similar to: Plotting confidence bands around regression line

Dear R-helpers, This may sound simple to you, but I'm a beginner in this, so please be forgiving. I have a following problem: two analytes were measured in patient's blood on 4 occasions: ProteinA and ProteinB. How to correctly evaluate correlation between ProteinA and ProteinB? I tried: x <- data.frame(Patient.ID=rep(1:10, each=4), Visit=rep(c(1:4),10), ProteinA=rnorm(m=10,

Dear R-Helpers, I am a novice in survival analysis. I have the following code: for (i in 3:12) print(coxph(Surv(time, status)~a[,i], data=a)) I used it to fit the Cox Proportional Hazard models separately for every available parameter (columns 3:12) in my data set - with intention to compare the Hazard Ratios. However, some of my variables are in range 0.1 to 1.6, others in range 5000 to

Dear R-helpers, I am working with 'cph' models from 'rms' library. When I build simple survival models, based on 'Surv(time, event)', everything is fine and I can make nice plots using plot(Predict(f, time=3)). However, recently I tried to be more specific and used 'Surv(start, stop, event)' type model. Using this model 'plot(Predict(f))' works OK, but

Hello, I have made an R script that prepares a NONMEM dataset and I would like to start the NONMEM run right after the dataset is ready. I am using windows XP, R 2.9.1 and NONMEM 6. I have prepared a run.bat file that looks like this: ---------------------------------------- call K:\nmvi\NMdirectories.bat call K:\nmvi\nmfe6 "path\control.txt" "path\output.txt"

Dear R-Helpers, I'm working with immunoassay data and 5PL logistic model. I wanted to experiment with different forms of weighting and parameter selection, which is not possible in instrument software, so I turned to R. I am using R 2.14.2 under Win7 64bit, and the 'nls' library to fit the model - I started with the same model and weighting type (1/y) as in the instrument to see

Dear R-helpers I have a data set of roughly 10 million records, 7 columns. It has only about 500MB as a csv, so it fits in the memory. It's painfully slow to do anything with it, but it's possible. I also have another dataset of covariates that I would like to explore - with about 4GB of data... I would like to merge the two datasets and use lmer to build a mixed effects model. Is

Hello everyone, I am new to R, I have been using SAS for a while. Not surprisingly, I find R much better in graphics, which is publication ready right away. Recently, I have been trying to calculate some basic statistics using R. I have a dataset of multiple rows per subject. For example: Subject Date Factor1 Factor2 Factor3 P1 0.5 1 1 3 P1 1 3 2 5 P1 2 3 5 NA ... P2 0.5 1 6 4 P2 1 2 NA 7 P2

Dear R-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

Hello R-helpers, I have a problem with formatting a single number to show leading zeros. For example, I want "2" displayed as "002". My numbers have 1 to 3 digits and I would like them all to display 3 digits for printing. I know I could use "paste" in a loop with several "if"s, but I was wondering if there is a single function that can do this. I have

I'm using nonparametric regression (packeges ksmooth and ks). My question: is there any way to compute confidence bands (or variability bands) with R. Confidence bands for functions are intervals [CLO(x);CUP(x)] such that with probability 1-alpha the true curve is covered by the band [CLO(x);CUP(x)]. Thanks very much for any help you can offer. Michael G??lger

What's the best / simplest way to create 95% confidence bands for a model created with lm() that can be plotted around teh regression line? I've looked everywhere for this - I guess I must be missing something. - Jason

Is there a built-in function in R that will generate simultaneous confidence and prediction bands for linear regression? Tom -- View this message in context: http://www.nabble.com/Simultaneous-Confidence-Prediction-Bands-tp17941537p17941537.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I am trying to plot KM curves with confidence bands with function survplot under package rms. However, the following codes do not seem to work. The KM curves are produced, but the confidence bands are not there. Any insights? Thanks in advance. library(rms) ########data generation############ n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age"

Dear all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin

Dear R-list, I would like to show the implications of estimating a linear trend to time series, which contain significant serial correlation. I want to demonstrate this, comparing lm() and an gls() fits, using the LakeHuron data set, available in R. Now in my particular case I would like to draw confidence bands on the plot and show that there are differences. Unfortunately, I do not know how to

Hi, I am using ns() to model the effect of time on some outcome y [ specifically, I am using polr() in a model of the form mod1=polr(y~x1+x2*ns(Year,df=3),...) , with x1 and x2 denoting several covariates each ] I understand how to use the spline basis as recorded in the model matrix in order to reproduce the model fit and to generate curves of the point estimates of the time effect,

Hello R users, Given a linear (in the parameters) regression model where one predictor x interacts with time and time*time (ie, a quadratic effect of time t): y = b0 + b1(x) + b2(t) + b3(t^2) + b4(x*t) + b5(x*t^2) + e, I would like to construct 95% confidence bands (optimally, shaded) around this function: *dy* = b1 + b4(t) + b5(t^2) *dx* That is, the partial effect of x on y changing over

Hello, I am trying to plot confidence bands on the mean and prediction bands for the following nonlinear regression, using maximum likelihood via optim. A toy example with data and code of what I am trying to accomplish is: VOL<-c(0.01591475, 1.19147935 ,6.34102460, 53.68809287, 91.90143074, 116.21397007, 146.41843056, 215.64535337, 256.53149673, 315.73609232) Age <-c(1.622222, 2.833333