[R] Fwd: Joining two datasets - recursive procedure?

Luca Meyer
Sun, 22 Mar 2015 20:12:45 -0000

Hi Bert,

Maybe I did not explain myself clearly enough. But let me show you with a
manual example that indeed what I would like to do is feasible.

The following is also available for download from
https://www.dropbox.com/s/qhmpkkrejjkpbkx/sample_code.txt?dl=0

rm(list=ls())

This is usual (an extract of) the INPUT file I have:

f1 <- structure(list(v1 = c("A", "A", "A",
"A", "A", "A", "B", "B",
"B", "B", "B", "B"), v2 =
c("A", "B", "C", "A", "B",
"C", "A",
"B", "C", "A", "B", "C"), v3 =
c("B", "B", "B", "C", "C",
"C",
"B", "B", "B", "C", "C",
"C"), v4 = c(18.18530, 3.43806,0.00273, 1.42917,
1.05786, 0.00042, 2.37232, 3.01835, 0, 1.13430, 0.92872,
0)), .Names = c("v1", "v2", "v3", "v4"),
class = "data.frame", row.names c(2L,
9L, 11L, 41L, 48L, 50L, 158L, 165L, 167L, 197L, 204L, 206L))

This are the initial marginal distributions

aggregate(v4~v1*v2,f1,sum)
aggregate(v4~v3,f1,sum)

First I order the file such that I have nicely listed 6 distinct v1xv2
combinations.

f1 <- f1[order(f1$v1,f1$v2),]

Then I compute (manually) the relative importance of each v1xv2 combination:

tAA <-
(18.18530+1.42917)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=A & v2=A
tAB <-
(3.43806+1.05786)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=A & v2=B
tAC <-
(0.00273+0.00042)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=A & v2=C
tBA <-
(2.37232+1.13430)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=B & v2=A
tBB <-
(3.01835+0.92872)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=B & v2=B
tBC <-
(0.00000+0.00000)/(18.18530+1.42917+3.43806+1.05786+0.00273+0.00042+2.37232+1.13430+3.01835+0.92872+0.00000+0.00000)
# this is for combination v1=B & v2=C
# and just to make sure I have not made mistakes the following should be
equal to 1
tAA+tAB+tAC+tBA+tBB+tBC

Next, I know I need to increase v4 any time v3=B and the total increase I
need to have over the whole dataset is 29-27.01676=1.98324. In turn, I need
to dimish v4 any time V3=C by the same amount (4.55047-2.56723=1.98324).
This aspect was perhaps not clear at first. I need to move v4 across v3
categories, but the totals will always remain unchanged.

Since I want the data alteration to be proportional to the v1xv2
combinations I do the following:

f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="A" &
f1$v3=="B", f1$v4+(tAA*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="A" &
f1$v3=="C", f1$v4-(tAA*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="B" &
f1$v3=="B", f1$v4+(tAB*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="B" &
f1$v3=="C", f1$v4-(tAB*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="C" &
f1$v3=="B", f1$v4+(tAC*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="A" & f1$v2=="C" &
f1$v3=="C", f1$v4-(tAC*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="A" &
f1$v3=="B", f1$v4+(tBA*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="A" &
f1$v3=="C", f1$v4-(tBA*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="B" &
f1$v3=="B", f1$v4+(tBB*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="B" &
f1$v3=="C", f1$v4-(tBB*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="C" &
f1$v3=="B", f1$v4+(tBC*1.98324),
f1$v4)
f1$v4 <- ifelse (f1$v1=="B" & f1$v2=="C" &
f1$v3=="C", f1$v4-(tBC*1.98324),
f1$v4)

This are the final marginal distributions:

aggregate(v4~v1*v2,f1,sum)
aggregate(v4~v3,f1,sum)

Can this procedure be made programmatic so that I can run it on the
(8x13x13) categories matrix? if so, how would you do it? I have really hard
time to do it with some (semi)automatic procedure.

