Re: [R] [Q] Bayeisan Network with the
Dear Young-Jin Lee Please note that there is a 'deal' mailing list, see http://www.math.aau.dk/~dethlef/novo/deal R I followed the manual and paper from the author's web site to learn it, as R shown below, but I could not figure out how to access the local and R posterior probability of the nodes in the constructed network. To access the prior and posterior distributions, use localprior( nodes(ksl.nw)[[1]] ) localposterior( nodes(ksl.nw)[[1]] ) Best, Claus __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Time Series information in formulae
Dear List
The UKgas data is stored as an object of class 'ts'. I am trying to use UKgas
in a formula as argument to a function. However, I do not know how to access
the 'time series' information in the response (such as start() end() etc.).
Here is a boiled down example.
ssm - function(formula, data = list(),subset=NULL) {
cl - match.call()
if (missing(data))
data - environment(formula)
mf - match.call(expand.dots = FALSE)
mf$drop.unused.levels - TRUE
mf[[1]] - as.name(model.frame)
mf - eval(mf, .GlobalEnv)
mt - attr(mf, terms)
y - model.response(mf, numeric)
print(class(y))
}
R class(UKgas)
[1] ts
R ssm(UKgas~1)
[1] numeric
R ssm(log10(UKgas)~1)
[1] numeric
I want the latter two to be ts. I have tried putting any in place
of numeric in the call to model.response, but it does not change anything for
me.
How do I retrieve the response from a formula without loosing its ts status?
Best,
Claus
Claus Dethlefsen, MSc, PhD
Biostatistician at Center for Cardiovascular Research
Aalborg Hospital, Aarhus University Hospital, Denmark
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Re: [R] Kalman Smoothing - time-variant parameters (sspir)
Dear Tariq Khan The initial conditions m0 and C0 can be specified according to your needs. If you are a Bayesian (as in WestHarrison 1997), you will use m0 and C0 to express your prior information. If you use a vague prior, you will give a high weight to your observations in the beginning, and the influence of the prior will die out fast. The values of m0 and C0 could also stem from several time-series and express a random effect of the level of the individual series. Finally, you may estimate m0 and C0 using maximum likelihood estimation. This is not done in sspir (but the log-likelihood value is provided from a run of the filter). One crude way of specifying m0 and C0 would be to use the estimates from a static model, i.e. ss$ss$m0[1:2,] - coef(lm(y~x,data=dfrm)) ss$ss$C0[1:2,1:2] - summary(lm(y~x,data=dfrm))$cov.unscaled smooth.params3 - kfs(ss)$m ts.plot(t(smooth.params3)) Note that the 'kfs' function is a shortcut for using smoother(kfilter()). Note also, that your variance parameters are both set to unity. Again, you may discuss how to set these either by previous knowledge or by maximum likelihood estimation. It is set using ss$ss$phi[1] - 2 # observational variance ss$ss$phi[2] - .5# variance of the beta-parameter Hope this helps, Claus Claus Dethlefsen, Msc, PhD Statistiker ved Kardiovaskulært Forskningscenter Forskningens Hus Aalborg Sygehus Sdr. Skovvej 15 9000 Aalborg Tlf: 9932 6863 email: [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] Fra: ¨Tariq Khan [mailto:[EMAIL PROTECTED] Sendt: to 01-12-2005 13:12 Til: R-help@stat.math.ethz.ch; [EMAIL PROTECTED] Cc: Claus Dethlefsen / Aalborg Sygehus Emne: Kalman Smoothing - time-variant parameters (sspir) Dear R-brains, I'm rather new to state-space models and would benefit from the extra confidence in using the excellent package sspir. In a one-factor model, If I am trying to do a simple regression where I assume the intercept is constant and the 'Beta' is changing, how do I do that? How do i Initialize the filter (i.e. what is appropriate to set m0, and C0 for the example below)? The model I want is: y = alpha + beta + err1; beta_(t+1) = beta_t + err2 I thought of the