similar to: Problem extracting enough coefs from gam (mgcv package)

Dear useRs, This is the first time I post to this list and I would appreciate any help available. I've used the excellent mgcv package for a while now to investigate geographical patterns of language variation, and it has has always worked without any problems for me. The problem below occurs using R 2.14.0 (both 32 and 64 bit versions in Windows and the 64 bit version in Unix) and mgcv (both

Dear useRs, I reran an analysis with bam (mgcv, version 1.7-17) originally conducted using an older version of bam (mgcv, version 1.7-11) and this resulted in the same estimates, but much lower standard errors (in some cases 20 times as low) and lower p-values. This obviously results in a larger set of significant predictors. Is this result expected given the improvements in the new version? Or

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

Hello R users, I have a quick question I was hoping to get your input on. I am new to R and the smooth statistical regression world, and am trying to wrap my mind around the issues concerning using splines for mixed effect modeling. My question is the following: in the ?gamm? function, generalized additive mixed models can be estimated by including random components. These can be explicitly

Howdee, I am able to fit a 4-parameter logistic growth curve to a dataset which comprise many individuals (using R v. 2.3.1). Yet, if I want to obtain the parameters for each individual (i.e., for each 'id') using nlsList, then I obtain an Error message which I have trouble interpreting. Any advice as to how I can solve this problem? Thanks for your time, Marc > reg <-nls(mass ~

Hi, I have fit a series of ols() models, by group, in this manner: l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65

Hello! I would like to use constrain to sum coeficients of a factor to 0 instead of classical corner contraint i.e. I would like to fit a model like lm(y ~ 1 + effectA + effectB) and say get parameters intercept effectA_1 effectA_2 effectB_1 effectB_2 effectB_3 where effectA_1 represents deviation of level A_1 from intercept and sum(effectA_1, effectA_2) = 0 and the same for factor B. Is

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

Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope coefficients and sb1,b2 the pooled standard error of the slope (b) which can be calculated in R this way: > df1 <-

?Random.user says (in svn trunk) Optionally, functions \code{user_unif_nseed} and \code{user_unif_seedloc} can be supplied which are called with no arguments and should return pointers to the number of seeds and to an integer array of seeds. Calls to \code{GetRNGstate} and \code{PutRNGstate} will then copy this array to and from \code{.Random.seed}. And it offers as an example void

Hi All, I am confused with SMATR's test for common slope. My null hypothesis here is that all slopes are parallel (common slopes?), right? So if I get a p value < 0.05 means that we can have confidence to reject it? That slopes are different? Or the other way around? it means that we have statistical confidence that the slopes are parallel? thanks -- Eugenio Larios PhD Student University

Dear friends, I have install the last version 2.10 and now I have probles with 'mgcv' which can be load. I proved to eliminated and reinstalled but always obtain the same message: > library (mgcv) This is mgcv 1.6-1. For overview type `help("mgcv-package")'. Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error : .onAttach non

Hi ,When I try to load the 'mgcv" package, often, but not always, get this error message. What am I doing wrong? __________________ This is mgcv 1.4-1.1 Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error : .onAttach failed in 'attachNamespace' Error: package/namespace load failed for 'mgcv' -----------------------------------------

Dear community, I encountered a very disturbing phenomenon today: When I try to fit any gam() with mgcv R aborts. I could not find any post regarding this in google, which mades in even more strange. I am using the latest Ubuntu, latest R and latest mgcv everything up to date. The crash occured too with the last mgcv 1.7-11. This is the output from the R shell: <pre> R version 2.14.0

Hello, I am working in R 2.13.2 GUI 1.42 Leopard build 32-bit (5910) on a MacBook Pro. I am working with a data frame of 22 columns and approximately 7,100 rows. I have been using the package scatter3d routinely to work similar data sets. However, when I tried to use scatter3d to work with this new data set I get the following message... Error in dyn.load(file, DLLpath = DLLpath, ...) : ? unable

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

Dear friends, I cannot load the pachkages labdsv...which i do not understand is linked to 'mgcv' package. Anyway neither the last is loadable in my new R 2.10 version. Th emessage on the screen is the following: This is mgcv 1.6-1. For overview type `help("mgcv-package")'. Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error :

Hi All: I tried to update the package mgcv from my current version 1.1-8 to mgcv version 1.2. Using the command update.packages("mgcv") to update mgcv failed in my computer and the returning message was: "Error in old.packages(lib.loc=lib.loc, contriburl=contriburl, method=method, :no installed.packages for (?invalid) lib.loc=mgcv" When I typed installed.packages(), I got

Hola! I am sending this to the list since emails from me to Simon Wood has bounced earlier. I get: > library(tsDyn) Loading required package: mgcv Erro en `parent.env<-`(`*tmp*`, value = NULL) : use of NULL environment is defunct Error: package 'mgcv' could not be loaded > library(mgcv) Erro en `parent.env<-`(`*tmp*`, value = NULL) : use of NULL environment

Hi, I'm getting the following error when I try and load mgcv: > library(mgcv) Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared object '/usr/lib/R/library/Matrix/libs/Matrix.so': /usr/lib/R/library/Matrix/libs/Matrix.so: undefined symbol: R_check_class_and_super Error: package/namespace load failed for 'mgcv' I thought there might be a mismatch