Thank you for the very helpful tips. Just one last question:
- In the lattice method, how can I plot TIME vs OBSconcentration and TIME vs
PREDconcentration in one graph (per individual)? You said "in lattice you
would replace 'smooth' by 'l' in the type = argument of xyplot()" that just
means now the OBSconc is replaced by a line instead of points and PREDconc
is not plotted, right?
--> I tried to superimpose 2 lattices but that won't work. I can keep the
x-scale the same but not the y-scale. Ex: max pred conc = 60 and max obs
conc = 80 and thus for this case, there would be 2 different y-scales
superimposed on one another.
- the ggplot2 method works, just the weird 5,6,7,8,9,10 mg legends (there
are only 5,7,10 mg doses) but it's nothing that photoshop can't take care
of.
This is very cool, I think I understand it a lot better now. It was a lot
easier than what I was doing before, that's for sure. Thanks!
On Fri, Oct 15, 2010 at 2:32 AM, Dennis Murphy <djmu...@gmail.com> wrote:
> Hi:
>
> To get the plots precisely as you have given them in your png file, you're
> most likely going to have to use base graphics, especially if you want a
> separate legend in each panel. Packages ggplot2 and lattice have more
> structured ways of constructing such graphs, so you give up a bit of freedom
> in the details of plot construction to get nicer default configurations.
>
> Perhaps you've written a panel function in S-PLUS (?) to produce each graph
> in your png file - if so, you could simply add a couple of lines to
> determine the max y-value and from that the limits of the y scale. It
> shouldn't be at all difficult to make such modifications. Since R base
> graphics is very similar to base graphics in S-PLUS, you could possibly get
> away with popping your S-PLUS code directly into R and see how far that
> takes you.
>
> Lattice is based on Trellis graphics, but the syntax in lattice has changed
> a fair bit vis a vis Trellis as the package has developed. ggplot2 is a more
> recent graphics system in R predicated on the grammar of graphics exposited
> by Leland Wilkinson.
>
> For my example, I've modified the Theophylline data set in package nlme,
> described in Pinheiro & Bates (2000), Mixed Effects Models in S and S-PLUS,
> Springer. The original data set has eleven unique doses - I combined them
> into three intervals and converted them to factor with cut(). I also created
> four groups of Subjects and put them into a variable ID. The output data
> frame is called theo. I didn't fit the nlme models to the subjects -
> instead, I was lazy and used loess smoothing instead. The code to generate
> the data frame is given below; this is what we mean by a 'reproducible
> example', something that can be copied and pasted into an R session.
>
> # Use modified version of Theophylline data in package nlme
>
> library(nlme)
> theo <- Theoph
> theo <- subset(theo, Dose > 3.5)
> theo$dose <- cut(theo$Dose, breaks = c(3, 4.5, 5, 6), labels = c('4.25',
> '4.8', '5.5'))
> theo <- as.data.frame(theo)
> theo$ID <- with(theo, ifelse(Subject %in% c(6, 7, 12), 1,
> ifelse(Subject %in% c(2, 8, 10), 2,
> ifelse(Subject %in% c(4, 11, 5), 3, 4) )))
> # ID is used for faceting, dose for color and shape
>
> # lattice version:
>
> xyplot(conc ~ Time | factor(ID), data = theo, col.line = 1:3,
> pch = 1:3, col = 1:3, groups = dose, type = c('p', 'smooth'),
> scales = list(y = list(relation = 'free')),
> auto.key = list(corner = c(0.93, 0.4), lines = TRUE, points =
> TRUE,
> text = levels(theo$dose)) )
>
> # ggplot2 version:
> # scales = 'free_y' allows independent y scales per panel
> g <- ggplot(theo, aes(x = Time, y = conc, shape = dose, colour = dose,
> group = Subject))
> g + geom_point() + geom_smooth(method = 'loess', se = FALSE) +
> facet_wrap( ~ ID, ncol = 2, scales = 'free_y') +
> opts(legend.position = c(0.9, 0.4))
>
> This is meant to give you some indication how the two graphics systems work
> - it's a foundation, not an end product. There are a couple of ways I could
> have adjusted the y-scales in the lattice graphs (either directly in the
> scales = ... part or to use a prepanel function for loess), but since you're
> not likely to use loess in your plots, it's not an important consideration.
>
> Both ggplot2 and lattice place the legend outside the plot area by default;
> I've illustrated a couple of ways to pull it into the plot region FYI.
>
> One other thing: if your data set contains fitted values from your PK
> models for each subject * dose combination, the loess smoothing is
> unnecessary. In ggplot2, you would use geom_line(aes(y = pred), ...) in
> place of geom_smooth(), and in lattice you would replace 'smooth' by 'l' in
> the type = argument of xyplot().
