R help - Dec 2013 - ggplot2: stat_smooth for family=binomial with cbind(Y, N) formula

With ggplot2, I can plot the glm stat_smooth for binomial data when the 
response is binary or
a two-level factor as follows:

data("Donner", package="vcdExtra")
ggplot(Donner, aes(age, survived)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", family = binomial, formula = y ~ x,
alpha = 0.2, size=2)

But how can I specify the formula for stat_smooth when the response is 
cbind(successes, failures)?
The equivalent with plot (minus the confidence band) for the example I 
want is:

data("SpaceShuttle", package="vcd")

 > head(SpaceShuttle, 5)
   FlightNumber Temperature Pressure Fail nFailures Damage
1            1          66       50   no         0      0
2            2          70       50  yes         1      4
3            3          69       50   no         0      0
4            4          80       50 <NA>        NA     NA
5            5          68       50   no         0      0
 >

plot(nFailures/6 ~ Temperature, data = SpaceShuttle,
      xlim = c(30, 81), ylim = c(0,1),
      main = "NASA Space Shuttle O-Ring Failures",
      ylab = "Estimated failure probability",
      xlab = "Temperature (degrees F)",
      pch = 19, col = "blue", cex=1.2)
fm <- glm(cbind(nFailures, 6 - nFailures) ~ Temperature,
           data = SpaceShuttle,
           family = binomial)
pred <- predict(fm, data.frame(Temperature = 30 : 81), se=TRUE)
lines(30 : 81,
       predict(fm, data.frame(Temperature = 30 : 81), type =
"response"),
       lwd = 3)

-- 
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

Dear Michael,

Calculate the propotions. Then it is easy to use the weight option of glm

data("SpaceShuttle", package="vcd")
SpaceShuttle$trials <- 6

fm <- glm(cbind(nFailures, 6 - nFailures) ~ Temperature, data = SpaceShuttle,
family = binomial)
fm2 <- glm(nFailures/trials ~ Temperature, data = SpaceShuttle, family =
binomial, weight = trials)
all.equal(coef(fm), coef(fm2))

ggplot(SpaceShuttle, aes(x = Temperature, y = nFailures / trials)) +
geom_point() + geom_smooth(method = "glm", family = binomial,
aes(weight = trials))

Best regards,

Thierry

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens Michael Friendly
Verzonden: dinsdag 17 december 2013 14:58
Aan: R-help
Onderwerp: [R] ggplot2: stat_smooth for family=binomial with cbind(Y, N) formula

With ggplot2, I can plot the glm stat_smooth for binomial data when the response
is binary or a two-level factor as follows:

data("Donner", package="vcdExtra")
ggplot(Donner, aes(age, survived)) +
geom_point(position = position_jitter(height = 0.02, width = 0)) +
stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha
= 0.2, size=2)

But how can I specify the formula for stat_smooth when the response is
cbind(successes, failures)?
The equivalent with plot (minus the confidence band) for the example I want is:

data("SpaceShuttle", package="vcd")

 > head(SpaceShuttle, 5)
   FlightNumber Temperature Pressure Fail nFailures Damage
1            1          66       50   no         0      0
2            2          70       50  yes         1      4
3            3          69       50   no         0      0
4            4          80       50 <NA>        NA     NA
5            5          68       50   no         0      0
 >

plot(nFailures/6 ~ Temperature, data = SpaceShuttle,
      xlim = c(30, 81), ylim = c(0,1),
      main = "NASA Space Shuttle O-Ring Failures",
      ylab = "Estimated failure probability",
      xlab = "Temperature (degrees F)",
      pch = 19, col = "blue", cex=1.2)
fm <- glm(cbind(nFailures, 6 - nFailures) ~ Temperature,
           data = SpaceShuttle,
           family = binomial)
pred <- predict(fm, data.frame(Temperature = 30 : 81), se=TRUE)
lines(30 : 81,
       predict(fm, data.frame(Temperature = 30 : 81), type =
"response"),
       lwd = 3)

--
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

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