Displaying 20 results from an estimated 10000 matches similar to: "GAM vs. MGCV packages"
2006 Jun 18
1
GAM selection error msgs (mgcv & gam packages)
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
2013 Apr 17
1
mgcv: how select significant predictor vars when using gam(...select=TRUE) using automatic optimization
I have 11 possible predictor variables and use them to model quite a few
target variables.
In search for a consistent manner and possibly non-manual manner to identify
the significant predictor vars out of the eleven I thought the option
"select=T" might do.
Example: (here only 4 pedictors)
first is vanilla with "select=F"
>
2008 Nov 14
1
negative prediction by gam (mgcv package)
Hi
Gam in mgcv package is predicting negative values which should not be
the case despite all the predictors and response variables are positive.
Tried to use log link function but it did not help. Please help
sunil
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2008 Aug 03
1
output components of GAM
I would like to request help with the following:
I am trying to use a Generalized Additive Model (gam) to examine the density distribution of fish as a function of latitude and longitude as continuous variables, and year as a categorical variable. The model is written as:
gam.out <- gam(Density ~ s(Lat) + s(Lon) + as.factor(Year))
The fitted model prediction of the link function is
2013 Mar 21
1
[mgcv][gam] Odd error: Error in PredictMat(object$smooth[[k]], data) : , `by' variable must be same dimension as smooth arguments
Dear List,
I'm getting an error in mgcv, and I can't figure out where it comes
from. The setup is the following: I've got a fitted GAM object called
"MI", and a vector of "prediction data" (with default values for
predictors). I feed this into predict.gam(object, newdata = whatever)
via the following function:
makepred = function(varstochange,val){
for
2005 Oct 05
3
testing non-linear component in mgcv:gam
Hi,
I need further help with my GAMs. Most models I test are very
obviously non-linear. Yet, to be on the safe side, I report the
significance of the smooth (default output of mgcv's summary.gam) and
confirm it deviates significantly from linearity.
I do the latter by fitting a second model where the same predictor is
entered without the s(), and then use anova.gam to compare the
2023 Feb 11
1
GAM with binary predictors
Dear R-experts,
I am trying to fit a GAM with 2 binary predictors (variables coded 0,1). I guess I cannot just smooth binary variables. By the way I code them as 0=no,1=yes, then mgcv will think those variables are numeric.?
I have tried to change 0 and 1 in no and yes. It does not work.
How to solve my problem. Here below my toy example. Many thanks.
Best,
Sacha
?
########################
2018 Jan 17
1
mgcv::gam is it possible to have a 'simple' product of 1-d smooths?
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
2011 Feb 16
1
retrieving partial residuals of gam fit (mgcv)
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
2007 Dec 13
1
Two repeated warnings when runing gam(mgcv) to analyze my dataset?
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,
2003 Jun 04
2
gam()
Dear all,
I've now spent a couple of days trying to learn R and, in particular, the
gam() function, and I now have a few questions and reflections regarding
the latter. Maybe these things are implemented in some way that I'm not yet
aware of or have perhaps been decided by the R community to not be what's
wanted. Of course, my lack of complete theoretical understanding of what
2008 Jun 11
1
mgcv::gam error message for predict.gam
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
2023 Feb 16
1
GAM with binary predictors
Dear Sacha,
use glm() in this case. I'd rather code the covariable as TRUE / FALSE or
as a factor.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
2013 Jun 17
1
Can you use two offsets in gam (mgcv)?
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
2012 Aug 14
1
Random effects in gam (mgcv 1.7-19)
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)
2012 Jul 23
1
mgcv: Extract random effects from gam model
Hi everyone,
I can't figure out how to extract by-factor random effect adjustments from a
gam model (mgcv package).
Example (from ?gam.vcomp):
library(mgcv)
set.seed(3)
dat <- gamSim(1,n=400,dist="normal",scale=2)
a <- factor(sample(1:10,400,replace=TRUE))
b <- factor(sample(1:7,400,replace=TRUE))
Xa <- model.matrix(~a-1) ## random main effects
Xb <-
2008 May 06
1
mgcv::gam shrinkage of smooths
In Dr. Wood's book on GAM, he suggests in section 4.1.6 that it might be
useful to shrink a single smooth by adding S=S+epsilon*I to the penalty
matrix S. The context was the need to be able to shrink the term to zero if
appropriate. I'd like to do this in order to shrink the coefficients towards
zero (irrespective of the penalty for "wiggliness") - but not necessarily
all the
2012 Jul 30
2
mgcv 1.7-19, vis.gam(): "invalid 'z' limits'
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
2009 Jul 12
1
variance explained by each predictor in GAM
Hi,
I am using mgcv:gam and have developed a model with 5 smoothed predictors
and one factor.
gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + s(
Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts")
+factor(site),data=dat3)
The total deviance explained = 70.4%.
I would like to extract the variance explained
2012 Feb 03
1
GAM (mgcv) warning: matrix not positive definite
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