regression

I have a Java app which needs to perform partial least squares regression. It would appear there are no Java implementations of PLSR out there. Weka might have had something like it at some point, but it is no longer in the API. On the other hand, I have found a good R implementation, which has an added bonus to it. It was used by the people whose result I want to replicate, which means there is less chance that things will go wrong because of differences in the way PLSR is implemented. The question is: is there a good enough (and simple to use) package that enable Java to call R, pass in some

I want to perform a stepwise linear Regression using p-values as a selection criterion, e.g.: at each step dropping variables that have the highest i.e. the most insignificant p-values, stopping when all values are significant defined by some threshold alpha . I am totally aware that I should use the AIC (e.g. command step or stepAIC ) or some other criterion instead, but my boss has no grasp of statistics and insist on using p-values. If necessary, I could program my own routine, but I am wondering if there is an already implemented version of this. Show your boss the following : set.seed(100

I am trying to run a mixed-effects model that predicts F2_difference with the rest of the columns as predictors, but I get an error message that says fixed-effect model matrix is rank deficient so dropping 7 columns / coefficients. From this link, Fixed-effects model is rank deficient , I think I should use findLinearCombos in the R package caret . However, when I try findLinearCombos(data.df) , it gives me the error message Error in qr.default(object) : NA/NaN/Inf in foreign function call (arg 1) In addition: Warning message: In qr.default(object) : NAs introduced by coercion My data does not

Let me start by telling that what I get may not be what I expect and perhaps you can help me here. I have the following data: >>> x array([ 3.08, 3.1 , 3.12, 3.14, 3.16, 3.18, 3.2 , 3.22, 3.24, 3.26, 3.28, 3.3 , 3.32, 3.34, 3.36, 3.38, 3.4 , 3.42, 3.44, 3.46, 3.48, 3.5 , 3.52, 3.54, 3.56, 3.58, 3.6 , 3.62, 3.64, 3.66, 3.68]) >>> y array([ 0.000857, 0.001182, 0.001619, 0.002113, 0.002702, 0.003351, 0.004062, 0.004754, 0.00546 , 0.006183, 0.006816, 0.007362, 0.007844, 0.008207, 0.008474, 0.008541, 0.008539, 0.008445, 0.008251, 0.007974, 0.007608, 0.007193, 0.006752, 0.006269, 0.005799, 0.005302,

I am interested in a general root finding problem for an interpolation function. Suppose I have the following (x, y) data: set.seed(0) x <- 1:10 + runif(10, -0.1, 0.1) y <- rnorm(10, 3, 1) as well as a linear interpolation and a cubic spline interpolation: f1 <- approxfun(x, y) f3 <- splinefun(x, y, method = "fmm") How can I find x -values where these interpolation functions cross a horizontal line y = y0 ? The following is a graphical illustration with y0 = 2.85 . par(mfrow = c(1, 2)) curve(f1, from = x[1], to = x[10]); abline(h = 2.85, lty = 2) curve(f3, from = x[1], to = x[10]); abline(h =