following: library(mvtnorm) # (1) library(sspir) # Let's get some data so we can all try this at home dfrm - data.frame( y = c(0.02,0.04,-0.03,0.02,0,0.01,0.04,0.03,-0.01,0.04,-0.01,0.05,0.04, 0.03,0.01,-0.01,-0.01,-0.03,0.02,-0.04,-0.05,-0.02,-0.04,0,0.02,0, -0.01,-0.01,0.01,0.09,0.03,0.03,0.05,0.04,-0.01,0.05,0.03,0.01, 0.04,0.01,-0.01,-0.02,-0.01,-0.01, 0.06,0.03,0.02,0.03,0.03,0.04, 0.03,0.04,-0.02,-0.03,0.04,0.03,0.05,0.02,0.03,-0.1), x = c(-0.03,-0.01,0.07,-0.03,-0.07,0.05,0.02,-0.05,-0.04, -0.02,-0.19,0.07,0.09,0.01,0.01,0,0.05,0,-0.02,-0.09, -0.12,-0.01,-0.13,0.04,0.04,-0.07,-0.05,-0.03, -0.01,0.11,0.06,0.03,0.06,0.06,-0.01,0.07,0.01, 0,0.07,0.04,-0.02,0,-0.03,0.04,-0.04,-0.01,0.03,0.02,0.05,0.04, 0.05,0.03,0,-0.04,0.05,0.05,0.06,0.02,0.04,-0.06) ) ss - ssm(y ~ tvar(x), time = 1:nrow(dfrm), family=gaussian(link=identity), data=dfrm) smooth.params - smoother(kfilter(ss$ss))$m (1) I read in http://ww.math.aau.dk/~mbn/Teaching/MarkovE05/Lecture3.pdf that this is requred as there is a bug in sspir. To what should I set ss$ss$m0 and ss$ss$C0? (I did notice that smoother() replaces these, but it still matters what I initialize it to in the first place) Many thanks! Tariq Khan __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Advice on parsing formulae
Thank you for the advice. I have now boiled my problem down to the following: How do I create fm2 from fm1 ? fm1 - Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3)) fm2 - Y ~ 1 + x:A + z + u + B + poly(v, 3) Thus, how do I simply remove tvar( * ) from a formula? Do I have to write a function to parse the string and then re-create the formula? Is there an easy way of doing this? When my above problem is solved, I can (with the help from Heather Turner and Chuck Berry) do the following ## define som data x - z - u - v - rnorm(5) A - B - factor( rep( c(1,2), c(3,2) ) ) ## define my formula fm1 and manually create fm2. fm1 - Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3)) fm2 - Y ~ 1 + x:A + z + u + B + poly(v, 3) ## extract the term.labels from fm2, make the design matrix and extract 'assign' term.labels - unname(sapply(attr(terms(fm2), term.labels), removeSpace)) X - model.matrix(fm2,keep.order=TRUE) pAssign - attr(X, assign) ## Now, extract the tvar-terms from fm1 tvar.terms - terms( fm1, specials = tvar,keep.order=TRUE ) idx - attr(tvar.terms,specials)$tvar if (attr(tvar.terms,intercept)) idx - idx -1 tvar - attr(terms(fm2,keep.order=TRUE),term.labels)[idx] tvar - unname( sapply( tvar, removeSpace) ) ## Finally, combine the information to get the vector I asked for tvarAssign - match(pAssign, sort(match(tvar, term.labels))) tvarAssign[is.na(tvarAssign)] - 0 -Original Message- From: Heather Turner [mailto:[EMAIL PROTECTED] Sent: 15. december 2004 11:41 To: [EMAIL PROTECTED] Subject: Re: [R] Advice on parsing formulae I think this will do what you want: # Need this function to remove spaces from term labels later on removeSpace - function(string) gsub([[:space:]], , string) # Specify which terms are in a tvar group # (could remove spaces separately) tvar - unname(sapply(c(x:A, z, B, poly(v,3)), removeSpace)) # Use terms to get term labels from formula formula - Y ~ 1 + x:A + z + u + B + poly(v,3) term.labels - unname(sapply(attr(terms(formula), term.labels), removeSpace)) tvar [1] x:A z B poly(v,3) term.labels [1] z u B poly(v,3) x:A # Get assign variable for parameters # (You would use first two lines, but I don't have data so defined assign variable myself) #X - model.matrix(formula) #pAssign - attr(X, assign) pAssign - c(0,1,2,3,4,4,4,5,5) # Define tvarAssign tvarAssign - match(pAssign, sort(match(tvar, term.labels))) tvarAssign[is.na(tvarAssign)] - 0 tvarAssign [1] 0 1 0 2 3 3 3 4 4 HTH Heather Mrs H Turner Research Assistant Dept. of Statistics University of Warwick Claus Dethlefsen [EMAIL PROTECTED] 12/13/04 04:10pm Dear list I would like to be able to group terms in