User`s guide

Handling Missing Data and Outliers
Apply the misdata command to the new data object. For example:
dat1 = misdata(dat);
plot(dat,dat1) % Check how the missing data
% was estimated on a time plot
Handling Outliers
Malfunctions can produce errors in measured values, called outliers.Such
outliers might be caused by signal spikes or by m easurement malfunctions.
If you do not remove outliers from your data, this can adv ersely affect the
estimated models.
Toidentifythepresenceofoutliers,perform o ne of the following tasks:
• Before estimating a model, plot the data on a time plot and identify values
that appear out of range.
• After estimating a model, plot the residuals and identify unusually large
values. For more information about plo tting residuals, see “Using Residual
Analysis Plots to Validate M odels” on page 8-16. Evaluate the original
data that is responsible for large residuals. For example, for the model
Model and valida tion data Data, you can use the following commands to
plot the residuals:
% Compute the residuals
E = resid(Model,Data)
% Plot the residuals
plot(E)
Next, try these techniques for removing or minimizing the ef fects of outliers:
• Extract the informative data portions into segments and merge them into
one multiexperiment data set ( see “Example – Extracting and Modeling
Specific Data Segm ents” on page 1-92). For more information about
selecting and extracting data segments, see “Selecting Subsets of Data”
on page 1-86.
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