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SAAM
II Other Features
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Data and Parameters
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Data may be
entered directly in the Data window or loaded from
other applications; SAAM II formatting can be added
after data is loaded.
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Model
parameters are automatically determined from
equation variables; parameters may be defined as
fixed, adjustable or Bayesian; user can easily edit
values and types.
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General
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Flexible, efficient and accurate
Optimizer for
computational optimization:
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Choose Data or Model variance as
the Variance Model.
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Choose Absolute or Relative for
data weighting.
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Choose Forward Derivative
(faster) or Central Derivative (smaller total
error).
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Set convergence criteria to
control optimization speed versus accuracy.
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Allows use of a Bayesian term to
incorporate prior knowledge of an adjustable
parameter.
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User’s Notes
can be included in Study Files to document
particular items of interest in a model and/or
experiment: setup, conclusions, etc.
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Includes Users Guide with examples
and detailed on-line Help.
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Progress Display during analysis.
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Compartmental and Numerical Models
are solved and fitted to data automatically.
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Output
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Study results may be
plotted or listed in
tables;
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Results may be exported to other
applications.
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QuickPlot
displays plots for individual Compartmental objects
or single defined Numerical variables.
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Complete results
may be automatically saved to a text file.
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Statistics
for Correlation Matrix, Covariance Matrix and
Objective Function are calculated.
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Confidence
Intervals on study parameters provide upper and
lower bounds on each parameter.
 Data may be entered directly in the
Data window or loaded from other applications
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The data window uses
tables in a specific format that allows
sophisticated combinations of values, weighting and
unweighting for each of the data elements.
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The data can be listed in multiple
columns in one or more tables.
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Each table can include weighting
combinations of:
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Data constants can also be defined.
 Model parameters are
automatically determined from equation variables
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The Parameters window lists all the
parameters in the model, along with their type (fixed,
adjustable, or Bayesian) and values.
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In the SAAM II context, parameters
are variables that appear in equations, but are not
explicitly defined.
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The parameter values may be modified
during optimization to provide an optimal fit to the
data.
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The Pop. Mean and SD columns are
shown only if the Include Bayesian Term check box in the
Computational Settings dialog box has been checked.
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The user modifies a parameter type,
value and limits in the boxes at the bottom of the
dialog box and then saves the entries back into the
parameter list.
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Flexible, efficient and accurate
Optimizer for computational optimization
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The way in which the Optimizer "fits" the
model to the data is determined in the Optimizer pane of
the Computational Settings window. The user can limit
the number of iterations that the optimizer can use in
"fitting" the model to the data.
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The variance model can be based upon
model or data. Data weighting is used when a measurement
of confidence in each particular datum is provided.
Weights for each datum are calculated from the standard
deviations assigned to the data. Model weighting
expresses the confidence in each of the calculated
Sample values rather than in the data.
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Weights in SAAM II can be absolute or
relative. Absolute weighting assumes that the overall
weighting of each data element (variable) is the same,
while in relative weighting each data element is given
an additional weight that indicates how well that
element fits the resulting model sample curve.
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SAAM II uses approximate derivatives of
the model function "s(p, ti,j)" during the "fitting"
process, and when computing statistics. The optimizer
computes a step size for each parameter equal to "d(pU -
pL)", where "pU" and "pL" are the upper and lower limits
for "p", and "d" is the convergence criterion (see
below). The step size is used to form either a forward
or central difference approximation to the derivative of
"s(p, ti,j)" with respect to "p". Central difference
approximations have a smaller truncation error,
resulting in a smaller total error, but usually require
twice the execution time.
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Two different convergence tests are used
to terminate the "fitting" process: parameter
convergence and objective convergence. The user can
specify the value for the convergence criterion "d" in
this field or accept the default value of "0.0001".
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Bayesian estimation incorporates prior
knowledge of an adjustable parameter's value into the
fitting process. Check Include Bayesian Term to allow
Bayesian information to be provided for any adjustable
parameters, and Bayesian inference to be used during a
fit.
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In Bayesian estimation, a mean value and
a standard deviation are specified for one or more
adjustable parameters. If the mean value of a parameter
is specified but the standard deviation is not
specified, lambda is used to assign a default standard
deviation to that parameter. If "pU" and "pL" are the
upper and lower limits for the parameter, the default
standard deviation is "s = lambda(pU - pL)".
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 User’s
Notes can be included in Study Files
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The Notes window
can be used to create and retain information of
interest for a given model or experiment, such as a
description of what is being modeled, assumptions,
comments on certain aspects of the model, a
description of sampling techniques, etc.
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The Notes window is a standard
text window.
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Notes are automatically saved
with the model. The window may be left open while
you are working in other areas.
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 Study
results may be plotted or
listed in tables
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SAAM II offers extensive plotting
capabilities with the Plot command.
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This allows users to choose plots
for several variables.
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Users can customize the plot by
choosing a title, labels for the X-axis and
Y-axis, and the font size.
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Users can let SAAM II select the
plot scale settings or specify them.
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The user can use the
Table
window to view the values for
selected variables at each calculation
point. In this window, the data are
displayed in columns with the independent
variable (time) in the first column.
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 NOTE: The variables the user
selects apply to both the Plot window and the
Table window. This means that the Plot and Table
windows work independently, but share the same
variable set.
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 QuickPlot displays plots
for individual Compartmental objects or single
defined Numerical variables
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In the Numerical
application, QuickPlot
can be used to plot the values of any
selected variable defined explicitly on the
left-hand side of an equation.
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In the
Compartmental application,
QuickPlot can be used to plot the
solution values of a selected object.
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 Complete results may be automatically
saved to a text file
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The Save Results option in
the Computational Settings window produces a
readable summary of results after a "Fit" or
"Solve" and stores the results in a
user-accessible text file. The file can then
be opened in other applications such as
spreadsheets, word processors, or report
generators. Three levels of report details
are available:
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Basic Output, containing
the most commonly used results from a
Solve or Fit
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Detailed Output,
containing more complete results
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Model and Detailed
Output, containing a complete
description of the results and the model
itself.
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Statistics for Correlation
Matrix, Covariance Matrix and Objective Function are
calculated
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Confidence Intervals
on study parameters provide upper and lower
bounds on each parameter.
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After a successful fit, the
user can open the Statistics window to view:
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The value, standard
deviation, coefficient of variation, and
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