similar to: mgcv gam modeling trend variation over cases

Simon, in your recent reply to me, you noted that EDF=1 corresponds to a straight line fit. Does the edf always indicate the polynomial exponent directly? We ask because Hothorn and Everitt said "Roughly, the complexity of a cubic spline is about the same as a polynomial of degree one less than the degrees of freedom." Sorry we attributed the latter to you, by the way. Will --

Dear R-helpers, I'd like to understand how to test the statistical significance of a random effect in gamm. I am using gamm because I want to test a model with an AR(1) error structure, and it is my understanding neither gam nor gamm4 will do the latter. The data set includes nine short interrupted time series (single case designs in education, sometimes called N-of-1 trials in medicine)

Dear R users, I have seven regression lines I´d like to compare, in order to find out if these are significatively different. The main problem is that these are curves, non normal, non homogeneous data, I´ve tried to linearize them but it has not worked. So I´d like to know if you know any command or source in R which explains how to perform this kind of comparison. Thanks in advance for your

University of California Merced Merced, CA Programmer Analyst II/III (Research Assistant) Job Code SSNRI723A Open until filled. ? In the Sierra Nevada Research Institute at UC Merced, act in support of research in applied climatology and statistical modeling for wildfire, energy and water resource management applications and assist the Principle Investigator with the development of software,

Greetings, R users! I'm excited and pleased to announce that the weightr package was released to CRAN today: https://cran.r-project.org/web/packages/weightr/ Our package provides a function for meta-analysts to implement the Vevea and Hedges (1995) weight-function model for publication bias in R and, by specifying an additional argument, to implement the modified Vevea and Woods (2005)

Dear R users, I'm pleased to announce that the first major update to the MAVIS package (Meta Analysis via Shiny) has been released to CRAN. MAVIS: Meta Analysis via Shiny v1.1 "Smiling Fox" has expanded greatly over the last couple of months to include the following. - User interface has been improved with the ?shinyBS? package and icons. - Support for single case design,

Dear R users, I'm pleased to announce that the first major update to the MAVIS package (Meta Analysis via Shiny) has been released to CRAN. MAVIS: Meta Analysis via Shiny v1.1 "Smiling Fox" has expanded greatly over the last couple of months to include the following. - User interface has been improved with the ?shinyBS? package and icons. - Support for single case design,

Hello, Please let me know how I can troubleshoot this - driving me crazy! I have the following dovecot-ldap.conf file: hosts = ldap.ucmerced.edu dn=cn=natsciadmin,ou=Special Users,dc=ucmerced,dc=edu dnpass=******** ldap_version = 3 base = ou=People,dc=ucmerced,dc=edu deref = never scope = subtree user_attrs = uid user_filter = (&(ou=Natural

I received the warning "DNS updated failed!" during net join. Is this an important warning? Will there be problems? Immediately after the join, clients using smbclient were able to authenticate and access shares on this server - even though "net ads testjoin" complained about kerberos problems. # net join -U Administrator Administrator's password: Using short domain name

Hello, I'm running mixed models in GAMM4 with 2 (non-nested) random intercepts and I want to include a spline term for one of my exposure variables. However, when I include a spline term, I always get reported degrees of freedom of less than 1, even when I know that my spline is using more than 1 degree of freedom. For example, here is the code for my model: >

Dear all, I am writing as I would really need your help on the problem with gamm4. I have tried to find a solution online but I wasn't very successful. I am running a gamm4 model with an interaction between two variable using the tensor term, t2. I have a group variable (super end group) with six factors; I would like to run the model to see the how the interaction term varies across the

My problem is relatively straight forward, but I cannot seem to find a way to make it work. I have a RCBD with repeated measurements over time. I have created a fit using the gamm4 package. My model is: fit4a <- gamm4(Rate ~ s(Time,by=trt,bs="cr")+trt,data=qual.11.dat, random=~(1|block),correlation=corARH1()) What I would like to create is plots with the X-axis

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

Hi, I've been using gamm4 to build GAMMs for exploring environmental influences on genetic ancestry. Things have gone well and I have 2 very straightforward questions: 1. I've used method=REML. Am I correct that this is an alternative method for estimating the smooth functions in GAMMs rather than GCV that is often used for GAMs? I've read up on REML and it makes sense, but I'm

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

Dear R-help users, I am trying to analyze some visual transect data of organisms to generate a habitat distribution model. Once organisms are sighted, they are followed as point data is collected at a given time interval. Because of the autocorrelation among these "follows," I wish to utilize a GAM-GEE approach similar to that of Pirotta et al. 2011, using packages 'yags' and

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

Dear R users I have a question related to the interpretation of results based on GAMMs using Simon Woods package gamm4. I have repeated measurements (hours24) of subjects (vpnr) and one factor with three levels (pred). The outcome (dv) is binary. In the first model I'd like to test for differences among factor levels (main effects only): gamm.11<-gamm4(dv ~ pred +s(hours24), random = ~

I almost go this liked but then I get the following: Mar 7 18:09:43 malaga dovecot: auth(default): ldap(jnorris,76.20.83.98): pass search: base=ou=People,dc=ucmerced,dc=edu scope=subtree filter=(&(uid=jnorris)) fields=uid,userPassword Mar 7 18:09:43 malaga dovecot: auth(default): ldap(jnorris,76.20.83.98): result: uid(user)=jnorris Mar 7 18:09:43 malaga dovecot: auth(default):

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two