similar to: Loess fit

Greetings, A couple general questions regarding the use of splines to interpolate depth profile data. Here is an example of a set of depths, with associated attributes for a given soil profile, along with a function for calculating midpoints from a set of soil horizon boundaries: #calculate midpoints: mid <- function(x) { for( i in 1:length(x)) { if( i > 1) { a[i] = (x[i] -

Example: define i8 @narrow_add(i8 %x, i8 %y) { %x32 = zext i8 %x to i32 %y32 = zext i8 %y to i32 %add = add nsw i32 %x32, %y32 %tr = trunc i32 %add to i8 ret i8 %tr } With no data-layout or with an x86 target where 8-bit integer is in the data-layout, we reduce to: $ ./opt -instcombine narrowadd.ll -S define i8 @narrow_add(i8 %x, i8 %y) { %add = add i8 %x, %y ret i8 %add } But on

Hello Thanks for looking into this. I can't be very confident what the knock on result of a change like that would be, especially on architectures that are not Arm. What I can do though, is run some benchmarks and look at that results. Using this patch: --- a/lib/Transforms/InstCombine/InstructionCombining.cpp +++ b/lib/Transforms/InstCombine/InstructionCombining.cpp @@ -150,6 +150,9 @@

Hi, I'm Luca from Savona, Italy. I'm using Compiz and I'm writing an application with Xlib to draw two windows, let's say window_A and window_B. I would like to keep fixed the relative position between two established corners of them that they have when mapped. So that if I move or resize one of the window, the other if necessary automatically moves to observe the condition.

Thanks for the perf testing. I assume that DAG legalization is equipped to handle these cases fairly well, or someone would've complained by now... FWIW (and at least some of this can be blamed on me), instcombine already does the narrowing transforms without checking shouldChangeType() for binops like and/or/xor/udiv. The justification was that narrower ops are always better for

Hi Luca, In recent versions of compiz (0.9.10) if you have lazy positioning on (org.compiz.move 'lazy-positioning') other clients won't get a configure notification until the end of the move. This is a performance optimization to avoid a slow path on the nvidia drivers with vsync. If you want such notifications you need to turn this option off. Either that or implement this as a

Hi everyone, this should be pretty basic but I need asking for help as I got stuck. I am running simple linear regression models on R with k regressors where k > 1. In order to automate my code I packed all the regressors in a matrix X so that lm(y~X) will always produce the results I want regardless of the variables in X. I am new to R but I found this advice somewhere so I guess it is

Buenas Tengo un Data table de la siguiente manera: datos<-data.table(grupo=rep(c("a","b"),5),x=c(1:10),y=rnorm(10,2,1)) Lo que quiero es añadir una fila por cada grupo y en esa nueva fila, al valor de la x ponerle el valor anterior de la y Lo que hago es añadir una nueva fila por grupo, con: datos[,.SD[1:(.N+1)],by=grupo] Y para añadir el valor anterior uso la función

Hello, I'm attempting to evaluate the accuracy of the probability predictions for my model. As previously discussed here, the AUC is not a good measure as I'm not concerned with classification accuracy but probability accurcy. It was suggested to me that the loess function would be a good measure to look at. I can see some libraries (Design) will plot the loess function as a curve

Hi, I'm trying to normalize data by fitting a line through the highest density of points (in a 2D plot). In other words, if you visualize the data as a density plot, the fit I'm trying to achieve is the line that goes through the "crest" of the mountain. This is similar yet different to what LOESS does. I've been using loess before, but it does not exactly that as it takes

I'm at my wit's end, and have searched all of my sources. I need to generate a relatively large number of individual LOESS fits each month of data (I have about 16 months of data). Fitting the polynomial is not my problem, figuring out what the formula that describes that polynomial is. apologies in advance if this is so simple, but I need a hand here. Thanks. -- View this message in

Hi everyone. I'm trying to do a loess with missing value on independant variable. doc = c(2.27904, 2.59536, 7.44696, NA, 6.24264, 4.58400, 5.79192, 5.39502, 7.41216, 4.09440, 4.22868, 4.24620, 5.43804, 1.95528); distance = c(26.5,56.5, 90.3, 123.0, 147.5, 176.0, 215.7, 229.3, 252.0, 325.3, 362.0, 419.3, 454.6, 470.0); myloess = loess(doc ~ distance, na.action = na.omit); plot(distance,

Dear R community, I am trying to understand how the predict function, specifically, the predict.loess function works. I understand that the loess function calculates regression parameters at each data point in 'data'. lo <- loess ( y~x, data) p <- predict (lo, newdata) I understand that the predict function predicts values for 'newdata' according to the loess regression

Hi, I was wondering if anyone knows of a way by which one can estimate values from a contour plot created by using the loess function? I am hoping to use the loess contour plot as a means of interpolation to identify the loess created values at points at pre-defined (x,y) locations. Could anyone point me in the right direction please? Thanks. Laura Quinn Institute of Atmospheric Science School

I have a two-dimensional loess fit, and need to calculate the gradient field from it. Even after looking at loess.c and loess.f, I don't understand the meaning of the returned polynomial coefficients. Or is the brute force method of using a tangential approx to the fitted values the way to go? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074

Hi! In a current project, I am fitting loess models to subsets of data in order to use the loess predicitons for normalization (similar to what is done in many microarray analyses). While working on this I ran into a problem when I tried to predict from the loess models and the data contained NAs or NaNs. I tracked down the problem to the fact that predict.loess will not return a value at all

Good morning, I am Marta Colombo,student at Politecnico,Milan. I am studying local regression models and I am using loess function. My problem is that when I have a loess object I don't know how to display the fitted surface; in fact, while in S when you have a loess object you can see it writing plot(object), in R this dosen't work. Also I'd like to know if there is something like the

Hello, I need help with the details of loess prediction algorithm. I would like to get it implemented as a part of a measurement system programmed in LabView. My job is provide a detailed description of the algorithm. This is a simple one-dimensional problem - smoothing an (x, y) data set. I found quite a detailed description of the fitting procedure in the "white book". It is also

I'm updating the loess routines to allow for, among other things, arbitrary local polynomial degree and number of predictors. For now, I've given the updated package its own namespace. The trouble is, panel.loess still calls the original code in package:stats instead of the new loess package, regardless of whether package:loess or package:lattice comes first in the search list. If I

Thanks, John. However, loess.smooth() is producing a very different curve compared to the one that results from applying predict() on a loess(). I am guessing they are using different defaults. Correct? On Thu, 23 Mar 2023 at 20:20, John Fox <jfox at mcmaster.ca> wrote: > Dear Anupam Tyagi, > > You didn't include your data, so it's not possible to see exactly what >