similar to: Estimate predictor contribution in GAM models

hi, i'm using gam() function from package mgcv with default option (edf estimated by GCV). >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) >SG=summary(G) Formula: y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 Approximate significance of smooth

Hello, i got an warning message in the following code: f<-1:100 t<-1:100 b<-100 ll2 <- function(b,f,t) { t<-cumsum(t) tn<-t[length(t)] i<-seq(along=f) s1<-(tn*exp(-b*tn)*sum(f[i]))/(1-exp(-b*tn)) s2<-sum((f[i]*(t[i]*exp(-b*t[i])-t[i-1]*exp(b*t[i-1])))/(exp(-b*t[i-1])-exp(-b*t[i]))) s1-s2 } ll2(b,f,t) i think, the problem here is, that t[0] doesn't

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hi Everyone, I have a question regarding the construction of 3D graphs in 'R', BUT these graphs also need to illustrate movement (with time) of the prostate gland (using radiological techniques). I am not sure how to do this in 'R' although I'm sure there is some way of doing it. Below, I have copied and pasted some of the data with which I'm working on. The data

I''m switching all my fulltext searching over to ferret using the acts_as_ferret plugin on windows (for development, will be deployed on linux). In my tests I have a setup method as follows to ensure the index is reset for each test: def setup Item.rebuild_index end This works fine for the initial few tests but then suddenly fails for the last few tests when it seems to be failing

Hi all, I'm running the following code to generate 40 different jpegs based on the resulting data. I'd like the file names to be 'Cluster1.jpeg', however the code write filenames like 'Cluster 1 .jpeg'. How can I get rid of the unwanted spaces? I've looked at ?format and it doesn't seem to work - at least in this context. ################### ClusCount <- 40

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hi everyone. I have have a dataset with daily measurement from January 1st of 1966 up to December 31th of 2011. Here's the first part of the data: Date SLEV 1/1/1966 1.086 1/2/1966 1.079 1/3/1966 1.133 1/4/1966 1.261 1/5/1966 1.391 1/6/1966 1.571 1/7/1966 1.728 1/8/1966 1.823 1/9/1966 1.97 1/10/1966 1.804 1/11/1966 2.02 1/12/1966 2.017 1/13/1966 1.86 1/14/1966 1.96 1/15/1966 1.813 1/16/1966

Hello, I'm new to R and have a very basic syntax question for the functionglm. I am using the function glm() to do a regression on a data set. I want to run this function in a while loop, so I need to be able to do two things: (1) I need to be able to have my list of variables in the regression be based from a variable vector instead of hard code. (2) I need to be able to collect the

I''m switching all my fulltext searching over to ferret using the acts_as_ferret plugin on windows (for development, will be deployed on linux). In my tests I have a setup method as follows to ensure the index is reset for each test: def setup Item.rebuild_index end This works fine for the initial few tests but then suddenly fails for the last few tests when it seems to be

Hello everyone, I have been doing a binary classification using random forest from the library "randomForest". One of the predictors is a factor variable, which is known to be highly related to the binary response I am trying to predict. Other 80 predictors are numeric. Totally I have 44 subjects. However, the random forest returns the factor variable as the least important one based on

Hi all, I am applying a Presence/absence Generalized additive model to model the distribution of marine algae species in R. I have found that log transforming the environmental variables improves the explained deviance of the model considerably. While log transforming is common practice in GLM, I have been unable to find any papers where this is performed in a GAM. Im wondering whether this

I?m running a model on animal behavior in response to shipping. In most cases, there is only one ship in the study area at one time. Ship length, distance from the animals, speed, angle from animals, and ship direction (as east/west bound) are among shipping-related covariates (with multiple interactions). The tricky part is that sometimes there are 2 ships in the area. I could add all the same

Hi Michael, > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Michael > Friendly > Sent: Thursday, June 29, 2017 9:04 AM > To: Beno?t PELE <benoit.pele at acoss.fr>; r-help at r-project.org > Subject: Re: [R] Help : glm p-values for a factor predictor > > On 6/29/17 11:13 AM, Beno?t PELE wrote: > > My question is

On 9 May 2018 at 19:48, Dávid Bolvanský via llvm-dev <llvm-dev at lists.llvm.org> wrote: > But a fix needs to be made since branch predictor hints are broken in a > valid C++20 code: They don't affect performance in the expected way, but they also don't actually break code. I'm not saying it's not a bug, but it's certainly not on the same level as a miscompilation.

On 04 Jan 2011, at 08:30, Jakob Stoklund Olesen wrote: > I noticed that we generate code like this for i386 PIC: > > calll L0$pb > L0$pb: > popl %eax > movl %eax, -24(%ebp) ## 4-byte Spill > > I worry that this defeats the return address prediction for returns > in the function because calls and returns no longer are matched. According to benchmarks by

Hi, is there a way in R to generate a polynomial spline with multiple predictor variables? I have one response and two predictors and I'm trying to fit a spline model for this... Please cc me on the reply.. Thanks, nirmal

Hello, can someone give me an example of how to fit a linear model where the response is a matrix, please? (other than a loop over lm calls) The ?lm points me to model.matrix but it is not clear to me how to use a model matrix in an lm call. fit<-lm(headmatrix ~ tailmatrix) causes problems as soon as I use predict(fit,newframe). The variables in tailmatrix are not found in

When one is doing simple regression and needs to force a zero intercept ( for whatever reason. I realize it's a controversial issue ), then subtracting the means of the left hand side and the right hand side from themselves does the trick. Does anyone know if there is a similar trick when the RHS has two variables ? Thanks. -------------------------------------------------------- This is

If I understand correctly, rpart() will pick predictor at each node automatically. I am wondering if there is a way to force rpart() including a specific predictor. The reason I am asking is that I'd like to use rpart() to detect interaction terms for some variables. Thanks.