>
> HTH,
> Dennis
>
>
>
> On Fri, Oct 15, 2010 at 12:46 AM, Anh Nguyen <eataban...@gmail.com> wrote:
>
>> Hello Dennis,
>>
>> That's a very good suggestion. I've attached a template here as a .png
>> file, I hope you can view it. This is what I've managed to achieve in S-Plus
>> (we use S-Plus at work but I also use R because there's some very good R
>> packages for PK data that I want to take advantage of that is not available
>> in S-Plus). The only problem with this is, unfortunately, I cannot figure
>> out how make the scale non-uniform and I hope to fix that. My data looks
>> like this:
>>
>> ID Dose Time Conc Pred ...
>> 1 5 0 0 0
>> 1 5 0.5 6 8
>> 1 5 1 16 20
>> ...
>> 1 7 0 0 0
>> 1 7 0.5 10 12
>> 1 7 1 20 19
>> ...
>> 1 10 3 60 55
>> ...
>> 2 5 12 4 2
>> ...
>> ect
>>
>>
>> I don't care if it's ggplot or something else as long as it looks like how
>> I envisioned.
>>
>>
>>
>>
>> On Fri, Oct 15, 2010 at 12:22 AM, Dennis Murphy <djmu...@gmail.com>wrote:
>>
>>> I don't recall that you submitted a reproducible example to use as a
>>> template for assistance. Ista was kind enough to offer a potential solution,
>>> but it was an abstraction based on the limited information provided in your
>>> previous mail. If you need help, please provide an example data set that
>>> illustrates the problems you're encountering and what you hope to achieve -
>>> your chances of a successful resolution will be much higher when you do.
>>> BTW, there's a dedicated newsgroup for ggplot2:
>>> look for the mailing list link at http://had.co.nz/ggplot2/
>>>
>>> HTH,
>>> Dennis
>>>
>>>
>>> On Thu, Oct 14, 2010 at 10:02 PM, Anh Nguyen <eataban...@gmail.com>wrote:
>>>
>>>> I found 2 problems with this method:
>>>>
>>>> - There is only one line for predicted dose at 5 mg.
>>>> - The different doses are 5, 7, and 10 mg but somehow there is a legend
>>>> for
>>>> 5,6,7,8,9,10.
>>>> - Is there a way to make the line smooth?
>>>> - The plots are also getting a little crowded and I was wondering if
>>>> there a
>>>> way to split it into 2 or more pages?
>>>>
>>>> Thanks for your help.
>>>>
>>>> On Thu, Oct 14, 2010 at 8:09 PM, Ista Zahn <iz...@psych.rochester.edu
>>>> >wrote:
>>>>
>>>> > Hi,
>>>> > Assuming the data is in a data.frame named "D", something like
>>>> >
>>>> > library(ggplot2) # May need install.packages("ggplot2") first
>>>> > ggplot(D, aes(x=Time, y=Concentration, color=Dose) +
>>>> > geom_point() +
>>>> > geom_line(aes(y = PredictedConcentration, group=1)) +
>>>> > facet_wrap(~ID, scales="free", ncol=3)
>>>> >
>>>> > should do it.
>>>> >
>>>> > -Ista
>>>> > On Thu, Oct 14, 2010 at 10:25 PM, thaliagoo <eataban...@gmail.com>
>>>> wrote:
>>>> > >
>>>> > > Hello-- I have a data for small population who took 1 drug at 3
>>>> different
>>>> > > doses. I have the actual drug concentrations as well as predicted
>>>> > > concentrations by my model. This is what I'm looking for:
>>>> > >
>>>> > > - Time vs Concentration by ID (individual plots), with each subject
>>>> > > occupying 1 plot -- there is to be 9 plots per page (3x3)
>>>> > > - Observed drug concentration is made up of points, and predicted
>>>> drug
>>>> > > concentration is a curve without points. Points and curve will be
>>>> the
>>>> > same
>>>> > > color for each dose. Different doses will have different colors.
>>>> > > - A legend to specify which color correlates to which dose.
>>>> > > - Axes should be different for each individual (as some individual
>>>> will
>>>> > have
>>>> > > much higher drug concentration than others) and I want to see in
>>>> detail
>>>> > how
>>>> > > well predicted data fits observed data.
>>>> > >
>>>> > > Any help would be greatly appreciated.
>>>> > > --
>>>> > > View this message in context:
>>>> >
>>>> http://r.789695.n4.nabble.com/Time-vs-Concentration-Graphs-by-ID-tp2996431p2996431.html
>>>> > > Sent from the R help mailing list archive at Nabble.com.
>>>> > >
>>>> > > ______________________________________________
>>>> > > R-help@r-project.org mailing list
>>>> > > 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.
>>>> > >
>>>> >
>>>> >
>>>> >
>>>> > --
>>>> > Ista Zahn
>>>> > Graduate student
>>>> > University of Rochester
>>>> > Department of Clinical and Social Psychology
>>>> > http://yourpsyche.org
>>>> >
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list
>>>> 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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