search for: untransforming

Hi Dr. Snow, I am a graduate student working on analyzing data for my thesis and came across your post on an R forum: The default link function for the glm poisson family is a log link, which means that it is fitting the model: log(mu) ~ b0 + b1 * x But the data that you generate is based on a linear link. Therefore your glm analysis does not match with how the data was generated

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

Dear all,?I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality 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

Hi to all This is the first time I am quoting a question and I hope, my question is not too basic... For the following data, I wish to draw a fitted curve. x <- c(123,129,141,144,144,145,149,150,158,159,163,174,183,187,242,248) y <- c(14.42,26.96,31.3,19.95,36.36,15.4,24.76,35.39,28.07,40.97,26.23,42.83,46.53,14.79,49.18,48.08) If I plot the data, it looks somehow that a logistic

Dear R-Help, I am using the 'mfp' package. It produces three plots (as I am using the Cox model) simultaneously which can be viewed together using the following code: fit <- mfp(Surv(rem.Remtime,rem.Rcens)~fp(age)+strata(rpa),family=cox,data=nearma,select=0.05,verbose=TRUE) par(mfrow=c(2,2)) plot(fit) They can be viewed separately but the return key must be pressed after each graph

Hello, R-Help answers questions on R code, your question is about statistics. You should try posting the question to https://stats.stackexchange.com/ Hope this helps, Rui Barradas Em 23-10-2017 18:54, kende jan via R-help escreveu: > Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...). I have

I thought the "apply" functions are faster than for loops, but my most recent test shows that apply actually takes a significantly longer than a for loop. Am I missing something? It doesn't matter much if I do column wise calculations rather than row wise ## Example of how apply is SLOWER than for loop: #rm(list=ls()) ## DEFINE VARIABLES mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ;

On Wednesday 17 December 2003 15:53, Macsym wrote: > Hi everybody, > > If I understand well, the purpose of XSL files is re-formatting XML files. > When I checked the admin folder, I saw status.xsl but I did not see the > source XML file (status.xml?). > > If status.XML (NOT status.XSL) exists, where is it located? Is there any > way I can store status.xml or status.xsl on

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

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-sample qqmath() plot in lattice. Rich

Hi, I'm using R version 1.2.0 (2000-12-15), on RedHat Linux 6.2 (kernel 2.2.14). The following command ought to plot a straight-line on a log-log graph but instead plots two line segments, both with the wrong slope: plot(c(1,10,100),c(100,10,1),type="l",xlim=c(1,20),ylim=c(1,20),log="xy") Only clipped lines seem to have this problem. Changing to type "b" or

Hi there I'm trying to find the mle fo a multinomial model ->*L(N,h,S?x)*. There is only *N* I want to estimate, which is used in the number of successes for the last cell probability. These successes are given by: p^(N-x1-x2-...xi) All the other parameters (i.e. h and S) I know from somewhere else. Here is what I've tried to do so far for a imaginary data set:

Hello, I've some questions concerning log-transformations and plotting of the regression lines. So far as I know is it a problem to log-transform values smaller than 1 (0-1). In my statistics lecture I was told to do a log(x+1) transformation in such cases. So I provide here a small example to explain my questions: # Some example data for testing a1

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)

Hi! I am a psychologist who suspects that the only sensible way to analyse a particular data set is to use generalised linear mixed models. I am hoping that someone might be able to point me in the right direction to find some very practical hands on documentation that might be able to talk me through actually doing such an analysis? So far in my searches the most useful document I have turned

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,

Hi R-Users, I have a student doing work with lionfish and she has been trying to analyse a multivariate dataset to see what variables/factors are influencing the behaviour of lionfish. We have attempted a number of analyses, including rpart, relimpo and standard linear regression but we are not having much luck with quality output. The data is very non-normal and we would appreciate some advice

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