Thank you very much indeed once more :)

Luca


2015-03-22 18:32 GMT+01:00 Bert Gunter <gunter.berton at gene.com>:

> Nonsense. You are not telling us something or I have failed to
> understand something.
>
> Consider:
>
> v1 = c("a","b")
> v2 = "c("a","a")
>
> It is not possible to change the value of a sum of values
> corresponding to v2="a" without also changing that for v1, which
is
> not supposed to change according to my understanding of your
> specification.
>
> So I'm done.
>
> -- Bert
>
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
> (650) 467-7374
>
> "Data is not information. Information is not knowledge. And knowledge
> is certainly not wisdom."
> Clifford Stoll
>
>
>
>
> On Sun, Mar 22, 2015 at 8:28 AM, Luca Meyer <lucam1968 at gmail.com>
wrote:
> > Sorry forgot to keep the rest of the group in the loop - Luca
> > ---------- Forwarded message ----------
> > From: Luca Meyer <lucam1968 at gmail.com>
> > Date: 2015-03-22 16:27 GMT+01:00
> > Subject: Re: [R] Joining two datasets - recursive procedure?
> > To: Bert Gunter <gunter.berton at gene.com>
> >
> >
> > Hi Bert,
> >
> > That is exactly what I am trying to achieve. Please notice that
negative
> v4
> > values are allowed. I have done a similar task in the past manually by
> > recursively alterating v4 distribution across v3 categories within fix
> each
> > v1&v2 combination so I am quite positive it can be achieved but
honestly
> I
> > took me forever to do it manually and since this is likely to be an
> > exercise I need to repeat from time to time I wish I could learn how
to
> do
> > it programmatically....
> >
> > Thanks again for any further suggestion you might have,
> >
> > Luca
> >
> >
> > 2015-03-22 16:05 GMT+01:00 Bert Gunter <gunter.berton at
gene.com>:
> >
> >> Oh, wait a minute ...
> >>
> >> You still want the marginals for the other columns to be as
originally?
> >>
> >> If so, then this is impossible in general as the sum of all the
values
> >> must be what they were originally and you cannot therefore choose
your
> >> values for V3 arbitrarily.
> >>
> >> Or at least, that seems to be what you are trying to do.
> >>
> >> -- Bert
> >>
> >> Bert Gunter
> >> Genentech Nonclinical Biostatistics
> >> (650) 467-7374
> >>
> >> "Data is not information. Information is not knowledge. And
knowledge
> >> is certainly not wisdom."
> >> Clifford Stoll
> >>
> >>
> >>
> >>
> >> On Sun, Mar 22, 2015 at 7:55 AM, Bert Gunter <bgunter at
gene.com> wrote:
> >> > I would have thought that this is straightforward given my
previous
> >> email...
> >> >
> >> > Just set z to what you want -- e,g, all B values to 29/number
of B's,
> >> > and all C values to 2.567/number of C's (etc. for more
categories).
> >> >
> >> > A slick but sort of cheat way to do this programmatically --
in the
> >> > sense that it relies on the implementation of factor() rather
