[R] Time vs Concentration Graphs by ID
Ista Zahn
izahn at psych.rochester.edu
Fri Oct 15 21:59:10 CEST 2010
On Fri, Oct 15, 2010 at 7:29 AM, Anh Nguyen <eatabanana at gmail.com> wrote:
> 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.
No need to resort to photoshop... see the breaks argument of ?scale_x_continous
-Ista
>
> 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 <djmuser at 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 <eatabanana at 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 <djmuser at 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 <eatabanana at 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 <izahn at 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 <eatabanana at 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 at 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 at 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.
>>>>>
>>>>
>>>>
>>>
>>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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
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