Hi Rosa,
I have had a moment to look at your code. First I think you should start
your device as:
quartz(width=12,height=5)
The split.screen code that I sent seems to work for me, giving the
3 4 5 6
2
7 8 9 10
layout of screens. To get the aspect ratio of the plots more similar, try
this:
# do the first split, to get the rightmost screen for the legend
split.screen(figs=matrix(c(0,0.84,0,1,0.84,1,0,1),nrow=2,byrow=TRUE))
# now split the first screen to get your eight screens (numbered 3 to 10)
for the plots
split.screen(figs=matrix(c(0,0.31,0.5,1,
0.31,0.54,0.5,1,
0.54,0.77,0.5,1,
0.77,1,0.5,1,
0,0.31,0,0.5,
0.31,0.54,0,0.5,
0.54,0.77,0,0.5,
0.77,1,0,0.5),
ncol=4,byrow=TRUE),screen=1)
I'm not sure of which plots should go on the top line and which on the
bottom, but I think you want margins like this:
screen(3)
par(mar=c(0,3.5,3,0))
screen(4)
par(mar=c(0,0,3,0))
screen(5)
par(mar=c(0,0,3,0))
screen(6)
par(mar=c(0,0,3,0))
screen(7)
par(mar=c(3,3.5,0,0))
screen(8)
par(mar=c(3,0,3,0))
screen(9)
par(mar=c(3,0,3,0))
screen(10)
par(mar=c(3,0,3,0))
Perhaps this will help.
Jim
On Fri, Sep 18, 2015 at 6:14 AM, Jim Lemon <drjimle...@gmail.com> wrote:
> Hi Rosa,
> I don't think the problem is with the split.screen command, for you are
> getting the eight plots and the screen at the right as you requested. It
> looks like your margins for each plot need adjusting, and I also think you
> should have about a 2.2 to 1 width to height ratio in the graphics device.
> I can't analyze the rest of the code at the moment, but perhaps tomorrow if
> you can't work it out I can provide some suggestions.
>
> Jim
>
>
> On Fri, Sep 18, 2015 at 1:16 AM, Rosa Oliveira <rosit...@gmail.com> wrote:
>
>> Dear Jim,
>>
>> It works, nonetheless, it doesn't slip the screen correctly :(
>>
>> Do you have any idea?
>>
>>
>> I used the code:
>>
>>
>> #setwd("/Users/RO/Dropbox/LMER - 3rdproblem/R/latest_version/graphs/data")
>> setwd("~/Dropbox/LMER - 3rdproblem/R/latest_version/graphs/data")
>>
>>
>> library(ggplot2)
>> library(reshape)
>> library(lattice)
>>
>>
>> # read in what looks like half of the data
>>
>> bias.alpha2<-read.csv("graphs_bias_alpha2.csv")
>> SE.alpha2<-read.csv("graphs_SE_alpha2.csv")
>> bias.alpha1<-read.csv("graphs_bias_alpha1.csv")
>> SE.alpha1<-read.csv("graphs_SE_alpha1.csv")
>>
>>
>>
>> quartz(width=10,height=6)
>>
>> # do the first split, to get the rightmost screen for the legend
>> split.screen(figs=matrix(c(0,0.84,0,1,0.84,1,0,1),nrow=2,byrow=TRUE))
>> # now split the first screen to get your eight screens (numbered 3 to 10)
>> for the plots
>> split.screen(figs=matrix(c(0,0.25,0.5,1,
>> 0.25,0.5,0.5,1,
>> 0.5,0.75,0.5,1,
>> 0.75,1,0.5,1,
>> 0,0.25,0,0.5,
>> 0.25,0.5,0,0.5,
>> 0.5,0.75,0,0.5,
>> 0.75,1,0,0.5),
>> ncol=4,byrow=TRUE),screen=1)
>>
>>
>>
>> #split.screen(figs=matrix(c(0,0.5,0.5,1,#primeira linha primeira coluna
>> # 0.5,1,0.5,1,#primeira linha segunda coluna
>> # 0,0.5,0,0.5,#segunda linha primeira coluna
>> # 0.5,1,0,0.5),#segunda linha segunda coluna
>> # ncol=4,byrow=TRUE),screen=1)
>>
>>
>> # this produces seven screens numbered like this:
>> # 3 4 5 6
>> # 2
>> # 7 8 9 10
>> # select the upper left screen
>>
>>
>>
>> screen(3)
>> par(mar=c(0,3.5,3,0))
>> # now the second set
>> n250<-bias.alpha1$nsample==250
