similar to: mulitple mixed regression with spline

Hello, I am trying to estimate a multivariate regression of Y on X with regression splines. Y is (nx1), and X is (nxd), with d>1. I assume the data is generated by some unknown regression function f(X), as in Y = f(X) + u, where u is some well-behaved regression error. I want to estimate f(X) via regression splines (tensor product splines). Then, I want to get the predicted values for some new

Dear all, I'm trying to use the Pspline add-on package to fit a quintic spline (norder =3), but I keep running into a Singularity error. > traj.spl <- smooth.Pspline(time, x, norder=3 ) Error in smooth.Pspline(time, x, norder = 3) : Singularity error in solving equations > Playing around with the other parameters produces an "unused arguments" error: > traj.spl

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Dear listeRs; I am hoping to provide myself the ability to interpolate mortality estimates within intervals for a range of conditions and ages. I have four mortality tables derived from the Society of Actuaries 2001 VBT tables that contains interval mortality rates by starting age and year of duration. I would like to flexibly interpolate using a large set (~4MM) of ages and conditions

Is there any way to identify or infer the inflection points in a smooth spline object? I am doing a comparison of various methods of time-series analysis (polynomial regression, spline smoothing, recursive partitioning) and I am specifically interested in obtaining the julian dates associated with the inflection points inferred by the various models. Tyler e.g.

Dear list, I've had this problem for a while and I'm looking for a more general and robust technique than I've been able to imagine myself. I need to find N (typically N= 3 to 5) zeros in a function that is not a polynomial in a specified interval. The code below illustrates this, by creating a noisy curve with three peaks of different position, magnitude, width and

hola soy nuevo usuario de R y necesito crear un objeto p tal que, p=lambda*integral[f´´(x)^2 dx ] donde "lambda" es uno de los parametros que resultan de la funcion "smooth.spline()" y la integral es sobre la derivada 2 de esa misma funcion... dos cuestiones: 1) como extraigo lambda de los resultados de smooth.spline() para usarlo como objeto cuando lo requiera y 2) como

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

What should have been a routine upgrade to ZFS 0.6.4.1-1.el6.x86_64 somehow failed, and now I've lost access to a 2T raidz2 array. I've tried removing all zfs and related packages (spl*), then starting fresh. For reasons that have have nothing to do with this problem (I don't think), I'm still booted to kernel 2.6.32-431.29.2.el6.x86_64, and there are no other kernels installed.

Dear all, After struggling for some time with *apply() and eva() without success, I decided to ask for help. I have 3 lists labeled with, each contains 3 different interpolation functions with identical names: > names(missgp0) [1] "spl.1mb" "spl.2mb" "spl.5mb" > > names(missgp1) [1] "spl.1mb" "spl.2mb" "spl.5mb" > >

Hi R-users, I inted to make multiple plots using for loop. The question is how can I adjust the left hand side margin of the plot according to the names.arg argument in barplot2. In every plot I have different annotations in the y axis and they vary in length. Now when I have fixed margins opar <- par(mar=c(3,15,0,2)... I get the same margins in all of the plots. That leaves lots of white

Hello, I have fit a simple spline model to the following data. Data x y 0 1.298 2 0.605 3 0.507 4 0.399 5 0.281 6 0.203 7 0.150 8 0.101 Model Sp.1=lm(y~bs(x,df=4)) Now I wish to inverse predict the x for y=.75, say. Optimize works fine for a polynomial but I can figure out how to get the spline model into the function argument. Can anyone help me out. Thanks!! Jeff Jeff Morris Sanofi

Dear R users, Suppose I have a data set with inconsistent names for a field. I desire to make these to consistent names. i.e "University of New Jersey", "New Jersey Uni", "New Jersey University" (3 different inconsistent names) to "The University of New Jersey" (consistent name) Below are arbitrary data set produced from "state.name" (built

I am not certain that this is the right place to post this, but maybe some kind soul can point me in the right direction if not. And I apologize if this is the wrong spot...The reason I posted here is that I recall running across a syslinux changlog that said "..added support for reading .spl..." files. I want to customize the suse boot up splash screens (for a CD install). I have been

Shuji, I suspect that bruto blows up because your data are linearly separable. To see this (if you didn't already know), try library(lattice) splom(~x, groups = y) and look at the first row. If you are trying to do classification, there are a few methods that would choke on this (logistic regression) and a few that won't (trees, svms etc). I would guess that bruto is in the latter

Hi, If I set df=2 in my smooth.spline function, is that equivalent to running a linear regression through my data? It appears that df=# of data points gives the interpolating spline and that df = 2 gives the linear regression, but I just want to confirm this. Thank you, Steven

I successfully combined my data frames, and am now on my next hurdle. I had combined the data and quarter, and used tapply to count the entries for each unique date/quarter pair. ar= tapply(ewrgnd$gw, list(ewrgnd$dq), sum) #for each date/quarter combination sums the gw (which are all 1) dq=row.names(ar) spl=strsplit(dq) But I need to split them back into the separate date and quarter. So I used

I've been trying to figure out how to read in a large file for a few days now, and after extensive research I'm still not sure what to do. I have a large comma delimited text file that contains 59 fields in each record. There is also a header every 121 records This function works well for smallish records getcsv=function(fname){ ff=file(description = fname) x <- readLines(ff)

Hi, How can I extract part of an array? I would like to extract table Supported from this array. If this is not possible, how do I convert array to list? I'm sorry this is not an reproducible example. > spl <- tapply(temp$var1, list(temp$var2, temp$var3, temp$var3), mean) > spl , , Supported 07 08 A 68.38710 71.48387 B 21.67742 20.83871 C

hi all I have R object look like this: > spl $SB012XSB044 DPW Cross 1 66.6 SB012XSB044 2 96.5 SB012XSB044 3 78.8 SB012XSB044 4 68.6 SB012XSB044 5 62.0 SB012XSB044 6 72.1 SB044XSB012 7 72.2 SB044XSB012 8 69.6 SB044XSB012 9 87.9 SB044XSB012 10 84.4 SB044XSB012 11 51.9 SB044XSB012 12 65.5 SB044XSB012 $SB012XSB099 DPW Cross 13 100.9 SB012XSB099 14 44.4 SB012XSB099