenceIntervalTableEntries"	unformatted array of values from the table
	"SinglePredictionErrors"	standard errors for the predicted response of single observations

Properties that measure goodness of fit include:

	"AdjustedRSquared"	adjusted for the number of model parameters
	"AIC"	Akaike Information Criterion
	"BIC"	Bayesian Information Criterion
	"RSquared"	coefficient of determination

In LinearModelFit[{m, v }], the design matrix m is formed from the values of basis functions f_i at data points in the form {{f₁, f₂, ...}, {f₁, f₂, ...}, ...}. The response vector v is the list of responses {y₁, y₂, ...}.

For a design matrix m and response vector v, the model is where is the vector of parameters to be estimated.

When a design matrix is used, the basis functions f_i can be specified using the form LinearModelFit[{m, v}, {f₁, f₂, ...}].

EXAMPLES

CLOSE ALL

Basic Examples (1)

Fit a linear model to some data:

Obtain the functional form:

Evaluate the model at a point:

Visualize the fitted function with the data:

Extract information about the fitting:

Plot the residuals:

Fit a linear model to some data:

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Obtain the functional form:

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Evaluate the model at a point:

In[4]:=

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Visualize the fitted function with the data:

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Extract information about the fitting:

Plot the residuals:

Scope (11)

Fit a model of more than one variable:

Fit data to a linear combination of nonlinear functions of predictor variables:

Fit a model with categorical predictor variables:

Obtain an analysis of variance table for the model:

Extract the unformatted array of values:

Extract the mean squares values from the table:

Fit a model given a design matrix and response vector:

See the functional form:

Fit the model referring to the basis functions as x and y:

Obtain a list of available properties for a linear model:

Fit a linear model:

Extract the original data:

Obtain and plot the best fit:

Obtain the fitted function as a pure function:

Get the design matrix and response vector for the fitting:

Examine residuals for a fit:

Visualize the raw residuals:

Visualize scaled residuals in stem plots:

Plot the absolute differences between the standardized and Studentized residuals:

Fit a linear model to some data:

Extract the estimated error variance and coefficient of variation:

Obtain the analysis of variance table:

Get the F statistics from the table:

Extract the numeric entries from the table:

Use Grid to add formatting:

Obtain a formatted table of parameter information:

Extract the column of

statistic values:

Get the unformatted array of values from the table:

Add formatting using Grid:

Add formatting via TableForm:

Fit some data containing extreme values to a linear model:

Use single deletion variances to check the impact on the error variance of removing each point:

Check Cook distances to identify highly influential points:

Use DFFITS values to assess the influence of each point on the fitted values:

Use DFBETAS values to assess the influence of each point on each estimated parameter:

Fit a linear model:

Plot the predicted values against the observed values:

Obtain tabular results for mean and single prediction confidence intervals:

Get the single prediction intervals from the table:

Extract 99% mean prediction bands:

Obtain a table of goodness of fit measures for a linear model:

Compute goodness of fit measures for all possible linear submodels:

Rank the models by

:

Rank the models by adjusted

which penalizes for adding terms:

Generalizations & Extensions (1)

Perform other mathematical operations on the functional form of the model:

Integrate symbolically and numerically:

Find a predictor value that gives a particular value for the model:

Options (7)

The default gives 95% confidence intervals:

Use 99% intervals instead:

Set the level to 90% within FittedModel:

Fit a simple linear regression model:

Fit the linear model with intercept zero:

Fit data to a linear model:

Fit data to a linear model with a known Sqrt[x] term:

Fit data treating the first variable as a nominal variable:

Treat both variables as nominal:

Use the default unbiased estimate of error variance:

Assume a known error variance:

Estimate the variance by the mean squared error:

Fit a model using equal weights:

Give explicit weights for the data points:

Use WorkingPrecision to get higher precision in parameter estimates:

Obtain the fitted function:

Reduce the precision in property computations after the fitting:

Applications (6)

Fit the first 100 primes to a linear model:

Visualize the fit:

The systematic trend in the residuals violates the assumption of independent normal errors:

Fit a linear model of multiple variables:

Visually inspect the residuals by data point:

Plot the residuals against each predictor variable:

Plot Cook's distances to diagnose leverage:

Find the positions of distances above a given cutoff value:

Extract the associated data points:

Use q-q plots to check the assumption of normal errors:

Compare standardized residuals to standard normal values:

Do the comparison with Studentized residuals:

Simulate some data with a continuous and a nominal variable:

Fit an analysis of covariance model to the data:

Obtain the ANOVA table:

Group the data by treatment:

Visualize the grouped data and associated curves:

Use properties to compute additional results:

Extract the design matrix and residuals:

Compute White's heteroskedasticity-consistent covariance estimate:

Compare with the covariance assuming homoskedasticity:

Compare standard errors based on the two covariance estimates:

Perform a Breusch Pagan test:

Fit a model:

Fit the squared errors to a model with the same predictors:

Compute the Breusch Pagan test statistic:

Compute the p-value:

Properties & Relations (6)

DesignMatrix constructs the design matrix used by LinearModelFit:

By default LinearModelFit and GeneralizedLinearModelFit fit equivalent models:

LinearModelFit fits linear models assuming normally distributed errors:

NonlinearModelFit fits nonlinear models assuming normally distributed errors:

Fit and LinearModelFit fit equivalent models:

LinearModelFit allows for extraction of additional information about the fitting:

Fit a linear model to data:

Perform the same fitting using a design matrix and response vector:

Obtain the parameter estimates via LeastSquares:

LinearModelFit fits linear models:

FindFit gives parameter estimates for linear and nonlinear models: