R help - Sep 2014 - plots on log="y" scale with smooths

In the following example, I am trying to plot a response on a log scale, 
and add one or more smoothed
curves, e.g., lowess and abline.  In base graphics, I'd like to do this 
using log="y", so that the Y axis is
spaced on the log scale, but labeled as the actual response values. 
Using ggplot2, I'm using
scale_y_log10 to achieve the same purpose.  However, my attempts to add 
the smooths differ
considerably, so I must be missing something.

Here's the data I'm working with for one example:

data("CodParasites", package = "countreg")
## omit NAs in response & predictor
CodParasites <- subset(CodParasites, !(is.na(intensity) | is.na(length)))
## plot only positive values of intensity
CPpos <- subset(CodParasites, intensity>0)

Here's the base graphics plot.  The abline() is clearly wrong and the 
lowess smooth looks too low.
How does one meld plots using log="y" with such additional plot 
annotations ?

plot(jitter(intensity) ~ length, data = CPpos, log = "y")
with(CPpos, lines(lowess(length, log(intensity)), col="red", lwd=2) )
abline(lm(log(intensity) ~ length, data=CPpos))

Here's an attempt at a ggplot2 version, that actually looks more 
reasonable, but I'm not sure that it
is correct:

library(ggplot2)
ggplot(CPpos, aes(x=length, y=intensity)) +
     geom_jitter(position=position_jitter(height=.1), alpha=0.25) +
     scale_y_log10(breaks=c(1,2,5,10,20,50,100, 200)) +
     stat_smooth(method="loess", color="red", size=1.5) +
     stat_smooth(method="lm")


-- 
Michael Friendly     Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University      Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street    Web:http://www.datavis.ca
Toronto, ONT  M3J 1P3 CANADA

On Sep 17, 2014, at 4:34 PM, Michael Friendly wrote:
> In the following example, I am trying to plot a response on a log  
> scale, and add one or more smoothed
> curves, e.g., lowess and abline.  In base graphics, I'd like to do  
> this using log="y", so that the Y axis is
> spaced on the log scale, but labeled as the actual response values.  
> Using ggplot2, I'm using
> scale_y_log10 to achieve the same purpose.  However, my attempts to  
> add the smooths differ
> considerably, so I must be missing something.
>
> Here's the data I'm working with for one example:
>
> data("CodParasites", package = "countreg")
> ## omit NAs in response & predictor
> CodParasites <- subset(CodParasites, !(is.na(intensity) |  
> is.na(length)))
> ## plot only positive values of intensity
> CPpos <- subset(CodParasites, intensity>0)
>
> Here's the base graphics plot.  The abline() is clearly wrong and  
> the lowess smooth looks too low.
> How does one meld plots using log="y" with such additional plot  
> annotations ?
>
> plot(jitter(intensity) ~ length, data = CPpos, log = "y")
> with(CPpos, lines(lowess(length, log(intensity)), col="red",
lwd=2) )
> abline(lm(log(intensity) ~ length, data=CPpos))
This is what I would have expected to be a more accurate plot of the  
estimated central tendencies:

with(CPpos, lines(lowess(length, exp(log(intensity))), col="red",  
lwd=2) )
lines( CPpos$length, exp( lm(log(intensity) ~ length, data=CPpos) 
$fitted)   )

Just because you have changed the scaling of the plot axis does not  
mean that you would plot log()-ed values.
>
> Here's an attempt at a ggplot2 version, that actually looks more  
> reasonable, but I'm not sure that it
> is correct:
>
> library(ggplot2)
> ggplot(CPpos, aes(x=length, y=intensity)) +
>    geom_jitter(position=position_jitter(height=.1), alpha=0.25) +
>    scale_y_log10(breaks=c(1,2,5,10,20,50,100, 200)) +
>    stat_smooth(method="loess", color="red", size=1.5) +
>    stat_smooth(method="lm")
The loess line is somewhat different but the lm() prediction is the  
same as I expected.

-- 
David.


David Winsemius, MD
Alameda, CA, USA