similar to: Displaying Dichotomous Variables as fractions in gtsummary Tables

I think questions about gtsummary are better addressed to that project. They have a link "Getting Help" on their web page; I'd start there: https://www.danieldsjoberg.com/gtsummary/SUPPORT.html Duncan Murdoch On 27/03/2023 2:19 p.m., Deramus, Thomas Patrick wrote: > Hi R Experts. > > Apologies if this has been shared elsewhere, but I haven't been able to find a

Hi All, I stumbled upon some topics regarding interactions in anova and regression and packages for tabulating and visualizations the results of them. Here we are: https://stackoverflow.com/questions/77933272/how-to-add-a-reference-level-for-interaction-in-gtsummary-and-sjplot/77935742#77935742 ,

It is trivial in R to add whatever decorations to a plot that you would like, but that requires that you go beyond point and click production of graphics and write actual code. If you are unwilling or unable to do this, you are stuck with whatever various packaged graphics functionality provides.So you might want to search on "interaction plots for linear models in R" or similar at

Hi Thomas, Glad to hear the suggestion helped, and that switching to a `data.table` approach reduced the processing time and memory overhead?15 minutes for one of the smaller datasets is certainly better! Sounds like the adjustments you devised, especially keeping the multicore approach for `make_clean_names()` and ensuring that `ID_Key` values remain intact, were the missing components you

Hi Gregg. Just wanted to follow up on the solution you proposed. I had to make some adjustments to get exactly what I wanted, but it works, and takes about 15 minutes on our server configuration: temp <- ??????open_dataset( ????????????sources = input_files, ????????????format = 'csv', ????????????unify_schema = TRUE, ????????????col_types = schema( ????????????"ID_Key"

How is the server configured to handle memory distribution for individual users. I see it has over 700GB of total system memory, but how much can be assigned it each individual user? AAgain - just curious, and wondering how much memory was assigned to your instance when you were running R. regards, Gregg On Wednesday, December 11th, 2024 at 9:49 AM, Deramus, Thomas Patrick <tderamus at

Hi, I do not want to make a plot, I try to make an output table in R, (in GUI like Stata this is trivially easy task) with regard to SO OP question. As I mentioned, in paper I would not do this, but out of curiosity I use R this time trying to create it. If in R this is trivial task as well, could you please show how to do it ? After googling a lot I did not find solution. regards, Jacek niedz.,

It's Redhat Enterprise Linux 9 Specifically: OS Information: NAME="Red Hat Enterprise Linux" VERSION="9.3 (Plow)" ID="rhel" ID_LIKE="fedora" VERSION_ID="9.3" PLATFORM_ID="platform:el9" PRETTY_NAME="Red Hat Enterprise Linux 9.3 (Plow)" ANSI_COLOR="0;31" LOGO="fedora-logo-icon"

Thomas, I'm curious - what OS are you running this on, and how much memory does the computer have?? Let me know if that code worked out as I hoped. regards, gregg On Wednesday, December 11th, 2024 at 6:51 AM, Deramus, Thomas Patrick <tderamus at mgb.org> wrote: > About to try this implementation. > > As a follow-up, this is the exact error: > > Lost warning

Hello R-User! I appologise in advance if this should also go into statistics but I am presently puzzled. I have a data.frame (about 300 rows and about 80 variables) and my variables are dichotomous factors, continuous (numerical) and ordered factors. I would like to calculate the linear correlation between every pair of my variables, because I would like to perform a logistic regression (glm())

Hi everyone, Have sample of items for each one, a set of 20 dichotomous (absent-present) variables are expressed. I'm trying to understand how to explore the co-occurence of each variable. Read some papers concerning smallest space analysis, but it does not seems implemented in any R package (and my protamming skills are =0). Non metric MDS gives error messages, probably because of the

About to try this implementation. As a follow-up, this is the exact error: Lost warning messages Error: no more error handlers available (recursive errors?); invoking 'abort' restart Execution halted Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?) Error: cons memory exhausted (limit reached?)

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Hello, I'd like to know if there is a way to fit a Structural equation model with dichotomous indicators (ex: problem with a phone solved/ or not) having effects on a ordinal variable. How I do that using R? Do you have an example with the code in R that you can send to me? Thanks a lot! Renata Estrella UFRJ, Brasil, Rio de Janeiro Renata Leite Estrella Assistente de

hi, How can I compute a reliability score of a scale consisting only of dichotomous items? thanks for any help!

Newsgroup members, Does anyone have a clever way to simulate a correlation matrix such that each column contains dichotomous variables (0,1) and where each column has different prevalence rates. For instance, I would like to simulate the following correlation matrix: > CORMAT[1:4,1:4] PUREPT PTCUT2 PHQCUT2T ALCCUTT2 PUREPT 1.0000000 0.5141552 0.1913139 0.1917923 PTCUT2

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not

Hi everyone, I would like to fit a predictive model to my data in order to compare absorbance readings to a toxin standard. This data was obtained by exposing red blood cells to different toxin concentrations, which lead to the lysis of the red blood cells, increasing the absorbance (hemoglobin is freed). The data has a sigmoid shape (see below), so I thought about fitting a logistic model to the

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is

Newsgroup members, I appreciate the help on this topic. David Duffy provided a solution (below) that was quite helpful, and came close to what I needed. It did a great job creating two vectors of dichotomous variables with a known correlation (what I referred to as a phi-coefficient). My situation is a bit more complicated and I'm not sure it is easily solved. The problem is that I must