Displaying 20 results from an estimated 1000 matches similar to: "mgcv:gamm: predict to reflect random s() effects?"
2017 Jun 12
0
plotting gamm results in lattice
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
2012 Apr 02
1
gamm: tensor product and interaction
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
2011 Apr 11
1
Override col.lines and col.symbol in panel.xyplot with type='b'
Dear useRs,
I have a longitudinal experiment with several treatment groups, ~20 subjects per group, ~6 timepoints and a continuous dependent variable. I have been successfully been using lattice::xyplot with this data. However, I have been stumped with a particular application of it.
I would like to use xyplot on my data, broken into treatment groups with the groups argument, using
2006 Jul 03
1
gamm
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
2011 Oct 05
2
gamm: problems with corCAR1()
Dear all,
I?m analyzing this dataset containing biodiversity indices, measured over
time (Week), and at various contaminant concentrations (Treatment). We have
two replicates (Replicate) per treatment.
I?m looking for the effects of time (Week) and contaminant concentration
(Treatment) on diversity indices (e.g. richness).
Initial analysis with GAM models showed temporal autocorrelation of
2010 May 28
1
Comparing and Interpreting GAMMs
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 = ~
2006 Nov 07
1
gamm(): nested tensor product smooths
I'd like to compare tests based on the mixed model representation of additive models, testing among others
y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2)
(testing for additivity)
In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be:
y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2
vs
y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12
2011 Mar 23
1
how to add in interaction terms in gamm
I want to use gamm to generate smoothed trend line for three groups
identified by dummy variable genea and geneb. My question is how to add in
an interaction term between the time and another dummy variable such as
gender?
fitm<-gamm(change_gfr~
genea+geneb+s(timea_n,bs="ps")+s(timeb_n,bs="ps")+s(timec_n,bs="ps"),data=mm,random=list(time_n=~1|PID))
--
View this
2017 Jun 12
2
plotting gamm results in lattice
Dear all,?
I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution.
I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model:
model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,
2009 Mar 01
1
gamm (mgvc) and time-varying coefficient model
Dear R users,
I have repeated measurements on individuals. I want to estimate the
time-varying effect of a factor variable X (taking three levels), e.g. a
model in the spirit of Hastie and Tibshirani (1993).
I am considering using the package "mgvc" which implements generalized
additive models, especially the function gamm, which estimates
generalized additive mixed models, and thus,
2010 Jan 26
1
AIC for comparing GLM(M) with (GAM(M)
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
2010 Apr 14
2
GAMM : how to use a smoother for some levels of a variable, and a linear effect for other levels?
Hi,
I was reading the book on "Mixed Effects Models and Extensions in
Ecology with R" by Zuur et al.
In Section 6.2, an example is discussed where a gamm-model is fitted,
with a smoother for time, which differs for each value of ID (4
different bird species). In earlier versions of R, the following code
was used
BM2<-gamm(Birds~Rain+ID+
2010 Aug 04
2
more questions on gam/gamm(mgcv)...
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
2008 Nov 19
2
GAMM and anove.lme question
Greetings all
The help file for GAMM in mgcv indicates that the log likelihood for a
GAMM reported using
summary(my.gamm$lme) (as an example) is not correct.
However, in a past R-help post (included below), there is some indication
that the likelihood ratio test in anova.lme(mygamm$lme, mygamm1$lme) is
valid.
How can I tell if anova.lme results are meaningful (are AIC, BIC, and
logLik
2009 May 27
1
Deviance explined in GAMM, library mgcv
Dear R-users,
To obtain the percentage of deviance explained when fitting a gam model using the mgcv library is straightforward:
summary(object.gam) $dev.expl
or alternatively, using the deviance (deviance(object.gam)) of the null and the fitted models, and then using 1 minus the quotient of deviances.
However, when a gamm (generalizad aditive mixed model) is fitted, the
2007 Oct 02
3
mcv package gamm function Error in chol(XVX + S)
Hi all R users !
I'm using gamm function from Simon Wood's mgcv package, to fit a spatial
regression Generalized Additive Mixed Model, as covariates I have the
geographical longitude and latitude locations of indexed data. I include a
random effect for each district (dist) so the code is
fit <- gamm(y~s(lon,lat,bs="tp", m=2)+offset(log(exp.)),
random=list(dist=~1),
2011 Mar 17
2
fitting gamm with interaction term
Hi all,
I would like to fit a gamm model of the form:
Y~X+X*f(z)
Where f is the smooth function and
With random effects on X and on the intercept.
So, I try to write it like this:
gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) )
but I get the error message :
Error in MEestimate(lmeSt, grps) :
Singularity in backsolve at level 0, block 1
2009 Jan 22
1
convergence problem gamm / lme
Hope one of you could help with the following question/problem:
We would like to explain the spatial
distribution of juvenile fish. We have 2135 records, from 75 vessels
(code_tripnr) and 7 to 39 observations for each vessel, hence the random effect
for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and
sub sampling factor. There are no extreme outliers in lat/lon. The model
2011 Sep 22
1
negative binomial GAMM with variance structures
Hello,
I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me.
I was previously using this script for my overdispersed gam data:
M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus)
My gam.check gave me the attached result. In order to
2006 Dec 04
1
package mgcv, command gamm
Hi
I am an engineer and am running the package mgcv and specifically the
command gamm (generalized additive mixed modelling), with random
effects. i have a few queries:
1. When I run the command with 1000/2000 observations, it runs ok.
However, I would like to see the results as in vis.gam command in the
same package, with the 3-d visuals. It appears no such option is
available for gamm in the