>> matplot(x=bias.alpha1$lambda[n250],y=bias.alpha1[n250,3:5],
>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",ylim=c(-.1,
>> .6),main="nsample=250",ylab="", cex.main=1)
>> abline(h = 0, col = "gray60")
>> mtext(expression(paste("Bias av. for ",alpha[1])),side=2,line=2,
>> cex.main=1)
>>
>> screen(4)
>> par(mar=c(0,0,3,0))
>> # now the second set
>> n1000<-bias.alpha1$nsample==1000
>> matplot(x=bias.alpha1$lambda[n1000],y=bias.alpha1[n1000,3:5],
>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",yaxt="n",ylim=c(-.1,
>> .6),main="nsample=1000",ylab="")
>> abline(h = 0, col = "gray60")
>>
>>
>>
>> screen(5)
>> par(mar=c(0,3.5,3,0))
>> # now the second set
>> par(mar=c(3,3.5,0,0))
>> # now the second set
>> n250<-bias.alpha2$nsample==250
>> matplot(x=bias.alpha2$lambda[n250],y=bias.alpha2[n250,3:5],
>> type="l",pch=1:3,col=c(4,2,3),ylim=c(-.1, .6),ylab="")
>> abline(h = 0, col = "gray60")
>> mtext(expression(paste("Bias av. for ",alpha[2])),side=2,line=2,
>> cex.main=1.5)
>>
>> screen(6)
>> par(mar=c(3,0,0,0))
>> # now the second set
>> n1000<-bias.alpha2$nsample==1000
>> matplot(x=bias.alpha2$lambda[n1000],y=bias.alpha2[n1000,3:5],
>> type="l",pch=1:3,col=c(4,2,3),yaxt="n",ylim=c(-.1, .6))
>> abline(h = 0, col = "gray60")
>>
>>
>>
>>
>> screen(7)
>> par(mar=c(0,3.5,3,0))
>> # now the second set
>> n250<-SE.alpha1$nsample==250
>> matplot(x=SE.alpha1$lambda[n250],y=SE.alpha1[n250,3:5],
>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",ylim=c(0,
>> 1.1),main="nsample=250",ylab="", cex.main=1)
>> abline(h = -1, col = "gray60")
>> mtext(expression(paste("SE av. for ",alpha[1])),side=2,line=2,
>> cex.main=1)
>> mtext(expression(paste(lambda)),side=1,line=2, cex.main=1.5)
>>
>>
>> screen(8)
>> par(mar=c(0,0,3,0))
>> # now the second set
>> n1000<-SE.alpha1$nsample==1000
>> matplot(x=SE.alpha1$lambda[n1000],y=SE.alpha1[n1000,3:5],
>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",yaxt="n",ylim=c(0,
>> 1.1),main="nsample=1000",ylab="")
>> abline(h = -1, col = "gray60")
>>
>>
>>
>>
>> screen(9)
>> par(mar=c(3,3.5,0,0))
>> # now the second set
>> n250<-SE.alpha2$nsample==250
>> matplot(x=SE.alpha2$lambda[n250],y=SE.alpha2[n250,3:5],
>> type="l",pch=1:3,col=c(4,2,3),ylim=c(0, 1.1),ylab="")
>> abline(h = -.5, col = "gray60")
>> mtext(expression(paste("SE av. for ",alpha[2])),side=2,line=2,
>> cex.main=1.5)
>> mtext(expression(paste(lambda)),side=1,line=2, cex.main=1.5)
>>
>>
>> screen(10)
>> par(mar=c(3,0,0,0))
>> # now the second set
>> n1000<-SE.alpha2$nsample==1000
>> matplot(x=SE.alpha2$lambda[n1000],y=SE.alpha2[n1000,3:5],
>> type="l",pch=1:3,col=c(4,2,3),yaxt="n",ylim=c(0, 1.1))
>> abline(h = -.5, col = "gray60")
>> mtext(expression(paste(lambda)),side=1,line=2, , cex.main=1.5)
>>
>>
>>
>> screen(2)
>> par(mar=c(0,0,0,0))
>> # plot an empty plot to get the coordinates
>> plot(0:1,0:1,type="n",axes=FALSE)
>> legend(0,0.6,c("OLS", "GLS", "Reg. Cal.", "0"),bty = "n",
>> lty=1:3,col=c(4,2,3,"gray60"),xpd=TRUE)
>>
>>
>> close.screen(all=TRUE)
>>
>>
>> and I attach the output graph.
>>
>>
>>
>> Best,
>> RO
>>
>> Atenciosamente,
>> Rosa Oliveira
>>
>> _________________________________
>>
>> Antes de imprimir este e-mail pense bem se tem mesmo que o fazer.
>> Há cada vez menos árvores.
>> Não imprima, pense na sua responsabilidade e compromisso com o MEIO
>> AMBIENTE!