than its
> >> > API -- is:
> >> >
> >> > y <- f1$v3  ## to simplify the notation; could be done
using with()
> >> > z <- (c(29,2.567)/table(y))[c(y)]
> >> >
> >> > Then proceed to z1 as I previously described
> >> >
> >> > -- Bert
> >> >
> >> >
> >> > Bert Gunter
> >> > Genentech Nonclinical Biostatistics
> >> > (650) 467-7374
> >> >
> >> > "Data is not information. Information is not knowledge.
And knowledge
> >> > is certainly not wisdom."
> >> > Clifford Stoll
> >> >
> >> >
> >> >
> >> >
> >> > On Sun, Mar 22, 2015 at 2:00 AM, Luca Meyer <lucam1968 at
gmail.com>
> wrote:
> >> >> Hi Bert, hello R-experts,
> >> >>
> >> >> I am close to a solution but I still need one hint w.r.t.
the
> following
> >> >> procedure (available also from
> >> >>
https://www.dropbox.com/s/qhmpkkrejjkpbkx/sample_code.txt?dl=0)
> >> >>
> >> >> rm(list=ls())
> >> >>
> >> >> # this is (an extract of) the INPUT file I have:
> >> >> f1 <- structure(list(v1 = c("A",
"A", "A", "A", "A", "A",
"B", "B",
> "B",
> >> >> "B", "B", "B"), v2 =
c("A", "B", "C", "A", "B",
"C", "A", "B", "C",
> "A",
> >> >> "B", "C"), v3 = c("B",
"B", "B", "C", "C", "C",
"B", "B", "B", "C",
> "C",
> >> >> "C"), v4 = c(18.18530, 3.43806,0.00273,
1.42917, 1.05786, 0.00042,
> >> 2.37232,
> >> >> 3.01835, 0, 1.13430, 0.92872, 0)), .Names =
c("v1", "v2", "v3",
> "v4"),
> >> class
> >> >> = "data.frame", row.names = c(2L, 9L, 11L, 41L,
48L, 50L, 158L, 165L,
> >> 167L,
> >> >> 197L, 204L, 206L))
> >> >>
> >> >> # this is the procedure that Bert suggested (slightly
adjusted):
> >> >> z <- rnorm(nrow(f1)) ## or anything you want
> >> >> z1 <- round(with(f1,v4 + z -ave(z,v1,v2,FUN=mean)),
digits=5)
> >> >> aggregate(v4~v1*v2,f1,sum)
> >> >> aggregate(z1~v1*v2,f1,sum)
> >> >> aggregate(v4~v3,f1,sum)
> >> >> aggregate(z1~v3,f1,sum)
> >> >>
> >> >> My question to you is: how can I set z so that I can
obtain specific
> >> values
> >> >> for z1-v4 in the v3 aggregation?
> >> >> In other words, how can I configure the procedure so that
e.g. B=29
> and
> >> >> C=2.56723 after running the procedure:
> >> >> aggregate(z1~v3,f1,sum)
> >> >>
> >> >> Thank you,
> >> >>
> >> >> Luca
> >> >>
> >> >> PS: to avoid any doubts you might have about who I am the
following
> is
> >> my
> >> >> web page: http://lucameyer.wordpress.com/
> >> >>
> >> >>
> >> >> 2015-03-21 18:13 GMT+01:00 Bert Gunter <gunter.berton
at gene.com>:
> >> >>>
> >> >>> ... or cleaner:
> >> >>>
> >> >>> z1 <- with(f1,v4 + z -ave(z,v1,v2,FUN=mean))
> >> >>>
> >> >>>
> >> >>> Just for curiosity, was this homework? (in which case
I should
> >> >>> probably have not provided you an answer -- that is,
assuming that I
> >> >>> HAVE provided an answer).
> >> >>>
> >> >>> Cheers,
> >> >>> Bert
> >> >>>
> >> >>> Bert Gunter
> >> >>> Genentech Nonclinical Biostatistics
> >> >>> (650) 467-7374
> >> >>>
> >> >>> "Data is not information. Information is not
