similar to: Need a variant of rbind for datasets with different numbers of columns

I have a list of ICD9 (disease) codes with various formats - 3 digit, 4 digit, 5 digit. The first three digits of these codes are what I am most interested in. I would like to either add zeros to the 3 and 4 digit codes to make them 5 digit codes or add decimal points to put them all in the format ###.##. I did not see a function that allows me to do this in the formatting command. This seems

Hello everyone, I have a .csv file with the following format: uniqueID SubjectID Distance_miles Tag 1 1001 5.5 3 2 1001 7 1 3 1001 6.5 1 4 1001 5 1 5 1002

I would like to code records in a dataset with a 1 if any of the columns 9-67 contain a particular code, and zero if they don't. I've been working with "subset" and it seems that something like subset(data, data[9:67]--"12345") would work, but I have been unsuccessful so far. It seems like a simple problem - any help is appreciated!

I am interested in locating a script to implement a sampling scheme that would basically make it more likely that a particular observation is chosen based on a weight associated with the observation. I am trying to select a sample of ~30 census blocks from each ZIP code area based on the proportion of women in a ZCTA living in a particular block. I want to make it more likely that a block will

I have a dissimilarity dataset with the form: 1 1 dissimilarity value 1 2 ... 1 3 1 4 2 2 2 3 2 4 ... I would like to do nonmetric multidimensional scaling with this data, but I am having trouble using this format. I would like to either find a function that accepts this format or find a way to easily convert this format to a matrix for use with existing functions. Thanks!

Hello: I am working on getting some statistics related to clinical trials and stuff. I have to work with ICD9 codes. Is anyone aware of any R method that deals with ICD9 codes verification and manipulation. Thanks Vishwanath

Dear R experts, I want to plot a line chart with another secondary axis placed right to the standard secondary axis which one can access with the axis command, so that the data lines are seen in the same plot. Is there any way to do this in R? Many thanks, Kirsten.

Hello, I have a problem with using replace() to convert a vector of dates from yyyy-mm-dd to julian date. For example, I type replace(x,2004-05-14,134) and I receive an error: Error in `[<-.data.frame`(`*tmp*`, list, value = 134) : new columns would leave holes after existing columns If I can successfully convert, I have a script that will convert all of the dates in

I have a researcher who is consistently get the warning message: In max(i) : no non-missing arguments to max; returning -Inf Best as I can tell the code is working properly and the output is as expected. I would like some help in understanding why he is getting this error message and what its implications are. I have his code. Sincerely, Kirsten Miles Support Specialist Research Computing Lab

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

Hi Jim, Thanks for your help, I really appreciate it. Perhaps I'm misunderstanding, but does this formula run different ajustment values for this function? logit(p = doc$value, adjust = 0.025) I'm looking to plot the p-values of different adjustment values. Thanks so much, Kirsten On Wed, Jul 12, 2017 at 8:49 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Kirsten,

Hello all, I have a data set where the response variable is the percent cover of a specific plant (represented in cover classes 0,1,2,3,4,5, or 6). This data set has a lot of zeros (plots where the plant was not present). I am trying to model cover class of the plant as a function of both total nitrogen and shrub cover. After quite a bit of research I have come across a conditional approach

Hi all, Thank you for taking the time to read my message. I'm trying to make a figure that plots p-values by a range of different adjustment values. (Using the **logit** function in package **car**) My Statistical analyses were conducted on probability estimates ranging from 0% to 100%. As it's not ideal to run linear models on percentages that are bounded between 0 and 1, these

Hi again, I changed from 3.4.5 to 3.3.10 after an AD upgrade to 2008R2 and prinitng is working mor ore less but it is incredibly sloooow. My server has no remarkable load so it is not due to hardware bottlenecks. Are there any ways to 'tune' 3.3.10 or any tricks to make it faster ? cheers ~christoph -- /* Christoph Beyer | Office: Building 2b / 23 *\ * DESY

There appears to be an issue with the decreasing=TRUE option on the order() function that indicates either a bug or perhaps a design flaw (potentially flawed because I would suggest the majority of users would expect different behaviour). # demonstration of problem: x <- c(2,1,3,4,5) order(x) order(x, decreasing=TRUE) order(x) correctly reports the order as: 2 1 3 4 5 I expected the result

Kirsten, this may not be the most elegant approach, but I think I would create dummy variables on the fly for each disease code you have, then use the "with" function to get means. Give this a try: # setup some fake data as an example icd9 <- c(rep("dis1",4), rep("dis2",5), rep("dis3",3)) n <- length(icd9) exposure <- rnorm(n) working <-

Hi Kirsten, Perhaps this will help: set.seed(3) kmdf<-data.frame(group=rep(1:4,each=20), prop=c(runif(20,0.25,1),runif(20,0.2,0.92), runif(20,0.15,0.84),runif(20,0.1,0.77))) km.glm<-glm(prop~group,kmdf,family=quasibinomial(link="logit")) summary(km.glm) pval<-0.00845 padjs<-NA npadj<-1 # assume you have five comparisons in this family for(method in p.adjust.methods) {

Hi! Thanks for taking the time to read this. The code below creates a graph that takes 100 samples that are between 5% and 15% of the population (400). What I'd like to do, however, is add two other sections to the graph. It would look something like this: from 1-100 samples take 100 samples that are between 5% and 15% of the population (400). From 101-200 take 100 samples that are between

Hi Ben, That's exactly right! Except for each set it's the sample population that is 400, 800 or 300. I want to take 3 samples, each of 100, where only the population differs. I can do this separately, but I'm having trouble putting them all on the same graph. I'd like to have sample on the x axis (1-300) and estimate on the y axis. I want to show how population affects the

Hey everyone, I'm working on a contour plot depicting asymptomatic prevalence at varying durations of infectiousness and force of infection. I've been able to work everything out except for this one - my legend title keeps getting cut off. Here's what I have: filled.contour(x=seq(2,30,length.out=nrow(asym_matrix)), y=seq(1,2,length.out=ncol(asym_matrix)), asym_matrix, color =