search for: untransform

...lp> 801.408.8111 I am not using R at the moment (working in SPSS, have to love the GUI) but my question is quite related: I am running a generalized linear model on data highly skewed to the right with a bunch of zeroes, so I decided to use the Tweedie distribution. In the model I ran both untransformed data (with link=log) as well as log(x+1) transformed data (with link=identity). The latter model had a much smaller (more negative) AICc value than the untransformed data with link=log. Is it valid to run the GLM with log(x+1) transformed data if link=identity? Or am I violating some kind of as...

Hi there, I have a question regarding model comparisons that seems simple enough but to which I cannot find an answer. I am interested in developing a predictive model relating some measure of a tree's stem to the total leaf area (TLA) of the tree. Predictor variables might include, for example, the total cross-sectional area of the tree (commonly referred to as basal area) or the amount

...ity assumption was not verified...).?I have realised a sqrt(variable) transformation...?The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in the original untransformed dependant variable ??Thank you very much for your help!No?mie? [[alternative HTML version deleted]]

...3,42.83,46.53,14.79,49.18,48.08) If I plot the data, it looks somehow that a logistic function would render good results. My questions are: How do I use nls and/or SSlogis (or other) to fit the curve? How can I see the summary statistics of the fit? How do I finally draw the line to my x,y (untransformed data) plot? Any help would be highly appreciated. Thank you and cheers Pascale -- ____________________________________..___________________ Dr. Pascale Weber Swiss Federal Research Institute WSL Zuercherstrasse 111 CH-8903 Birmensdorf Switzerland

...key must be pressed after each graph appears (Click or hit ENTER for next page). I'd like to isolate the second plot produced (the estimated functional form of the influence of age on the log relative hazard) so that I can use the 'points' function to add the linear predictors for the untransformed and the log-transformed models. In the usual situation one would produce a plot and then type: coxfitu <- coxph(Surv(rem.Remtime,rem.Rcens)~age+strata(rpa),data=nearma) points(coxfitu$linear.predictor,col=2) coxfitl <- coxph(Surv(rem.Remtime,rem.Rcens)~log(age)+strata(rpa),data=nearma) po...

...ity assumption was not verified...). I have realised a sqrt(variable) transformation... The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in the original untransformed dependant variable ? Thank you very much for your help!No?mie > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE...

...NE VARIABLES mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ; sims=1e5 timesteps = T/dt ## MAKE PHI AND DS phi = matrix(rnorm(timesteps*sims), nrow=sims, ncol=timesteps) ds = mu*dt + sigma * sqrt(dt) * phi ## USE APPLY TO CALCULATE ROWWISE CUMULATIVE PRODUCT system.time(y1 <- apply(1+ds, 1, cumprod)) ## UNTRANSFORM Y1, BECAUSE ROW APPLY FLIPS THE MATRIX y1=t(y1) ## USE FOR LOOP TO CALCULATE ROWWISE CUMULATIVE PRODUCT y2=matrix(NA,nrow(ds),ncol(ds)) system.time( for (i in 1:nrow(ds)){ y2[i,]<-cumprod(1+ds[i,]) } ) ## COMPARE RESULTS TO MAKE SURE THEY DID THE SAME THING str(y1) str(y2) all(...

...located? Is there any > way I can store status.xml or status.xsl on a remote HTTP/FTP (not an > Icecast2 relay) server? tatus.xml does not exist as a real file. It is generated by the icecast2 server on demand, and served after going through an XSL transformation (it may also be available untransformed - it was a while ago, but I'm not certain that's still available). tatus.xsl must live in the local filesystem for icecast2 to find it. Mike --- >8 ---- List archives: http://www.xiph.org/archives/ icecast project homepage: http://www.icecast.org/ To unsubscribe from this list, se...

Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks are independent is no longer true and therefore I cannot use the Cox model....

The data are not normally distributed when untransformed and I'm trying various transformations to see if any would be appropriate to use. The lattice book (fig. 3.10) shows a 2-sample Q-Q plot with an abline but the code for the figure does not include the line. I'd appreciate a pointer to a reference on how to add an abline to a one-sampl...