knowledge. And
> knowledge
> >> >>> is certainly not wisdom."
> >> >>> Clifford Stoll
> >> >>>
> >> >>>
> >> >>>
> >> >>>
> >> >>> On Sat, Mar 21, 2015 at 7:53 AM, Bert Gunter
<bgunter at gene.com>
> wrote:
> >> >>> > z <- rnorm(nrow(f1)) ## or anything you want
> >> >>> > z1 <- f1$v4 + z -
with(f1,ave(z,v1,v2,FUN=mean))
> >> >>> >
> >> >>> >
> >> >>> > aggregate(v4~v1,f1,sum)
> >> >>> > aggregate(z1~v1,f1,sum)
> >> >>> > aggregate(v4~v2,f1,sum)
> >> >>> > aggregate(z1~v2,f1,sum)
> >> >>> > aggregate(v4~v3,f1,sum)
> >> >>> > aggregate(z1~v3,f1,sum)
> >> >>> >
> >> >>> >
> >> >>> > Cheers,
> >> >>> > Bert
> >> >>> >
> >> >>> > Bert Gunter
> >> >>> > Genentech Nonclinical Biostatistics
> >> >>> > (650) 467-7374
> >> >>> >
> >> >>> > "Data is not information. Information is
not knowledge. And
> knowledge
> >> >>> > is certainly not wisdom."
> >> >>> > Clifford Stoll
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> >
> >> >>> > On Sat, Mar 21, 2015 at 6:49 AM, Luca Meyer
<lucam1968 at gmail.com>
> >> wrote:
> >> >>> >> Hi Bert,
> >> >>> >>
> >> >>> >> Thank you for your message. I am looking
into ave() and tapply()
> as
> >> you
> >> >>> >> suggested but at the same time I have
prepared a example of input
> >> and
> >> >>> >> output
> >> >>> >> files, just in case you or someone else
would like to make an
> >> attempt
> >> >>> >> to
> >> >>> >> generate a code that goes from input to
output.
> >> >>> >>
> >> >>> >> Please see below or download it from
> >> >>> >>
https://www.dropbox.com/s/qhmpkkrejjkpbkx/sample_code.txt?dl=0
> >> >>> >>
> >> >>> >> # this is (an extract of) the INPUT file I
have:
> >> >>> >> f1 <- structure(list(v1 =
c("A", "A", "A", "A", "A",
"A", "B",
> "B",
> >> >>> >> "B", "B", "B",
"B"), v2 = c("A", "B", "C",
"A", "B", "C", "A",
> >> >>> >> "B", "C", "A",
"B", "C"), v3 = c("B", "B",
"B", "C", "C", "C",
> >> >>> >> "B", "B", "B",
"C", "C", "C"), v4 = c(18.18530, 3.43806,0.00273,
> >> >>> >> 1.42917,
> >> >>> >> 1.05786, 0.00042, 2.37232, 3.01835, 0,
1.13430, 0.92872,
> >> >>> >> 0)), .Names = c("v1",
"v2", "v3", "v4"), class = "data.frame",
> >> >>> >> row.names > >> >>>
>> c(2L,
> >> >>> >> 9L, 11L, 41L, 48L, 50L, 158L, 165L, 167L,
197L, 204L, 206L))
> >> >>> >>
> >> >>> >> # this is (an extract of) the OUTPUT file I
would like to obtain:
> >> >>> >> f2 <- structure(list(v1 =
c("A", "A", "A", "A", "A",
"A", "B",
> "B",
> >> >>> >> "B", "B", "B",
"B"), v2 = c("A", "B", "C",
"A", "B", "C", "A",
> >> >>> >> "B", "C", "A",
"B", "C"), v3 = c("B", "B",
"B", "C", "C", "C",
> >> >>> >> "B", "B", "B",
"C", "C", "C"), v4 = c(17.83529, 3.43806,0.00295,
> >> >>> >> 1.77918,
> >> >>> >> 1.05786, 0.0002, 2.37232, 3.01835, 0,
1.13430, 0.92872,
> >> >>> >> 0)), .Names = c("v1",
"v2", "v3", "v4"), class = "data.frame",
> >> >>> >> row.names > >> >>>
>> c(2L,
> >> >>> >> 9L, 11L, 41L, 48L, 50L, 158L, 165L, 167L,
