search for: penalised

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&quo...

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 elements |ki-kj|^3. Rather than fit basis functions Zk I have transformed the basis to Z = Zk %*% penalty^(-1/2). Full details are in section 2: Bayesian analysis for penalised spline regression u...

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-pr...

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-pr...

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

...~s(x1)+s(x2),family="poisson",method="REML") phi<-m1$scale #formula #1st term S1<-m1$smooth[[1]]$S[[1]]*m1$sp[1] S2<-m1$smooth[[2]]$S[[1]]*m1$sp[2] S<-matrix(0,19,19) for (i in 2:10) { for (j in 2:10) { S[i,j]=S1[i-1,j-1] S[i+9,j+9]=S2[i-1,j-1] } } beta<-m1$coef #penalised deviance Dp<-m1$dev+t(beta)%*%S%*%beta F1<-Dp/(2*phi) #2nd term F2<-sum(ifelse(y==0,0,y*log(y)-y-log(factorial(y)))) #3rd term X<-predict(m1,type="lpmatrix") W<-diag(fitted(m1)) H<-t(X)%*%W%*%X ldhs<-determinant(H+S,log=TRUE)$modulus[1] eigS<-eigen(S,only.values=...

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 variables are from...

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 models. User-defined penalties are also possible. It is in the devel area for two reasons (1) There are some tests I want to run against S-PLUS, and we don't have survival5 installed for S-PL...

On 2024-07-20 at 16:30 -0400, James Ralston wrote: > The real issue here is that the Mailman configuration for the > openssh-unix-dev list does not appear to set > `dmarc_moderation_action` > (in `Privacy options` - `Sender filters`) to either `Munge From` or > `Wrap Message`, which is necessary for lists where ... "Necessary" if the client machines re going to penalize

...tter penalty function (2), because this penalty is NOT equal to the negative log-likelihood (1). Finally, it's not clear to me whether we gain significant convergence speed and accuracy by using the penalty (2) as opposed to (1). Furthermore, the help file for frailty() says, "The penalised likelihood method is equivalent to maximum (partial) likelihood for the gamma frailty but not for the others." In the current state of the package, I would think that this should be the other way around. That is, "The penalised likelihood method is equivalent to maximum (parti...

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, 18 Jun 2024, Joseph S. Testa II wrote: > In the upcoming v9.8 release notes I see "the server will now block > client addresses that repeatedly fail authentication, repeatedly > connect without ever completing authentication or that crash the > server." Has this new PerSourcePenalties config directive been tested > against the DHEat attack? Not explicitly but

...> because this penalty is NOT equal to the negative log-likelihood (1). > Finally, it's not clear to me whether we gain significant convergence > speed and accuracy by using the penalty (2) as opposed to (1). > > Furthermore, the help file for frailty() says, > "The penalised likelihood method is equivalent to maximum (partial) > likelihood for the gamma frailty but not for the others." > > In the current state of the package, I would think that this should be the > other way around. That is, > "The penalised likelihood method is equiv...

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

...le > > #formula > #1st term > S1<-m1$smooth[[1]]$S[[1]]*m1$sp[1] > S2<-m1$smooth[[2]]$S[[1]]*m1$sp[2] > S<-matrix(0,19,19) > for (i in 2:10) > { > for (j in 2:10) > { > S[i,j]=S1[i-1,j-1] > S[i+9,j+9]=S2[i-1,j-1] > } > } > beta<-m1$coef > #penalised deviance > Dp<-m1$dev+t(beta)%*%S%*%beta > F1<-Dp/(2*phi) > > #2nd term > F2<-sum(ifelse(y==0,0,y*log(y)-y-log(factorial(y)))) > > #3rd term > X<-predict(m1,type="lpmatrix") > W<-diag(fitted(m1)) > H<-t(X)%*%W%*%X > ldhs<-determinant(H...

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