>>
>> <http://pt.dreamstime.com/cora-ccedil-atildeo-criado-das-folhas-de-aacutervores-diferentes-thumb12275776.jpg>
>>
>> <http://pt.dreamstime.com/cora-ccedil-atildeo-criado-das-folhas-de-aacutervores-diferentes-thumb12275776.jpg>
>>
>> 2015-09-17 12:18 GMT+01:00 Jim Lemon <drjimle...@gmail.com>:
>>
>>> Hi Rosa,
>>> Try this:
>>>
>>> # do the first split, to get the rightmost screen for the legend
>>> split.screen(figs=matrix(c(0,0.84,0,1,0.84,1,0,1),nrow=2,byrow=TRUE))
>>> # now split the first screen to get your eight screens (numbered 3 to
>>> 10) for the plots
>>> split.screen(figs=matrix(c(0,0.25,0.5,1,
>>> 0.25,0.5,0.5,1,
>>> 0.5,0.75,0.5,1,
>>> 0.75,1,0.5,1,
>>> 0,0.25,0,0.5,
>>> 0.25,0.5,0,0.5,
>>> 0.5,0.75,0,0.5,
>>> 0.75,1,0,0.5),
>>> ncol=4,byrow=TRUE),screen=1)
>>>
>>> Jim
>>>
>>>
>>> On Thu, Sep 17, 2015 at 2:45 AM, Rosa Oliveira <rosit...@gmail.com>
>>> wrote:
>>>
>>>> Dear all,
>>>>
>>>> I’m trying to do a graph,
>>>>
>>>> 3 rows, 5 columns, with the design:
>>>> # 3 4 5 6
>>>> # 2
>>>> # 7 8 9 10
>>>>
>>>> I had a code for 3 rows, 3 columns, with the design::
>>>> # 3 4
>>>> # 2
>>>> # 7 8
>>>> and I tried to modify it, but I had no success :(
>>>>
>>>> I suppose the problem is in the slip.screen code (red part of the code).
>>>>
>>>> I attach my code, can anyone please help me?
>>>>
>>>>
>>>> Best,
>>>> RO
>>>>
>>>>
>>>> setwd("/Users/RO/Dropbox/LMER -
>>>> 3rdproblem/R/latest_version/graphs/data")
>>>>
>>>> library(ggplot2)
>>>> library(reshape)
>>>> library(lattice)
>>>>
>>>>
>>>> # read in what looks like half of the data
>>>>
>>>> bias.alpha2<-read.csv("graphs_bias_alpha2.csv")
>>>> SE.alpha2<-read.csv("graphs_SE_alpha2.csv")
>>>> bias.alpha1<-read.csv("graphs_bias_alpha1.csv")
>>>> SE.alpha1<-read.csv("graphs_SE_alpha1.csv")
>>>>
>>>>
>>>>
>>>> quartz(width=10,height=6)
>>>> # do the first split, to get the rightmost screen for the legend
>>>> split.screen(figs=matrix(c(0,0.8,0,1,0.8,1,0,1),nrow=2,byrow=TRUE))
>>>> # now split the first screen to get your six screens for the plots
>>>>
>>>>
>>>>
>>>> split.screen(figs=matrix(c(0,0.5,0.5,1,#primeira linha primeira coluna
>>>> 0.5,1,0.5,1,#primeira linha segunda coluna
>>>> 0,0.5,0,0.5,#segunda linha primeira coluna
>>>> 0.5,1,0,0.5),#segunda linha segunda coluna
>>>> ncol=4,byrow=TRUE),screen=1)
>>>>
>>>>
>>>> # this produces seven screens numbered like this:
>>>> # 3 4 5 6
>>>> # 2
>>>> # 7 8 9 10
>>>> # select the upper left screen
>>>>
>>>>
>>>>
>>>> screen(3)
>>>> par(mar=c(0,3.5,3,0))
>>>> # now the second set
>>>> n250<-bias.alpha1$nsample==250
>>>> matplot(x=bias.alpha1$lambda[n250],y=bias.alpha1[n250,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",ylim=c(-.1,
>>>> .6),main="nsample=250",ylab="", cex.main=1)
>>>> abline(h = 0, col = "gray60")
>>>> mtext(expression(paste("Bias av. for ",alpha[1])),side=2,line=2,
>>>> cex.main=1)
>>>>
>>>> screen(4)
>>>> par(mar=c(0,0,3,0))
>>>> # now the second set
>>>> n1000<-bias.alpha1$nsample==1000
>>>> matplot(x=bias.alpha1$lambda[n1000],y=bias.alpha1[n1000,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",yaxt="n",ylim=c(-.1,
>>>> .6),main="nsample=1000",ylab="")
>>>> abline(h = 0, col = "gray60")
>>>>
>>>>
>>>>
>>>> screen(5)
