nonlinear regression -凯发k8网页登录

perform least-squares estimation to fit grouped or pooled data, compute confidence intervals, and plot fit quality statistics

perform using local, global, or hybrid estimation methods. fit each group in your data independently to obtain group-specific estimates or fit all groups simultaneously to get one set of parameter estimates. you can also compute confidence intervals for estimated parameters and predicted model responses. use various plots to visualize and assess fit quality measures.

apps

	build qsp, pk/pd, and mechanistic systems biology models interactively
simbiology model analyzer	analyze qsp, pk/pd, and mechanistic systems biology models

functions

estimate parameters

	perform parameter estimation using simbiology problem object
	perform nonlinear least-squares regression
	perform nonlinear least-squares regression using simbiology models (requires statistics and machine learning toolbox software)
	create dose objects from groupeddata object
	create variant objects from groupeddata object
	return simulation results of simbiology model fitted using least-squares regression
	return simbiology dose object
	simulate and evaluate fitted simbiology model
	simulate simbiology model, adding variations by sampling error model
	reset optional simbiology fit problem properties

plot quality statistics of fit results

	create box plot showing the variation of estimated simbiology model parameters
	compare simulation results to the training data, creating a time-course subplot for each group
	compare predictions to actual data, creating a subplot for each response
	plot residuals for each response, using time, group, or prediction as x-axis
	plot the distribution of the residuals
	return structure array that contains estimated values and fit quality statistics

compute confidence intervals

`sbioparameterci`	compute confidence intervals for estimated parameters (requires statistics and machine learning toolbox)
	compute confidence intervals for model predictions (requires statistics and machine learning toolbox)
	return summary table of confidence interval results
	plot parameter confidence interval results
	plot confidence interval results for model predictions

objects

data variables and problem object for fitting

	simbiology problem object for parameter estimation
	table-like collection of data and metadata for fitting in simbiology
	object containing information about estimated model quantities

fit results

	results object containing estimation results from least-squares regression
	estimation results object for any supported algorithm except `nlinfit`
	estimation results object for `nlinfit` algorithm

confidence intervals

	object containing confidence interval results for estimated parameters
	object containing confidence interval results for model predictions
	object containing confidence interval results

topics

nonlinear regression basics

simbiology supports several fitting, parameter transformations, and estimation options for nonlinear regression.
supported methods for parameter estimation in simbiology
simbiology^® supports a variety of optimization methods for least-squares and mixed-effects estimation problems.
simbiology supports constant, proportional, combined, and exponential error models.
the progress plot provides the live feedback on the status of parameter estimation while using sbiofit, sbiofitmixed, or the fit data program in the simbiology model analyzer app.

app workflow

calculate nca parameters and fit model to pk/pd data using simbiology model analyzer
calibrate model parameters by performing noncompartmental analysis and fitting to experimental pkpd data using nonlinear regression.

programmatic workflow

estimate model parameters using a simbiology problem object.
fit an individual's pk profile data to one-compartment model and estimate pharmacokinetic parameters.
estimate pharmacokinetic parameters of multiple individuals using a two-compartment model.
estimate category-specific (such as young versus old or male versus female in a hierarchical model), individual-specific, and population-wide parameters using pk profile data from multiple individuals.
configure sbiofit to perform a hybrid optimization.