similar to: mgcv with discrete covariates, interaction term

Dear list, does anybody know whether there is a way to easily retrieve the so called "partial residuals" of a gam fit with package mgcv? The partial residuals are the residuals you would get if you would "leave out" a particular predictor and are the dots in the plots created by plot(gam.object,residuals=TRUE) residuals.gam() gives me whole model residuals and

Hi, I am using the gam function in the mgcv package, I have random effects in my model (bs="re") this has worked fine, but after I updated the mgcv package to version 1.7-19 I recive an error message when I run the model. > fit1<-gam(IV~s(RUTE,bs="re")+s(T13)+s(H40)+factor(AAR)+s(V3)+s(G1)+s(H1)+s(V1)+factor(LEDD),data=data5,method="ML") > summary.gam(fit1)

Hello, I am trying to fit a gamm (package mgcv) model with a smooth term, a linear term, and an interaction between the two. The reason I am using gamm rather than gam is that there are repeated measures in time (which is the smooth term x1), so I am including an AR1 autocorrelation term. The model I have so far ended up with is of the type gamm(y ~ s(x1) + s(x1, by=x2), correlation =

Dear UseRs, I built varying coefficient models (in mgcv) for two groups separately, with one explanatory and one moderator variable (see the example below). # ------- #  Example: # ------ # generate moderator variable (can the same for both groups) modvar <- c(1:1000) # generate group1 values x1 <- rnorm(1000) y1 <- scale(cbind(1,poly(modvar,2))%*%c(1,2,1)*x1 + rnorm(1000,0,0.3)) #

Hi there, I'm using presence-absence data in a gam (i.e. 0 or 1 as values) I am trying to run a gam with 'dummy covariates' i.e. 1~1 unfortunately my model: * model<-gam(1~1, data=bats, family=negbin)* keeps putting out: * Error in gam(1 ~ 1, data = bats, family = negbin) : Not enough (non-NA) data to do anything meaningful* Is there a specific reason it would do this? I have

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

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Hello, I have been trying to find out whether it is possible to use more than one offset in a gam (in mgcv). The reason I would like to do this is to 1) account for area surveyed in a Poisson model of sightings of porpoises within defined grid cells (each cell has a slightly different area) and 2) account for detection probability within each grid cell (some grid cells are further away from the

Hi, I am using GAMMs to show a relationship of temperature differential over time with a model that looks like this:- gamm(Diff~s(DaysPT)+AirToC,method="REML") where DaysPT is time in days since injury and Diff is repeat measures of temperature differentials with regards to injury sites compared to non-injured sites in individuals over the course of 0-24 days. I use the following

Dear list, I?m using the ?mgcv? package to fit some GAMs. Some of my covariates are derived quantities and have an associated standard error, so I would like to incorporate this uncertainty into the GAM estimation process. Ideally, during the estimation process less importance would be given to observations whose covariates have high standard errors. The gam() function in the ?mgcv? package

Hello, I am trying to update the mgcv package on my Linux box and I keep getting an "Out of memory!" error. Does anyone know of a fix for this? Below is a snippet of the message that I keep getting: Thank you. Geoff ** R ** inst ** preparing package for lazy loading ** help *** installing help indices >>> Building/Updating help pages for package 'mgcv' Formats:

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Dear all, I run the GAMs (generalized additive models) in gam(mgcv) using the following codes. m.gam <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbinary,family=binomial(logit),data=point) And two repeated warnings appeared. Warnings$B!'(B 1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ... : Algorithm did not converge 2: In gam.fit(G,

I am using gam in mgcv (1.3-22) and trying to use gcv to help with model selection. However, I'm a little confused by the process of assessing GCV scores based on their magnitude (or on relative changes in magnitude). Differences in GCV scores often seem "obvious" with my poisson gams but with negative binomial, the decision seems less clear. My data represent a similar pattern as

Dear list, I fitted the same GAM model using directly the function gam(mgcv) ... then as a parameter of another function that capture the warnings messages (see below). In the first case, there is no warning message printed, but in the last one, the function find two warning messages stating "matrix not positive definite" So my question is: Do I have to worry about those warnings and

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 all, I need some solution in the following problem. The following error appears when i use "mgcv" package for implementing GAM. But the same formula works fine in "gam" package. > model.gam <- gam(formula = RES ~ > CAT01+s(NUM01,5)+CAT02+CAT03+s(NUM02,5)+CAT04+ + CAT05+s(NUM03,5)+CAT06+CAT07+s(NUM04,5)+CAT08+s(NUM05,5)+CAT09+ +

Sometimes, for specific models, I get this error from predict.gam in library mgcv: Error in complete.cases(object) : negative length vectors are not allowed Here's an example: model.calibrate <- gam(meansalesw ~ s(tscore,bs="cs",k=4), data=toplot, weights=weight, gam.method="perf.magic") > test <- predict(model.calibrate,newdata) Error in

Hi everyone, I ran a binomial GAM consisting of a tensor product of two continuous variables, a continuous parametric term and crossed random intercepts on a data set with 13,042 rows. When trying to plot the tensor product with vis.gam(), I get the following error message: Error in persp.default(m1, m2, z, col = col, zlim = c(min.z, max.z), xlab = view[1], : invalid 'z' limits In

I am writing my thesis with the function gam(), with the package {mgcv}. My command is: gam(y~s(x1,bs="cr")+s(x2, bs="cr")). I need help to know what are the default basis funcitons for gam. I have not found any detailed reference for this. Can anyone help me with this?? -- View this message in context: