Re: [R] Overlaying lattice graphs (continued)
Hi Deepayan,
The following code creates a dummy dataset which has the same similar as
my usual datasets. I did not try to implement the changes proposed by
Hadley, hoping that a solution can be found using the original dataset.
# My code
# Creating dataset
nPts-10# number of time points
nInd-6 # number of individuals
nModel-3 # number of models
TimePts-rep(1:nPts,nInd*nModel)#
creates the Time column
Coef-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a
vector of coefficients for generating the observations
Obs-10*exp(-Coef*TimePts) #
creates the observations
for (i in 1:60){
Pred[i]-jitter(10*exp(-Coef[i]*TimePts[i]))
Pred[i+60]-jitter(5)
Pred[i+120]-jitter(10-Coef[i+120]*TimePts[i])
}
# creates the predicted values
colPlot-rep(1,nPts*nInd*nModel)
# creates the Plot column
colModel-gl(nModel,nPts*nInd,labels=c(A,B,C)) #
creates the Model column
colID-gl(nInd,nPts,nPts*nInd*nModel)
# creates the ID column
mydata-data.frame(colPlot,colModel,colID,TimePts,Obs,Pred)
# creates the dataset
names(mydata)-c(Plot,Model,Individuals,Time,Observed,Predicted)
# Plotting as indicated by Deepayan
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals))) #,
#...)
### End of code
This codes is not exactly what I am looking for, although it is pretty
close. In the present case, I would like to have a Trellis plot with 6
panels (one for each individual), where the Observations and the
Predicted are plotted as symbols and lines, respectively. All three
models should be plotted on the same panel. Unfortunately, it looks to
me as 3 successives xyplots are created by the code above but only the
last one remains displayed. I tried to play with
panel.superpose,panel.superpose.2 and type, without much success.
I also tried the following code that creates 18 panels and distinguish
all (Individuals,Model) couples... so, not what I want.
xyplot(Observed + Predicted ~ Time | Individuals+Model, data = mydata,
type = c(p, l), distribute.type = TRUE)
Sebastien
Deepayan Sarkar a écrit :
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
Yes, and ?make.groups (and reshape of course) could help with that.
This might not be strictly necessary though.
However, I'm finding your pseudo-code confusing. Could you create a
small example data set that can be used to try out some real code?
Just from your description, I would have suggested something like
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals)),
...)
If all you want is to plot one page at a time, there are easier ways
to do that.
-Deepayan
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of
data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context
of the
example I gave.
I'd like to go a little bit further on this topic using a more
complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically,
this
dataset summarizes the t measurements obtained in n individuals as
well
as the predicted values from m different modeling approaches
(applied to
all individuals).
Re: [R] Overlaying lattice graphs (continued)
Hi Sebastian,
I think the following does what you want:
library(ggplot2)
names(mydata) - tolower(names(mydata))
obs - rename(subset(mydata, model==A, -predicted), c(observed = value))
obs$model - factor(observed)
pred - rename(mydata[, -5], c(predicted = value))
all - rbind(obs, pred)
ggplot(all, aes(x = time, y = value, colour=model)) +
geom_point(data = subset(all, model != Observed)) +
geom_line(data= subset(all, model == Observed)) +
facet_grid(. ~ individuals)
Hadley
On 6/22/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Deepayan,
The following code creates a dummy dataset which has the same similar as
my usual datasets. I did not try to implement the changes proposed by
Hadley, hoping that a solution can be found using the original dataset.
# My code
# Creating dataset
nPts-10# number of time points
nInd-6 # number of individuals
nModel-3 # number of models
TimePts-rep(1:nPts,nInd*nModel)#
creates the Time column
Coef-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a
vector of coefficients for generating the observations
Obs-10*exp(-Coef*TimePts) #
creates the observations
for (i in 1:60){
Pred[i]-jitter(10*exp(-Coef[i]*TimePts[i]))
Pred[i+60]-jitter(5)
Pred[i+120]-jitter(10-Coef[i+120]*TimePts[i])
}
# creates the predicted values
colPlot-rep(1,nPts*nInd*nModel)
# creates the Plot column
colModel-gl(nModel,nPts*nInd,labels=c(A,B,C)) #
creates the Model column
colID-gl(nInd,nPts,nPts*nInd*nModel)
# creates the ID column
mydata-data.frame(colPlot,colModel,colID,TimePts,Obs,Pred)
# creates the dataset
names(mydata)-c(Plot,Model,Individuals,Time,Observed,Predicted)
# Plotting as indicated by Deepayan
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals))) #,
#...)