197L, 204L, 206L))
> >> >>> >>
> >> >>> >> # please notice that while the aggregated v4
on v3 has changed ?
> >> >>> >>
aggregate(f1[,c("v4")],list(f1$v3),sum)
> >> >>> >>
aggregate(f2[,c("v4")],list(f2$v3),sum)
> >> >>> >>
> >> >>> >> # ? the aggregated v4 over v1xv2 has
remained unchanged:
> >> >>> >>
aggregate(f1[,c("v4")],list(f1$v1,f1$v2),sum)
> >> >>> >>
aggregate(f2[,c("v4")],list(f2$v1,f2$v2),sum)
> >> >>> >>
> >> >>> >> Thank you very much in advance for your
assitance.
> >> >>> >>
> >> >>> >> Luca
> >> >>> >>
> >> >>> >> 2015-03-21 13:18 GMT+01:00 Bert Gunter
<gunter.berton at gene.com>:
> >> >>> >>>
> >> >>> >>> 1. Still not sure what you mean, but
maybe look at ?ave and
> >> ?tapply,
> >> >>> >>> for which ave() is a wrapper.
> >> >>> >>>
> >> >>> >>> 2. You still need to heed the rest of
Jeff's advice.
> >> >>> >>>
> >> >>> >>> Cheers,
> >> >>> >>> Bert
> >> >>> >>>
> >> >>> >>> Bert Gunter
> >> >>> >>> Genentech Nonclinical Biostatistics
> >> >>> >>> (650) 467-7374
> >> >>> >>>
> >> >>> >>> "Data is not information.
Information is not knowledge. And
> >> knowledge
> >> >>> >>> is certainly not wisdom."
> >> >>> >>> Clifford Stoll
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>>
> >> >>> >>> On Sat, Mar 21, 2015 at 4:53 AM, Luca
Meyer <
> lucam1968 at gmail.com>
> >> >>> >>> wrote:
> >> >>> >>> > Hi Jeff & other R-experts,
> >> >>> >>> >
> >> >>> >>> > Thank you for your note. I have
tried myself to solve the
> issue
> >> >>> >>> > without
> >> >>> >>> > success.
> >> >>> >>> >
> >> >>> >>> > Following your suggestion, I am
providing a sample of the
> >> dataset I
> >> >>> >>> > am
> >> >>> >>> > using below (also downloadble in
plain text from
> >> >>> >>> >
> https://www.dropbox.com/s/qhmpkkrejjkpbkx/sample_code.txt?dl=0):
> >> >>> >>> >
> >> >>> >>> > #this is an extract of the overall
dataset (n=1200 cases)
> >> >>> >>> > f1 <- structure(list(v1 =
c("A", "A", "A", "A", "A",
"A", "B",
> >> "B",
> >> >>> >>> > "B", "B",
"B", "B"), v2 = c("A", "B",
"C", "A", "B", "C", "A",
> >> >>> >>> > "B", "C",
"A", "B", "C"), v3 = c("B",
"B", "B", "C", "C", "C",
> >> >>> >>> > "B", "B",
"B", "C", "C", "C"), v4 =
c(18.1853007621835,
> >> >>> >>> > 3.43806581506388,
> >> >>> >>> > 0.002733567617055,
1.42917483425029, 1.05786640463504,
> >> >>> >>> > 0.000420548864162308,
> >> >>> >>> > 2.37232740842861, 3.01835841813241,
0, 1.13430282139936,
> >> >>> >>> > 0.928725667117666,
> >> >>> >>> > 0)), .Names = c("v1",
"v2", "v3", "v4"), class = "data.frame",
> >> >>> >>> > row.names
> >> >>> >>> > > >> >>>
>>> > c(2L,
> >> >>> >>> > 9L, 11L, 41L, 48L, 50L, 158L, 165L,
167L, 197L, 204L, 206L))
> >> >>> >>> >
> >> >>> >>> > I need to find a automated
procedure that allows me to adjust
> v3
> >> >>> >>> > marginals
> >> >>> >>> > while maintaining v1xv2 marginals
unchanged.
> >> >>> >>> >
