Plotting Confidence Intervals into a density plot

Hang on, maybe you mean something like this:

erupt_dens<-density(faithful$eruptions)
plot(erupt_dens,ylim=c(0,0.65))
dispersion(erupt_dens$x,erupt_dens$y,ulim=erupt_dens$y/5,
 type="l",fill="lightgray",interval=TRUE)
lines(erupt_dens)

Jim


On Fri, Dec 2, 2016 at 9:36 PM, Jim Lemon <drjimlemon at gmail.com> wrote:

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.mitova at 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 <drjimlemon at 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.mitova at 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 <

dwinsemius at comcast.net>

wrote:

On Dec 1, 2016, at 12:10 PM, Elysa Mitova <elysa.mitova at 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!

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==

David Winsemius
Alameda, CA, USA

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