### End of code
This codes is not exactly what I am looking for, although it is pretty
close. In the present case, I would like to have a Trellis plot with 6
panels (one for each individual), where the Observations and the
Predicted are plotted as symbols and lines, respectively. All three
models should be plotted on the same panel. Unfortunately, it looks to
me as 3 successives xyplots are created by the code above but only the
last one remains displayed. I tried to play with
panel.superpose,panel.superpose.2 and type, without much success.
I also tried the following code that creates 18 panels and distinguish
all (Individuals,Model) couples... so, not what I want.
xyplot(Observed + Predicted ~ Time | Individuals+Model, data = mydata,
type = c(p, l), distribute.type = TRUE)
Sebastien
Deepayan Sarkar a écrit :
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
Yes, and ?make.groups (and reshape of course) could help with that.
This might not be strictly necessary though.
However, I'm finding your pseudo-code confusing. Could you create a
small example data set that can be used to try out some real code?
Just from your description, I would have suggested something like
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals)),
...)
If all you want is to plot one page at a time, there are easier ways
to do that.
-Deepayan
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of
data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context
of the
example I gave.
I'd like to go a little bit further on this topic using a more
complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1
Re: [R] Overlaying lattice graphs (continued)
On 6/22/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Deepayan,
The following code creates a dummy dataset which has the same similar as
my usual datasets. I did not try to implement the changes proposed by
Hadley, hoping that a solution can be found using the original dataset.
# My code
# Creating dataset
nPts-10# number of time points
nInd-6 # number of individuals
nModel-3 # number of models
TimePts-rep(1:nPts,nInd*nModel)#
creates the Time column
Coef-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a
vector of coefficients for generating the observations
Obs-10*exp(-Coef*TimePts) #
creates the observations
for (i in 1:60){
Pred[i]-jitter(10*exp(-Coef[i]*TimePts[i]))
Pred[i+60]-jitter(5)
Pred[i+120]-jitter(10-Coef[i+120]*TimePts[i])
}
# creates the predicted values
colPlot-rep(1,nPts*nInd*nModel)
# creates the Plot column
colModel-gl(nModel,nPts*nInd,labels=c(A,B,C)) #
creates the Model column
colID-gl(nInd,nPts,nPts*nInd*nModel)
# creates the ID column
mydata-data.frame(colPlot,colModel,colID,TimePts,Obs,Pred)
# creates the dataset
names(mydata)-c(Plot,Model,Individuals,Time,Observed,Predicted)
The way you have structured your data makes no sense to me. In
particular, your 'Observed' data is the same set of 60 numbers
repeated 3 times, and this is not reflected in the data structure at
all. What would you want to happen if the numbers were not repeated?
Would you always plot the first 60, or would plot all of them?