> >> >>> >>> > That is: modify the v4 values you
can find by running:
> >> >>> >>> >
> >> >>> >>> >
aggregate(f1[,c("v4")],list(f1$v3),sum)
> >> >>> >>> >
> >> >>> >>> > while maintaining costant the
values you can find by running:
> >> >>> >>> >
> >> >>> >>> >
aggregate(f1[,c("v4")],list(f1$v1,f1$v2),sum)
> >> >>> >>> >
> >> >>> >>> > Now does it make sense?
> >> >>> >>> >
> >> >>> >>> > Please notice I have tried to build
some syntax that tries to
> >> modify
> >> >>> >>> > values
> >> >>> >>> > within each v1xv2 combination by
computing sum of v4, row
> >> percentage
> >> >>> >>> > in
> >> >>> >>> > terms of v4, and there is where my
effort is blocked. Not
> really
> >> >>> >>> > sure
> >> >>> >>> > how I
> >> >>> >>> > should proceed. Any suggestion?
> >> >>> >>> >
> >> >>> >>> > Thanks,
> >> >>> >>> >
> >> >>> >>> > Luca
> >> >>> >>> >
> >> >>> >>> >
> >> >>> >>> > 2015-03-19 2:38 GMT+01:00 Jeff
Newmiller <
> >> jdnewmil at dcn.davis.ca.us>:
> >> >>> >>> >
> >> >>> >>> >> I don't understand your
description. The standard practice on
> >> this
> >> >>> >>> >> list
> >> >>> >>> >> is
> >> >>> >>> >> to provide a reproducible R
example [1] of the kind of data
> you
> >> are
> >> >>> >>> >> working
> >> >>> >>> >> with (and any code you have
tried) to go along with your
> >> >>> >>> >> description.
> >> >>> >>> >> In
> >> >>> >>> >> this case, that would be two
dputs of your input data frames
> >> and a
> >> >>> >>> >> dput
> >> >>> >>> >> of
> >> >>> >>> >> an output data frame (generated
by hand from your input data
> >> >>> >>> >> frame).
> >> >>> >>> >> (Probably best to not use the
full number of input values
> just
> >> to
> >> >>> >>> >> keep
> >> >>> >>> >> the
> >> >>> >>> >> size down.) We could then make
an attempt to generate code
> that
> >> >>> >>> >> goes
> >> >>> >>> >> from
> >> >>> >>> >> input to output.
> >> >>> >>> >>
> >> >>> >>> >> Of course, if you post that
hard work using HTML then it will
> >> get
> >> >>> >>> >> corrupted (much like the text
below from your earlier emails)
> >> and
> >> >>> >>> >> we
> >> >>> >>> >> won't
> >> >>> >>> >> be able to use it. Please learn
to post from your email
> software
> >> >>> >>> >> using
> >> >>> >>> >> plain text when corresponding
with this mailing list.
> >> >>> >>> >>
> >> >>> >>> >> [1]
> >> >>> >>> >>
> >> >>> >>> >>
> >> >>> >>> >>
> >>
>
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
> >> >>> >>> >>
> >> >>> >>> >>
> >> >>> >>> >>
> >>
> ---------------------------------------------------------------------------
> >> >>> >>> >> Jeff Newmiller
The     .....
> >>  .....  Go
> >> >>> >>> >> Live...
> >> >>> >>> >> DCN:<jdnewmil at
dcn.davis.ca.us>        Basics: ##.#.
>  ##.#.
> >> >>> >>> >> Live
> >> >>> >>> >> Go...
> >> >>> >>> >>
Live:   OO#.. Dead:
> OO#..
> >> >>> >>> >> Playing
> >> >>> >>> >> Research Engineer
(Solar/Batteries            O.O#.
>  #.O#.
