sbiofitstatusplot

Plot status of sbionlmefit or sbionlmefitsa

Syntax

stop = sbiofitstatusplot(beta, status, state)

Description

stop = sbiofitstatusplot(beta, status, state) initializes or updates a plot with the fixed effects, beta, the log likelihood status.fval, and the variance of the random effects, diag(status.Psi).

The function returns an output (stop) to satisfy requirements for the 'OutputFcn' option of nlmefit or nlmefitsa. For sbiofitstatusplot, the value of stop is always false.

Use sbiofitstatusplot to obtain status information about NLME fitting when using the sbionlmefit or sbionlmefitsa function . Specify sbiofitstatusplot as a function handle by using the optionStruct (options structure) input argument to sbionlmefit or sbionlmefitsa. Use sbiofitstatusplot or customize your own function to use in the options structure.

Input Arguments

beta

The current fixed effects.

status

Structure containing several fields.

FieldValue
inner

Structure describing the current status of the inner iterations within the ALT and LAP procedures, with the fields:

  • procedure

    • 'PNLS', 'LME', or 'none' when the procedure is 'ALT'

    • 'PNLS', 'PLM', or 'none' when the procedure is 'LAP'

  • state'init', 'iter', 'done', or 'none'

  • iteration — Integer starting from 0, or NaN

procedure'ALT' or 'LAP'
iterationInteger starting from 0
fvalCurrent log-likelihood
PsiCurrent random-effects covariance matrix
thetaCurrent parameterization of Psi
mseCurrent error variance

state

Either 'init', 'iter', or 'done'.

Examples

Obtain status information for NLME fitting:

% Create options structure with 'OutputFcn'.
optionStruct.Options.OutputFcn = @sbiofitstatusplot;
% Pass options structure with OutputFcn to sbionlmefit function.
results = sbionlmefit(..., optionStruct);

More About

expand all

Alt

Alternating algorithm for the optimization of the LME or RELME approximations

FO

First-order estimate

FOCE

First-order conditional estimate

LAP

Optimization of the Laplacian approximation for FO or FOCE

LME

Linear mixed-effects estimation

NLME

Nonlinear mixed effects

PLM

Profiled likelihood maximization

PNLS

Penalized nonlinear least squares

RELME

Restricted likelihood for the linear mixed-effects model

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