search for: ssq

# Dear R list, # # A have a doubt about SSQ decomposition and contrasts with ANOVAs. # So, I would like a tip from more advanced R users. # Below my data, the basic script and my doubts: # Data r = paste('r', gl(3, 8), sep='') e = paste('e', rep(gl(2, 4), 3), sep='') tra = sort(paste('t', rep(1:6...

Dear R list, Please, could someone help me with SSQ decomposition and contrasts. Below my data, graphic, ANOVAs and my doubt: # Data a = paste('a', gl(3, 8), sep='') b = paste('b', gl(2, 4, 24), sep='') tra = sort(paste('t', rep(1:6, 4), sep='')) y = c(26.2, 26.0, 25.0, 25.4, 24.8, 24.6, 26.7, 2...

...the sum of squares of these. As the x values of the estimates are not the same, and I would prefer not to restrict the estiomate to a certain range of x values, how can I do the calculation? Lets say: d1 <- density(Data1) d2 <- density(Data2) If the x values would be the same, I would: ssq <- sum( (d1$y - d2$y)^2 ) but as it isn't, I can't do that. is there an easy way to get the sum of squares? Thanks, Rainer

Hi I am using ggplot2 at the moment and I must say it is definitely better then ggplot - good work. My problem is that I am using facet_grid() in the following way: > p <- ggplot(ssq, aes(x=year, y=-log(ssq))) > p + geom_point() + facet_grid(me*gi~cs*rz) and it works nicely, except that I would like to have, in naddition to the values of me, gi, cs and rz the name of the variable. I.e: if gi is 1, 2 and 3 I would like to have "gi = 1:", "gi = 2" and &...

Hi, Within my R code, I am using a C++ function as below: overlaps <- matrix(0, nrow=B, ncol=length(N)) overlaps.P <- matrix(0, nrow=B, ncol=length(N)) .C("speedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), ssq[i], i, as.integer(B), overlaps, overlaps.P, DUP=FALSE) the function "speedUp", is supposed to update matrices overlaps and overlaps.P and it works with official R versions. However, using the same code in R-devel, it does not update matrices and they remain all zero without returning a...

...ues and that this gives a negative gain in the Kalman filter, which in turn gives a likelihood equal to NaN. Here is a reproducible example where I forced the use of 'ML'. > set.seed(1) > x <- arima.sim(100,model=list(ar=phi,ma=theta)) > KalmanLike(x,mod=out,fast=FALSE) $Lik ssq NaN $s2 ssq 0.971436 > arima(x,order=c(1,0,1),seasonal=list(period=12,order=c(1,0,0)),include.mean=FALSE,init=c(phi1,theta,phis),method='ML') Erreur dans optim(init[mask], armafn, method = optim.method, hessian = TRUE, : valeur non-finie fournie par optim If needed, I can...

...trace = T) > bc <- coef(storm.fm) > se <- sqrt(diag(vcov(storm.fm))) > dv <- deviance(storm.fm) > par(pty = "s") > b1 <- bc[1] + seq(-3*se[1], 3*se[1], length = 51) > b2 <- bc[2] + seq(-3*se[2], 3*se[2], length = 51) > bv <- expand.grid(b1,b2) > ssq <- function(b) + sum((Time - b[1]*Viscosity/(Wt-b[2]))^2) > dbetas <- apply( bv, 1 , ssq) > attach(stormer) > cc <- matrix(Time - rep(bv[,1],rep(23,2601)) * + Viscosity/(Wt - rep(bv[,2], rep(23, 2601))), 23) > dbetas <- matrix(drop(rep(1,23) %% cc^2), 51) > fstat <- ma...

> From: kjetil halvorsen [mailto:kjetilh at umsanet.edu.bo] > > Martin Hoyle wrote: > > > > Dear Greg, > > Thanks for the new release. The decomposition of the SSQ is > just what I need! > > > I thought decomposition of the SSQ can be obtained with the split= > argument to summary.aov? That argument seems tro be new --- it didnt > exist two years ago, and I was reacently made aware of it. > It might not be well known? You are correct t...

