R help - May 2014 - Lattice Histogram with Normal Curve - Y axis as percentages

Hello,

This may seem like a simple problem, but it's frustrating me immensely. 
I'm
trying to overlay a normal curve (dnorm) on top of a histogram using the
code below.  This works find when the type = "density", but the person
for
whom I'm making the plot wants the y axis in percent of total rather than
density.  When I change type to "percent", I get the histogram scale
I'm
after, but the dnorm plot is greatly reduced.  How could I scale the density
plot to the percent of total axis.  Alternatively, perhaps there is a way to
add density to a secondary y axis?

Thanks in advance for your help.

Jimdare


plot<-histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip my.strip,xlab
= j,
   type = "percent",layout=c(2,1),
   panel=function(x, ...) {                            
   panel.histogram(x, ...)

   panel.mathdensity(dmath=dnorm, col="black", 
  # Add na.rm = TRUE to mean() and sd()
    args=list(mean=mean(x, na.rm = TRUE),
    sd=sd(x, na.rm = TRUE)), ...)            
                                        }) 



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Hi Jim

Without going to the histogram function to study it in detail it appears 
when there is  density used  in the histogram function the data has to be in
the original scale to to send to the panel function.

As you example is not reproducible use the singer data to get the ylimits
for the histogram and density plot separately

For density
d <-
histogram( ~ height | voice.part, data = singer,
          xlab = "Height (inches)", type = "density",
          panel = function(x, ...) {
             # panel.histogram(x, ...)
              panel.abline(v= 70)
              panel.mathdensity(dmath = dnorm, col = "black",
                                args = list(mean=mean(x),sd=sd(x)))
          } )

d$y.limits ...

and the same for the histogram with type = percent

if you divide the last by the former there is a difference of 192 (ie
scaling factor)

Using the full dataset as it is quicker to demonstrate

histogram( ~ height, data = singer,
          type = "percent", border = "transparent", col.line
= "grey60",
          xlab = "Height (inches)",
          ylab = "Density Histogram\n with Normal Fit" )

trellis.focus("panel", 1, 1, clip.off=F, highlight = FALSE)
llines(density(singer$height)$x, density(singer$height)$y*192)
trellis.unfocus()

This will give you the idea that it is possible. y axes labels etc are all
funny now

By using a user defined panel function  plotting the histogram and the
density output plotted as panel.lines after scaling within the function you
should be able to get a plot
It may be easier to start off with an xyplot to send the data to the panel
function as you will need 2 types of data as original and that for the
histogram.
Whether you need to use panel.groups is another mater

HTH

Duncan

Duncan Mackay
Department of Agronomy and Soil Science
University of New England
Armidale NSW 2351
Email: home: mackay at northnet.com.au


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On
Behalf Of jimdare
Sent: Tuesday, 6 May 2014 06:23
To: r-help at r-project.org
Subject: [R] Lattice Histogram with Normal Curve - Y axis as percentages

Hello,

This may seem like a simple problem, but it's frustrating me immensely. 
I'm
trying to overlay a normal curve (dnorm) on top of a histogram using the
code below.  This works find when the type = "density", but the person
for
whom I'm making the plot wants the y axis in percent of total rather than
density.  When I change type to "percent", I get the histogram scale
I'm
after, but the dnorm plot is greatly reduced.  How could I scale the density
plot to the percent of total axis.  Alternatively, perhaps there is a way to
add density to a secondary y axis?

Thanks in advance for your help.

Jimdare


plot<-histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip my.strip,xlab
= j,
   type = "percent",layout=c(2,1),
   panel=function(x, ...) {                            
   panel.histogram(x, ...)

   panel.mathdensity(dmath=dnorm, col="black", 
  # Add na.rm = TRUE to mean() and sd()
    args=list(mean=mean(x, na.rm = TRUE),
    sd=sd(x, na.rm = TRUE)), ...)            
                                        }) 



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percentages-tp4690000.html
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______________________________________________
R-help at r-project.org mailing list
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.

This illustrates why you really do not want percents... (I never quite
understand why people do want them - I can understand raw counts wheh used in
teaching as a precursor to the concept of a density, but percentages is an odd
in-between sort of thing.)

Anyways, the scaling factor is the bin width (you figure out whether to multiply
or divide, I get it wrong every second time), possibly multiplied by 100%. A
pragmatic way out would seem to be switch out dnorm with dnorm_scaled <-
function(...) scale*dnorm(...) in the call to panel.mathdensity.

-Peter D

On 05 May 2014, at 22:23 , jimdare <james.dare at es.govt.nz> wrote:
> Hello,
> 
> This may seem like a simple problem, but it's frustrating me immensely.
I'm
> trying to overlay a normal curve (dnorm) on top of a histogram using the
> code below.  This works find when the type = "density", but the
person for
> whom I'm making the plot wants the y axis in percent of total rather
than
> density.  When I change type to "percent", I get the histogram
scale I'm
> after, but the dnorm plot is greatly reduced.  How could I scale the
density
> plot to the percent of total axis.  Alternatively, perhaps there is a way
to
> add density to a secondary y axis?
> 
> Thanks in advance for your help.
> 
> Jimdare
> 
> 
> plot<-histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip >
my.strip,xlab = j,
>   type = "percent",layout=c(2,1),
>   panel=function(x, ...) {                            
>   panel.histogram(x, ...)
> 
>   panel.mathdensity(dmath=dnorm, col="black", 
>  # Add na.rm = TRUE to mean() and sd()
>    args=list(mean=mean(x, na.rm = TRUE),
>    sd=sd(x, na.rm = TRUE)), ...)            
>                                        }) 
> 
> 
> 
> --
> View this message in context:
http://r.789695.n4.nabble.com/Lattice-Histogram-with-Normal-Curve-Y-axis-as-percentages-tp4690000.html
> Sent from the R help mailing list archive at Nabble.com.
> 
> ______________________________________________
> R-help at r-project.org mailing list
> 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.
-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com

Thanks for your help Duncan and Peter!  I ended up using a combination of
your suggestions.  I used Duncan's y.limits ration of two plots with
differing types (percent and density), to provide me with a scale variable. 
I then used this in Peter's dnorm_scaled function and called it using
panel.mathdensity.  See below for my amended code.

Regards,
Jim


x1<-histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip = my.strip,xlab
= j,  
                  type = "density",layout=c(2,1))
  
x2<-histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip = my.strip,xlab
= j,  
                type = "percent",layout=c(2,1))

scale <- x2$y.limits/x1$y.limits
  
dnorm_scaled <- function(...){ scale[1]*dnorm(...)}
  
histogram(~rdf[,j]|Year,nint=20, data=rdf,main = i,strip = my.strip,xlab j,  
            type = "percent",layout=c(2,1),
            panel=function(x, ...) {
                            
              panel.histogram(x, ...)
              
              panel.mathdensity(dmath=dnorm_scaled, col="black", 
                                # Add na.rm = TRUE to mean() and sd()
                                args=list(mean=mean(x, na.rm = TRUE),
                                          sd=sd(x, na.rm = TRUE)), ...)

            })



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