In order to display a polygon, you need x/y pairs for each point. If
you just want a rectangle, you only need four x/y pairs, e.g.:
plot(0,xlim=x(2.44,2.57),ylim=c(0,1),type="n")
polygon(c(2.44,2.57,2.57,2.44),c(0,0,1,1),col="lightgray")
Now if you have a series of x values and want to display a band of
constant width around it:
y_values<-runif(14)
plot(seq(2.44,2.57,by=0.01),y_values,ylim=c(-2,3))
dispersion(seq(2.44,2.57,by=0.01),y_values,ulim=rep(0.5,14),
type="l",interval=TRUE,col="lightgray")
lines(seq(2.44,2.57,by=0.01),y_values)
Jim
On Fri, Dec 2, 2016 at 8:59 PM, Elysa Mitova <elysa.mit...@gmail.com> wrote:
> Thank you,
>
> this seems to work, but it is not exactly what I need (it indeed looks
> great, but a bit beyond my understanding)
>
> I just need a shaded area between 2.44 to 2.57 along the x-axis - a polygon
> inserted into my density plot (and not a confidence line along a scatter
> plot like your suggested solution)
>
> My x-axis is an index (a data frame), my y-axis is the automatically
> constructed density
>
> On Fri, Dec 2, 2016 at 10:01 AM, Jim Lemon <drjimle...@gmail.com> wrote:
>>
>> Hi Elysa,
>> I think you are going a bit off course in your example. Try this and
>> see if it is close to what you want:
>>
>> data<-rnorm(100)+runif(100,0,15)
>> smu_data<-supsmu(1:100,data)
>> rollfun<-function(x,window=10,FUN=sd) {
>> xlen<-length(x)
>> xout<-NA
>> forward<-window%/%2
>> backward<-window-forward
>> for(i in 1:xlen) {
>> xstart<-i-backward
>> if(xstart < 1) xstart<-1
>> xend<-i+forward-1
>> if(xend > xlen) xend<-xlen
>> xout[i]<-do.call(FUN,list(x[xstart:xend],na.rm=TRUE))
>> }
>> return(xout)
>> }
>> mad_data<-rollfun(data,10,mad)
>> plot(data,ylim=c(0,17))
>> library(plotrix)
>> dispersion(smu_data$x,smu_data$y,mad_data,type="l",interval=TRUE,
>> fill="lightgray")
>> lines(smu_data,lwd=2)
>> points(1:100,data)
>>
>> Jim
>>
>>
>> On Fri, Dec 2, 2016 at 7:18 PM, Elysa Mitova <elysa.mit...@gmail.com>
>> wrote:
>> > Hi, thank you!
>> >
>> > I've constructed the upper and lower bounds with
>> >
>> > a <- 2.505766
>> > s <- 0.7789832
>> > n <- 607
>> > error <- qnorm(0.975)*s/sqrt(n)
>> > left <- a-error
>> > right <- a+error
>> > left
>> > right
>> >
>> > Now, I have the numbers I need, but I have no idea how to plot them. I
>> > was
>> > thinking of using a polygon, but somehow it doesn't work out, because my
>> > y-axis shows only density and is in itself not a variable?
>> >
>> > xx <- data
>> >
>> > fit1 <- density(data,na.rm=TRUE)
>> >
>> > fit2 <- replicate(10000, { x <- sample(xx, replace=TRUE);
>> > density(x, na.rm=TRUE, from=min(fit1$x), to=max(fit1$x))$y } )
>> >
>> > fit3 <- apply(fit2, 1, quantile, c(0.025,0.975) ) - Probably herein
>> > lies the problem?
>> >
>> > plot(fit1, ylim=range(fit3))
>> > polygon( c(fit1$x, rev(fit1$x)), c(fit3[1,], rev(fit3[2,])),
>> > col='grey', border=F)
>> > lines(fit1)
>> >
>> > I tried working with this solution I found on the internet, but
>> > somehow now the lines the shaded areas sporadically everywhere around
>> > my density plot? I just want a polygon spreading from 2.44 to 2.57
>> > along the x-axis.
>> >
>> >
>> > Any tipps?
>> >
>> >
>> >
>> >
>> > On Fri, Dec 2, 2016 at 1:24 AM, David Winsemius <dwinsem...@comcast.net>
>> > wrote:
>> >
>> >>
>> >> > On Dec 1, 2016, at 12:10 PM, Elysa Mitova <elysa.mit...@gmail.com>
>> >> wrote:
>> >> >
>> >> > Hi,
>> >> >
>> >> > I am desperately looking for a way to plot confidence intervals into
>> >> > a
>> >> > density plot of only one variable (not a scatter plot etc.)
>> >> >
>> >> > Have you any advice how to do this?
>> >> >
>> >> > I've only found manual ways to do with "abline", but this is a rather
>> >> > bothersome method and only works with ggplot (and not ggplot2).
>> >>
>> >> This makes it appear that you expect this to be done in ggplot2
>> >> automagically. I suspect you must instead first find the right approach
>> >> to
>> >> construction of those upper and lower bounds before plotting. It's not
>> >> clear what methods you expect to be needed. Your desperation is not a
>> >> guide. Perhaps trying a bit of searching?
>> >>
>> >> install.packages("sos")
>> >> library(sos)
>> >> findFn("confidence intervals density estimates")
>> >>
>> >>
>> >> Delivers quite a few results. Then searching on the text within that
>> >> webpage you find
>> >>
>> >>
>> >> 208 2 27 54 nprobust kdrobust
>> >> 2016-11-14
>> >> 16:41:50 27 Kernel Density Estimation with Robust Confidence
>> >> Intervals
>> >> 209 2 27 54 nprobust lprobust
>> >> 2016-11-14
>> >> 16:41:50 27 Local-Polynomial Estimation with Robust Confidence
>> >> Intervals
>> >>
>> >> Is that what you seek?
>> >>
>> >> >
>> >> > Thank you!
>> >> >
>> >> > [[alternative HTML version deleted]]
>> >> I know you just subscribed, so now is the time to read the Posing
>> >> Guide.
>> >>
>> >> ==
>> >>
>> >> David Winsemius
>> >> Alameda, CA, USA
>> >>
>> >>
>> >
>> > [[alternative HTML version deleted]]
>> >
>> > ______________________________________________
>> > 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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