search for: misspecif

hi, I have following data and code; cov <- c (1.670028 ,-1.197685 ,-2.931445,-1.197685,1.765646,3.883839,-2.931445,3.883839,12.050816) cov.matrix <- matrix(cov, 3, 3, dimnames=list(c("y1","x1","x2"), c("y1","x1","x2"))) path.model <- specify.model() x1 -> y1, x1-y1 x2 <-> x1, x2-x1 x2 <->

...toregressive processes with reduced rank structure, J. Econ. (91). Or any other systematic procedure for the consistent selection of lag order and cointegration rank? I understand that the usual procedure of first selecting the lag order (by AIC, etc.) and then the rank (by Johansen) can result in misspecification. Thanks, Murali [[alternative HTML version deleted]]

...e been trying to carry out some variance ratio tests such as Lo-MacKinlay test and Chow-Denning test. However, When I write the function LM <- Lo.Mac(y,kvec) or any other functions I don’t get the results displayed. The only sign I get is < I don’t understand what’s wrong (is it the package, misspecification of the function, Vista). I am really stuck in and I would be grateful for any hints that may help me get the results displayed. Many thanks in advance, Ben. [[alternative HTML version deleted]]

Hi, Can you tell me if there are an R package or code for STAR model estimation and test misspecification. If no, how i could do this. Thanks in advance Best regards AJMI Noomen Phd student TUNISIA --------------------------------- [[alternative HTML version deleted]]

...when used in a normal R session. However, the function fails the R CMD CHECK command with the error Error in get(as.character(FUN), mode = "function", envir = envir) : object '.fooInternal' of mode 'function' was not found I'm wondering if this is some environment misspecification issue? I also tried replacing .fooInternal by packageName:::.fooInternal (replacing packageName with actual package name, of course), to no avail. Thanks in advance for all replies. Peter

Dear Frauke, good afternoon, Could you tell me which excel function didnt work for regression analysis and what excel version where you using? Best regards, Paul El 07/11/2012 11:55, "frauke" <fhoss@andrew.cmu.edu> escribió: > Hi David, hi Rui, > > thanks for your quick replies. I have replicated David's R results and > confirmed them with Minitab. Though

Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works? Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335) analyzed some simple clinical trials data using a logistic random effects model. Several packages and methods MIXOR, SAS NLMIXED were employed. They reported obtaining very different parameter estimates and P

....org/web/packages/spsur/index.html This is a specific R package for the estimation of Spatial Seemingly Unrelated Regression models by maximum likelihood or instrumental variable procedures. Moreover, 'spsur' implements a collection of Lagrange Multipliers and Likelihood Ratios to test for misspecifications in SUR models. If you would like, you can install 'spsur' directly from CRAN repository. There you will find also a vignette including some examples, available also in https://cran.r-project.org/web/packages/spsur/vignettes/spsur-vignette.html This is the first version of 'sps...

....org/web/packages/spsur/index.html This is a specific R package for the estimation of Spatial Seemingly Unrelated Regression models by maximum likelihood or instrumental variable procedures. Moreover, 'spsur' implements a collection of Lagrange Multipliers and Likelihood Ratios to test for misspecifications in SUR models. If you would like, you can install 'spsur' directly from CRAN repository. There you will find also a vignette including some examples, available also in https://cran.r-project.org/web/packages/spsur/vignettes/spsur-vignette.html This is the first version of 'sps...

...t;< white.test()). > > > > The test's statistic is beta / sqrt(W), where W is Var(beta) "? la White", > > that is the beta(i) matrix is replaced by the residuals matrix. I think what you mention here is not a test for heteroskedasticity but a robustified (against misspecification, e.g., heteroskedasticity) t-test. > > > > > Anyway, whatever anybody may haev, I'm interested, else I'll write it this > > week-end. > > Have a look at the package 'tseries', available at CRAN. > > Uwe Ligges > The White test which Vinc...

Dear all:   I have this:  A1 B1 C1 D1 E1 A2 B2 C2 D2 E2 A3 B3 C3 D3 E3   And I want this A1 E1 B1 E1 C1 E1 D1 E1 A2 E2 B2 E2 C2 E2 D2 E2 A3 E3 B3 E3 C3 E3 D3 E3   Example:   m<- matrix(1:15,nrow=3,byrow=T) m v<- unlist(list(t(m[,1:4]))) u<-

...me model using rq() and dummy variables for the groups. The coefficient estimates differ substantially between the two approaches (rqpd() produces substantially larger coefficients). Should the two approaches deliver similar estimates (as for plm() and lm() plus dummies)? I.e. does this indicate a misspecification in one of the models? Best regards, sseemann -- View this message in context: http://r.789695.n4.nabble.com/Comparing-rqpd-and-rq-tp4643479.html Sent from the R help mailing list archive at Nabble.com.

How does the R module ARIMA account for unspecified deterministic structure such as seasonal pulses, level shifts, local time trends and regular pulses without needing to ask the user to intervene to specify this? I have attached a Makradakis paper which hammers Box-Jenkins approach to this problem of nonstationarity. I have also included a recent discussion from stackexchange which you might

Dear all, I would like to use a GWR model in order to spatially predict food insecurity in Africa. I have a georeferenced village data-bases and I've run a "classic" regression model (taking into account the spatial dependence of the errors) that works fairly well for Niger that is a quite homogenous country. Now, I would like to make the same thing for countries where it should be

Hi what would you recommend to compare classification methods such as LDA, classification trees (rpart), bagging, SVM, etc: 10-fold cv (as in Ripley p. 346f) or leaving-one-out (as e.g. implemented in LDA)? my data-set is not that huge (roughly 200 entries) many thanks for a hint Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

I don't usually do much with graphs in R, and this is my first time adding a line of best fit. Hopefully this is an easy problem to solve. I'm looking at a variable called soloKills along the range 5:28. Here are all my commands, in script form: range=5:28 graph=soloKills title="Solo kill/death data" xlabel="Number of deaths/1 game" ylabel="Mean number of kills/1

As I understand it lme (in R v1.9.x) estimates random effect variances on a log scale, constraining them to be positive. Whilst this seems sensible, it does lead to apparently biased estimates if the variance is actually zero - which makes our simulation results look strange. Whilst we need to think a bit deeper about it - I still haven't got my head around what a negative variance could

Hi I am actually running the White test for correcting Heteroscedasticity. I used sandwich() & car(), however the output shows the updated t test of coefficients, with revised Standard Errors, however the estimates remained same. My problem is that the residuals formed a pattern in the original regression equation. After running the White's test, I got some new standard errors - but

Hi All, I am running quantile regression in a "for loop" starting with 1 variable and adding a variable at a time reaching a maximum of 20 variables. I get the following warning messages after my "for" loop runs. Should I be concerned about these messages? I am building predictive models and am not interested in inference. Warning messages: 1: In

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-