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 class 'lm'
predict(object, ...)

Arguments

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.

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:

Author(s)

R Development Core Team (for code for R version of predict.lm).

Steven P. Millard (for modification to add compenent n.coefs; EnvStats@ProbStatInfo.com)

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

  # 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()

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(EnvStats)

Attaching package: 'EnvStats'

The following objects are masked from 'package:stats':

    predict, predict.lm

The following object is masked from 'package:base':

    print.default

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/EnvStats/predict.lm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.lm
> ### Title: Predict Method for Linear Model Fits
> ### Aliases: predict.lm
> ### Keywords: models regression
> 
> ### ** Examples
> 
>   # 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

>   #$fit
>   #       1        2 
>   #2.697810 4.101808 
>   #
>   #$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()
> 
> 
> 
> 
> 
> dev.off()
Error in dev.off() : cannot shut down device 1 (the null device)
Execution halted