R help - Nov 2011 - lines and points in xyplot()

Given the following data, I want a scatterplot with the data points and the
predictions from the regression.

Sigma <- matrix(c(1,.6,1,.6), 2)
mu <- c(0,0)
dat <- mvrnorm(5000, mu, Sigma)

x <- dat[,1] * 50 + 200
y <- dat[,2] * 50 + 200

fm <- lm(y ~ x)

### This gives the regression line, but not the data
xyplot(y ~ x,
               type = c('g', 'p'),
               panel = function(x, y){
               panel.lines(x, predict(fm))
               }
)

### This gives both data but as point
xyplot(y + predict(fm) ~ x,
               type = c('g', 'p'),
               )

I know I can add an abline easily, but my problem is a bit more complex and the
code above is just an example. What is the best way for the predicted data to
form a solid line and let the data points remain as points

Harold

	[[alternative HTML version deleted]]

On Wed, Nov 23, 2011 at 10:48 PM, Doran, Harold <HDoran at air.org>
wrote:
> Given the following data, I want a scatterplot with the data points and the
predictions from the regression.
>
> Sigma <- matrix(c(1,.6,1,.6), 2)
> mu <- c(0,0)
> dat <- mvrnorm(5000, mu, Sigma)
>
> x <- dat[,1] * 50 + 200
> y <- dat[,2] * 50 + 200
>
> fm <- lm(y ~ x)
>
> ### This gives the regression line, but not the data
> xyplot(y ~ x,
> ? ? ? ? ? ? ? type = c('g', 'p'),
> ? ? ? ? ? ? ? panel = function(x, y){
> ? ? ? ? ? ? ? panel.lines(x, predict(fm))
> ? ? ? ? ? ? ? }
> )
>
> ### This gives both data but as point
> xyplot(y + predict(fm) ~ x,
> ? ? ? ? ? ? ? type = c('g', 'p'),
> ? ? ? ? ? ? ? )
>
> I know I can add an abline easily, but my problem is a bit more complex and
the code above is just an example.
> What is the best way for the predicted data to form a solid line and let
the data points remain as points
See

http://lattice.r-forge.r-project.org/Vignettes/src/lattice-tricks/regression-lines.pdf

(This is a work in progress, so feedback would be appreciated.)

-Deepayan

Hi:

Try this:

library('lattice')
xyplot(y ~ x,
              type = c('g', 'p'),
              panel = function(x, y, ...){
              panel.xyplot(x, y, ...)
              panel.lines(x, predict(fm), col = 'black', lwd = 2)
              }
         )

HTH,
Dennis

On Wed, Nov 23, 2011 at 9:18 AM, Doran, Harold <HDoran at air.org>
wrote:
> Given the following data, I want a scatterplot with the data points and the
predictions from the regression.
>
> Sigma <- matrix(c(1,.6,1,.6), 2)
> mu <- c(0,0)
> dat <- mvrnorm(5000, mu, Sigma)
>
> x <- dat[,1] * 50 + 200
> y <- dat[,2] * 50 + 200
>
> fm <- lm(y ~ x)
>
> ### This gives the regression line, but not the data
> xyplot(y ~ x,
> ? ? ? ? ? ? ? type = c('g', 'p'),
> ? ? ? ? ? ? ? panel = function(x, y){
> ? ? ? ? ? ? ? panel.lines(x, predict(fm))
> ? ? ? ? ? ? ? }
> )
>
> ### This gives both data but as point
> xyplot(y + predict(fm) ~ x,
> ? ? ? ? ? ? ? type = c('g', 'p'),
> ? ? ? ? ? ? ? )
>
> I know I can add an abline easily, but my problem is a bit more complex and
the code above is just an example. What is the best way for the predicted data
to form a solid line and let the data points remain as points
>
> Harold
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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