Clsti

Temperature Interpolated Classical Least Squares models.

model = clsti(files); %builds CLSTI model (calibration step)

model = clsti(files,options); %builds CLSTI model (calibration step)

pred = clsti(x,temps,model);%makes predictions with a new data and temperatures

pred = clsti(x,temps,model,options);%makes predictions with a new data and temperatures

CLSTI models will interpolate a test temperature from a give set of pure spectra at certain temperatures.

files = cell array of definition files, that can be:
- .xlsx, .csv, or .txt file formats, with pure component name in first cell and filenames for pure component data in column 1 and corresponding temperatures in column 2, or
- a cell array of DataSet Objects of pure component spectra at different temperatures with corresponding temperatures in the .axisscale{1,1} field.
x = DSO of test data with temperatures in .axisscale{1,1} field
temps = vector of temperatures (if not in x.axisscale{1,1})
model = CLSTI model to apply to x

model = standard model structure containing the CLSTI model (See Standard Model Structure).
pred = prediction object.

options = a structure array with the following fields:

plots: [ {'none'} | 'final' ] governs plotting of results.
display: [ 'off' | {'on'} ] governs level of display to command window.
blockdetails: [ 'compact' | {'standard'} | 'all' ] level of detail (predictions, raw residuals, and calibration data) included in the model.

‘Standard’ = the predictions and raw residuals for the X-block as well as the X-block itself are not stored in the model to reduce its size in memory. Specifically, these fields in the model object are left empty: 'model.pred{1}', 'model.detail.res{1}', 'model.detail.data{1}'.
‘Compact’ = for this function, 'compact' is identical to 'standard'.
'All' = keep predictions, raw residuals for both X- & Y-blocks as well as the X- & Y-blocks themselves.

Contents