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PLS regression (Partial Least Squares or Projection to Latent structures - both mean the same thing and underlying process), as suggested by user257566 is a very useful way to go for this type of query. Unlike PCA it uses external information to guide the act of data reduction, meaning that underlying data processes related to that external information are ...


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I would run a simple linear regression of the following form: $R_i = \beta _0 + \beta _1D_i + \beta _2G_i + \beta _3G_i\times D_i + \epsilon_i,$ where i would be a pair of two individuals $R_i$ - the measure of their relatedness $D_i$ - the distance between them, $G_i$ - a dummy variable which is one if both belong to the same group and zero if they dont. ...


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The most probable cause for what you are seeing is collinearity, i.e. your 3 independent variables are correlated. Collinearity in Normal Linear Regression One assumption of the linear regression is "no or little (Multi-)Collinearity". If we violate this assumption we get biased estimates (coefficients). Sometimes this is exactly what we want, e.g. ...


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Two features can be considered similar with respect to a response if they provide similar information about the response. In other words: if they're redundant for the purpose of predicting the response. I'll describe how to formalize this intuition using information theory, and use it to construct a measure of dissimilarity between features. Proposed ...


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Summary: Standard triplots for ecological data analyzed by canonical correspondence analysis (CCA*) provide a way to gauge both the strengths of relationships of individual environmental variables to species distributions and the similarities among environmental variables in these respects. You might, however, want to do some dimension reduction on the set ...


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This link provides a simple answer to the question: https://stat.ethz.ch/pipermail/r-sig-teaching/2011q4/000387.html


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So one caveat, there are a lot of different ways to go about this, and it really depends on your field. That being said, there are some general principles for variable selection. Instead of using a distance matrix (which can be really really hard to view with 100 variables), I would suggest heading right to a model selection method, like stepwise model ...


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GLM-Univariate is for analyses involving a single dependent variable, while GLM-Repeated Measures is for designs involving two or more dependent variables that form part of a repeated-measures or within-subjects structure. I'm not entirely clear what you have here, but if there are three variables where you want to test equality, that would be repeated ...


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SPSS Statistics currently doesn't offer anything designed to do any type of power or sample size analysis for a Cox regression model. Since the program has some general mathematical functions, distribution functions, and a matrix programming language, if you know the math you can program pretty much anything, but I assume that's not what you're looking to do....


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I suggest using forward selection for variables, basing on p-values in logistic regression. That is, first we choose the first variable, that has the least p-value as a covariate in the univariate logistic regression model, where we take the presence of the species as the outcome. Then we add to the model another covariate with the least p-value, then the ...


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Handling this type of data as predictors in any regression is tricky. However you treat the below-detection or extreme-outlier cases, you still are faced with the requirement that the outcome in the appropriate scale (log-odds of disease status in this case) must be linearly related to the predictor value (perhaps after some transformation). Furthermore, ...


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In principle, Factor Analysis was devised to deal with exactly your problem. It tries to achieve three aims: Identify groups of strongly correlated features. Identify "irrelevant" features, i.e. features not "contributing" to any of the groups. Replace each group by a new value called factor that is a linear combination of the features of the respective ...


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As a less technical point of view, often times it's not very helpful just explaining the trend; that is, to treat time as the predictor of primary interest. The variation of a series over time often imply the underlying effects of other variables, including autoregressive and/or exogenous processes, which is more conceptually relevant to investigate. It ...


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This problem has often been discussed; one keyword is comparision of "centroid-rotation" vs. "pc-rotation", another one is "parcels" vs. "item-factor-analysis" (or so, I may not be up-to-date). The computation of the items-mean is here in principle equivalent with determining the "centroid", and if multiple means are taken from multiple subsets of items, ...


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I was asking myself the same question and I just found a method that works. You can simply extract it using the basing quantile() function included with R. Here with your model it would give something like this: alpha <- 0.99 (your df) VaR_extract <- as.numeric(quantile(DCC_fit, probs = alpha))


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One possibility is to choose a random point $x_0$ to start, this is $t=0$. Choose another random point $x_1$. This is $t=1/N$ where $N$ is the size of your dataset. We can interpolate between these two so that, for $0<t<1/N$, we take $f(t) = Ntx_1 + (1-Nt)x_0$. This gives a linear interpolation between the two points. Repeat this process until you'...


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The correlation of 0.5 is an assumption, it is not derived. I will not accept this as an answer to my question because I do not have a source. This was based on a conversation with an expert, for whatever that's worth. The assumption is made when there are equal sample sizes in the experimental arm and the control arm. The numerator of the hazard ratio is ...


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