Results object containing estimation results from leastsquares regression
The LeastSquaresResults
object is a superclass
of two results objects: NLINResults object
and OptimResults object
. These objects contain
estimation results from fitting a SimBiology^{®} model to data using sbiofit
with any supported algorithm.
If sbiofit
uses the nlinfit
estimation
algorithm, the results object is the NLINResults
object.
If sbiofit
uses any other supporting algorithm,
then the results object is an OptimResults
object.
See the sbiofit
function for
the list of supported algorithms.
boxplot(LeastSquaresResults,OptimResults,NLINResults)  Create box plot showing the variation of estimated SimBiology model parameters 
fitted(LeastSquaresResults,OptimResults,NLINResults)  Return simulation results of SimBiology model fitted using leastsquares regression 
plot(LeastSquaresResults,OptimResults,NLINResults)  Compare simulation results to the training data, creating a timecourse subplot for each group 
plotActualVersusPredicted(LeastSquaresResults,OptimResults,NLINResults)  Compare predictions to actual data, creating a subplot for each response 
plotResidualDistribution(LeastSquaresResults,OptimResults,NLINResults)  Plot the distribution of the residuals 
plotResiduals(LeastSquaresResults,OptimResults,NLINResults)  Plot residuals for each response, using time, group, or prediction as xaxis 
predict(LeastSquaresResults,OptimResults,NLINResults)  Simulate and evaluate fitted SimBiology model 
random(LeastSquaresResults,OptimResults,NLINResults)  Simulate SimBiology model, adding variations by sampling error model 
summary(LeastSquaresResults,OptimResults,NLINResults)  Plot a summary figure that contains estimated values and estimation statistics 
GroupName  Categorical variable representing the name of the group associated
with the results, or [] if the 'Pooled' namevalue
pair argument was set to true when you ran sbiofit . 
Beta  Table of estimated parameters where the jth
row represents the jth estimated parameter β_{j}.
It contains transformed values of parameter estimates if any parameter
transform is specified. Standard errors of these parameter estimates
( It can also contain the following variables:

ParameterEstimates  Table of estimated parameters where the jth
row represents the jth estimated parameter β_{j}.
This table contains untransformed values of parameter estimates. Standard
errors of these parameter estimates ( It can also contain the following variables:

J  Jacobian matrix of the model, with respect to an estimated
parameter, that is, $$J(i,j,k)={\frac{\partial {y}_{k}}{\partial {\beta}_{j}}}_{{t}_{i}}$$ where t_{i} is the ith time point, β_{j} is the jth estimated parameter in the transformed space, and y_{k} is the kth response in the group of data. 
COVB  Estimated covariance matrix for Beta , which
is calculated as: COVB = inv(J'*J)*MSE . 
CovarianceMatrix  Estimated covariance matrix for ParameterEstimates ,
which is calculated as: CovarianceMatrix = T'*COVB*T ,
where T = diag(JInvT(Beta)) .
For
instance, suppose you specified the logtransform for an estimated
parameter 
R  Residuals matrix where R_{ij} is the residual for the ith time point and the jth response in the group of data. 
LogLikelihood  Maximized loglikelihood for the fitted model. 
AIC  Akaike Information Criterion (AIC), calculated as AIC
= 2*(LogLikelihood + P) , where P is
the number of parameters. 
BIC  Bayes Information Criterion (BIC), calculated as BIC
= 2*LogLikelihood + P*log(N) , where N is
the number of observations, and P is the number
of parameters. 
DFE  Degrees of freedom for error, calculated as DFE =
NP , where N is the number of observations
and P is the number of parameters. 
MSE  Mean squared error. 
SSE  Sum of squared (weighted) errors or residuals. 
Weights  Matrix of weights with one column per response and one row per observation. 
EstimatedParameterNames  Cell array of character vectors specifying estimated parameter names. 
ErrorModelInfo  Table describing the error models and estimated error model
parameters.
There are four builtin error models. Each model defines the error using a standard meanzero and unitvariance (Gaussian) variable e, the function value f, and one or two parameters a and b. In SimBiology, the function f represents simulation results from a SimBiology model.

EstimationFunction  Name of the estimation function. 
Loglikelihood
, AIC
, and BIC
properties
are empty for LeastSquaresResults
objects that
were obtained before R2016a.
NLINResults object
 OptimResults object
 sbiofit
 sbiofitmixed