search for: explained

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...

Hi all, This is pretty basic but I am not an expert and I couldn't find anything in the forum or my statistics book about it. I was reading a paper and the authors were using both "explained deviance" and "explained variance" as synonyms. They were describing a GAM regression. Is that right? I performed an analysis in R to take a look to the output of GAM regression and I think that: - 'R-sq. (adj)' is the percentage of variance explained by the regression, i.e...

After doing a PCA using princomp, how do you view how much each component contributes to variance in the dataset. I'm still quite new to the theory of PCA - I have a little idea about eigenvectors and eigenvalues (these determine the variance explained?). Are the eigenvalues related to loadings in R? Thanks, Paul -- View this message in context: http://www.nabble.com/PCA-and---variance-explained-tp19388970p19388970.html Sent from the R help mailing list archive at Nabble.com.

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 "wiggliness" of the fitted smooth (edf), but (as far as I understand) there is no information on the proportion of variance explained. One alternative m...

Hello again- Thanks to Prof. Ripley for responding to my previous question. I would like to clarify my question using sample code. I will use some sample code taken from ?prcomp Again, I would like to compare the % variance explained by each PC before and after rotation. < code follows > data(USArrests) pca = prcomp(USArrests, scale = TRUE) # proportion variance explained by each PC prop = pca$sdev^2/sum(pca$sdev^2) # cumulative proportion variance explained by each PC cumProp = cumsum(prop) # following print state...

Dear all, I am trying to calculate some measure of the amount of variability in the response variable that is explained by a model fitted in lmer m1<-lmer(response-var ~ Condition+(1|Site/Area/Transect),family="binomial") . I've seen from the literature that the precentage of explained deviance is a common measure. How can I calculate it? Thanks a lot for your help, I hope this is not too shallow....

Dear list, I have read several posts on this topic. I would use the same methodology as proposed by Simon Wood in this post: http://r.789695.n4.nabble.com/variance-explained-by-each-term-in-a-GAM-td836513.html My first question is: Does anyone know a scientific source (paper, book,...) that explains or uses this methodology. I have read several articles, particularly in the field of ecology, that use GAM and I've never read one that dealt with the proportionof va...

Hi, Could someone point me to documentation or explain in detail, process of creating file.(space reservation, inode....) What is happen at low lavel? Thanks -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://listman.redhat.com/archives/ext3-users/attachments/20060713/2c862514/attachment.htm>

Hi, I am unable to use the Searcher''s explain method. Anytime I call it, I get Segmentation Faults and it kills the process I have running my Rails site. Has anyone else had this problem? Here is some code I am trying to use it in... search = Search.create(:query => query) @quotations = [] searcher = Ferret::Search::Searcher.new("index") # FerretConfig::INDEX bq =

Hello, I think to use FastICA package for microarray data clusterization, but one question stops me: can I know how much variance explain each component (or all components together) ? I will be very thankful for the help. Thanks, Pavel [[alternative HTML version deleted]]

Hi Celine, GLM outputs usually give the null deviance and residual deviance in the summary() term - so you can work out % deviance explained for a variable/model from this. Hope this helps. Best wishes, Clare ---------------------------- Dr Clare B Embling Visiting Research Fellow Marine Institute University of Plymouth Plymouth, UK.

...lows: b1 <- gam(y~s(x1)+s(x2)+s(x3),sp=b$sp) b2 <- gam(y~fv1+s(x2)+s(x3),sp=b$sp[-1]) b3 <- gam(y~fv1+s(x1)+s(x3),sp=b$sp[-2]) b4 <- gam(y~fv1+s(x1)+s(x2),sp=b$sp[-3]) b0 <- gam(y~1) And then proceed as in Simon's example, i.e. (deviance(b1)-deviance(b))/deviance(b0) ## prop explained by fv1 (deviance(b2)-deviance(b))/deviance(b0) ## prop explained by x1 etc. Am I more or less on the right track? Thanks, Pierre On Tue Oct 9 20:25:24 CEST 2007, Simon Wood wrote: > I think that your approach is reasonable, except that you should use the same > smoothing parame...

Any quick tips? I''ve been getting the SQL Server adapter up to passing for 3.2 and I wanted to test our explain printer. It will be very much like mysql2''s printer. But I think my brain is mush and my meta-fu weak at the moment and I need some help. I tried stubbing ActiveRecord::Base.logger.warn but to no avail. Any thoughts? - Ken -- You received this message because you

...- gam (resp ~ Culture + Clust**er**:Culture + s(**Year**,k=4, by=Culture) + s(**X1**, by=Culture) + s(**X2**, by=Culture) + s(Depts, bs="re", by=Culture) * *, family=betar(link="logit"),method="REML",data=data,select=FALSE)* Then, I estimated the part of the model explained deviance provided by each covariate. For that, I run the model without the given covariate (keeping smooth parameters constant between models), and compute the difference in deviance between the Full model (with the given covariate) and the penalized model (without the given covariate): (Full...

Hello everybody After doing a MANOVA on a bunch of data, I want to be able to make some comment on the amount of variation in the data that is explained by the factor of interest. I want to say this in the following way: XX% of the data is explained by A. I can acheive something like what I want by doing the following: X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3, 2))) Y <- structure(c(0, 2, 4, 0), .Dim = as.integer(c...

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Hi All, I have a left truncated, right censored cox model: coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to know how much of the observed variance (as a number between 0 and 1) is explained by each variable. How could I do that? Adding terms sequentially and then using anova(mod1, mod2) tells me whether I get a significant improvement of the fit, but does not tell me how much variance I actually explain? BW Federico -- Federico C. F. Calboli Neuroepidemiology and Ageing Research...

I have a problem with ifelse(), I do not understand how it works. > X<-c(2,2,1,1,0,0) > str(X) num [1:6] 2 2 1 1 0 0 > Y<-ifelse(X>0,1,0) > Y [1] 1 1 1 1 0 0 > Can some one explain what is going on, I do not understand what ifelse is doing in this case. Can someone explain the output Y. Thanks -- View this message in context:

...would help me a lot to be a better R programmer, that I think would probably help many others as well. I put the wish out there in the hopes that someone might think it was worth doing at some point. I wish I had the code of some substantial, widely used package ? lm, say ? heavily annotated and explained at roughly the level of R knowledge of someone who has completed an intro statistics course using R and picked up some R along the way. The idea is that you would say what the various blocks of code are doing, why the authors chose to do it this way rather than some other way, point out coding tec...

...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 by each predictor. Is there a straightforward way to do this? I have tried dropping a term and recalculating the model, but the edf's change if there is any correlation among variables, thereby making all of the relationships different...