If I understand what you are trying to do, this might be a more
transparent approach:
nPts-10 # number of time points
nInd-6# number of individuals
TimePts - rep(1:nPts, nInd)
Coef - rep(rnorm(6,0.1,0.01), each = nPts)
Obs - 10 * exp(-Coef * TimePts)
colID - gl(nInd, nPts)
mydata - data.frame(Time = TimePts, Observed = Obs, Individuals = colID)
fmA - lm(Observed ~ Time, mydata)
fmB - lm(Observed ~ poly(Time, 2), mydata)
fmC - lm(Observed ~ poly(Time, 2) * Individuals, mydata)
mydata$PredA - predict(fmA)
mydata$PredB - predict(fmB)
mydata$PredC - predict(fmC)
xyplot(Observed + PredA + PredB + PredC ~ Time | Individuals,
data = mydata,
type = c(p, l, l, l),
distribute.type = TRUE)
-Deepayan
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs (continued)
Hadley,
I have some troubles to run your code with ggplot version 0.4.1. Is the
package ggplot2 mandatory ?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think the following does what you want:
library(ggplot2)
names(mydata) - tolower(names(mydata))
obs - rename(subset(mydata, model==A, -predicted), c(observed =
value))
obs$model - factor(observed)
pred - rename(mydata[, -5], c(predicted = value))
all - rbind(obs, pred)
ggplot(all, aes(x = time, y = value, colour=model)) +
geom_point(data = subset(all, model != Observed)) +
geom_line(data= subset(all, model == Observed)) +
facet_grid(. ~ individuals)
Hadley
On 6/22/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Deepayan,
The following code creates a dummy dataset which has the same similar as
my usual datasets. I did not try to implement the changes proposed by
Hadley, hoping that a solution can be found using the original dataset.
# My code
# Creating dataset
nPts-10# number of time points
nInd-6 # number of individuals
nModel-3 # number of models
TimePts-rep(1:nPts,nInd*nModel)#
creates the Time column
Coef-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a
vector of coefficients for generating the observations
Obs-10*exp(-Coef*TimePts) #
creates the observations
for (i in 1:60){
Pred[i]-jitter(10*exp(-Coef[i]*TimePts[i]))
Pred[i+60]-jitter(5)
Pred[i+120]-jitter(10-Coef[i+120]*TimePts[i])
}
# creates the predicted values
colPlot-rep(1,nPts*nInd*nModel)
# creates the Plot column
colModel-gl(nModel,nPts*nInd,labels=c(A,B,C)) #
creates the Model column
colID-gl(nInd,nPts,nPts*nInd*nModel)
# creates the ID column
mydata-data.frame(colPlot,colModel,colID,TimePts,Obs,Pred)
# creates the dataset
names(mydata)-c(Plot,Model,Individuals,Time,Observed,Predicted)
# Plotting as indicated by Deepayan
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals))) #,
#...)
### End of code
This codes is not exactly what I am looking for, although it is pretty
close. In the present case, I would like to have a Trellis plot with 6
panels (one for each individual), where the Observations and the
Predicted are plotted as symbols and lines, respectively. All three
models should be plotted on the same panel. Unfortunately, it looks to
me as 3 successives xyplots are created by the code above but only the
last one remains displayed. I tried to play with
panel.superpose,panel.superpose.2 and type, without much success.
I also tried the following code that creates 18 panels and distinguish
all (Individuals,Model) couples... so, not what I want.
xyplot(Observed + Predicted ~ Time | Individuals+Model, data = mydata,
type = c(p, l), distribute.type = TRUE)
Sebastien
Deepayan Sarkar a écrit :
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and
lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you
need to
put observed on the same footing as the different models, so you
would
have a new column in your data called value (previously observed
and
predicted) and a new model type (observed). Then you could do:
Yes, and ?make.groups (and reshape of course) could help with that.
This might not be strictly necessary though.
However, I'm finding your pseudo-code confusing. Could you create a
small example data set that can be used to try out some real code?
Just from your description, I would have suggested something like
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals)),
...)
If all you want is to plot one page at a time, there are easier ways
to do that.
-Deepayan
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of
data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context
of the
example I gave.
I'd like to go a little bit further on this topic using a more
complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
2
Re: [R] Overlaying lattice graphs (continued)
Yes - you'll need ggplot2.
Hadley
On 6/22/07, Sébastien [EMAIL PROTECTED] wrote:
Hadley,
I have some troubles to run your code with ggplot version 0.4.1. Is the
package ggplot2 mandatory ?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think the following does what you want:
library(ggplot2)
names(mydata) - tolower(names(mydata))
obs - rename(subset(mydata, model==A, -predicted), c(observed =
value))
obs$model - factor(observed)
pred - rename(mydata[, -5], c(predicted = value))
all - rbind(obs, pred)
ggplot(all, aes(x = time, y = value, colour=model)) +
geom_point(data = subset(all, model != Observed)) +
geom_line(data= subset(all, model == Observed)) +
facet_grid(. ~ individuals)
Hadley
On 6/22/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Deepayan,
The following code creates a dummy dataset which has the same similar as
my usual datasets. I did not try to implement the changes proposed by
Hadley, hoping that a solution can be found using the original dataset.
