similar to: Understanding why a GAM can't have an intercept

Hi everybody, I am trying to fit a GAM model without intercept using library mgcv. However, the result has nothing to do with the observed data. In fact the predicted points are far from the predicted points obtained from the model with intercept. For example: #First I generate some simulated data: library(mgcv) x<-seq(0,10,length=100) y<-x^2+rnorm(100) #then I fit a gam model with

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

Get some question about the function "gam". Suppose I have a semiparametric model, Y~x1+x2+s(z1). Using "gam", how could I get the estimates for the parametric part and nonparametric part respectively? And another question: we could find the coefficients for both parametric term and nonparametric term, what do these coefficients for the nonparametric term stand for, the

1) I have a semiparametric model, like *Y~x1+s(x2)+s(x3)* When I rum gam package I only obtained the estimates and the statistics of the nonparametric part. How can I get the parametric part? Please could you give me the complete comand to do it. 2) How are the negative coefficients identified. I run different examples and I never got any negative parameters. Thank you, Dacha [[alternative

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

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 =

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Hi, Is it possible to compare two GAM objects created with the gam() function from the mgcv package. I use a slightly modified version of anova.glm() named anova.gam(), modified from John Fox (2002). It often gives me some aberant responses, especially with "F" test. I use a quasibinomial model and scale (dispersion) is calculated and used in the calculation of the F value. Does someone

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if

Hello, I am a bit confused about gamm in mgcv. Consulting Wood (2006) or Ruppert et al. (2003) hasn't taken away my confusion. In this code from the gamm help file: b2<-gamm(y~s(x0)+s(x1)+s(x2)+s(x3),family=poisson,random=list(fac=~1)) Am I correct in assuming that we have a random intercept here....but that the amount of smoothing is also changing per level of the

We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone can advise us on a problem we are encountering as we analyze many short time series datasets. For each dataset, we have four models, each with intercept, predictor x (trend), z (treatment), and int (interaction between x and z). Our models are Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths Model

Hi all, I constructed a GAM model with a linear term and two smooth terms, all of them statistically significant but the intercept was not significant. The adjusted r2 of this model is 0.572 and the deviance 65.3. I decided to run the model again without intercept, so I used in R the following instruction: regression= gam(dependent~ +linear_independent +s(smooth_independent_1)

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!

Sorry if this is an obvious question... I'm estimating a simple binomial generalized additive model using the gam function in the package mgcv. The model makes sense given my data, and the predicted values also make sense given what I know about the data. However, I'm having trouble interpreting the y-axis of the plot of the gam object. The y-axis is labeled "s(x,2.52)"

For summary(GAM) in the mgcv package smooth the degrees of freedom for the F value for test of smooth terms are the rank of covariance matrix of \hat{beta} and the residuals df. I've noticed that in a lot of GAMs I've fit the rank of the covariance turns out to be 9. In Simon Wood's book, the rank of covariance matrix is usually either 9 or 99 (pages 239-230 and 259). Can anyone

Dear all, I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1). The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as

I am a little bit confusing about the following help message on how to fit a GAM model with interaction between factor and smooth terms from http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html: ?Sometimes models of the form: E(y)=b0+f(x)z need to be estimated (where f is a smooth function, as usual.) The appropriate formula is: y~z+s(x,by=z) - the by argument ensures that the smooth

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Dear All: I apologize for asking such an elementary question, but I could not find an adequate response on line. I am hoping to receive some help with the interpretation of the Intercept coefficient in the gam model below. I1 through I3 are dummy coded "Item difficulty" parameters in a data set that includes 4 items. If the Intercept is the value of Y when all other terms are 0, am I

Hello, I have been using mgcv to run GAM hurdle models, analyzing presence/absence data with GAM logistic regressions, and then analyzing the data conditional on presence (e.g. without samples with no zeros) with GAMs with a negative binomial distribution. It occurs to me that using the negative binomial distribution on data with no zeros is not right, as the negative binomial allows zeros.