[R] graphs
John Kane
jrkrideau at inbox.com
Fri Mar 13 15:12:17 CET 2015
@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.pikal at precheza.cz
> Sent: Fri, 13 Mar 2015 11:34:42 +0000
> To: mir.salam at uef.fi, r-help at 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-bounces at r-project.org] On Behalf Of Mir
>> Salam
>> Sent: Sunday, March 08, 2015 10:57 PM
>> To: r-help at 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]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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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.
>
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