similar to: Problem in step() and stepAIC() when a name of a regressors has b (PR#3991)

Hi, I have an interesting issue. I?m running a redhat 9 box with samba 3.0.7, and Kerberos 1.3.1. I?ve joined the machine to the AD2k3 domain, and all the informational commands respond as expected, getent's, wbinfo ?g ?t, and net ads*** and also 'smbclient -k //otherdomainserver/share' works as it should from the server's terminal. when connecting to the server with a windows

I was having a problem with my Samba PDC with LDAP backend. Some of my workstations (W2kSP4) couln't log into the domain. I removed the machines from the domain, changed the name, created a new machine-account, but I still can't add the machine to the domain. smbclient -L localhost Enter root's password: Anonymous login successful Domain=[DCHOMO] OS=[Unix]

The model matrix for the code at the end the email is shown below. Since the model matrix doesn't have -1, I think that it is made of dummy regressors rather than deviation regressors. I'm wondering how to make a model matrix using deviation regressors. Could somebody let me know? > model.matrix(aaov) (Intercept) A2 B2 B3 A2:B2 A2:B3 1 1 0 0 0 0 0 2

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0,

Hi everybody, I have built a model that includes subject ID as a random effect, and has a continous variable (time) and I want to test whether the slope of this line differs between treatments (this is tested with the interaction between treatment and "time"). My question now is that I also want to include regressors that might explain variation in this slope between subjects (and of

Hi, This is really more a stats question than a R one, but.... Does anyone have any familiarity with using the mean prediction squared error scaled by the variance of the response, as a 'scale free' criterion for evaluating different regression algorithms. E.g. Generate X_train, Y_train, X_test, Y_test from true f. X_test/Y_test are generated without noise, maybe? Use X_train, Y_train

Hi, Using arima on data that are in a data frame, especially when adding xreg, would be much easier if the arima function contained 1) a "data=" argument 2) the possibility to include the covariate(s) in a formula style. Ideally the call could be something like > arima(symptome, order=c(1,0,0), xreg=~trait01*mesure0, data=anxiete) ( or arima(symptome~trait01*mesure0,

Hi, All. I have a logistic regression model that I have run. The question came up: which of these regressors is more important than another? (I'm using Design) Logistic Regression Model lrm(formula = iconicgesture ~ ST + SSP + magnitude + Condition + Expertise, data = d) Coef S.E. Wald Z P Intercept -3.2688 0.2854 -11.45 0.0000 ST 2.0871 0.2730 7.64

Hello, I need to perform a robust regression on data which contains errors in BOTH observations and regressors. Right now I am using rlm from the MASS package with 'method="MM"' and get visually very nice results. MASS is quite clear, however, that the described methodologies are only applicable to observation-error only data (p. 157, 4th Ed.). So here's the questions now:

Does anyone know if there is a way to specify regressors dynamically rather than explicitly? More specifically, I have a data set in "long format" that details a number of individuals and their responses to a question (which can be positive, negative, or no answer). Each individual answers as many questions as they want, so there are a different number of rows per individual. For each

dear r-experts: there is probably a very easy way to do it, but it eludes me right now. I have a large data frame with, say, 26 columns named "a" through "z". I would like to define "sets of regressors" from this data frame. something like myregressors=c("b", "j", "x") lm( l ~ myregressors, data=... ) is the best way to create new

To make a long story short I was doing some in-sample testing in which some dynamically created regressors would end up either all true or all false based on the validation portion. In my case a new mainframe configuration (this is a crappy way to handle a level shift but I do what I can.) So here is the code snippet that finally let me pre-check my regressors and drop any of them that were all

Hi everyone, this should be pretty basic but I need asking for help as I got stuck. I am running simple linear regression models on R with k regressors where k > 1. In order to automate my code I packed all the regressors in a matrix X so that lm(y~X) will always produce the results I want regardless of the variables in X. I am new to R but I found this advice somewhere so I guess it is

Hello, after upgrading from 3.0.8 to 3.0.14a I started have problems in my network. Environment: - clients with W2KSP4 - Linux Debian Sarge server with Samba acting as PDC. There was no other changes on server configuration. Problems: 1) Only some users cannot reach their home share automatically after login. I am using "login drive = J:" to auto-setup connection after login. 2) Only

Dear All, I was wondering if it is possible to generate a regression summary (it does not matter at this stage if from an lm or for example a glm estimate) in which to obtain the joint significance of a set of regressors? Examples could be looking at the joint significance level of a polynomial, or of a set of exogenous variables of which is of interest the linear combination suggested by the

Hi all, I've experienced this problem using StructTS(x) when the *first* element of x is a NA (R:R1.7.0, os: w2ksp4). Please look at the following code: a=rep(1:7,10) library(ts) #this works StructTS(a) #this works x=a x[2]=NA StructTS(x) #this doesn't work x=a x[1]=NA StructTS(x) The last command returns this error "Error in optim(init[mask], getLike,

##### BEGIN CODE ###### #!/usr/bin/perl ###### # # --start, -s = The date you would like to start generating regressors #--end, -e = When to stop generating holiday regressros # --scope, -c = D, W for Daily or Weekly respectively (e.g. Does this week have a particular holiday) # --file, -f = Ummm where to write the output silly! # # **NOTE** The EOM holiday is "End of Month" for

Dear: Does anyone use keep function in stepAIC command? If so, could you give an example? I try to use this function to choose some variables in all of possible models. Many Thanks! Xin [[alternative HTML version deleted]]

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?

Hello dear R mailing list members. I have recently became curious of the possibility applying model selection algorithms (even as simple as AIC) to regressions of large datasets. I searched as best as I could, but couldn't find any reference or wrapper for using step or stepAIC to packages such as biglm. Any ideas or directions of how to implement such a concept ? Best, Tal --