# My code
# Creating dataset
nPts-10# number of time points
nInd-6 # number of individuals
nModel-3 # number of models
TimePts-rep(1:nPts,nInd*nModel)#
creates the Time column
Coef-rep(rnorm(6,0.1,0.01),each=nPts,nModel) # Creates a
vector of coefficients for generating the observations
Obs-10*exp(-Coef*TimePts) #
creates the observations
for (i in 1:60){
Pred[i]-jitter(10*exp(-Coef[i]*TimePts[i]))
Pred[i+60]-jitter(5)
Pred[i+120]-jitter(10-Coef[i+120]*TimePts[i])
}
# creates the predicted values
colPlot-rep(1,nPts*nInd*nModel)
# creates the Plot column
colModel-gl(nModel,nPts*nInd,labels=c(A,B,C)) #
creates the Model column
colID-gl(nInd,nPts,nPts*nInd*nModel)
# creates the ID column
mydata-data.frame(colPlot,colModel,colID,TimePts,Obs,Pred)
# creates the dataset
names(mydata)-c(Plot,Model,Individuals,Time,Observed,Predicted)
# Plotting as indicated by Deepayan
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals))) #,
#...)
### End of code
This codes is not exactly what I am looking for, although it is pretty
close. In the present case, I would like to have a Trellis plot with 6
panels (one for each individual), where the Observations and the
Predicted are plotted as symbols and lines, respectively. All three
models should be plotted on the same panel. Unfortunately, it looks to
me as 3 successives xyplots are created by the code above but only the
last one remains displayed. I tried to play with
panel.superpose,panel.superpose.2 and type, without much success.
I also tried the following code that creates 18 panels and distinguish
all (Individuals,Model) couples... so, not what I want.
xyplot(Observed + Predicted ~ Time | Individuals+Model, data = mydata,
type = c(p, l), distribute.type = TRUE)
Sebastien
Deepayan Sarkar a écrit :
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and
lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you
need to
put observed on the same footing as the different models, so you
would
have a new column in your data called value (previously observed
and
predicted) and a new model type (observed). Then you could do:
Yes, and ?make.groups (and reshape of course) could help with that.
This might not be strictly necessary though.
However, I'm finding your pseudo-code confusing. Could you create a
small example data set that can be used to try out some real code?
Just from your description, I would have suggested something like
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals)),
...)
If all you want is to plot one page at a time, there are easier ways
to do that.
-Deepayan
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of
data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context
of the
example I gave.
I'd like to go a little bit further on this topic using a more
complex
dataset
Re: [R] Overlaying lattice graphs (continued)
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context of the
example I gave.
I'd like to go a little bit further on this topic using a more complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically, this
dataset summarizes the t measurements obtained in n individuals as well
as the predicted values from m different modeling approaches (applied to
all individuals). Therefore, the observations are repeated m times in
the Observed columns, while the predictions appears only once for a
given model an a given individual.
What I want to write is a R batch file creating a Trellis graph, where
each panel corresponds to one individual and contains the observations
(as scatterplot) plus the predicted values for all models (as lines of
different colors)... $Plot is just a token: it might be used to not
overload graphs in case there are too many tested models. The fun part
is that the values of p, m, n and t might vary from one dataset to the
other, so everything has to be coded dynamically.
For the plotting part I was thinking about having a loop in my code
containing something like that:
for (i in 1:nlevels(mydata$Model)) {
subdata-subset(mydata,mydata$Model=level(mydata$Model)[i])
xyplot(subset(Observed + Predicted ~ Time | Individuals, data =
subdata) #plus additionnal formatting code
}
Unfortunately, this code simply creates a new Trellis plot instead of
adding the model one by one on the panels. Any idea or link to a useful
command will wellcome.
