similar to: Using linear formula inside MLE

I have used the following code to obtain a max likelihood estimator for a logit regression. The final command invokes ‘optim’ to obtain the parameter estimates. The code works OK but I want to use the ‘mle’ function in the ‘stats4’ package instead of directly calling ‘optim’. Can someone please figure out the command to do this? Thank you in advance. Martin # mlelo.r - maximum

To the R development team: First of all, thank you so much for maintaining wonderful R software. Perhaps, Dr. Ahn has just reported an error on the wilcox.test() function, and suggesting that an error may arise from abs() and rank(). I just had a quick check that the problem may come from the precision of the results of arithmetic functions. 87.7-89.1+1.4 # > 87.7-89.1+1.4 # [1]

Hi, I am having a problem figuring out how to extract a subset of rows. I have a list with 68 similar data.frames. Each data.frame is 500 rows by 5 columns. I want to take one row from each data.frame based upon the data in a particular column (i.e. it matches a symbol). For example: > str(database) List of 68 $ X2011.01.11:'data.frame': 500 obs. of 5 variables: ..$ Symbol

Hi All, Thanks to your previous assistance, I'm able to compile virt-v2v 1.42.0 on Debian testing. However, when I run `make check` tests/test-v2v-oa-option.sh fails with the following log: -8<------------------------------------------------------------------ test-v2v-oa-option.sh: info: you can skip this test by setting SKIP_TEST_V2V_OA_OPTION_SH=1 [ 0.0] Opening the source -i libvirt

TEQL and Subnet problem I have a network topology shown below, and I am trying to use TEQL. My problem is: When I ping to P3.teql0 from P2 ("[P2]# ping 16.119.144.66"), the traffic can never go from P2.eth1, and all traffic only goes to P1.eth0. What P2.eth1 (16.119.144.33) did is broadcasting an ARP asking for the MAC address of 16.119.144.66, although I have specified the route to

Hi R Users, I am going to run a multiple linear regression with around 57 independent variables. Each time I run the model with just 11 variables, the results are reasonable. With increasing the number of independent variables more than 11, the coefficients will get ?NA? in the output. Is there any limitation for the number of independent variables in multiple linear regressions in R? I attached

TEQL and Subnet problem I have a network topology shown below, and I am trying to use TEQL. P1, P2 and P3 are three linux machines, and each has two network cards. P2 and P3 are connected in two ways: (1) directed connection between P2.eth0 and P3.eth0; (2) connection via P1. P1 acts as the gateway. TEQL is installed on P2 and P3 to load-balance packet transmission by round-robin sending packets

Hi, I recently updated from puppet 3.1.1 to 3.2.1 and noticed quite a bit of increased load on the puppetmaster machine. I''m using the Apache/passenger/rack way of puppetmastering. Main symptom is: higher load on puppetmaster machine (8 cores): - 3.1.1: around 4 - 3.2.1: around 9-10 Any idea why there''s more load on the machine with 3.2.1? -- You received this

> On Apr 15, 2016, at 6:07 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > Keep your answers to R help (others can help you too) > > From the warning message it seems to me possible that function ReadExi needs to write something to the working directory. As I said I am not an expert in this package, but commands from help > > R>

Hi Keep your answers to R help (others can help you too) From the warning message it seems to me possible that function ReadExi needs to write something to the working directory. As I said I am not an expert in this package, but commands from help R> make.gal.env(galname='galenv', gal.path='Exiqon') R> ebatch <- ReadExi(galname='galenv',

Dear CentOS users, I run a small Facebook game at a CentOS 6.3 machine with PostgreSQL 8.4.3 + few PHP scripts + 1 Perl daemon and even though the server worked ok, I've suggested my users to double up the RAM to 32 GB and they have collected money for that. Now my problem is that I don't know, which knob to turn and how to really use the additional memory. Below is my top output at the

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating