search for: explains

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

...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., I can write "The regression explains xx% of variance". - 'Deviance explained' is a simple measure of the quality of the fit but it is not related to the percentage of variance that is explained by the regression. Am I right? Thank you so much Ram?n -- View this message in context: http://r.789695.n4.nabble.com/devianc...

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:

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

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

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

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 variance explained by each term of a model... and I think it's an important topic, at least for my study! My second question:...

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.

Picking up an ancient thread (from Oct 2007), I have a somewhat more complex problem than given in Simon Wood's example below. My full model has more than two smooths as well as factor variables as in this simplified example: b <- gam(y~fv1+s(x1)+s(x2)+s(x3)) Judging from Simon's example, my guess is to fit reduced models to get components of deviance as follows: b1 <-

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

Dear R users, I am using the *mgcv package* to model the ratio of hectares of damaged culture by wild boar in french departments according to some environmental covariates. I used a _Beta distribution_ for the response. For each department, we estimated the damaged in 3 different culture types (??Culture??). Our statistical individual are therefore the department crossed by the culture

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,

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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

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:

There is something that I wish I had that I think 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

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