Sebastien
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs (continued)
Sorry, I have forgotten to tell that I work on R version 2.5.0 on
Windows XP sp2.
Sébastien a écrit :
Dear R Users,
I recently posted an email on this list about the use of data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context of the
example I gave.
I'd like to go a little bit further on this topic using a more complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically, this
dataset summarizes the t measurements obtained in n individuals as well
as the predicted values from m different modeling approaches (applied to
all individuals). Therefore, the observations are repeated m times in
the Observed columns, while the predictions appears only once for a
given model an a given individual.
What I want to write is a R batch file creating a Trellis graph, where
each panel corresponds to one individual and contains the observations
(as scatterplot) plus the predicted values for all models (as lines of
different colors)... $Plot is just a token: it might be used to not
overload graphs in case there are too many tested models. The fun part
is that the values of p, m, n and t might vary from one dataset to the
other, so everything has to be coded dynamically.
For the plotting part I was thinking about having a loop in my code
containing something like that:
for (i in 1:nlevels(mydata$Model)) {
subdata-subset(mydata,mydata$Model=level(mydata$Model)[i])
xyplot(subset(Observed + Predicted ~ Time | Individuals, data =
subdata) #plus additionnal formatting code
}
Unfortunately, this code simply creates a new Trellis plot instead of
adding the model one by one on the panels. Any idea or link to a useful
command will wellcome.
Sebastien
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Re: [R] Overlaying lattice graphs (continued)
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context of the
example I gave.
I'd like to go a little bit further on this topic using a more complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically, this
dataset summarizes the t measurements obtained in n individuals as well
as the predicted values from m different modeling approaches (applied to
all individuals). Therefore, the observations are repeated m times in
the Observed columns, while the predictions appears only once for a
given model an a given individual.
What I want to write is a R batch file creating a Trellis graph, where
each panel corresponds to one individual and contains the observations
(as scatterplot) plus the predicted values for all models (as lines of
different colors)... $Plot is just a token: it might be used to not
overload graphs in case there are too many tested models. The fun part
is that the values of p, m, n and t might vary from one dataset to the
other, so everything has to be coded dynamically.
For the plotting part I was thinking about having a loop in my code
containing something like that:
for (i in 1:nlevels(mydata$Model)) {
subdata-subset(mydata,mydata$Model=level(mydata$Model)[i])
xyplot(subset(Observed + Predicted ~ Time | Individuals, data =
subdata) #plus additionnal formatting code
}
Unfortunately, this code simply creates a new Trellis plot instead of
adding the model one by one on the panels. Any idea or link to a useful
command will wellcome.
Sebastien
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Overlaying lattice graphs (continued)
Sebastian,
You should be able to, but I don't know how to do it with lattice. In
ggplot (http://had.co.nz/ggplot2) you would do it as follows:
ggplot(mydata, aes(x = time, y = value, colour=model)) +
geom_point(subset(data, model != observed)) +
geom_line((subset(data, model == observed)) +
facet_grid(. ~ individuals)
or if you only wanted the models coloured:
ggplot(mydata, aes(x = time, y = value)) +
geom_point(subset(data, model != observed), aes(colour=model)) +
geom_line((subset(data, model == observed)) +
facet_grid(. ~ individuals)
Although the way the panels are arranged is probably suboptimal if you
have many individuals. It's something I plan to fix in the future, so
that + facet_wrap(individuals) would give you a display like lattice
does.
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context of the
example I gave.
I'd like to go a little bit further on this topic using a more complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically, this
dataset summarizes the t measurements obtained in n individuals as well
as the predicted values from m different modeling approaches (applied to
all individuals). Therefore, the observations are repeated m times in
the Observed columns, while the predictions appears only once for a
given model an a given individual.
What I want to write is a R batch file creating a Trellis graph, where
each panel corresponds to one individual and contains the observations
(as scatterplot) plus the predicted values for all models (as lines of
different colors)... $Plot is just a token: it might be used to not
overload graphs in case there are too many tested models. The fun part
is that the values of p, m, n and t might vary from one dataset to the
other, so everything has to be coded dynamically.
