mgcv

In R, I would like to fit a gam model with categorical variables. I thought I could do it like with (cat is the categorical variable). lm(data = df, formula = y ~ x1*cat + x2 + x3); But I can't do things like : gam(data = df, formula = y ~ s(x1)*cat + s(x2) + x3) but the following works: gam(data = df, formula = y ~ cat + s(x1) + s(x2) + x3) How do I add a categorical variable to just one of the splines? One of the comments has more or less told you how. Use by variable: s(x1, by = cat) This creates the "factor smooth" smoothing class fs , where a smooth function of x1 is created for each

I am fitting a model using gam from the mgcv package and store the result in model and so far I have been looking at the smooth components using plot(model) . I have recently started using ggplot2 and like its output. So I am wondering, is it possible to plot these graphs using ggplot2? Here is an example: x1 = rnorm(1000) x2 = rnorm(1000) n = rpois(1000, exp(x1) + x2^2) model = gam(n ~ s(x1, k=10) + s(x2, k=20), family="poisson") plot(model, rug=FALSE, select=1) plot(model, rug=FALSE, select=2) And I am interest in s(x1, k=10) and s(x2, k=20) not in the fit. Partial answer: I dug deeper into

I'm using the mgcv package in R to fit some polynomial splines to some data via: x.gam <- gam(cts ~ s(time, bs = "ad"), data = x.dd, family = poisson(link = "log")) I'm trying to extract the functional form of the fit. x.gam is a gamObject , and I've been reading the documentation but haven't found enough information in order to manually reconstruct the fitted function. x.gam$smooth contains information about whether the knots have been placed; x.gam$coefficients gives the spline coefficients, but I don't know what order polynomial splines are used and looking in the code has not revealed

This thread from a couple of years ago describes how to extract data used to plot the smooth components of a fitted gam model. It works, but only when there is one smooth variable. I've got more than one smooth variable, and unfortunately I can only extract the smooths from the last of the series. Here is an example: library(mgcv) a = rnorm(100) b = runif(100) y = a*b/(a+b) mod = gam(y~s(a)+s(b)) summary(mod) plotData <- list() trace(mgcv:::plot.gam, at=list(c(25,3,3,3)), #this gets you to the location where plot.gam calls plot.mgcv.smooth (see ?trace) #plot.mgcv.smooth is the function that

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: 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? How can I extract the matrix from the fitted GAM so that I can use it in as an impute for a different model? The types of models I am running are to the following form: gam <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+ s(birth_year

On running a gam model using the mgcv package, I encountered a strange error message which I am unable to understand: “Error in model.frame.default(formula = death ~ pm10 + Lag(resid1, 1) + : variable lengths differ (found for 'Lag(resid1, 1)')”. The number of observations used in model1 is exactly the same as the length of the deviance residual, thus I think this error is not related to difference in data size or length. I found a fairly related error message on the web here , but that post did not receive an adequate answer, so it is not helpful to my problem. Reproducible example and data