> >> >>> >>> >> with
> >> >>> >>> >> /Software/Embedded Controllers)
.OO#.
>  .OO#.
> >> >>> >>> >> rocks...1k
> >> >>> >>> >>
> >> >>> >>> >>
> >> >>> >>> >>
> >>
> ---------------------------------------------------------------------------
> >> >>> >>> >> Sent from my phone. Please
excuse my brevity.
> >> >>> >>> >>
> >> >>> >>> >> On March 18, 2015 9:05:37 AM
PDT, Luca Meyer <
> >> lucam1968 at gmail.com>
> >> >>> >>> >> wrote:
> >> >>> >>> >> >Thanks for you input
Michael,
> >> >>> >>> >> >
> >> >>> >>> >> >The continuous variable I
have measures quantities (down to
> the
> >> >>> >>> >> > 3rd
> >> >>> >>> >> >decimal level) so
unfortunately are not frequencies.
> >> >>> >>> >> >
> >> >>> >>> >> >Any more specific
suggestions on how that could be tackled?
> >> >>> >>> >> >
> >> >>> >>> >> >Thanks & kind regards,
> >> >>> >>> >> >
> >> >>> >>> >> >Luca
> >> >>> >>> >> >
> >> >>> >>> >> >
> >> >>> >>> >> >==> >>
>>> >>> >> >
> >> >>> >>> >> >Michael Friendly wrote:
> >> >>> >>> >> >I'm not sure I
understand completely what you want to do,
> but
> >> >>> >>> >> >if the data were
frequencies, it sounds like task for
> fitting a
> >> >>> >>> >> >loglinear model with the
model formula
> >> >>> >>> >> >
> >> >>> >>> >> >~ V1*V2 + V3
> >> >>> >>> >> >
> >> >>> >>> >> >On 3/18/2015 2:17 AM, Luca
Meyer wrote:
> >> >>> >>> >> >>* Hello,
> >> >>> >>> >> >*>>* I am facing a
quite challenging task (at least to me)
> and
> >> I
> >> >>> >>> >> > was
> >> >>> >>> >> >wondering
> >> >>> >>> >> >*>* if someone could
advise how R could assist me to speed
> the
> >> >>> >>> >> > task
> >> >>> >>> >> > up.
> >> >>> >>> >> >*>>* I am dealing
with a dataset with 3 discrete variables
> and
> >> one
> >> >>> >>> >> >continuous
> >> >>> >>> >> >*>* variable. The
discrete variables are:
> >> >>> >>> >> >*>>* V1: 8 modalities
> >> >>> >>> >> >*>* V2: 13 modalities
> >> >>> >>> >> >*>* V3: 13 modalities
> >> >>> >>> >> >*>>* The continuous
variable V4 is a decimal number always
> >> greater
> >> >>> >>> >> > than
> >> >>> >>> >> >zero in
> >> >>> >>> >> >*>* the marginals of
each of the 3 variables but it is
> >> sometimes
> >> >>> >>> >> > equal
> >> >>> >>> >> >to zero
> >> >>> >>> >> >*>* (and sometimes
negative) in the joint tables.
> >> >>> >>> >> >*>>* I have got 2
files:
> >> >>> >>> >> >*>>* => one with
distribution of all possible combinations
> of
> >> >>> >>> >> > V1xV2
> >> >>> >>> >> >(some of
> >> >>> >>> >> >*>* which are zero or
neagtive) and
> >> >>> >>> >> >*>* => one with the
marginal distribution of V3.
> >> >>> >>> >> >*>>* I am trying to
build the long and narrow dataset
> V1xV2xV3
> >> in
> >> >>> >>> >> > such
> >> >>> >>> >> >a way
> >> >>> >>> >> >*>* that each V1xV2 cell
does not get modified and V3 fits
> as
> >> >>> >>> >> > closely
> >> >>> >>> >> >as
> >> >>> >>> >> >*>* possible to its
marginal distribution. Does it make
