similar to: choosing a proper knot in GAM mgcv package

Hi, there, I have 5 datasets. I would like to choose a basis spline with same knots in GAM function in order to obtain same basis function for 5 datasets. Moreover, the basis spline is used to for an interaction of two covarites. I used "cr" in one covariate, but it can only smooth w.r.t 1 covariate. Can anyone give me some suggestion about how to choose a proper smoothing spline

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Dear all, I have a problem with specifying the no. of knots in our function which include gam(). I last worked with this in mid September but since then I have reinstalled R and Simon Wood's library(mgcv), which he has changed since then. The statistician (and good R-coder) with whom I co-operate is now unfortunately overloaded with teaching, and I'm in the sprut of my thesis.... I

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients? Sincerely, Bill

Hi, all I want to choose same knots in GAM for 10 different studies so that they has the same basis function. Even though I choose same knots and same dimensions of basis smoothing, the basis representations are still not same. My command is as follows: data.gam<-gam(y~s(age,bs='cr',k=10)+male,family=binomial,knots=list(age=seq(45,64,length=10))) What is my mistake for choice of

Hello, We have a question about how to retrieve the nonparametric curve in gam() function. Now, we get the estimates and draw the fitted curve using the following code. You can see that our fitted curve is parallel to the output from gam() function but differs by a constant shift. However, the magnitude of the shift is not the intercept term. Also, gam's estimate appears more close to the

Dear R-hep, I'm trying to get the first derivative of a smooth from a gam model like: model<-gam(y~s(x,bs="cr", k=5)+z) and need the derivative: ds(x)/dx. Since coef(model) give me all the parameters, including the parameters of the basis, I just need the 1st derivative of the basis s(x).1, s(x).2, s(x).3, s(x).4. If the basis were generated with the function

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

Hi R-users, I'm using R 2.11.1, mgcv 1.6-2 to fit a generalized additive mixed model. I'm new to this package...and just got more and more problems... 1. Can I include correlation and/or random effect into gam( ) also? or only gamm( ) could be used? 2. I want to estimate the smoothing function s(x) under each level of treatment. i.e. different s(x) in each level of treatment. shall I

Dear listers, I'm using gam(from mgcv) for semi-parametric regression on small and noisy datasets(10 to 200 observations), and facing a problem of overfitting. According to the book(Simon N. Wood / Generalized Additive Models: An Introduction with R), it is suggested to avoid overfitting by inflating the effective degrees of freedom in GCV evaluation with increased "gamma"

Dear all, Following the help from gam(mgcv) help page, i tried to analyze my dataset with all the default arguments. Unfortunately, it can't be run successfully. I list the errors below. #m.gam<-gam(mark~s(x,y)+s(lstday2004)+s(slope)+s(ndvi2004)+s(elevation)+s(disbinary),family=binomial(logit),data=point)

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 =

Hi, I am using GAMs (package mgcv) to smooth event rates in a penalized regression setting and I was wondering if/how one can select the order of the derivative penalty. For my particular problem the order of the penalty (parameter "m" inside the "s" terms of the formula argument) appears to have a larger effect on the AIC/deviance of the estimated model than the

Hi all, My question concerns 2 error messages; one in the gam package and one in the mgcv package (see below). I have read help files and Chambers and Hastie book but am failing to understand how I can solve this problem. Could you please tell me what I must adjust so that the command does not generate error message? I am trying to achieve model selection for a GAM which is required for

Hi all, I am using GAM to model time trends in a logistic regression. Yet I would like to extract the the fitted spline from it to add it to another model, that cannot be fitted in GAM or GAMM. Thus I have 2 questions: 1) How can I fit a smoother over time so that I force one knot to be at a particular location while letting the model to find the other knots? 2) how can I extract the matrix

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

Suppose I have two variables, x and y. For a fixed number of knots, I want to create a spline transformation of x such that a loss function is minimized. Presumably, this loss function would be least squares, i.e. sum (f(x)-y)^2. The spline transformations would be linear, quadratic or cubic. I know I can solve this problem using some optimization function in R, but I was wondering if anyone

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise