search for: penalisations

Hey all, I'm using the gam() function inside the mgcv package to fit a penalised spline to some data. However, I don't quite understand what exactly the intercept it includes by default is / how to interpret it. Ideally I'd like to understand what the intercept is in terms of the B-Spline and/or truncated power series basis representation. Thanks!

Hi, I have been trying to fit an un-penalised gam in mgcv (in order to get more reliable p-values for hypothesis testing), but I am struggling to get the model to fit sucessfully when I add in a te() interaction. The model I am trying to fit is: gam(count~ s(x1, bs = "ts", k = 4, fx = TRUE) + s(x2, bs = "ts", k = 4, fx = TRUE) + te(x2, x3, bs =

Good Afternoon Everyone, I am looking for advice fitting a linear mixed model where the random components do not seem to fit within the model formulae for lmer. The columns of Z are not stratified and have the notional random formula (z1 | 1) + ... + (zk | 1). Context I am fitting a penalised thin plate spline with knots k1 to kn. The basis functions Zk are |x-ki|^3 and the penalty matrix has

Anyone using the penalised partial likelihood routines in survival5 should update their version. A bug has been fixed in the S package: in coxph() models with penalised likelihood and strata it was possible in some circumstances to get an infinite loop or perhaps an incorrect answer. The new version (2.3) is on cran.r-project.org and will percolate through CRAN in the next few days. -thomas

Anyone using the penalised partial likelihood routines in survival5 should update their version. A bug has been fixed in the S package: in coxph() models with penalised likelihood and strata it was possible in some circumstances to get an infinite loop or perhaps an incorrect answer. The new version (2.3) is on cran.r-project.org and will percolate through CRAN in the next few days. -thomas

Hi all, I have been trying to investigate traffic shaping in an effort to solve the "unfriendly network apps" problem on a test network. I have a basis by which I''d like to shape traffic, but studying the howto doesn''t uncover and existing qdisc that seems to fit what I would like to do. The problem I would like to address is to prevent an IP address opening 10

hi I'm puzzled as to the relation between the REML score computed by gam and the formula (4) on p.4 here: http://opus.bath.ac.uk/22707/1/Wood_JRSSB_2011_73_1_3.pdf I'm ok with this for poisson, or for quasipoisson when phi=1. However, when phi differs from 1, I'm stuck. #simulate some data library(mgcv) set.seed(1) x1<-runif(500) x2<-rnorm(500)

Hi, I have a scenario that I am failing to implement using the Queue app, but which I had thought would be commonplace... 1) (this bit works fine) I want a queue caller to have access to the basic set of agents initially, with an overflow to additional agents if they are busy - This is done using penalty: queues.conf: member => SIP/dev1,0,Agent1 member => SIP/dev2,0,Agent2 member =>

Hello, I have posted this mail a few days ago but I did it wrong, I hope is right now: I have the following doubts related with lm.ridge, from MASS package. To show the problem using the Longley example, I have the following doubts: First: I think coefficients from lm(Employed~.,data=longley) should be equal coefficients from lm.ridge(Employed~.,data=longley, lambda=0) why it does not happen?

** High Priority ** Hello R users My issue is both theorical and technical. I would like to run a penalised discriminant analysis with the fda() function, but I don''t know all the details of splines theory. I try on the example of the phonems from the article "Penalised Discriminant Analysis" of Hastie, Buja and Tibshirani 1994 : 5 groups and 256 variables. The 256

A nearly complete port of the new survival5 package has been sent to CRAN and will soon be appearing on a mirror near you in the contrib/devel area. This new package, the successor to survival4, has a more stable likelihood maximiser for parametric survival models and incorporates penalised likelihoods for adding smoothing splines, ridge regression, and (approximately) frailties to survival

I am confused on how the log-likelihood is calculated in a parametric survival problem with frailty. I see a contradiction in the frailty() help file vs. the source code of frailty.gamma(), frailty.gaussian() and frailty.t(). The function frailty.gaussian() appears to calculate the penalty as the negative log-density of independent Gaussian variables, as one would expect: >

Hi, For the following code: void foo (); int main () {foo();} llvm emits "bl foo" Then I set foo at a far address in linking: aarch64-linux-gnu-gcc -Wl,--defsym=foo=0x80000000 a.o -o a.exe I got an error from ld: a.c:(.text+0x8): relocation truncated to fit: R_AARCH64_CALL26 against symbol `foo' define in *ABS* section in a.exe The question is: do I

On Tue, 6 May 2003, Jerome Asselin wrote: > > I am confused on how the log-likelihood is calculated in a parametric > survival problem with frailty. I see a contradiction in the frailty() help > file vs. the source code of frailty.gamma(), frailty.gaussian() and > frailty.t(). > > The function frailty.gaussian() appears to calculate the penalty as the > negative

Hi R-users I am trying to use the GAM function of the mgcv package. But I am having problem trying to specify the k parameter. Although I managed to run some models by giving to the parameter some (random) value, and it is explained by Wood (2006) that it does not seem to "really" affect the final result, I would like to grasp better its meaning. I understand that is the

Hi, I am new to R and AIC scores but what I get from coxme seems wrong. The AIC score increases as p-values decrease. Since lower AIC scores mean better models and lower p-values mean stronger effects or differences then shouldn't they change in the same direction? I found this happens with the data set rats as well as my own data. Below is the output for two models constructed with the rats

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions. please send comments + looking for nice not too big multi-arrays for the next release demos Didier --...

PTAk_1.1-1 ( Principal Tensor Analysis on k modes) has been released on CRAN A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions. please send comments + looking for nice not too big multi-arrays for the next release demos Didier --...

Dear R Helpers I am having difficulty understanding how to use the penalty matrix for the nomROC function in package 'nonbinROC'. The documentation says that the values of the penalty matrix code the penalty function L[i,j] in which 0 <= L[i,j] <= 1 for j > i. It gives an example that if we have an ordered response with 4 categories, then we might wish to penalise larger