...=c(1,100),log="xy") abline has the same problem. Try following the above calls with abline(2,-1). (This is supposed to plot a straight line in the log-log domain, and does, but it's the wrong straight line.) I can also get the problem where only one axis is logarithmic. Let's untransform the x-axis from the original command: plot(log(c(1,10,100)),c(100,10,1),type="l",xlim=log(c(1,20)),ylim=c(1,20),log="y") Thanks! - Jason Eisner -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.t...

...prod<-(1-h[1])*S[1] for (i in 2:(l-1)){ p[i]<-prod*h[i] prod<-prod*(1-h[i])*S[i] } p[l]<-1-sum(p[-l]) #last cell probability -dmultinom(exp(d),prob=p,log=TRUE) #exp(d)->backtransform the estimates } c<-c(cohort,100) #untransformed initial values for the optimization,->100=N-x1-x2-x3 nvec<-c(rep("x",l-1),"N") #names for the initial vector nrs<-c(1:(l-1),1) #nrs for the initial vector svec = log(c) #transformation of the data to avoid constraints (x>0) names(svec)...

..._point(aes(b1,a1,data=data1))+ geom_abline(aes(intercept=coef(model)[1],slope=coef(model)[2]))+ scale_y_log()+ scale_x_log() # Plot with standard plot plot(b1,a1,log="xy") abline(model,untf=T) abline(model,untf=F) 1) The regression lines are different for plot vs. ggplot(transformed or untransformed). So what is actually the correct line? 2) The regression line was calculated on basis of log(x+1), but the log scale on my axis is just simple log (without +1). So how are such cases usually treated? I thought about subtracting the value 1 from the intercept? So my simple question: What is the...

Hi i have seen that the dbPowerAmp ripping and encoding software supports a new so-called "FLAC uncompressed" format, e.g. http://www.audiostream.com/content/dbpoweramps-flac-lossless-uncompressed-wish-come-true i know only the normal flac compression levels from 0 to 8. have i missed an option on the flac comamnd line tool or how could i achieve that on the linux command line flac

I have a statistical question. The data sets I am working with are right-skewed so I have been plotting the log transformations of my data. I am using a Grubbs Test to detect outliers in the data, but I get different outcomes depending on whether I run the test on the original data or the log(data). Here is one of the problematic sets: fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047)

...s measured in each subject in three different task types. There is furthermore a context variable with two levels. Each task was administered in each context, but not for every single subject. So the design is quite simple - two fixed factors (task and context), one random factor (subject), and an untransformably skewed dependent variable. I might want to add some additional fixed factors (age group) in future but for now I would like to keep it simple. I guess this is straightforward for those in the know. Any help at all much appreciated! Cheers, Ben -- Dr. Ben Kenward Department of Psychology, Up...

Hi R Users This is a code I wrote and just want to confirm if the first 1000 values are raw gamma (z) and the next 1000 values are transformed gamma (k) or not. As I get 2000 rows once I import into excel, the p - values beyond 1000 dont look that good, they are very high. -- sink("a1.txt"); for (i in 1:1000) { x<-rgamma(10, 2.5, scale = 10) y<-rgamma(10, 2.5, scale = 10)

Hi. Suppose I have a vector that I partition into disjoint, contiguous subvectors. For example, let v = c(1,4,2,6,7,5), partition it into three subvectors, v1 = v[1:3], v2 = v[4], v3 = v[5:6]. I want to find the maximum element of each subvector. In this example, max(v1) is 4, max(v2) is 6, max(v3) is 7. If I knew that the successive subvector maxima would never decrease, as in the example,

..., log(x+1), ln, sqrt, fourth root). What type of analyses have I tried? (1) Regression trees. Using categorical variables as categorical without changing into numerical. This was coded with package rpart and is the preferred analyses due to ease of interpretation. The response variable was untransformed and the distribution chosen Poisson. Result was a tree with immediately increasing error (cp) which picked 0 splits as the best tree. (2) Multiple regression Tried using package relaimpo to obtain a classification on the importance of explanatory variables. Used different transformations to...

Hi all, Im a little bit confused concerning the effect() command, effects package. I have done several glm models with family=quasipoisson: model <-glm(Y~X+Q+Z,family=quasipoisson) and then used results.effects <-effect("X",model,se=TRUE) to get the "adjusted means". I am aware about the debate concerning adjusted means, but you guys just have to trust me - it