R help - Jul 2005 - Overlying a Normal Dist in a Barplot

R-Users,
Hopefully someone can shed some light on these questions as I had
little luck searching the archives (although I probably missed something
in my search due to the search phrase).  I estimated multinomial
probabilities for some count data (number successful offspring) ranging
from 0 to 8 (9 possible response categories).  I constructed a barplot
(using barplot2) and I want to "overlay" a normal distribution on the
figure (using rnorm (1000, mean, sd)).  My intent is to show that using
a mean(and associated sd) estimated from discrete count data may not be
a valid representation of the distribution of successful offspring.  

Obviously the x and y axes (as structured in barplot2) will not be
equivalent for these 2 sets of information and this shows up in my
example below. 

1)  Is it possible to somehow reconcile the underlying x-axis to the
same scale as would be needed to overly the normal distribution (e.g.
where 2.5 would fall on the normal density, I could relate it to 2.5 on
the barplot)?  Then, using axis (side=4) I assume I could insert a
y-axis for the normal distribution.

2)  Is lines(density(x)) the appropriate way to insert a normal
distribution into this type of figure?  Should I use 'curve'?

If someone could point me in the right direction, I would appreciate
it.

TIA, Bret

Example:

testdata 
0    0.196454948
1    0.063515510
2    0.149187592
3    0.237813885
4    0.282127031
5    0.066469719
6    0.001477105
7    0.001477105
8    0.001477105


x<-rnorm(1000, 2.84, 1.57)
barplot2(testdata, xlab="Fledgling Number", 
             ylab="Probability", ylim=c(0, 1), col="black", 
             border="black", axis.lty=1)
lines(density(x))


--Version--
platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    0.1            
year     2004           
month    11             
day      15             
language R

On 7/8/05, Bret Collier <bret at tamu.edu> wrote:
> R-Users,
> Hopefully someone can shed some light on these questions as I had
> little luck searching the archives (although I probably missed something
> in my search due to the search phrase).  I estimated multinomial
> probabilities for some count data (number successful offspring) ranging
> from 0 to 8 (9 possible response categories).  I constructed a barplot
> (using barplot2) and I want to "overlay" a normal distribution on
the
> figure (using rnorm (1000, mean, sd)).  My intent is to show that using
> a mean(and associated sd) estimated from discrete count data may not be
> a valid representation of the distribution of successful offspring.
> 
> Obviously the x and y axes (as structured in barplot2) will not be
> equivalent for these 2 sets of information and this shows up in my
> example below.
> 
> 1)  Is it possible to somehow reconcile the underlying x-axis to the
> same scale as would be needed to overly the normal distribution (e.g.
> where 2.5 would fall on the normal density, I could relate it to 2.5 on
> the barplot)?  Then, using axis (side=4) I assume I could insert a
> y-axis for the normal distribution.
> 
> 2)  Is lines(density(x)) the appropriate way to insert a normal
> distribution into this type of figure?  Should I use 'curve'?
> 
> If someone could point me in the right direction, I would appreciate
> it.
> 
> TIA, Bret
> 
> Example:
> 
> testdata
> 0    0.196454948
> 1    0.063515510
> 2    0.149187592
> 3    0.237813885
> 4    0.282127031
> 5    0.066469719
> 6    0.001477105
> 7    0.001477105
> 8    0.001477105
> 
> 
> x<-rnorm(1000, 2.84, 1.57)
> barplot2(testdata, xlab="Fledgling Number",
>             ylab="Probability", ylim=c(0, 1),
col="black",
>             border="black", axis.lty=1)
> lines(density(x))
> 
Maybe something like this using rect and curve:

# data from your post
testdata <- c(0.196454948, 0.06351551, 0.149187592, 0.237813885, 
  0.282127031, 0.066469719, 0.001477105, 0.001477105, 0.001477105)
x <- 0:9

# setup plot ranges noting max of normal density is at mean
xrange <- range(x) + c(-0.5,+0.5)
yrange <- range(c(testdata, dnorm(2.84, 2.84, 1.57), 0))
plot(xrange, yrange, type = "n", xlab = "X", ylab =
"Probability", xaxt = "n")
axis(1, x)

# draw bars using rect and density using curve
rect(x - 0.5, 0, x + 0.5, testdata, col = "lightgrey")
curve(dnorm(x, 2.84, 1.57), min(xrange), max(xrange), add = TRUE)