On Mar 13, 2015, at 9:27 AM, Mir Salam wrote:
>
> __Dear all,
>
> You can see the plot specif curves in the enclosed document. Inclusion of all
> plot specif curves in one plot may not be look good and finally it will be
> unreadable. I am agree with Petrr and John. This graph is more readable.
>
You should remember that both lattice (and, I seem to vaguely remember,
ggplot2) allow specification of three "dimensions" for plot layout, the third
being for pages. For xyplot which I think is the underlying mechanism for
nmle-plot methods, the parameter is `layout` which can be a vector of length 2
or 3. This would only apply to multipage graphics devices but you seem to ahve
figured out how to make PDFs so that should apply.
>
> Best regards
> Salam
>
>
>
> ______________________________________
> From: John Kane <jrkrid...@inbox.com>
> Sent: Friday, March 13, 2015 4:12 PM
> To: PIKAL Petr; Mir Salam; r-help@r-project.org
> Subject: Re: [R] graphs
>
> @Petr
>
> I agree. I think Mir would get a totally unreadable graph. I occasionally
> look at some spagetti graphs from climate research, and I find 8 - 12 lines
> are incomprehensible (I'm not a subject matter expert)'
>
> @Mir
>
> Is there any logical way to break down the data ? I think Petr is correct in
> that ggplot2 should be able to do it but I'd suggest thinking about, perhaps,
> facetting the data to produce some reasonable number of panels.
>
> Here is a simple three-panel plot to illustrate what I mean but I have easily
> produced a 9 or 10 panel plot which reduces visual clutter immensely.
>
> library(plyr) library(ggplot2)
>
> df1 = data.frame(basel_asset_class =
> c("bank","bank","bank","bank","bank","bank","bank","corporate","corporate","corporate","corporate","corporate","corporate","corporate","sovereign","sovereign","sovereign","sovereign","sovereign","sovereign","sovereign"),
> ratings =
> c("AAA","AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC"),
> default_probability =
> c(0.0027,0.0029,0.0031,0.0034,0.0037,0.004,0.0043,0.0025,0.0024,0.0024,0.0023,0.0022,0.0021,0.0021,0.003,0.0031,0.0032,0.0033,0.0034,0.0035,0.0036))
>
> names(df1) <- c("class", "rate", "default")
>
> p <- ggplot(df1, aes(rate, default,colour=class)) + geom_point() +
> facet_grid(class ~.) + theme(legend.position="none")
> p
>
> John Kane
> Kingston ON Canada
>
>
>> -----Original Message-----
>> From: petr.pi...@precheza.cz
>> Sent: Fri, 13 Mar 2015 11:34:42 +0000
>> To: mir.sa...@uef.fi, r-help@r-project.org
>> Subject: Re: [R] graphs
>>
>> Hi
>>
>> Your example is not reproducible, but I presume you could use ggplot
>> together with predict. However, I wonder how do you want to distinguish
>> 68 curves in one picture.
>> I would
>>
>> Cheers
>> Petr
>>
>>
>>> -----Original Message-----
>>> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Mir
>>> Salam
>>> Sent: Sunday, March 08, 2015 10:57 PM
>>> To: r-help@r-project.org
>>> Subject: [R] graphs
>>>
>>> Dear all,
>>>
>>> I need help to get different 68 plots specifc fitted curves in one
>>> plot with respective field data observations (age vs dominant height).
>>>
>>>
>>>
>>> aspdomH2<-groupedData(domH2~age|plotno,data=aspdomH2)
>>>
>>>
>>>
>>> names(aspdomH2)
>>>
>>> plotno, age, origin, soilcharacter, domH2,
>>>
>>>
>>>
>>> plotno-different plot no. I have 68 plots
>>>
>>> age- every plot have from age 5 to 30 years
>>>
>>> origin- two, native aspen and hybrid aspen
>>>
>>> domH2<-dominant height
>>>
>>> soilcharacter-3, clay, silt and mold. both origin have different soil
>>> charcter
>>>
>>>
>>>
>>> #### then I fit model
>>>
>>> fm2cham.nlme<-nlme(domH2~cham(age,b0,b1,b2),
>>> data=aspdomH2,
>>> fixed = list(b0~1+origin+soilcharacter,b1~ 1,b2 ~
>>> 1+origin+soilcharacter),
>>> random = b0+b2~1|plotno,
>>> start=c(b0=26.3387,0,0,0,b1=0.1065,b2=1.9453,0,0,0),
>>> weights=varPower(form = ~age, 0.5),
>>> correlation=corAR1())
>>>
>>>
>>>
>>> #### parameter values
>>>
>>> Fixed effects: list(b0 ~ 1 + origin + soil character, b1 ~ 1, b2 ~ 1 +
>>> origin + soil character)
>>>
>>> Value
>>> b0.(Intercept) 21.081124
>>> b0.origin1 7.735064
>>> b0.soilcharactermold 10.689051
>>> b0.soilcharactersilt 3.906585
>>> b1 0.079035
>>> b2.(Intercept) 1.616360
>>> b2.origin1 -0.384421
>>> b2.soilcharactermold 0.612285
>>> b2.soilcharactersilt 0.527462
>>>
>>>
>>>
>>> ##### I can easily get the augmented plot. I got different 68 plots
>>> specific curves.
>>>
>>> #######
>>>
>>> Any body can help me how can I will get all 68 plots specific fitted
>>> curves in one plot with respective plot specific age and dominant
>>> height obervations? (x axis will represent age, y axis will represent
>>> dominant height and fitted curves of all 68 plots)
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
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> <aug-2.pdf>______________________________________________
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