a formula using a function that I will call tvar(), eg. the formula Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3)) where x,u and v are numeric and A and B are factors - binary, say. As output, I want the model.matrix as if tvar had not been there at all. In addition, I would like to have information on the grouping, as a vector as long as ncol( model.matrix ) with zeros corresponding to terms outside tvar and with an index grouping the terms inside each tvar(). In the (sick) example: model.matrix(Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3))) (Intercept) z u B2 poly(v, 3)1 poly(v, 3)2 poly(v, 3)3 x:A1 x:A2 11 -1.55 -1.03 0 0.160 -0.350 -0.281 0.66 0.00 21 -1.08 0.55 0 -0.164 -0.211 0.340 0.91 0.00 31 0.29 -0.26 0 -0.236 -0.073 0.311 -1.93 0.00 41 -1.11 0.96 0 0.222 -0.285 -0.385 -0.23 0.00 51 0.43 -0.76 1 -0.434 0.515 -0.532 0.22 0.00 I would like the vector c(0,1,0,2,3,3,3,4,4) pointing to the tvar-grouped terms. Thus what I would like, looks a bit like the 'assign' attribute of the model.matrix() output. I have not figured out a way of doing this in a nice way and would like some help, please. I hope somebody can help me (or point the manual-pages I should read), Best, Claus Dethlefsen -- Assistant Professor, Claus Dethlefsen, Ph.D. mailto:[EMAIL PROTECTED], http://www.math.auc.dk/~dethlef Dpt. of Mathematical Sciences, Aalborg University Fr. Bajers Vej 7G, 9220 Aalborg East Denmark -- No virus found in this outgoing message. Checked by AVG Anti-Virus. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Advice on parsing formulae
Dear list I would like to be able to group terms in a formula using a function that I will call tvar(), eg. the formula Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3)) where x,u and v are numeric and A and B are factors - binary, say. As output, I want the model.matrix as if tvar had not been there at all. In addition, I would like to have information on the grouping, as a vector as long as ncol( model.matrix ) with zeros corresponding to terms outside tvar and with an index grouping the terms inside each tvar(). In the (sick) example: model.matrix(Y ~ 1 + tvar(x:A) + tvar(z) + u + tvar(B) + tvar(poly(v,3))) (Intercept) z u B2 poly(v, 3)1 poly(v, 3)2 poly(v, 3)3 x:A1 x:A2 11 -1.55 -1.03 0 0.160 -0.350 -0.281 0.66 0.00 21 -1.08 0.55 0 -0.164 -0.211 0.340 0.91 0.00 31 0.29 -0.26 0 -0.236 -0.073 0.311 -1.93 0.00 41 -1.11 0.96 0 0.222 -0.285 -0.385 -0.23 0.00 51 0.43 -0.76 1 -0.434 0.515 -0.532 0.22 0.00 I would like the vector c(0,1,0,2,3,3,3,4,4) pointing to the tvar-grouped terms. Thus what I would like, looks a bit like the 'assign' attribute of the model.matrix() output. I have not figured out a way of doing this in a nice way and would like some help, please. I hope somebody can help me (or point the manual-pages I should read), Best, Claus Dethlefsen --- Assistant Professor, Claus Dethlefsen, Ph.D. mailto:[EMAIL PROTECTED], http://www.math.auc.dk/~dethlef Dpt. of Mathematical Sciences, Aalborg University Fr. Bajers Vej 7G, 9220 Aalborg East Denmark -- No virus found in this outgoing message. Checked by AVG Anti-Virus. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] [R-pkgs] Package deal version 1.2-17
A new version of the package deal is now available on CRAN. The package is for learning (parameters and structure) of Bayesian networks and provide an interface to Hugin. In the new version there is an interface to the package dynamicGraph which allows for editing and callbacks of graphs in the displayed window. Try install.packages(c(dynamicGraph,deal)) library(deal) demo(reinis) Best, Claus --- Assistant Prof. Claus Dethlefsen, PhD Department of Mathematical Sciences, Aalborg University Fredrik Bajers Vej 7G, DK-9220 Aalborg, Denmark [EMAIL PROTECTED]; www.math.aau.dk/~dethlef Ph: +45 9635 8878; Fax: +45 9815 8129 --- ___ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
