similar to: Detailed contingency tables

Is there a way to associate text descriptions with variables in a data.frame? For example... Let's say that in my data.frame I have a variable named var1. var1 represents the responses to the question "When was the last time you saw your physician?" When I tabulate the variable var1 I'd like the output to be a bit more descriptive and contain the more descriptive

Is there any such thing as a program file in R? That is, is it possible to compile and save code for performing data management and analytic tasks for a given project into a single/multiple file(s) (i.e., like a script file (S-Plus), a do file (STATA), or a *.sas file (SAS))? Any words of wisdom or a point in the direction of some documentation would be helpful. If it is not possible to work

I need some guidance from someone who is familiar/has some experience with the survey package. The data that I am using is from the Medical Expenditure Panel Survey (www.meps.ahrq.gov <http://www.meps.ahrq.gov/> ). The STRATA and PSU variables are varstr01 and varpsu01 respectively. When I try to specify them with the svydesign function I get an error message. An excerpt of my session

Is there a function in R which performs stepwise estimation in ways similar to SAS/STATA (i.e., allows the analyst to specify the significance levels for removal/addition of terms). I've been asked to evaluate two final models: one resulting from a backwards selection in R (stepAIC) and one resulting from a backwards selection using PROC LOGISTIC in SAS. The final terms are slightly

I'm trying to create a function which will allow me to subset a data set based on values of various specified variables. I also want to then apply some other function(s) (e.g., summary). This is what I've tried so far.... > test.fx <- function(dta, expvar, expval) { + newdta <- subset(dta, eval(expvar)>expval) + summary(newdta$eval(expvar)) + } > >

What is the best way to sort a dataframe? For example, how would I go about sorting a dataframe (with variables V1-V5) by ascending V1, V2 and descending V3 while retaining V4 and V5. Also, is there a relatively easy way by which to re-order my columns? Thank you for your time. Marc Marc Zodet [[alternative HTML version deleted]]

How do I rotate 3D plots/surfaces generated by either cloud or wireframe? Thanks. Marc Marc W. Zodet, MS Health Statistician Agency for Healthcare Research and Quality [[alternative HTML version deleted]]

I'm trying to simulate trend data over a five year period. I want different trend profiles...the simplest being a linear trend. I've been using the following code: patBdta1 <- NULL for(i in 1:100) patBdta1 <- rbind(patBdta1,c(yr1= mean(rbinom(50,1,.50)), yr2 =mean(rbinom(50,1,.51)), yr3 =mean(rbinom(50,1,.52)),

How do I go about generating a WEIGHTED mean (and standard error) of a variable (e.g., expenditures) for each level of a categorical variable (e.g., geographic region)? I'm looking for something comparable to PROC MEANS in SAS with both a class and weight statement. Thanks. Marc [[alternative HTML version deleted]]

I'm attempting to select a model using stepAIC. I want to use a forward selection procedure. I have specified a "scope" option, but must not be understanding how this works. My results indicate that the procedure begins and ends with the "full" model (i.e., all 17 independent variables)...not what I expected. Could someone please point out what I'm not

All: Is there any way to run an R script file (i.e., *.R) from the command prompt in the console window. Ultimately, I'm looking to put such code in a script file so that it can set off other R scripts/programs as needed. Thanks. Marc [[alternative HTML version deleted]]

I'm attempting to use stepAIC to select a model through a forward procedure. I want to consider up to all 3-way interactions. I've attempted to use the following code: m2.Fwd3way <- stepAIC(m1.Ionly, direction="forward", scope=list(upper=~(var1 + var2 + var3 + var4)^3, lower=~1)) When I submit this the trace indicates that only 2-way

I hesitate to post this question in the light of recent threads, indeed I have hesitated for several weeks, however I have come to a full stop and really need some help if I am going to progress. I am a new user of R for medical statistics. I have attempted to read all the relevant documents, but would welcome any suggestions as to what I have missed. I am trying to contruct "table 1"

Hello all, i think i am to silly. I have installed R 1.7.1 (2003-06-16). Installed some packages like xtables ore xml. I tried out this to installing packages. Then i tried to make a crosstable like i know it from spss. They say in this list that it would be going. I made a table in asci-format, seperated with tabs or blanks and than i use something like this:

svymean w/ svyby is working for me... > svyby(~visitcnt, ~agegrp3.f, svymean, design=svydes) agegrp3.f visitcnt se.visitcnt 18-44 18-44 8.755552 0.4953235 45-64 45-64 10.131555 0.5347806 65+ 65+ 9.588802 0.4323629 svyquantile is working for me... > svyquantile(~visitcnt, quantiles=c(.25, .5, .75), ties="rounded", design=svydes) 0.25

I'm running 1.9.1 on Mac OS X 10.1. My simple question is whether there is a crosstabs-like command (I know about "xtabs" which is much like "table") that computes not only cell counts but also row, column, and cell percents. Something like crosstabs(~x+y) in S-PLUS. Thank you.

I've been playing around with various table tools, trying to construct a fairly simple cross-tab. It shouldn't be hard, but for some reason it turning out to be (for me). If I want to see how many men and how many women agree with a agree/disagree question (coded 1,0), I can do this: >attach(mydata) >mytable <- table(male, q1.bin) # gender and a binary response variable

If anyone wants to see a prime example of how inefficient it is to program in SAS, take a look at the SAS programs provided by the US Agency for Healthcare Research and Quality for risk adjusting and reporting for hospital outcomes at http://www.qualityindicators.ahrq.gov/software.htm . The PSSASP3.SAS program is a prime example. Look at how you do a vector product in the SAS macro

Dear list. New to R, I'm looking for a way of using crosstab to output low-dimensional (higher than 2) contingency tables (frequencies, per-cents by rows, % by columns, mean, quantiles....) I'm looking for something of the following sort dataframe: singers, categorical variates: voice category (soprano,mezzo-soprano, ...) , voice type( drammatic, spinto, lirico-spinto, lirico,

Is there a crosstabs function like the one found in S-Plus available in any of the R addon packages? I can't seem to locate anything other than the chisq.test, which is adequate but does not yield the output customarily required with any journal submission (in my field). (S-Plus, SPSS, SAS all provide examples of the required output). If there isn't such a function already