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The spectral data were then analyzed using some computational approaches consisted of SPLINE (moving cubic spline), NIPALS (principal component analysis), CLASS (classification and class modelling techniques), MRM (multivariate range modelling), TREE (classification trees), and PLS2 (correlation between blocks of variables)).
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PLS can explore patterns of covariation between two blocks of variables.
An O-PLS model may reveal relationships between two blocks of variables, predictor (X) and response (Y) variables [ 30].
The linear combinations are formulated so that the new variables explain as much as possible of the covariation between the two original blocks of variables.
We distinguish the available information into 22 blocks of variables.
Treating {x(i)} and {z ij } as two blocks of variables, the two-block ADM is applied to problem (4).
Two blocks of variables were entered separately; block 1: Background variables (age, gender, educational level, number of comorbid diseases); block 2: Illness and treatment perceptions variables.
The regression procedures included several blocks of variables; some blocks used Stepwise entry, while others forced predictor variables into the equation.
PLS yielded five dimensions of covariation between the two blocks of variable sets (i.e., head shape variables and trophic traits), of which only the first dimension was significantly greater than expected by chance (p<0.05).
Balanced centre-stratified randomization with blocks of variable length.
We use block randomisation (blocks of variable and unknown size) to ensure an equal group size.
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