similar to: max / pmax

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

I just started using R and I'm having all sorts of "fun" trying different things. I'm going to document the different things I'm doing here as a kind of case study. I'm hoping that I'll get help from the community so that I can use R properly. Anyways, in this study, I have demographic data, drug usage data, and side effect data. All of this is loaded into a csv

the data is : >table.8.3<-data.frame(expand.grid( marijuana=factor(c("Yes","No"),levels=c("No","Yes")), cigarette=factor(c("Yes","No"),levels=c("No","Yes")), alcohol=factor(c("Yes","No"),levels=c("No","Yes"))), count=c(911,538,44,456,3,43,2,279))

I am trying to get multi_search to search across multiple models. I have the following: class Drug < ActiveRecord::Base acts_as_ferret :store_class_name => true end class Target < ActiveRecord::Base acts_as_ferret :store_class_name => true end and I am trying to query via: @drugs = Drug.multi_search(params[:query], [Target]) But I get no results. If I go: @drugs =

Hi Bert and David, Thank you so much for willingness to spend some time on my problem!!! I have some statistical knowledge (going to get a master in applied statisitics), but do not have a chance to purse a phD for statistics, so I am always be careful before starting to do analysis and hope to gather supportive information from real statisticians. Sorry that I did not tell more info about

David: I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected: For example, if baseline control (no drugs) has a response of 0, drugA has an effect of 1, drugB has an effect of 2, and the effects are additive, with no noise we

But of course the whole point of additivity is to decompose the combined effect as the sum of individual effects. "Mislead" is a subjective judgment, so no comment. The explanation I provided is standard. I used it for decades when I taught in industry. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into

> On Mar 5, 2018, at 3:04 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > But of course the whole point of additivity is to decompose the combined effect as the sum of individual effects. Agreed. Furthermore your encoding of the treatment assignments has the advantage that the default treatment contrast for A+B will have a statistical estimate associated with it. That was a

Hello All, Want very much to learn how to plot patient drug timelines. Trouble is I need to figure out how to do this today. So not much time for me to struggle with it. Hoping someone can just help me out a bit. Below are some sample data and code that produces what I think is the beginning of a very nice graph. Need to alter the code to: 1. Get the lines for the drugs to appear on the

Hello, I previously sent this message which was stripped off due to HTML mail. Sorry! Thanks. Hello All, I am new to the list. I have been learning to use R recently and its been great to see so much help available. However I must admit, I have stumbled upon a problem with correlation matrices and was hoping if someone could help. I have an excel workbook with a sheet per drug. Each sheet

Hello All, I was wondering if it's possible to reshape data from long to wide in R without using a "timevar". I've pasted some sample data below along with some code. The data are sorted by Subject and Drug. I want to transpose the Drug variable into multiple columns in alphabetical order. My data have a variable called "RowNo" that functions almost like a

Hi all, thank you for your patience. I am dealing with a large dataset detailing patients and medications Medications are hard to code, as they are (usually) meaningless unless matched with doses. I have a dataframe with vectors (Drug1, Drug2..... Drug 16) and individual patients are represented by rows. The vectors are actually factors, with 100s of possible levels (all the drugs the patient

> On Mar 5, 2018, at 8:52 AM, Ding, Yuan Chun <ycding at coh.org> wrote: > > Hi Bert, > > I am very sorry to bother you again. > > For the following question, as you suggested, I posted it in both Biostars website and stackexchange website, so far no reply. > > I really hope that you can do me a great favor to share your points about how to explain the

> On Mar 5, 2018, at 2:27 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > David: > > I believe your response on SO is incorrect. This is a standard OFAT (one factor at a time) design, so that assuming additivity (no interactions), the effects of drugA and drugB can be determined via the model you rejected: >> three groups, no drugA/no drugB, yes drugA/no drugB,

Good Afternoon, I am inexperienced in data visualization R. so I wonder if someone can help me. I am using the generic database mtcars. I would like to change the chart plot, instead of appearing the name of the medicine, I wanted a symbol in the chart drugX arise for example in a yellow circle, and so for the rest of the drugs. I am using the following code. dataset Age Sex BP

Hi Bert, I am very sorry to bother you again. For the following question, as you suggested, I posted it in both Biostars website and stackexchange website, so far no reply. I really hope that you can do me a great favor to share your points about how to explain the coefficients for drug A and drug B if run anova model (response variable = drug A + drug B). is it different from running three

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

Hello fellow R-ers, I have spent some time on this and it is driving me NUTS! I am sure there is a solution, so please help. I am trying to create a function that will plot different lines for subsets of a dataset. For example, I am trying to look at different drug groups (drug2), let's say 1,2,3,4, and 5. The data has 2 different rates, college students and high school students (var names cs

Dear R-maillit, I have to perform a stastistical test to asses if two grug show the same or different effect on my prepartion. I tested on the same preparation only one drug and I have to use normalized data due to high variability in the control condictions. What type can I use? A non paramatric test right? And how can do it on R software? Thanks in advance Simone [[alternative HTML version

Dear R users, is the following OK? > max(NA, NaN) [1] NA > max(NaN, NA) [1] NA > pmax(NaN, NA) [1] NA > pmax(NA, NaN) [1] NaN ...or is it a bug? Documentation says that NA has a higher priority over NaN. Best regards, Michal Burda [[alternative HTML version deleted]]