similar to: the observed "log odds" in logistic regression

I have run a t.test in R, and received these results: Two Sample t-test data: rsa and umple t = 0.9819, df = 10, p-value = 0.3493 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -76.1541 196.1541 sample estimates: mean of x mean of y 508.3333 448.3333 Can someone give me a detailed interpretation of the above results? Specifically,

Hi, I'm trying to fit a smooth line in a plot(y ~ x) graph. x is continuous variable y is a proportion of success in sub-samples, 0 <= y <= 1, from a Monte Carlo simulation. For each x there may be several y-values from different runs. Each run produces several sub-samples, where "0" mean no success in any sub- sample, "0.5" means success in half of the

Hi, I'm trying to calculate p-value to findout definitely expressed genes compare A to B situation. I got this data(this is a part of data) from whole organism , and each number means each expression values (that means, we could think 'a' gene is 13 in A situation, and it turns 30 in B situation) To findout probability, I'm going to use Audic - Claverie Method. ( The significance

Hi there Yes, I know very little of ruby. I must take a crash course? Probabily. Anyway, I try to ask this here. I have a control: customer_controller. From here, I can do something like: @id = 1 @customer = Customer.find(id) Now @customer will contain all the fetched record for customer with id = 1. Great. From this controller I will render an html page. Somewhere the view will print test.

I have an action and I am testing it like this: The action: ========== def create @event = Evento.new(params[:event]) @event.aproved = false if @event.save redirect_to :action => "index" else render :action => "new" end end The RSpec test: ============== before do @event = mock_model(Event)

I'm running Samba 4.1.6 with Ubuntu 14.04.1 LTS Little Endian on a Power 8 box. As I said in a previous post, I can't mount any share from machine. I tried the same configuration with Samba 4.1.11 on Ubuntu 14.10 Little Endian on Power 8, and it works fine. I'm probabily wrong, but is there a bug in the binary distribution of Samba on Ubuntu 14.04.01 Little Endian, maybe

hi, when i change the OBJ_ROOT and recompile the llvm, it's successful. but run following test, then the error occues: ----------------------------------------------------- [yue at RH9 obj]$ make -C ./test/Programs make: Entering directory `/home/yue/llvm/obj/test/Programs' make[1]: Entering directory `/home/yue/llvm/obj/test/Programs/SingleSource' make[2]: Entering directory

Dear yueqiang, I tracked this down this morning, and it is a bug with our Python code that runs the tests. Essentially what is happening is that the code is finding the temporary directory it created the first time it ran the tests and believes that there are tests inside of it which it needs to run. It then gets confused and quits. This bug only shows up when the source tree and the

hi, I compile a pthread program using llvmgcc, but when i run pthread.ll, it show: ------------------ [yue at RH9 test]$ llvmgcc pthread.c -o pthread.ll [yue at RH9 test]$ ls pthread pthread.c pthread.ll pthread.ll.bc [yue at RH9 test]$ ./pthread.ll Creating thread 0 WARNING: Cannot resolve fn 'pthread_create' using a dummy noop function instead! ERROR; return code from

Hi, stepAIC generic plot function creates useful graphics for the diagnosis of multiple regressions. To create predicted versus observed plots, I use to look for the coefficients, copy them by hand, calculate R?, then plot. Is there a more automated way to plot predicted versus observed with its associated R? output using stepAIC, or another function? Kind regards, S.-?. Parent Universit?

Dear useR's, I was comparing results for a logistic regression model between different library's. themodel formula is arranged as follows: response ~ (intercept) + value + group OR: glm( response ~ (intercept) + value + group , family=binomial(link='logit')) lrm( response ~ (intercept) + value + group ) ROC( from = response ~ (intercept) + value + group ,

Dear , This is Elaine. I am computing moran's I using moran.test for a generalized linear model (multiregression). The following contents are the results, and I cannot find the observed Moran's I mentioned as estimate in the manual. Please kindly help indicate if there is observed Moran's I did not notice or other method for calculation. Thanks Elaine data: residuals(modg1)

The following code library(Hmisc) x = 1:10 y = x latex(summary(x~y),table.env=FALSE) latex(summary(cbind(x,y)),table.env=FALSE) should produce latex output that is not a table. The second one produces just a tabular, as it should. However, the first one produces a tabular embedded in a table. (This is the effect if you leave table.env=FALSE out).

Hi, I've two fitted models, one binomial model with presence-absence data that predicts probability of presence and one gaussian model (normal or log-normal abundances). I would like to evaluate these models not on their capability of adjustment but on their capability of prediction by calculating the (log)likelihood between predicted and observed values for each type of model. I found

Hi All, Not sure exactly a memory leak or not. I was porting my nagios from Redhat 7.3 to CentOS 5.4 and I observed the memory usage was gradually increasing on the new centos box. When I ran all my perl plugins with Valgrind -3.2.1, all the plugins complained about a memory leak. Not sure if it's a leak or if its fault with valgrind way of evaluating things. I have attached

(If I am so lucky, please include me in reply, I'm not on the list) Tony Breeds wrote (2004-10-13): > On Wed, Oct 13, 2004 at 04:10:23PM +1300, Michael Woodhams wrote: >> Background: Linux, Debian (Sarge). I want to auto-mount an smbfs at >> boot. smbmount version is 3.0.7-Debian. > > <snip> > >> username=<user>/<domain> >>

Dear friends. I believe this problem has been discussed in various forms now and then, so I hope you will forgive me I ask how to do a truncated model like this, where the observed y is recorded as 10 whenever it is higher or equal to that value x <- rnorm(1000) y <- 10*x + rnorm(1000) y[which(y>10)] <- 10 and recover the "true" model ? Best wishes Troels Troels Ring,

Hello R Users, I have two data sets i) TOMS aerosol optical depth(AOD) and ii) TOMS ozone(O3). AOD data is on 1x1 grid and O3 data is on 5x5 grid. First I want to read AOD and O3 as it is and then I want to regrid AOD on 5x5 grid as O3. Reading is first problem. FIRST PROBLEM READING AOD: AOD data is in following format: ######### Latitute: 89.5 167 0 0 0 0 0 182 0 0 0 0 0 0 0 0 0 0 0 0 200

To: cran at r-project.org Subject: New package: pomp, inference for partially-observed Markov processes The new package 'pomp' is built around a very general realization of nonlinear partially-observed Markov processes (AKA state-space models, nonlinear stochastic dynamical systems). The user provides functions specifying the model's process and measurement components. The

To: cran at r-project.org Subject: New package: pomp, inference for partially-observed Markov processes The new package 'pomp' is built around a very general realization of nonlinear partially-observed Markov processes (AKA state-space models, nonlinear stochastic dynamical systems). The user provides functions specifying the model's process and measurement components. The