>>>> par(mar=c(0,3.5,3,0))
>>>> # now the second set
>>>> par(mar=c(3,3.5,0,0))
>>>> # now the second set
>>>> n250<-bias.alpha2$nsample==250
>>>> matplot(x=bias.alpha2$lambda[n250],y=bias.alpha2[n250,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),ylim=c(-.1, .6),ylab="")
>>>> abline(h = 0, col = "gray60")
>>>> mtext(expression(paste("Bias av. for ",alpha[2])),side=2,line=2,
>>>> cex.main=1.5)
>>>>
>>>> screen(6)
>>>> par(mar=c(3,0,0,0))
>>>> # now the second set
>>>> n1000<-bias.alpha2$nsample==1000
>>>> matplot(x=bias.alpha2$lambda[n1000],y=bias.alpha2[n1000,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),yaxt="n",ylim=c(-.1, .6))
>>>> abline(h = 0, col = "gray60")
>>>>
>>>>
>>>>
>>>>
>>>> screen(7)
>>>> par(mar=c(0,3.5,3,0))
>>>> # now the second set
>>>> n250<-SE.alpha1$nsample==250
>>>> matplot(x=SE.alpha1$lambda[n250],y=SE.alpha1[n250,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",ylim=c(0,
>>>> 1.1),main="nsample=250",ylab="", cex.main=1)
>>>> abline(h = -1, col = "gray60")
>>>> mtext(expression(paste("SE av. for ",alpha[1])),side=2,line=2,
>>>> cex.main=1)
>>>> mtext(expression(paste(lambda)),side=1,line=2, cex.main=1.5)
>>>>
>>>>
>>>> screen(8)
>>>> par(mar=c(0,0,3,0))
>>>> # now the second set
>>>> n1000<-SE.alpha1$nsample==1000
>>>> matplot(x=SE.alpha1$lambda[n1000],y=SE.alpha1[n1000,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),xaxt="n",yaxt="n",ylim=c(0,
>>>> 1.1),main="nsample=1000",ylab="")
>>>> abline(h = -1, col = "gray60")
>>>>
>>>>
>>>>
>>>>
>>>> screen(9)
>>>> par(mar=c(3,3.5,0,0))
>>>> # now the second set
>>>> n250<-SE.alpha2$nsample==250
>>>> matplot(x=SE.alpha2$lambda[n250],y=SE.alpha2[n250,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),ylim=c(0, 1.1),ylab="")
>>>> abline(h = -.5, col = "gray60")
>>>> mtext(expression(paste("SE av. for ",alpha[2])),side=2,line=2,
>>>> cex.main=1.5)
>>>> mtext(expression(paste(lambda)),side=1,line=2, cex.main=1.5)
>>>>
>>>>
>>>> screen(10)
>>>> par(mar=c(3,0,0,0))
>>>> # now the second set
>>>> n1000<-SE.alpha2$nsample==1000
>>>> matplot(x=SE.alpha2$lambda[n1000],y=SE.alpha2[n1000,3:5],
>>>> type="l",pch=1:3,col=c(4,2,3),yaxt="n",ylim=c(0, 1.1))
>>>> abline(h = -.5, col = "gray60")
>>>> mtext(expression(paste(lambda)),side=1,line=2, , cex.main=1.5)
>>>>
>>>>
>>>>
>>>> screen(2)
>>>> par(mar=c(0,0,0,0))
>>>> # plot an empty plot to get the coordinates
>>>> plot(0:1,0:1,type="n",axes=FALSE)
>>>> legend(0,0.6,c("OLS", "GLS", "Reg. Cal.", "0"),bty = "n",
>>>> lty=1:3,col=c(4,2,3,"gray60"),xpd=TRUE)
>>>>
>>>>
>>>> close.screen(all=TRUE)
>>>>
>>>>
>>>>
>>>>
>>>> Best,
>>>> RO
>>>>
>>>>
>>>> Atenciosamente,
>>>> Rosa Oliveira
>>>>
>>>> --
>>>>
>>>> ____________________________________________________________________________
>>>>
>>>>
>>>> Rosa Celeste dos Santos Oliveira,
>>>>
>>>> E-mail: rosit...@gmail.com
>>>> Tlm: +351 939355143
>>>> Linkedin: https://pt.linkedin.com/in/rosacsoliveira
>>>>
>>>> ____________________________________________________________________________
>>>> "Many admire, few know"
>>>> Hippocrates
>>>>
>>>> ______________________________________________
>>>> R-help@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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