similar to: PRESS alternative

Hi, I am trying to use boot() to refit an ordinal logit (polr in MASS) model. (A very basic bootstrap which samples from the data frame without replacement and updates the model.) I need to extract two statistics per run (the coefficients and zeta) and I tried concatenating them into a single vector after fitting, but I get the following error: Error in "[<-"(*tmp*, r, ,

Der useRs, I'm working on meta analysis using rmeta package. Using code below I plot the forest plot: library(rmeta) data (catheter) a<-meta.MH (n.trt, n.ctrl, col.trt, col.ctrl, data=catheter, names=Name, subset=c(13,6,5,3,7,12,4,11,1,8,10,2)) summary(a) # odds ratio values and confidence intervals metaplot(a$logOR, a$selogOR, nn=a$selogOR^-2,a$names, summn=a$logMH, sumse=a$selogMH,

Hello I am having some problems for extracting the residuals from models working with ordinal multinomial data. Either working with the polr() function or the plsRglm () function, the residuals are "NULL". I guess this is because the data is multinomial but I do not know how to solve it. I have read the following in internet: "can you tell us how residuals would be defined in

Hello, I would like to create a plot composed of stacked boxes (squares or rectangles), where the size of the box would represent the frequency of observations based on a categorical variable (group), the color would represent the proportion of success (binary) within that group (outcome) on a predetermined color scale. Ideally the boxes can be stacked from the bottom left to the top right based

Greetings, I'd be grateful if a good Samaritan helps me to approach this problem.... with my data, I've created the following model lm(formula = OUTCOME ~ VAR1 + VAR2) summary(model) Call: lm(formula = OUTCOME ~ VAR1 + VAR2) Residuals: Min 1Q Median 3Q Max -1.4341 -0.3621 0.1879 0.4994 0.7696 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.89020

Dear useRs, I have two different length vectors: one column (1...m) and one row vector (1...n): 20 40 20 60 5 4 2 Now I have to calculate ratios between column vector elements and each row vector elements: 4 5 10 8 10 20 4 5 20 15 12 30 Thank's in advance for any suggestions, Andrej

Dear all, I try to install the latest R version using checkinstall (v. 1.6.2) on Ubuntu 11.10. After solving all the dependencies (installed using apt-get build-dep r-base) checkinstall fails to build and install R package with the following error (the same commands build and install R-2.13.2 on the same machine without any problem): wget

Dear R useRs, I have a vector with positive and negative numbers: A=c(0,1,2,3,0,4,5) Now if i-th element in vector A is > 0, then i-th element in vector B is a+1 else i-th element in vector b=a (or 0) vector A: 0 1 2 3 0 4 5 vector B: 0 2 3 4 0 5 6 What's the right way to do this. I still have some problems with for and if statements... Cheers, Andrej

Hello everyone. This is a question regarding generation of the concordance index (c index) in R using the function rcorr.cens. In particular about interpretation of its direction and form of the 'predictor'. One of the arguments is a "numeric predictor variable" ( presumably this is just a *single* predictor variable). Say this variable takes numeric values.... Am I

Hi, I am simulating an event that has 15 possible outcomes and I have a vector 'pout' that gives me the probability of each outcome - different outcomes have different probabilities. Does anyone know a simple way of simulating the outcome of my event? If my event had only two possible outcomes (0/1) I would pick a uniform random number between 0 and 1 and use it to choose between the two

Dear R useRs, I have big (23000 rows), vertical bar delimited file: e.g. A00001|Text a,Text b, Text c|345 A00002|Text bla|456 ... .. . Try using A <- read.table('filename.txt', header=FALSE,sep='\|') process stop at line 11975 with warning message: number of items read is not a multiple of the number of columns I have no problems with processing similar file, which is

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

HI all, I might add some more information in order to possibly solve my problem. I'm really stuck and no obvious solutions do the trick. I'm using R 1.7.1 on Windows 2000 with the packages regurarly updated. I'm using hypothetical data constructed as a pseudo population conforming to a certain Var-Cov structure. I might add that just > predict(level2) works. But when I add the

Hello, I am having a problem figuring out how to model a continuous outcome (y) given a continuous predictor (x1) and two levels of nested categorical predictors (x3 nested in x2). The data are observational, not from a designed experiment. There are about 15 levels of x2 and between 3 and 14 levels of x3 nested within each level of x2. There are between 6 and 50 x1,y observations for each unique

Hi All I found survfit function was very slow for a large dataset and I am looking for an alternative way to quickly get the predicted survival probabilities. My historical data set is a pool of loans with monthly observed default status for 24 months. I would like to fit the proportional hazard model with time varying covariate such as unemployment rates and time constant variables at loan

Dear listers, First let me appologize if the same mail arrives multiple times. Recently I had some probelms sending my e-mails to the list. I encountered a problem when running glmmPQL procuedure doing multilevel modeling with a dichotomous outcome. Those are the two error messages I usually get: Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3)

Dear all , I want to do PRESS (prediction error sums of squares) or the residual mean square error of prediction (RMSEP) which will give me value that is valid for 'future predictions of independent data'. I am using different methods for example, Multiple Linear Regression, LASSO regression, Ridge Regression, Elastic Net regression etc. I am wandering if there are

Dear all, I encounter some discrepancies when comparing the deviance of a weighted and unweigthed model with the AIC values. A general example (from 'nls'): DNase1 <- subset(DNase, Run == 1) fm1DNase1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1) This is the unweighted fit, in the code of 'nls' one can see that 'nls' generates a vector

Dear R-help, Does anybody know where can I find R/Splus code for computing PREdiction Sum of Squares (PRESS) in a linear regression model? I have a large regression model (~100 predictors, ~30,000 cases) and good prediction is the main goal. I remember there is a faster way to compute it (no need to repeatedly fit models), but if there is existing code...... Thanks. Mai Z

I have a situation where the parameter estimates from lrm identify a binary predictor variable ("X") as clearly non-significant (p>0.3), but the ANOVA of that same model gives X a chi^2-df rank of > 200, and adjudicates X and one interaction of X and a continuous measure as highly significant. The N is massive and X has two categories, each with > 100,000 observations.