similar to: Password changing?

Dear list, I'm currently analyzing some count data using a hurdle model. I've used the rcspline.eval function in the Hmisc-library to contruct the spline terms for the regression model, and what I want in the end is the ability to compute coefficients and confidence intervals for different changes in the smooth function as well as plotting the smooth function along with the

On 29 September 2014 15:16, Robinson, Paul <Paul_Robinson at playstation.sony.com> wrote: > That promise is what I understood from a discussion within the past month, > e.g. http://lists.cs.uiuc.edu/pipermail/llvmdev/2014-September/076815.html > If I misunderstood, clarification on the clarification would be helpful. ;-) I see, I missed that one. My concerns seem to be similar to

I've uninstalled windows and this seems to be the final hurdle as there doesn't seem to be a comparable linux app (thermodynamics equation solver). I absolutely need this program for my engineering classes. Wine looks great, and the gui winetools is pretty neat too. While running the program i get: X Error of failed request: BadDrawable (invalid Pixmap or Window parameter) Major opcode

Hello all, I am using the hurdle model for fitting my count data using the pscl package which is working fine. However, I am stuck with the problem of calculating the percent correctly predicted (PCP) zeros for hurdle model. The method I am trying to use to achieve this is 'hitmiss' in the pscl package (ref: http://www.inside-r.org/packages/cran/pscl/docs/hitmiss). When I do: >

I ran a negative binomial logit hurdle model and am now trying to plot the effects of a continuous predictor variable (the only variable in my model) on the count and zero component and the overall mean response. I'm confused because for some values, the predicted overall mean is higher than the mean of the non-zero counts (range of predicted overall means=2.2-11.0; range of non-zero count

Good afternoon, I?m in need of an advice regarding a proper use of glm.nb, zeroinfl or hurdle with my dataframe. I can not provide a self-contained example, since I need an advice on this current dataset and its ?contradictory? results. So.... i have a dataset which contains 1309 cases and 11 variables, highly right-skewed and heavily zeroinflated (with over 1100 cases that have 0 value

Hello, I'm using pscl to run a hurdle model. Everything works great until I get to the point of making predictions. All of my "count" predictions are lower than my actual data, and lower than the "response" predictions, similar to the issue described here ( https://stat.ethz.ch/pipermail/r-help/2012-August/320426.html) and here (

Hello- Sorry if this is a noob question, but I cant seem to get my password_query to work with dovecot + mysql. Im using 'Password verification by SQL server' at: http://wiki2.dovecot.org/AuthDatabase/SQL trying to modify it to work with my encrypted passwords in the DB. Im using the following which isnt working: password_query = SELECT NULL AS password, \ 'Y' as

I am attempting to join a new samba server to Active Directory. The join is successful but I am getting a kerberos_kinit_password failed error message. The following is cut-&-paste of the end section of the screen out from the join attempt. Using short domain name -- domain Joined 'EXPRESS' to realm 'mydomain.x.CA' [2011/07/19 11:51:07.978076, 0] libads/kerberos.c:333()

Dear All, I am wondering about the fitting negative binomial(NB) hurdle model with random effect using REML estimation method in R. We can fit regular hurdle model without random effect using ML method as following. hurdle(pkg).... But, I couldn't figure out how I can fit NB hurdle model with random effect using REML in R. Please give me a tip. Thank you so much. Sincerely, SK

I assume most people here know what a joke DIDX is -- but in case you didn't already know, please avoid these people. Basic features of their service don't work, their tech support refuses/drags their feet to fix them for a month and if you post publicly about them, they terminate your service. Instead of investing their effort in reading mailinglists to terminate customers maybe they

Dear list, I am using the hurdle model for modelling the habitat of rare fish species. However I do get an error message when I try to model my data: > test_new1<-hurdle(GALUMEL~ depth + sal + slope + vrm + lat:long + offset(log(haul_numb)), dist = "negbin", data = datafit_elasmo) Error in hurdle(GALUMEL ~ depth + sal + slope + vrm + lat:long + offset(log(haul_numb)), :

I am interested in utilizing this so called "double hurdle" model in my study. We can write the model in the following way: if (z'a + u > 0 & x'b + e > 0) y = x'b + e, else y = 0 In the model, consumption y is the (left-) censored dependent variable. e and u are the normally distributed error terms. z'a is the participation equation and x'b is the

Maybe I am grasping the full usage of this protect_from_forgery function, but it does not seem to work for me. Imagine the following: A simple website with a user that needs to log in to do certain stuff and a closed off admin section that only certain users can access that have the is_admin field set to true. So to be clear, my User model has a login, password and is_admin. When displaying the

Dear all, A question related to the following has been asked on R-help before, but I could not find any answer to it. Input will be much appreciated. I got an unexpected sign of the "slope" parameter associated with a covariate (diam) using zeroinfl(). It led me to compare the estimates given by zeroinfl() and hurdle(): The (significant) negative estimate here is surprising, given

I am new to SAMBA. I am using it with LINUX and I love it! Nice change from you know who. I am sorry if this is a lame question, but I am setting up a Primary Domain Controller and a Backup Domain Controller. All the clients are Windows 2K or XP. I need the clients to reset their passwords every 30 days. I am having a hard time locating a procedure to set this feature. LINUX has 'chage'

Hello, I have been using mgcv to run GAM hurdle models, analyzing presence/absence data with GAM logistic regressions, and then analyzing the data conditional on presence (e.g. without samples with no zeros) with GAMs with a negative binomial distribution. It occurs to me that using the negative binomial distribution on data with no zeros is not right, as the negative binomial allows zeros.

# Global parameters [global] workgroup = MYDOMAIN server string = Samba Server %v on %L security = DOMAIN log file = /var/log/samba/%m.log max log size = 50 socket options = TCP_NODELAY SO_RCVBUF=8192 SO_SNDBUF=8192 printcap name = /etc/printcap local master = No dns proxy = No wins server = MYWINSERVER idmap

Hi all, I apologize if I am getting into a subject that has been hashed and rehashed endlessly on this list, but I am just not finding exactly what I am looking for in terms of a response. At the moment, I have Linux and Solaris servers happily authenticating to my MIT Kerberos KDC, and fetching user information via an OpenLDAP server with a StartTLS connection (and authenticating to the LDAP

Some of you have already read about my newbie fortune with this Samba3 install. Thanks to those who answered my original questions. Now I have something which I know is probably academic, and perhaps still newbie-related. When I installed Samba3 on Friday, it took me a little while to figure out that my names required adding a 3 to the end (SWAT3, SAMBA3, TESTPARM3, et al). I did not uninstall