On Wed, 13 Sep 2017, Riccardo (Jack) Lucchetti wrote: > I was thinking that we might want to map what we call "discrete" series > into what R calls "factors". The idea is quite simple, and exemplified in > the script below. My question to the list is: is this a good idea? Is it > worth the coding effort (very small IMO)? > > Comments welcome. > > <hansl> > nulldata 50 > > cont1 = normal() > cont2 = normal() > disc1 = floor(uniform(1,5)) > disc2 = floor(uniform(4,18)) > > discrete disc1 disc2 > > list D = dataset > loop foreach i D > if !isdiscrete($i) > D -= $i > endif > endloop > > matrix mD = D > mwrite(mD, "discrete.mat", 1) > > foreign language=R --send-data > D <- gretl.loadmat("discrete.mat"); > for (i in D) {gretldata[,i] <- as.factor(gretldata[,i])}; > summary(gretldata); > end foreign > </hansl> Not sure about this, but my initial reaction is that it may be assuming too much about our "discrete" series. In R, isn't a "factor" a variable that (in gretl parlance) has to be "dummified" before use in regression? That is, an arbitrary encoding of a qualitative characteristic? If so, then I think the above is wrong, since a gretl-discrete series could be a perfectly valid (albeit quantized) quantitative variable; for example, years of education or number of bedrooms. But If I'm wrong about what a "factor" is to R, my objection may fall.

On Wed, 13 Sep 2017, Allin Cottrell wrote: >> Not sure about this, but my initial reaction is that it may be assuming >> too much about our "discrete" series. >> >> In R, isn't a "factor" a variable that (in gretl parlance) has to be >> "dummified" before use in regression? That is, an arbitrary encoding of a >> qualitative characteristic? Yes, you're right. >> If so, then I think the above is wrong, since a gretl-discrete series >> could be a perfectly valid (albeit quantized) quantitative variable; for >> example, years of education or number of bedrooms. >> >> But If I'm wrong about what a "factor" is to R, my objection may fall. > > Sorry, I should have added: we now have the facility, under the "setinfo" > command, of marking a series as "coded". And when we write a "coded" series > as CSV we quote the numerical values, in response to which R automatically > treats the series as a "factor". So I think we already have what you're > aiming at here. I agree that the mapping to R's factors is much more accurate if we used the "coded" bit. However, R doesn't seem to make this distinction automagically for integer-valued coded strings. Example: <hansl> nulldata 50 cont1 = normal() disc1 = floor(uniform(1,5)) disc2 = floor(uniform(4,18)) stringify(disc1, defarray("a", "b", "c", "d")) # string-valued series list D = disc1 disc2 loop foreach i D setinfo $i --coded endloop foreign language=R --send-data summary(gretldata); is.factor(gretldata$disc1); is.factor(gretldata$disc2); end foreign </hansl>

Perhaps we could force R to treat variables as factors via an additional option to foreign, something like foreign language=R --send-data --as-factors=X where X is a list.

As for me, the most tedious thing with Main/Tools/Start GNU R are time series In this case class(gretldata)->"mts" That's why I never use Tools/Start GNU R with time series: instead I run the following R script from gretl <R # choose interactive R session if (any(class(gretldata)!="data.frame")) {mts2frame2<-function(x) {ddd<-data.frame(x); if (length(class(x))>1) {m<-ncol(x);for (i in 1:m) ddd[,i]<-ts(ddd[,i],start=start(x),frequency=frequency(x))} else ddd[,1]<-ts(ddd[,1],start=start(x),frequency=frequency(x)); ddd}; gretldata<-mts2frame2(gretldata)} # make native gretl variables names searcheable attach(gretldata) R>

On Thu, 14 Sep 2017, Allin Cottrell wrote:

> Is that the general opinion? That is, that R's multiple time series > object ("mts") is of no use and we're better creating a data frame and > copying the time-series attributes across per series.

Personally, I have a totally different opinion from Oleh's. I like the fact that we ship time-series data into R as a ts(), as opposed to a generic data frame. But, that said, I'm not a heavy R user, so I'd rather listen to more informed people than myself.

On Thu, 14 Sep 2017, oleg_komashko(a)ukr.net wrote: [...] > As for me, the most tedious thing > with Main/Tools/Start GNU R > are time series > In this case class(gretldata)->"mts" [W]e could easily revise our R-data exporter [...]