...n R? Not that I would like to use them, I know they are problematic. I would like to know how to calculate them in order to demonstrate that strange things happen when you use them (for a course for example). I know you can use drop1(lm(), test="F") but for an lm(y~A+B+A:B), type III SSQs are only calculated for the A:B interaction. If I specify lm(y~A+B), it calculates type II SSQ for the main effects (as type III only differs from type II if interactions are included). Thus, this approach doesn't help. Another approach is the Anova(, type="III") function within...

...es this work for my code with R but not R-devel? And another question is related to using .Call. Based on the manual page, I do not need to change the function parameters when using .Call. So I can run like this: .Call("sppedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), ssq[i], i, as.integer(B), overlaps, overlaps.P) But I am receiving the memory(?) related error: terminate called after throwing an instance of 'std::bad_alloc' what(): std::bad_alloc Now that I am running the code using .Call. Thanks. On Mon, Mar 2, 2015 at 2:01 PM, Duncan Murdoch <mur...

...ed in the NEWS file. > And another question is related to using .Call. Based on the manual > page, I do not need to change the function parameters when using > .Call. So I can run like this: > .Call("sppedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), > ssq[i], i, as.integer(B), overlaps, overlaps.P) > > But I am receiving the memory(?) related error: > terminate called after throwing an instance of 'std::bad_alloc' > what(): std::bad_alloc > Now that I am running the code using .Call. Code using .Call is quite different fr...

Dear Greg, Thanks for the new release. The decomposition of the SSQ is just what I need! Regards, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle >>> gregory_r_warnes at groton.pfizer.com 10/30/02 07:16PM >>> Versi...

...combined into the final result. Further, the approach induces during execution a dynamic hierarchical grid that will better fit the dataset w.r.t. classical grid approaches that exploit a fixed grid instead. Finally, the algorithm exploits the analytical properties of the Quadratic Sums of Squares (SSQ in the following) function to minimize the cost of merge and split operations. you can refer the paper for more details about time complexity and performance http://web.cs.ucla.edu/~zaniolo/papers/chp%253A10.1007%252F978-3-642-37456-2_10.pdf for implementing it,we can use Documentsource class in...

I have a challenge that I want to share with the group. This is not homework (but I may assign it as such if I teach the appropriate class again) and I have found one solution, so don't need anything urgent. This is more for fun to see if others can find a better solution than I did. The challenge: I want to read a book in a given number of days. I want to read an integer number of

...ith repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I would like to know how to write the ANOVA model in R. I have done the following: aov(trait ~ species + strain/replicate) Is it accurate? Thanks a lot, Christian

Hello All,I am Nirmal Singhania from NIIT University,India. I am interested in Clustering of search results Topic. I have been in field of practical machine learning and information retrieval from quite some time. I took various courses/MOOC on Information retrieval and Text Mining and have been working on real life datasets(KDD99,AWID,Movielens). Because the problems you face in real life ML/IR

...ores = c("none", "regression", "Bartlett"), rotation = "varimax", control = NULL, ...) { sortLoadings <- function(Lambda) { cn <- colnames(Lambda) Phi <- attr(Lambda, "covariance") ssq <- apply(Lambda, 2, function(x) -sum(x^2)) Lambda <- Lambda[, order(ssq), drop = FALSE] colnames(Lambda) <- cn neg <- colSums(Lambda) < 0 Lambda[, neg] <- -Lambda[, neg] if(!is.null(Phi)) { unit <- ifelse(neg, -1, 1)...

...gt; > Within my R code, I am using a C++ function as below: > > overlaps <- matrix(0, nrow=B, ncol=length(N)) > overlaps.P <- matrix(0, nrow=B, ncol=length(N)) > > .C("speedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), > ssq[i], i, as.integer(B), overlaps, overlaps.P, DUP=FALSE) > > > the function "speedUp", is supposed to update matrices overlaps and > overlaps.P and it works with official R versions. > However, using the same code in R-devel, it does not update matrices and > they remai...

...W Test > # for Normality to Large Samples" from Applied Statistics, 31 no.2 > # pp. 115-124. > # > n <- length(x) > index <- 1:n > m <- qnorm((index - 0.375)/(n + 0.25)) > y <- sort(x) > mu <- mean(y) > SSq <- sum((y - mu)^2) > astar <- 2 * m > ends <- c(1, n) > astar.p <- astar[ - ends] > if(n <= 20) > m <- n - 1 > else m <- n > if(m < 20) > aa <- gamma(0.5 * (m + 1))/(sq...

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]