similar to: van der waerden test

Hello, I need help in performing a Van_der_Waerden normal scores test in R. I have two arrays of scores(final on therapy scores from drug and placebo) and want to use the normal scores procdeure to test for significance. (observations are unequal in number - due to dropouts). Could you please help me out with the coding or let me know if there is a package that can be used (for example,

Hello, I am running a few simulations for clinical trial anlysis. I want some help regarding the following. We know trhat as the sample size increases, the variance should decrease, but I am getting some unexpected results. SO I ran a code (shown below) to check the validity of this. large<-array(1,c(1000,1000)) small<-array(1,c(100,1000)) for(i in

In package fOptions, there are functions that generate Halton sequences. The van der Corput sequence for base 2 is a particular case of the Halton sequence generated by: n <- 8 # anything here... x <- runif.halton(n, 1) In fact, x <- runif.halton(n, dim) will generate the van der Corput sequences for the base b as the i-th prime number in x[,i]. (in other words, if I want the van der

Dear All, I am a bit struggling with the many packages for Markov models available in R. Apologies for now posting a code snippet, but I am looking for some guidance here. Please consider a set like the one below (which you can get with data<-read.csv('http://dl.dropboxusercontent.com/u/5685598/data_table.csv') ). ID therapy age1 age2 EFS 7308 ormo_lunga 78

Hi, I'm playing with moving some of my lattice graphics into ggplot2, and I'd like to ask how to achieve a couple of things, both of which are fully illustrated in self-contained code (and mostly minimal, although that left quite a bit) following this written description. 1. I quite often like to use a 'ghosted' reference across facets - for example, in my example program below,

In http://tolstoy.newcastle.edu.au/R/e2/help/06/10/3064.html a method was given for converting a frequency table to an expanded data frame representing each observation as a set of factors. A slightly modified version was later included in the NCStats package, only on http://rforge.net/ (and it has too many dependencies to be useful). I've tried to make it more general, allowing an input

COMPUTATIONAL ANALYSIS OF NEXT GENERATION SEQUENCE DATA (http://bioinformatics.nki.nl/vacancies.php) THREE BIOINFORMATICS POSITIONS PROJECT OUTLINE Genomic alterations are major determinant of responses to (targeted) therapies in cancer. In fact, the best positive and negative predictors of responses to targeted therapies are alterations in kinases or their direct downstream effectors. To

I'll take a risk following Uwe's wonderful response to that advert. I'm looking for someone, perhaps particularly a stats student, who might want to do a piece of work with me on using R to present and analyse routine data that psychotherapists might submit on a cgi-bin interface and perhaps cross-referencing that against some largish referential data to which I have access.

We just studied randomized block design analysis in my statistics class, and I'm trying to learn how to do them in R. I'm trying to duplicate a case study example from my textbook [1]: > # Case Study 13.2.1, page 778 > cd <- c(8, 11, 9, 16, 24) > dp <- c(2, 1, 12, 11, 19) > lm <- c(-2, 0, 6, 2, 11) > table <- data.frame(Block=LETTERS[1:5], "Score

Dear All, I met problems when doing contrast and now really need some help in the model below: Fit=gam(y~treat+SEQUENCE+PERIOD+SEX+s(x),data=dat, random=list(SUBJID=~1),correlation=corAR1(form=~1|SUBJID)) And error message keeps coming out when I want to compare the differences between treatments: Diff=contrast(Fit, list(treat=treatment[-placebo.pos]),list(treat="Placebo"),

Dear all, The incredibly useful Hmisc package provides a method to generate summary tables that can be typeset in latex. The Alzola and Harrell book "An introduction to S and the Hmisc and Design libraries" provides an example that generates mean and quartiles for continuous variables, and numbers and percentages for count variables: summary() with method = 'reverse'. I

Hi everyone, I have the following problem: I have three variables, 'group', 'city' and 'pressure' There is an interaction effect between group and city and I'd like to show this in an interaction plot: interaction.plot(group, city, pressure, type="b", col= c(1:2), leg.bty="o", leg.bg="blue", lwd=1,

I have problem getting the concept of a nested fixed variable into the nlme scheme. I fear the question is very stupid. In the past I had asked this before, and never got a reply (in other cases, the response was within hours). I also checked the S-list, where several similar enquiries of other people are orphaned. We have a cross-over design, where patient are treated two weeks with placebo,

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

I read in a thread in r-help today that the windows device in 2.6 supports transparency, so I tried an example and had some issues. The density plots should be filled with transparent color in the following example (similar to the points), however the color is "fully" transparent. This works in the Cairo device, but not in the windows device. Thanks, --Matt Matt Austin

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

Hi all, I am a biostatistician and I have developed my own ranking system for clinical data. I would like to test the efficiency of it w.r.t. to other ranking systems. I would like to simulate the data and after assigning ranks to my observed scores(after neglecting dropouts), observe the type I error. If I want to do a Kruuskal Wallis type of test, what test statistic should I use to test for a

Hallo! I have two groups (placebo/verum), every subject is measured at 5 times, the first time t0 is the baseline measurement, t1 to t4 are the measurements after applying the medication (placebo or verum). The question is, if there is a significant difference in the two groups and how large the differnce is (95% confidence intervals). Let me give sample data # Data

I am having two problems creating data frames that I have solutions, but they really seem like kludges and I assume I just don't understand the proper R way of doing things. The first situation is I have an set of uneven data vectors. When I try to use them to create a data frame I would like the bottoms of them padded with NAs, without explicitly specifying that. When I do: anxiety.data =

All, I can't seem to get auto.key to work properly in an xyplot that is employing panel.text. Specifically, I often change the default grouping colors then use auto.key accordingly, but for some reason the same functionality isn't working for this different type of plot. Any help much appreciated. Cheers, David library("lattice") dat = data.frame( Y = c(rnorm(18,1),