Hi John,
In scanning some of the more popular model functions (e.g. lm(), glm(),
lme(), coxph(), etc.), none seem to provide examples of the use of the
'subset' argument, even though it is documented for them.
That being said, there is some old (2003) documentation by Prof Ripley here:
https://developer.r-project.org/model-fitting-functions.html
that may be helpful, and where the link to the lm() function source code
on the above page should be:
https://svn.r-project.org/R/trunk/src/library/stats/R/lm.R
Within that source file, you might want to focus upon the
model.frame.lm() function, the basic form which is used internally in
many (most?, all?) of the typical model related functions in R to create
the internal data frame from the specified formula, that is then used to
create the model.
There is a parallel model.frame.glm() function for glm() here:
https://svn.r-project.org/R/trunk/src/library/stats/R/glm.R
There is also a 2003 paper by Thomas Lumley on non-standard evaluation
that may be helpful:
https://developer.r-project.org/nonstandard-eval.pdf
The help for the generic ?model.frame has the following text for the
'subset' argument:
"a specification of the rows to be used: defaults to all rows. This can
be any valid indexing vector (see|[.data.frame
<http://127.0.0.1:13384/library/stats/help/[.data.frame>|) for the rows
of|data|or if that is not supplied, a data frame made up of the
variables used in|formula|."
I cannot recall off-hand, using the 'subset' argument myself in ~20
years of using R, but do seem to recall some old discussions on the
e-mail lists, which I cannot seem to locate at present. A search via
rseek.org may yield some benefit.
Regards,
Marc Schwartz
J C Nash wrote on 7/13/21 7:21 PM:
In mentoring and participating in a Google Summer of Code project "Improvements to
nls()",
I've not found examples of use of the "subset" argument in the call to nls().
Moreover,
in searching through the source code for the various functions related to
nls(), I can't
seem to find where subset is used, but a simple example, included below,
indicates it works.
Three approaches all seem to give the same results.
Can someone point to documentation or code so we can make sure we get our
revised programs
to work properly? The aim is to make them more maintainable and provide
maintainer documentation,
along with some improved functionality. We seem, for example, to already be
able to offer
analytic derivatives where they are feasible, and should be able to add
Marquardt-Levenberg
stabilization as an option.
Note that this "subset" does not seem to be the "subset()" function of R.
John Nash
# CroucherSubset.R -- https://walkingrandomly.com/?p=5254
xdata = c(-2,-1.64,-1.33,-0.7,0,0.45,1.2,1.64,2.32,2.9)
ydata =
c(0.699369,0.700462,0.695354,1.03905,1.97389,2.41143,1.91091,0.919576,-0.730975,-1.42001)
Cform <- ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata)
Cstart<-list(p1=1,p2=0.2)
Cdata<-data.frame(xdata, ydata)
Csubset<-1:8 # just first 8 points
# Original problem - no subset
fit0 = nls(ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata), data=Cdata,
start=list(p1=1,p2=.2))
summary(fit0)
# via subset argument
fit1 = nls(ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata), data=Cdata,
start=list(p1=1,p2=.2), subset=Csubset)
summary(fit1)
# via explicit subsetting
Csdata <- Cdata[Csubset, ]
Csdata
fit2 = nls(ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata), data=Csdata,
start=list(p1=1,p2=.2))
summary(fit2)
# via weights -- seems to give correct observation count if zeros not recognized
wts <- c(rep(1,8), rep(0,2))
fit3 = nls(ydata ~ p1*cos(p2*xdata) + p2*sin(p1*xdata), data=Cdata,
weights=wts, start=list(p1=1,p2=.2))
summary(fit3)
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