EnvStats (version 2.3.1)

predict.lm: Predict Method for Linear Model Fits

Description

The function predict.lm in EnvStats is a modified version of the built-in R function predict.lm. The only modification is that for the EnvStats function predict.lm, if se.fit=TRUE, the list returned includes a component called n.coefs. The component n.coefs is used by the function pointwise to create simultaneous confidence or prediction limits.

Usage

# S3 method for lm
predict(object, ...)

Arguments

object

Object of class inheriting from "lm".

Further arguments passed to the R function predict.lm. See the R help file for the R function predict.lm.

Value

See the R help file for predict.lm.

The only modification is that for the EnvStats function predict.lm, if se.fit=TRUE, the list returned includes a component called n.coefs, i.e., the function returns a list with the following components:

fit

vector or matrix as above

se.fit

standard error of predicted means

residual.scale

residual standard deviations

df

degrees of freedom for residual

n.coefs

numeric scalar denoting the number of predictor variables used in the model

Details

See the R help file for predict.lm.

The function predict.lm in EnvStats is a modified version of the built-in R function predict.lm. The only modification is that for the EnvStats function predict.lm, if se.fit=TRUE, the list returned includes a component called n.coefs. The component n.coefs is used by the function pointwise to create simultaneous confidence or prediction limits.

References

Chambers, J.M., and Hastie, T.J., eds. (1992). Statistical Models in S. Chapman and Hall/CRC, Boca Raton, FL.

Draper, N., and H. Smith. (1998). Applied Regression Analysis. Third Edition. John Wiley and Sons, New York, Chapter 3.

Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with S-PLUS. CRC Press, Boca Raton, FL, pp.546-553.

Miller, R.G. (1981a). Simultaneous Statistical Inference. Springer-Verlag, New York, pp.111, 124.

See Also

Help file for R function predict, Help file for R function predict.lm, lm, calibrate, calibrate, inversePredictCalibrate, detectionLimitCalibrate.

Examples

# NOT RUN {
  # 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()
# }