similar to: dynamically specifying regressors/RHS variables in a regression

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

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

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,

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

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, 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:

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've experienced this problem using step() and stepAIC() when a name of a regressors has blanks in between (R:R1.7.0, os: w2ksp4). Please look at the following code: "x" <- c(14.122739306734, 14.4831100207131, 14.5556459667089, 14.5777151911177, 14.5285815352327, 14.0217803203846, 14.0732571632964, 14.7801310180502, 14.7839362960477, 14.7862217992577)

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 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

##### 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 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, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] How do I find the estimates of the coefficients in R? And also I would like to know what technique R employs to find the estimates? Any help is appreciated. Thanks,

Hi all, Suppose: y<-rnorm(100) x1<-rnorm(100) lm.yx<-lm(y~x1) To predict from a new data source, one can use: # works as expected dum<-data.frame(x1=rnorm(200)) predict(lm.yx, newdata=dum) Suppose lm.yx has been run and we have the lm object. And we have a dataframe that has columns that don't correspond by name to the original regressors. I very! naively assumed that doing

Dear R users, I want to estimate a Cox PH model with time-dependent covariates so I am using a counting process format with the following formula: Surv(data$start, data$stop, data$event.time) ~ cluster(data$id) + G1 + G2 + G3 + G4 + G5 +G6 Gs represent a B-spline basis functions so they sum to 1 and I can't estimate the model as is without getting the last coefficient to be NA, which

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

HI all, Somebody had tried to do a rebuild of RedHat Storage Server 2.0 and RHEV 3.2 hypervisor with CentOS? Mock is an option to accomplish this? Thanks.

Dear all, I was wondering if it is possible in TableGen to either: 1. Selectively define an instruction depending on an SDNode's properties, e.g. if the SDNode is not commutative. 2. Override/ignore the TableGen error given when a commutative node has an immediate on the LHS. My case comes from trying to define a generic ALU operation multiclass for my target, which includes a

Paul.White@dsto.defence.gov.au wrote: > > > Hello bug people, > > > > Version: > > platform = i686-pc-linux-gnu > > arch = i686 > > os = linux-gnu > > system = i686, linux-gnu > > status = > > major = 1 > > minor = 3.1 > > year = 2001 > > month = 08 > > day = 31 > > language = R > > > >