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As @theGD already pointed out in the comment, scaling is often not needed for spectroscopic data as the features already have a common intensity axis. Here's my guess what's happening when you scale: You have spectra with very nice zero baselines. In other words, all those features outside your analyte signal are constant mean + some noise. If you scale ...


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Assuming your independent variable matrix is $m\times n$, that you have $m$ observations and $n$ variables. For each PLS component (AKA latent variable), you get a loading vector ($n \times 1$), so for $h$ components the size of loading matrix ($P$) is $n \times h$. These loadings are calculated for both interpretation and algorithmic purposes but they have ...


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I was also looking for information on these parameters and found a good explanation in the book Eriksson et al. Multi- and Metavariate Data Analysis Principles and Applications. In general, I think you have the right idea. According to Eriksson et al, the fit tells us how well we are able to mathematically reproduce the data of the training set. The $R^2$ ...


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There are many possible reasons, but it seems like you may not have enough rows to estimate the model accurately. You have enough degrees of freedom to vastly overfit, and because PLS regression finds the latent space that best models the covariance between the regressors and the target, it will find a space that overfits the data. As you expand the model ...


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Some questions that may help digging down to what the actual issue is. I don't think cross validation itself is the problem here - it's probably just exposing problems in the model. All your tentative models do not show much improvement over the 0 component model: even at 10 latent variables, $RMSE_{CV}$* is still withing 95 % of the $RMSE_{CV}$ always ...


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First of all, PLS-DA means that you perform a PLS regression and then apply a threshold to assign class labels. Now, there are two very different situations where this is done: the underlying nature of the problem is metric, and the classes mean that the modeled property is above or below some threshold or limit. Presence/absence of an analyte (...


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Both are supervised classification methods. LDA aims to find projections that aims to minimize within class distance while maximizing between class distance. PLS-DA is basically PLS regression to class information (I think this kind of class information is called one-hot-encoding of classes in machine learning) and aims to maximize covariance between ...


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Different sources indicate that a PLS regression takes into account the variability of the dependent variables (while PCR doesn't). Why is this aspect so important and why it is considered to be an advantage over PCR? You may have confounders that contribute large variance to $\mathbf X$, but as they are confounders that variance does not help but rather ...


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pls::plsr centers both $\mathbf X$ and $\mathbf Y$, and the corresponding intercepts are in $Xmeans and $Ymeans. So in order to predict using the coefficients that map $\mathbf{Y_c} = \mathbf { X_c} \mathbf B$, you need to center $\mathbf X$: $\mathbf X_c = \mathbf X - \bar x$ ($\bar x$ is $Xmeans) matrix-multiply by $\mathbf B$: $\mathbf Y_c = \mathbf ...


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That (our) paper applies independent of the field, but wrt. to test sample size it is only about figures of merit that are proportions of test cases (sensitivity, specificity, ...). You'll find that these figures of merit are not recommended for many situations, among other reasons (they are no proper scoring rules) because they have high variance. This ...


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My Question Is: I'm confused why a single component solution is working equally well compared to 3 component solution for my simulated data below- as I think i've simulated 3 independent components explaining variance in Y, and a 4th component which is independent of Y. Your simulation has only one data generating process built into $FactorY$: FactorY=...


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PLS-DA is closely related to LDA: for n > p the full rank PLS-DA (i.e. using all latent variables) is the same as LDA. For 1 latent variable, PLS-DA yields the same classification as closest (Euclidean) distance in feature space. I.e. the regularization "squeezes" the pooled covariance matrix into spherical shape. A two class problem with both classes ...


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Further research has thrown up this webpage at purdue.edu which links to source code for various variants of PLS. On the latter page, the PLS1 method appears to be very similar to the algorithm shown on the PLS regression Wikipedia page. The purdue.edu implementation cites "Overview and Recent Advances in Partial Least Squares" by Roman Rosipal and Nicole ...


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It has nothing to do with PLS-DA, it is related to autoscaling spesifically. While taking derivative (or smoothing) is applied per spectrum, the autoscaling does the following: Calculate the mean of each variable using all calibration set samples Subtract this mean from from each variable on both calibration and validation set Calculate standard ...


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I'm going to break down my answer in a different way. 1. when is PCA or PLS preferred? PCA is an unsupervised data reduction, i.e. the data is compressed into its underlying components without any guidance from data external ($Y$) to the $X$ data. The top ranked components returned are those that dominate the variation in the $X$ data as it has been pre-...


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