similar to: filtering ts with arima

Is there a method with the current tcltk library in R to call back an R function within the .Tcl("...") interpreter? I can't find anything documented, and I don't understand the usefulness of the .Tcl.callback() function. Browsing through the tcltk source code I found two functions R_eval and R_call that seem to take input from the .Tcl interpreter and parse them through the R

I had an open session working in R and I regularly save the image with save.image(). However, after a period of inactivity of a few days. I quit the session. Now, when I try to restart R and load the same image I get the following result: > load("y:/grm/sdata/.rdata1") Error in load("y:/grm/sdata/.rdata1") : an xdr real data read error occured Can someone explain the cause

Thanks to Martyn Plummer and Vele Samak for taking the time to answer my problem. scan certainly will do the trick. However, having taken a look at the code for read.table, it seems to me that I might be able to add in a "what" argument and then just pass it down to the call to scan. But that won't be today's project . . . Dave Kane

Ok, Here's the c code that's loaded into R as dll on windows: #include <R.h> #include <Rdefines.h> SEXP test1(SEXP col); SEXP test1(SEXP col) { SEXP col2; Rprintf("no value!\n"); return col2; } Compiles ok with bcc32 into a dll, loads into R as part of a library, I Run library(rkdb), then > .Call("test1", 2) no value! And puf! Rterm crashes.

I'm trying to write some functions extending influence measures to multivariate linear models and also allow subsets of size m>=1 to be considered for deletion diagnostics. I'd like these to work roughly parallel to those functions for the univariate lm where only single case deletion (m=1) diagnostics are considered. Corresponding to stats::hatvalues.lm, the S3 method for class

I have just spent a day trying to determine why I seemed to be unable to read a file of microarray expression results into R properly. The file was produced by the Dchip software developed by Li and Wong at Harvard's Department of Biostatistics. It contains rows of tab-delimited fields in the order Probe set identifier Probe set description Array 1 expression Array 1 call Array 2 expression

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :

Hi, I found that using different contrasts (e.g. contr.helmert vs. contr.treatment) will generate different fitted probabilities from multinomial logistic regression using multinom(); while the fitted probabilities from binary logistic regression seem to be the same. Why is that? and for multinomial logisitc regression, what contrast should be used? I guess it's helmert? here is an example

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables

I would like to do a SS type III analysis on a proportional odds logistic regression model. I use drop1(), but dropterm() shows the same behaviour. It works as expected for regular main effects models, however when the model includes an interaction effect it seems to have problems with matching the parameters to the predictor terms. An example: library("MASS"); options(contrasts =

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example:

Hi! I'm trying to do a lm() test on three objects. My problem is that R protests and says that the variable lengths differ for one of the objects (Sweden.GDP.gap). But I have double checked that the number of observations are the same. All three objects should contain 9 observations but R only accepts 9 observations in two of the objects. The third must have 10! Very confusing because there

Hi Renato, > > Just wondering if LLVM has any command options or tool to allow for > > demangling C++ names from the LLVM bitcode? > > c++filt? Do you refer to any particular version of c++filt? I tried, but seemed not to work. For example, when I run a command as below: c++filt _ZNSt4listIiSaIiEEaSERKS1_ The output remains the same as the input symbol. By the way,

Hi, I want to calculate the t- and p-values for a linear model using the Newey West estimator. I tried this Code and it usually worked just fine: > oberlm <- lm(DYH ~ BIP + Infl + EOil, data=HU_H) > coeftest(oberlm, NeweyWest(oberlm, lag=2)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1509950 0.0743832 2.0300 0.179486 BIP

On Aug 27, 2010, at 11:43 AM, Xiaolong Tang wrote: > Hi Renato, > >>> Just wondering if LLVM has any command options or tool to allow for >>> demangling C++ names from the LLVM bitcode? >> >> c++filt? > > Do you refer to any particular version of c++filt? I tried, but seemed > not to work. For example, when I run a command as below: > >

Where can I find a package with an R implementation of the S-Plus "nlmib" general minimzation routine described in Venables & Ripley's "Modern Applie Statistics with S-Plus", 3rd Ed., pp 267 - 270? Thanks, David Stamps stamps.d.a at att.net -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, I want to do a global likelihood ratio test for the proportional odds logistic regression model and am unsure how to go about it. I am using the polr() function in library(MASS). 1. Is the p-value from the likelihood ratio test obtained by anova(fit1,fit2), where fit1 is the polr model with only the intercept and fit2 is the full polr model (refer to example below)? So in the case of the

Hi all, Two questions: 1. Is there a way to evaluate models from lmer() with a poisson distribution? I get the following error message: library(lme4) lmer(tot.fruit~infl.treat+def.treat+(1|initial.size),family=poisson)->model plot(fitted(model),resid(model)) Error: 'resid' is not implemented yet Are there any other options? 2. Why doesn't the function estimable() in gmodels