For the plotting part I was thinking about having a loop in my code
containing something like that:
for (i in 1:nlevels(mydata$Model)) {
subdata-subset(mydata,mydata$Model=level(mydata$Model)[i])
xyplot(subset(Observed + Predicted ~ Time | Individuals, data =
subdata) #plus additionnal formatting code
}
Unfortunately, this code simply creates a new Trellis plot instead of
adding the model one by one on the panels. Any idea or link to a useful
command will wellcome.
Sebastien
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs (continued)
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Hi Hadley,
Hopefully, my dataset won't be too hard to changed. Can I modify the
aspect of each group using your code (symbols for observed and lines for
predicted)?
Sebastien
hadley wickham a écrit :
Hi Sebastian,
I think you need to rearrange your data a bit. Firstly, you need to
put observed on the same footing as the different models, so you would
have a new column in your data called value (previously observed and
predicted) and a new model type (observed). Then you could do:
Yes, and ?make.groups (and reshape of course) could help with that.
This might not be strictly necessary though.
However, I'm finding your pseudo-code confusing. Could you create a
small example data set that can be used to try out some real code?
Just from your description, I would have suggested something like
xyplot(Observed + Predicted ~ Time | Individuals + Model,
data = mydata,
panel = panel.superpose.2, type = c(p, l),
layout = c(0, nlevels(mydata$Individuals)),
...)
If all you want is to plot one page at a time, there are easier ways to do that.
-Deepayan
xyplot(value ~ time | individauls, data=mydata, group=model)
Hadley
On 6/21/07, Sébastien [EMAIL PROTECTED] wrote:
Dear R Users,
I recently posted an email on this list about the use of data.frame and
overlaying multiple plots. Deepayan kindly indicated to me the
panel.superposition command which worked perfectly in the context of the
example I gave.
I'd like to go a little bit further on this topic using a more complex
dataset structure (actually the one I want to work on).
mydata
PlotModelIndividualsTimeObserved
Predicted
11A 1 0.05
1010.2
21A 1 0.10
2019.5
etc...
10 1B 1 0.05 10
9.8
11 1B 1 0.10 20
20.2
etc...
There are p levels in mydata$Plot, m in mydata$Model, n in
mydata$Individuals and t in mydata$Time (Note that I probably use the
word levels improperly as all columns are not factors). Basically, this
dataset summarizes the t measurements obtained in n individuals as well
as the predicted values from m different modeling approaches (applied to
all individuals). Therefore, the observations are repeated m times in
the Observed columns, while the predictions appears only once for a
given model an a given individual.
What I want to write is a R batch file creating a Trellis graph, where
each panel corresponds to one individual and contains the observations
(as scatterplot) plus the predicted values for all models (as lines of
different colors)... $Plot is just a token: it might be used to not
overload graphs in case there are too many tested models. The fun part
is that the values of p, m, n and t might vary from one dataset to the
other, so everything has to be coded dynamically.
For the plotting part I was thinking about having a loop in my code
containing something like that:
for (i in 1:nlevels(mydata$Model)) {
subdata-subset(mydata,mydata$Model=level(mydata$Model)[i])
xyplot(subset(Observed + Predicted ~ Time | Individuals, data =
subdata) #plus additionnal formatting code
}
Unfortunately, this code simply creates a new Trellis plot instead of
adding the model one by one on the panels. Any idea or link to a useful
command will wellcome.
Sebastien
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs
Thanks for the information. These commands work perfectly fine and the
?panel.superpose help was most informative.
If you don't mind, I will certainly come back to you as there will
certainly be additionnal complexities in my datasets that I will be
unable to handle (e.g. data in more than one data.frame, complex
conditions...).
Sebastien
Deepayan Sarkar a écrit :
On 6/11/07, Seb [EMAIL PROTECTED] wrote:
Hello
I apologize in advance if this question has already be posted on the
list, although I could not find a relevant thread in the archives.
I would like to overlay xyplots using different datasets for each plot.
