similar to: [Solved][Code Snippets] Dropping Empty Regressors

I am having trouble plotting outliers on time series. Give then following code: ############################################################ # find STL Outliers by weight and append sh2, use Robust # this should allow the initial outliers to be filtered # this section may be commented out. ############################################################ tsSourceDiag <-

I am working on a script that takes numeric performance indicators and runs them against a series of regressors (dummy regressors, yes\no stuff via 0 and 1, e.g. Was is Christmas this week 0=no, 1=yes). The script is as follows (Written as a function): -- Begin Script -- doEnv <- function(HOUR,ENVNAME,REPORTNAME) { library(RODBC) library(forecast) library("geneplotter")

to clean up some code I would like to make a list of arbitrary length to store?various objects for use in a loop sample code: ############ BEGIN SAMPLE ############## # You can see the need for a loop already linearModel1=lm(modelSource ~ .,mcReg) linearModel2=step(linearModel1) linearModel3=lm(modelSource ~ .-1,mcReg) linearModel4=step(linearModel3) #custom linearModel5=lm(modelSource ~ .

Dear R-users, I am a dentist (so forgive me if my question looks stupid) and came across a problem when I did simulations to compare a few single level and two level regressions. The simulations were interrupted and an error message came out like 'Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1'. My collegue suggested that this might be due to my codes

Hi list, I want to plot two plot in the same figure. I set par(new=TRUE). But it does not work. library(lattice) myPanel <- function(x,...) { panel.histogram(x,alpha=0.4,...) ltext(0.4,1.5,paste("Mean=","0.05",digit=2)),cex=0.8) ltext(0.8,1.5,paste("s.d.=","0.06",digit=2)),cex=0.8) } histogram(sh2,

I did have a verbose description of why but rather then make everyone's eyes bleed with useless details I ask the following :) To make a long story short: How can I make newmcReg[[i]]["PreIO308"] go away in the following list... er vector... no wait array.... dataframe.... awww crap... summary(newmcReg[[i]]) UNITBUILD UNITDB ITBUILD ITDB Mode :logical

I spent the last couple of hours trying to figure this out. We upgraded to sshd version OpenSSH_3.8.1p1 Debian and now "password" login no longer works... however keyboard-interactive login still works. the result of this is that while openSSH clients still function, ssh applications like MindTerm do not. here is the debug dump from the login session: Jun 25 21:47:50 m1

Hi, I am trying to get Openssh 5.5p1 to work with TACACS+. I have the TACACS + PAM module compiled on Ubuntu. I have compiled SSH --with-pam. When the user is defined in /etc/passwd, the SSH authentication to the TACACS+ server takes place successfully. If I REMOVE the user from /etc/passwd OpenSSH sends a string called INCORRECT to the TACACS+ server and it denies authentication. I am trying

I imported a spreadsheet into a variable sh e.g. sh$aaaa, sh$bbbb, etc... doing the following: tsSource <- ts(paste("sh$",NAMEVARIABLE,sep="") ... ) fails. The paste isn't evaluating properly. What is the proper way to concatenate a data source with a member name such that they evaluate properly. actual code below: doEnv <-

I have a weekly data set imported via: tsSource=ts(sh1$I000,start=c(2004,1),freq=52) I am now getting to some 'spit and polish' but I realize something I can't wrap my head around. Given an outlier I find at say tsSource[54] ... how can get translate index 54 into the date\week. I mean I can figure out obviously that entry 52 is last week of 2004 but since the data goes for many

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

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)