similar to: Improve image scaling

Hi all, I am trying to find estimates for 7 parameters of a model which should fit real data. I have a function for the negative log likelihood (NLL) of the data. With optim(method="L-BFGS-B",lower=0) I am now minimizing the NLL to find the best fitting parameters. My problem is that the algorithm does not converge for certain data sets. I have read that one should scale the fn

I've been experimenting with the ideas of Replay Gain[1] and find that ogg123 doesn't have a way of specifying the scaling applied to replayed samples (like -f in mpg123). Looking at libvorbisfile, I see no function exactly matching this possibly desirable behaviour. ov_read() scales by either 128 (byte output) or 32768 (word output), but there's nothing in between. ov_read_float()

Hello, We are trying to find a function to compute "stress" in our multidimensional scaling analysis of a dissimilarity matrix. We've used "dist()" to create the matrix and "cmdscale()" for the scaling. In order to determine the number of dimensions we would like to plot stress vs. dimensions. However, we cannot find a pre-made command. It seems that other

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 I've been looking fairly carefully at the Q-Q plots produced by plot.lm() and am having difficulty understanding why plot.lm() is doing what it's doing, specifically scaling the standardized residuals by the prior weights. Can anyone explain this to me ... ? Multiplying by the weights seems to give the wrong plot, at least for binomial

R 1.4.0 ?stars has scale: logical flag: if `TRUE', the columns of the data matrix are scaled independently so that the maximum value in each column is 1 and the minimum is 0. If `FALSE', the presumption is that the data have been scaled by some other algorithm to the range [0,1]. but the code has if (scale) { x <- sweep(x, 2,

Dear R list Would anyone be able to tell me whether it is possible to do "enhanced multidimensional scaling" (enhanced MDS) in R? In other words, something that goes beyond "cmdscale" by iteratively improving the fit between observed dissimilarities and inter-object distances, using the KYST algorithm (Kruskal, 1964). I have found several implementations of non-metric MDS

Dear all Problem: hist()-function, scale = ?percent? I want to generate histograms for changing underlying data. In order to make them comparable, I want to fix the y-axis (vertical-axis) to, e.g., 0%, 10%, 20%, 30% as well as to fix the spaces, too. So the y-axis in each histogram should be identical. Currently, I have 100 histograms and the y-axis scales changes in each. Here is my code:

Dear list (but probably mostly Hadley): In ggplot, operations to modify 'guides' are accessed through grid objects, but I did not find mention of creating new guides or possibly removing them altogether using ggplot functions. I wonder if this is something I need to learn grid to learn more about (which I hope to do eventually). Also, ggplot()+geom_object() [where 'object' can be

On Sun, Apr 15, 2012 at 3:53 AM, Chandler Carruth <chandlerc at google.com>wrote: > >> And, again, I think this should be function level model, unless specified >> otherwise in the instruction, as it will be the case in 99.9999% of the >> compilations. > > > I actually lobbied with Duncan to use a function default, with instruction > level overrides, but his

scaling<-4 xywidth<-480 resolution<-150 png(filename = "c:/r/anschluss/plots/4.png", width = xywidth*scaling, height = xywidth*scaling,pointsize = 12, bg = "white", res = resolution*scaling) ...... barplot(xrow,col = barcolors,cex.axis=scaling, ylab="mean time till attachment in sec",cex.lab=1.2*scaling) I tried to scale the barplot but there is one

I have a problem with lattice log scales that I could use some help with. I'm trying to print log y-axis scales without exponents in the labels. A similar thread with Deepayan' recommendation is here: http://tolstoy.newcastle.edu.au/R/e11/help/10/09/9865.html. For example, this code using xyplot produces a logged y-axis but the labels (e.g. "10^1.2") are not very

Hello! We are new in using R. We use the SVM module from the library ''e1071'' for training. Problem formulation: a classification has been performed using SVM module (linear kernel). Later, a new data set (test set) comparable to the training data shall be scaled in the same way as the training set (using the same scaling parameter set, but without using the SVM again

I just read this blog: https://mrotaru.wordpress.com/2013/10/10/scaling-to-12-million-concurrent-connections-how-migratorydata-did-it/ <https://mrotaru.wordpress.com/2013/10/10/scaling-to-12-million-concurrent-connections-how-migratorydata-did-it/> about scaling to 12 Million Concurrent Connections on a single server and it got me thinking. Would it be possible to scale Dovecot IMAP server

I feel like this discussion is getting a bit off track... On Sun, Apr 15, 2012 at 12:00 AM, Dmitry Babokin <babokin at gmail.com> wrote: > > I would define the set of transformations, such as (i can help with more > complete list if you prefer): > > - reassociation > - x+0.0=>x > - x*0.0=>0.0 > - x*1.0=>x > - a/b => a* 1/b > -

This may not be the best place to ask, but I was prompted by a question about guest cores on KVM. We currently use VMWare Server (v1.0) on CentOS5. It supports up to two virtual CPUs, but not very well, as I understand it. VMWare Server 2.0 might do better at supporting the same maximum of 2 CPUs, but if my research is correct, they both use what is called "strict co-scheduling".

I guess the question that is being asked here is what is the scaling matrix that is being returned in the qda object. The help file on qda() says: ... scaling: for each group ?i?, ?scaling[,,i]? is an array which transforms observations so that within-groups covariance matrix is spherical. ... This is a bit ambiguous. I tried a few cases (spectral, QR decomposition, especially given that it is an

I've been playing and comparing frequency scaling between AMD and Intel CPUs yesterday and there seem to be great differences between AMD and Intel and some gotchas. This is all on CentOS 5.2 with latest Xen kernels (which are supposed to be powersaving-enabled since 5.2). AMD: It seems once I get the AMD CPU to use the ondemand governor it works very well and very efficiently. But this

Hello R-help, I have a data.frame where I want to scatterplot variable1 versus variable2 using a third variable as a factor. For each factor I want to have a regression line in the same plot with different color and symbols. Var1, and var2 contain a lot of NA,sometimes the dependent var sometimes the predictor, sometimes both together. Three problems occure: Makeing subsets to split the data

Hi, I''ve recently installed xen-3.2.1 on my AMD Sempron(tm) machine. It runs gentoo 2.6.21 as dom0 and cpu frequency scaling works in dom0. But inside a domU, cpu frequency scaling is not working. By checking the boot log in domU I found that domU is unable to load the powernow driver: "powernow-k8: BIOS error - no PSB or ACPI _PSS objects" I wonder if it is possible to have

Hello, I am Souradeep Chattopadhyay and I am a graduate student at Iowa State University Department of Statistics. Can anyone please explain the mathematical formulation behind the scaling matrix returned by the qda function in MASS package. I want to understand how this scaling matrix is derived from the inputs given to the qda function. Example Code The following example is using