predict(object, ...)

## Default S3 method:
predict(object, ...)

# Using the data from the built-in data frame Air.df, 
  # fit the cube-root of ozone as a function of temperature, 
  # then compute predicted values for ozone at 70 and 90 degrees F,
  # along with the standard errors of these predicted values.

  # First look at the data
  #-----------------------
  with(Air.df, 
    plot(temperature, ozone, xlab = "Temperature (degrees F)", 
      ylab = "Cube-Root Ozone (ppb)"))


  # Now create the lm object 
  #-------------------------
  ozone.fit <- lm(ozone ~ temperature, data = Air.df) 


  # Now get predicted values and CIs at 70 and 90 degrees.
  # Note the presence of the last component called n.coefs.
  #--------------------------------------------------------
  predict.list <- predict(ozone.fit, 
    newdata = data.frame(temperature = c(70, 90)), se.fit = TRUE) 

  predict.list
  #$fit
  #       1        2 
  #2.697810 4.101808 
  #
  #$se.fit
  #         1          2 
  #0.07134554 0.08921071 
  #
  #$df
  #[1] 114
  #
  #$residual.scale
  #[1] 0.5903046
  #
  #$n.coefs
  #[1] 2

 
  #----------

  #Continuing with the above example, create a scatterplot of 
  # cube-root ozone vs. temperature, and add the fitted line 
  # along with simultaneous 95% confidence bands.

  with(Air.df, 
    plot(temperature, ozone, xlab = "Temperature (degrees F)", 
      ylab = "Cube-Root Ozone (ppb)"))

  abline(ozone.fit, lwd = 3, col = "blue")

  new.temp <- with(Air.df, 
    seq(min(temperature), max(temperature), length = 100))

  predict.list <- predict(ozone.fit, 
    newdata = data.frame(temperature = new.temp), 
    se.fit = TRUE)

  ci.ozone <- pointwise(predict.list, coverage = 0.95, 
    simultaneous = TRUE)

  lines(new.temp, ci.ozone$lower, lty = 2, lwd = 3, col = "magenta") 

  lines(new.temp, ci.ozone$upper, lty = 2, lwd = 3, col = "magenta") 

  title(main=paste("Scatterplot of Cube-Root Ozone vs. Temperature", 
    "with Fitted Line and Simultaneous 95% Confidence Bands", 
    sep="\n"))

  #----------

  # Clean up
  #---------
  rm(ozone.fit, predict.list, new.temp, ci.ozone)
  graphics.off()