I typically work on the following data.frame (mydata) structure
mydata
DrugTimeObserved Predicted
1 A0.05 10 10.2
2 A0.10 20 19.5
etc...
100 B0.05 11 12.7
101 B0.10 35 36
etc...
I want to plot the observed data as points and the predicted values as
lines. If I use the following commands, I don't have the possibility to
switch the y values from Observed for the scatterplot to Predicted for
the line.
xyplot(Observed ~ Time | Drug, data = mydata, panel = function(x,y,
...){
+panel.xyplot(x,y,...)
+panel.xyplot(x,y,type=l,...)})
I wonder if this problem can be solved using the trellis.focus family
commands but I have a hard time to understand how they work.
Please, let me know if a thread have already addressed this question.
Otherwise, I would grateful for any hint, comments or info you can
provide.
There are several possible solutions. In your case, the simplest one
would be something like (see ?panel.superpose for explanation):
xyplot(Observed + Predicted ~ Time | Drug, data = mydata,
type = c(p, l), distribute.type = TRUE)
This will work best if the Time values are ordered; otherwise you
could use
type = c(p, a)
instead, which will be a little slower. Let us know if this doesn't
give you what you want, preferably with a reproducible example
illustrating why.
-Deepayan
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs
On 6/12/07, Seb [EMAIL PROTECTED] wrote:
Hello
I apologize in advance if this question has already be posted on the
list, although I could not find a relevant thread in the archives.
I would like to overlay xyplots using different datasets for each plot.
I typically work on the following data.frame (mydata) structure
mydata
DrugTimeObserved Predicted
1 A0.05 10 10.2
2 A0.10 20 19.5
etc...
100 B0.05 11 12.7
101 B0.10 35 36
etc...
I want to plot the observed data as points and the predicted values as
lines. If I use the following commands, I don't have the possibility to
switch the y values from Observed for the scatterplot to Predicted for
the line.
xyplot(Observed ~ Time | Drug, data = mydata, panel = function(x,y, ...){
+panel.xyplot(x,y,...)
+panel.xyplot(x,y,type=l,...)})
I wonder if this problem can be solved using the trellis.focus family
commands but I have a hard time to understand how they work.
Another approach would be to use ggplot, http://had.co.nz/ggplot2.
Then your code might look something like:
ggplot(mydata, aes(x=Time)) +
geom_point(aes(y=Observed)) +
geom_line(aes(y = Predicted)) +
facet_grid(. ~ Drug)
Hadley
__
R-help@stat.math.ethz.ch 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.
Re: [R] Overlaying lattice graphs
On 6/11/07, Seb [EMAIL PROTECTED] wrote:
Hello
I apologize in advance if this question has already be posted on the
list, although I could not find a relevant thread in the archives.
I would like to overlay xyplots using different datasets for each plot.
I typically work on the following data.frame (mydata) structure
mydata
DrugTimeObserved Predicted
1 A0.05 10 10.2
2 A0.10 20 19.5
etc...
100 B0.05 11 12.7
101 B0.10 35 36
etc...
I want to plot the observed data as points and the predicted values as
lines. If I use the following commands, I don't have the possibility to
switch the y values from Observed for the scatterplot to Predicted for
the line.
xyplot(Observed ~ Time | Drug, data = mydata, panel = function(x,y, ...){
+panel.xyplot(x,y,...)
+panel.xyplot(x,y,type=l,...)})
I wonder if this problem can be solved using the trellis.focus family
commands but I have a hard time to understand how they work.
Please, let me know if a thread have already addressed this question.
Otherwise, I would grateful for any hint, comments or info you can provide.
There are several possible solutions. In your case, the simplest one
would be something like (see ?panel.superpose for explanation):
xyplot(Observed + Predicted ~ Time | Drug, data = mydata,
type = c(p, l), distribute.type = TRUE)
This will work best if the Time values are ordered; otherwise you could use
type = c(p, a)
instead, which will be a little slower. Let us know if this doesn't
give you what you want, preferably with a reproducible example
illustrating why.
-Deepayan
__
R-help@stat.math.ethz.ch 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.
