search for: anxieti

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

Hi All:-- I've started using the the Hmisc reporting facilities recently, mostly successfully. I'm having some trouble with mChoice() multiple-choice objects, though. Here's some example code and output from R-help a couple of years ago: library(Hmisc) Symptom1 <- c("Headache", "Headache", NA) Symptom2 <- c(NA, "Anxiety", NA) Symptoms <-

Hello ALL! I have a problem to plot factor (lets say gender) as a line, or at least both line and point, from ols model: ols1 <- ols(Y ~ gender, data=dat, x=T, y=T) plot(ols1, gender=NA, xlab="gender", ylab="Y", ylim=c(5,30), conf.int=FALSE) If I convert gender into discrete numeric predictor, and use forceLines=TRUE, plot is not nice and true, since it shows values

While looking further into our load problems with Samba, and specifically with logins, I bumped up our debug level to 10. This has me really worried: in log.smb, I now see that *every other* chdir logged is to *my* home directory. Why? It's not in the source, that I can see. It's not in smb.conf or in our logon script. It looks like each and every user that logs on "passes

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 =

Hi, I have a set of 30 binary variables measuring side effects after drug treatment. Since each subject can have multiple side effects, I want to display these side effects in a multiple choice table. I'm using the summary and mChoice functions of the Hmisc package, because it produces nicely formatted latex tables. My problem is, that table includes a category for people who have at

On Fri, 19 Jun 2020 14:46:19 +0000 "Keane, Erich via llvm-dev" <llvm-dev at lists.llvm.org> wrote: > If the name of our branch causes anxiety/difficulty for a significant > portion of our population, it is literally the least we can do to > choose a word that better respects the last few centuries of world > history. Honestly, if the name of a branch causes

Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to

On 2020-06-19, Justin Hibbits via llvm-dev wrote: >On Fri, 19 Jun 2020 17:38:02 +0100 >Renato Golin <rengolin at gmail.com> wrote: > >> On Fri, 19 Jun 2020 at 16:43, Robinson, Paul via llvm-dev >> <llvm-dev at lists.llvm.org> wrote: >> > If anyone's keeping track of the voting: >> > +1 for "dev" (contrasts with "release")

workaround can't be used in wine 1.3.21 winetricks wininet wine iexplore fixme:urlmon:create_object Could not find object for MIME L"application/x-gzip-compressed"

Hi all, I am trying to get a handle on gls (package nlme). I have a toy problem: 3 fixed factors (A, B, C), two levels each, 5 replicates per treatment. The response variable is continuous, normal. I have a correlation matrix of the form: > mat A B C A 1.00 0.75 0 B 0.75 1.00 0 C 0.00 0.00 1 which is common to all observations. How do I construct the call to gls? I think I need to

On Fri, 19 Jun 2020 14:45:37 +0100 Renato Golin via llvm-dev <llvm-dev at lists.llvm.org> wrote: > On Fri, 19 Jun 2020 at 12:57, Hal Finkel via llvm-dev > <llvm-dev at lists.llvm.org> wrote: > > I like "dev" as an option here. It's short, and in addition, > > conveys the fact that the development happens in that branch. > > "main" in

On Sat, Apr 19, 2014 at 05:42:12PM +0200, Patrik Horník wrote: > > Please confirm that this is fully correct solution (for my purpose, not > elegant clean way for official fix) and it has no negative consequences. It > seems that way but I did not analyze all code paths the fixed code is in. Yes, that's a fine solution. What I'll probably do is disable the check if

Dear R users: Could you please help me out how to use R to get and interpret Standardized discriminant functions and structure coefficients for sex, Degree_Program, and Enrol_USM Please find the data file at: http://www.usm.maine.edu/~aaboueissa/Rhelps/Rhelps.html data files are (xls file: dataforRxls or txt file: dataforRtxt) Output looks like this table. Standardized

Could anyone please explain how to set up contrasts between means in R. I want to know if "before I conduct an experiment and believe the mean for 1 and 2 will be different from means 3 and 4, Is this true?" That is what I have to prove or disprove, I thought that contrasts would be the way to go. Thanks for the help. Igor [[alternative HTML version deleted]]

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The irr package seems to install correctly: > install.packages("irr") trying URL 'http://cran.us.r-project.org/src/contrib/irr_0.61.tar.gz' Content type 'application/x-tar' length 13848 bytes opened URL ================================================== downloaded 13Kb * Installing *source* package 'irr' ... ** R ** data ** help >>>

Hello, I''ve completed the update of all my servers to the new mongrel. No problems whatsoever to report, well done. But, I have some colleagues who are wondering what the primary improvements are to mongrel with this new release. I read the Change log but it was a bit cryptic for me. Can anyone provide an executive summary of why 1.0.1 is better than 0.3.xx? a few main bullet

Dear R experts, I'm beginner. My question about ANOVA for unequal sample sized data should be obsolete but I can not clarify it. I have a dataset from 23 males and 18 females. I measured one condition('cond') with 4 levels. So I'd like to see main effect of gender, cond and gender by cond interaction and also postHoc test. (In fact, I have to do anova 90 times) * 1. Question