> sense?
> >> >>> >>> >> >*>>* To be even more
specific, my 2 input files look like
> the
> >> >>> >>> >> >following.
> >> >>> >>> >> >*>>* FILE 1
> >> >>> >>> >> >*>* V1,V2,V4
> >> >>> >>> >> >*>* A, A, 24.251
> >> >>> >>> >> >*>* A, B, 1.065
> >> >>> >>> >> >*>* (...)
> >> >>> >>> >> >*>* B, C, 0.294
> >> >>> >>> >> >*>* B, D, 2.731
> >> >>> >>> >> >*>* (...)
> >> >>> >>> >> >*>* H, L, 0.345
> >> >>> >>> >> >*>* H, M, 0.000
> >> >>> >>> >> >*>>* FILE 2
> >> >>> >>> >> >*>* V3, V4
> >> >>> >>> >> >*>* A, 1.575
> >> >>> >>> >> >*>* B, 4.294
> >> >>> >>> >> >*>* C, 10.044
> >> >>> >>> >> >*>* (...)
> >> >>> >>> >> >*>* L, 5.123
> >> >>> >>> >> >*>* M, 3.334
> >> >>> >>> >> >*>>* What I need to
achieve is a file such as the following
> >> >>> >>> >> >*>>* FILE 3
> >> >>> >>> >> >*>* V1, V2, V3, V4
> >> >>> >>> >> >*>* A, A, A, ???
> >> >>> >>> >> >*>* A, A, B, ???
> >> >>> >>> >> >*>* (...)
> >> >>> >>> >> >*>* D, D, E, ???
> >> >>> >>> >> >*>* D, D, F, ???
> >> >>> >>> >> >*>* (...)
> >> >>> >>> >> >*>* H, M, L, ???
> >> >>> >>> >> >*>* H, M, M, ???
> >> >>> >>> >> >*>>* Please notice
that FILE 3 need to be such that if I
> >> aggregate
> >> >>> >>> >> > on
> >> >>> >>> >> >V1+V2 I
> >> >>> >>> >> >*>* recover exactly FILE
1 and that if I aggregate on V3 I
> can
> >> >>> >>> >> > recover
> >> >>> >>> >> >a file
> >> >>> >>> >> >*>* as close as possible
to FILE 3 (ideally the same file).
> >> >>> >>> >> >*>>* Can anyone
suggest how I could do that with R?
> >> >>> >>> >> >*>>* Thank you very
much indeed for any assistance you are
> >> able to
> >> >>> >>> >> >provide.
> >> >>> >>> >> >*>>* Kind regards,
> >> >>> >>> >> >*>>* Luca*
> >> >>> >>> >> >
> >> >>> >>> >> >       [[alternative HTML
version deleted]]
> >> >>> >>> >> >
> >> >>> >>> >>
>______________________________________________
> >> >>> >>> >> >R-help at r-project.org
mailing list -- To UNSUBSCRIBE and
> more,
> >> see
> >> >>> >>> >>
>https://stat.ethz.ch/mailman/listinfo/r-help
> >> >>> >>> >> >PLEASE do read the posting
guide
> >> >>> >>> >>
>http://www.R-project.org/posting-guide.html
> >> >>> >>> >> >and provide commented,
minimal, self-contained, reproducible
> >> code.
> >> >>> >>> >>
> >> >>> >>> >>
> >> >>> >>> >
> >> >>> >>> >         [[alternative HTML version
deleted]]
> >> >>> >>> >
> >> >>> >>> >
______________________________________________
> >> >>> >>> > R-help at r-project.org mailing
list -- To UNSUBSCRIBE and more,
> >> see
> >> >>> >>> >
https://stat.ethz.ch/mailman/listinfo/r-help
> >> >>> >>> > PLEASE do read the posting guide
> >> >>> >>> >
http://www.R-project.org/posting-guide.html
> >> >>> >>> > and provide commented, minimal,
self-contained, reproducible
> >> code.
> >> >>> >>
> >> >>> >>
> >> >>
> >> >>
> >>
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>

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