search for: depressants

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'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

How should I define the :onsuccess option in my - link_to_remote - tag, to execute a Js function I wrote : :success => "toggleButton(this, /db_bfilter/i);" ''this'' is the current element defined in my view : <span id="db_bfilter0" class="depressed"><%= filter_link_helper "This week" %></span> <span

Dear all, I noticed the following bug in points.formula > library(DAAG) > data(roller) > fm <- lm(depression ~ weight, data=roller) > plot( depression ~ weight, data=roller, type="n") > abline(fm) > attach(roller) > points( depression~weight, subset=1:7) > points( depression~weight, subset=8:10, col="blue") Error in if (length(x) == l) x[s] else x :

I'm trying to extend some work in the car and heplots packages that requires getting a table of multivariate means for one (or later, more) terms in an mlm object. I can do this for concrete examples, using aggregate(), but can't figure out how to generalize it. I want to return a result that has the factor-level combinations as rownames, and the means as the body of the table

Hi, I recently built my new graphics workstation with the express idea of using Linux, Wine, and an AutoCAD clone called IntelliCAD. http://intellicadms.com/ Running Ubuntu 9.04 (Jaunty) on my old Intel system with Wine works just fine. IntelliCAD functions beautifully. However, when I installed the above on my all-new AMD Phenom II X4 955 system, IntelliCAD simply does not launch. I use: -

*Dear experts,* Please excuse me for disturbing... Right now I am struggling with GLM a bit... Would you be so kind to provide me a solution on using nuisance variables. The problem is that I have data on Depression (volumetric measurements of different brain regions) and I want to include age, gender and education as nuisance parameters in the model. In the other words I would like to model the

Hi, Here is (I hope) all the relevant output from R. > mean(s1$ZDEPRESSION, na.rm=T) [1] -1.041546e-16 > mean(s1$ZDIVERSITY_PA, na.rm=T) [1] -9.660583e-16 > mean(s1$ZMEAN_PA, na.rm=T) [1] -5.430282e-15 > lm.ridge(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)$coef ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA -0.3962254 -0.3636026

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

Hi all, I want to make a contingency table in R. I want to tabulate two variables, one as the independent and second as the dependent variable. The IV has two categories, namely, birth complications, and no birth complications. The frequency of birth complication category is fifty, and the frequency of no birth complication category is 34. The categories and frequencies of DV follows.

I am writing to inquire about a warning message that I receive when trying to model a random slope and intercept at the second level of a 3-level model. Specifically, I am testing a 3-level model in which time (WEEK) is nested in participants (PARTICIP) and participants are nested in dyads (DYADID). The goal is to examine how an interpersonal style (CORUMTO) one week predicts changes in

Thanks, I was getting to try this, but got side tracked by actual work... Your analysis reproduces the SPSS unscaled estimates. It still remains to figure out how Nick got > coefficients(lm(ZDEPRESSION ~ ZMEAN_PA * ZDIVERSITY_PA, data=s1)) (Intercept) ZMEAN_PA ZDIVERSITY_PA ZMEAN_PA:ZDIVERSITY_PA 0.07342198 -0.39650356

Hi John, Thanks for the comment... but that appears to mean that SPSS has a big problem. I have always been told that to include an interaction term in a regression, the only way is to do the multiplication by hand. But then it seems to be impossible to stop SPSS from re-standardizing the variable that corresponds to the interaction term. Am I missing something? Is there a way to perform the

Hi all, Is everyone switched over to the Google lists? I''m basically ready for the switch, I think, and doing so would be one more closed loop for me. I notice we''ve only got a bit over 300 subscribers to the Puppet Users list, while my madstop list has nearly 600. This is a bit depressing, but it probably represents a better idea of who''s actually using the

I''m trying to set up cucumber so we can try it in some Ruby automation scripts I''m working on. These will be straight Ruby, for use in BBEdit and TextMate (on the Mac). NOT Rails. The big early-stage stumbling block I''m having is setting up the initial Rakefile and directory structure. I think I''m rtfm''ing, but clearly I''m missing

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

I'm trying to write a function to find the means over factors of the responses in a mlm (something I would do easily in SAS with PROC SUMMARY). The not-working stub of a function to do what I want is below, and my problem is that I don't know how to call aggregate (or some other function) in the context of terms in a linear model extracted from a lm/mlm object. means.mlm <-

Both of these applications fulfill a great need of mine: to read data directly from google spreadsheets that are private to myself and one or two collaborators. Thanks to the authors. I had been using RGoogleDocs for the about 6 months (maybe more) but have had to stop using it in the past month since for some reason that I do not understand it no longer reads google spreadsheets. I loved it. Its

Dear Nick, On 2017-05-05, 9:40 AM, "R-devel on behalf of Nick Brown" <r-devel-bounces at r-project.org on behalf of nick.brown at free.fr> wrote: >>I conjecture that something in the vicinity of >> res <- lm(DEPRESSION ~ scale(ZMEAN_PA) + scale(ZDIVERSITY_PA) + >>scale(ZMEAN_PA * ZDIVERSITY_PA), data=dat) >>summary(res) >> would reproduce the