Displaying 20 results from an estimated 9000 matches similar to: "deviance and variance - GAM models"
2012 Jan 16
1
GAM without intercept reports a huge deviance
Hi all,
I constructed a GAM model with a linear term and two smooth terms, all of
them statistically significant but the intercept was not significant. The
adjusted r2 of this model is 0.572 and the deviance 65.3.
I decided to run the model again without intercept, so I used in R the
following instruction:
regression= gam(dependent~ +linear_independent +s(smooth_independent_1)
2011 Nov 10
1
Sum of the deviance explained by each term in a gam model does not equal to the deviance explained by the full model.
Dear R users,
I read your methods of extracting the variance explained by each
predictor in different places. My question is: using the method you
suggested, the sum of the deviance explained by all terms is not equal to
the deviance explained by the full model. Could you tell me what caused
such problem?
> set.seed(0)
> n<-400
> x1 <- runif(n, 0, 1)
> ## to see problem
2005 Sep 23
1
Smooth terms significance in GAM models
hi,
i'm using gam() function from package mgcv with default option (edf
estimated by GCV).
>G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3))
>SG=summary(G)
Formula:
y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3)
Parametric coefficients:
Estimate std. err. t ratio Pr(>|t|)
(Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16
Approximate significance of smooth
2007 Oct 08
2
variance explained by each term in a GAM
Hello fellow R's,
I do apologize if this is a basic question. I'm doing some GAMs using the mgcv package, and I am wondering what is the most appropriate way to determine how much of the variability in the dependent variable is explained by each term in the model. The information provided by summary.gam() relates to the significance of each term (F, p-value) and to the
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
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
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
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
2013 Mar 26
2
GAM model with interactions between continuous variables and factors
Hi all,
I am not sure how to handle interactions with categorical predictors in the
GAM models. For example what is the different between these bellow two
models. Tests are indicating that they are different but their predictions
are essentially the same.
Thanks a bunch,
> gam.1 <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+
+ s(birth_year,by=wealth) +
+
2012 May 29
1
GAM interactions, by example
Dear all,
I'm using the mgcv library by Simon Wood to fit gam models with interactions and I have been reading (and running) the "factor 'by' variable example" given on the gam.models help page (see below, output from the two first models b, and b1).
The example explains that both b and b1 fits are similar: "note that the preceding fit (here b) is the same as
2012 Jul 14
1
GAM Chi-Square Difference Test
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
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
2012 Aug 24
3
mgcv package, problems with NAs in gam
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
2009 May 05
1
A question about using “by” in GAM model fitting of interaction between smooth terms and factor
I am a little bit confusing about the following help message on how to fit a
GAM model with interaction between factor and smooth terms from
http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html:
?Sometimes models of the form:
E(y)=b0+f(x)z
need to be estimated (where f is a smooth function, as usual.) The
appropriate formula is:
y~z+s(x,by=z)
- the by argument ensures that the smooth
2009 Apr 28
2
Why there is no p-value from likelihood ratio test using anova in GAM model fitting?
Hello, everybody,
There is the first time for me to post a question, because I really cannot
find answer from books, websites or my colleagues. Thank you in advance for
your help!
I am running likelihood ratio test to find if the simpler model is not
significant from more complicated model. However, when I run LRT to compare
them, the test did not return F value and p-value for me. What's the
2005 Oct 24
2
GAM and AIC: How can I do??? please
Hello, I'm a Korean researcher who have been started to learn the "R"
package.
I want to make gam model and AIC value of the model to compare several
models.
I did the GAM model, but there were error for AIC.
SO, how can I do? pleas help me!!!
I did like below;
> a.fit <- gam(pi~ s(t1r), family = gaussian(link="log"))
>
2002 Nov 13
2
Comparing GAM objects using ANOVA
Hi,
Is it possible to compare two GAM objects created with the gam() function from the mgcv package. I use a slightly modified version of anova.glm() named anova.gam(), modified from John Fox (2002). It often gives me some aberant responses, especially with "F" test. I use a quasibinomial model and scale (dispersion) is calculated and used in the calculation of the F value. Does someone
2008 Mar 31
1
unexpected GAM result - at least for me!
Hi
I am afraid i am not understanding something very fundamental.... and does not matter how much i am looking into the book "Generalized Additive Models" of S. Wood i still don't understand my result.
I am trying to model presence / absence (presence = 1, absence = 0) of a species using some lidar metrics (i have 4 of these). I am using different models and such .... and when i
2005 Sep 26
4
p-level in packages mgcv and gam
Hi,
I am fairly new to GAM and started using package mgcv. I like the
fact that optimal smoothing is automatically used (i.e. df are not
determined a priori but calculated by the gam procedure).
But the mgcv manual warns that p-level for the smooth can be
underestimated when df are estimated by the model. Most of the time
my p-levels are so small that even doubling them would not result
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"
>