R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("dyn-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('dyn')
Loading required package: zoo
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "dyn-package"
> 
> ### * dyn-package
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dyn-package
> ### Title: Time Series Regression
> ### Aliases: dyn-package
> ### Keywords: models
> 
> ### ** Examples
> 
> x <- ts(seq(10)^2)
> dyn$lm(x ~ lag(x))

Call:
lm(formula = dyn(x ~ lag(x)))

Coefficients:
(Intercept)       lag(x)  
     -4.134        0.839  

> dyn$glm(x ~ lag(x))

Call:  glm(formula = dyn(x ~ lag(x))) 

Coefficients:
(Intercept)       lag(x)  
     -4.134        0.839  

Degrees of Freedom: 8 Total (i.e. Null);  7 Residual
Null Deviance:	    6308 
Residual Deviance: 8.261 	AIC: 30.77 
> dyn$loess(x ~ lag(x))
Call:
loess(formula = dyn(x ~ lag(x)))

Number of Observations: 9 
Equivalent Number of Parameters: 5.38 
Residual Standard Error: 0.118 
> 
> 
> 
> cleanEx(); ..nameEx <- "dyn"
> 
> ### * dyn
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dyn
> ### Title: dynamic regression class
> ### Aliases: dyn $.dyn model.frame.dyn model.frame.irts model.frame.its
> ###   model.frame.ts model.frame.zoo model.frame.zooreg predict.dyn
> ###   predict.ts predict.zoo fitted.dyn fitted.ts fitted.zoo residuals.dyn
> ###   residuals.ts residuals.zoo update.dyn anova.dyn fitted.irts
> ###   fitted.its fitted.zooreg predict.irts predict.its predict.zooreg
> ###   residuals.irts residuals.its residuals.zooreg
> ### Keywords: regression
> 
> ### ** Examples
> 
> y <- ts(1:12, start = c(2000,2), freq = 4)^3
> x <- ts(1:9, start = c(2000,3), freq = 4)^2
> 
> # can be used with numerous different regression functions
> y.lm <- dyn$lm( window(y, start = c(2000,4)) ~ diff(x) )
> y.lm <- dyn$lm( y ~ diff(x) )
> y.glm <- dyn$glm( y ~ diff(x) )
> y.loess <- dyn$loess( y ~ diff(x) )
> 
> y.lm <- dyn(lm(dyn(y ~ diff(x))))  # same
> y.lm

Call:
lm(formula = dyn(y ~ diff(x)))

Coefficients:
(Intercept)      diff(x)  
       -303           68  

> summary(y.lm)

Call:
lm(formula = dyn(y ~ diff(x)))

Residuals:
    Min      1Q  Median      3Q     Max 
-102.00  -75.00  -18.00   51.75  147.00 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -303.000     87.975  -3.444 0.013731 *  
diff(x)       68.000      7.998   8.502 0.000145 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 103.7 on 6 degrees of freedom
Multiple R-Squared: 0.9234,	Adjusted R-squared: 0.9106 
F-statistic: 72.29 on 1 and 6 DF,  p-value: 0.0001449 

> residuals(y.lm)
     Qtr1 Qtr2 Qtr3 Qtr4
2000                 126
2001   27  -48  -93 -102
2002  -69   12  147     
> fitted(y.lm)
     Qtr1 Qtr2 Qtr3 Qtr4
2000                 -99
2001   37  173  309  445
2002  581  717  853     
> y2.lm <- update(y.lm, . ~ . + lag(x,-1))
> y2.lm

Call:
lm(formula = y ~ diff(x) + lag(x, -1))

Coefficients:
(Intercept)      diff(x)   lag(x, -1)  
      77.25       -19.75        19.50  

> anova(y.lm, y2.lm)
Analysis of Variance Table

Model 1: y ~ diff(x)
Model 2: y ~ diff(x) + lag(x, -1)
  Res.Df   RSS Df Sum of Sq      F    Pr(>F)    
1      6 64476                                  
2      5   594  1     63882 537.73 2.775e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
> 
> # examples of using data
> dyn$lm(y ~ diff(x), list(y = y, x = x))

Call:
lm(formula = dyn(y ~ diff(x)), data = list(y = y, x = x))

Coefficients:
(Intercept)      diff(x)  
       -303           68  

> dyn$lm(y ~ diffx, list(y = y, diffx = diff(x)))

Call:
lm(formula = dyn(y ~ diffx), data = list(y = y, diffx = diff(x)))

Coefficients:
(Intercept)        diffx  
       -303           68  

> 
> # invoke model.frame on formula as a dyn object
> dyn$model.frame( y ~ diff(x) )
      y diff(x)
3    27       3
4    64       5
5   125       7
6   216       9
7   343      11
8   512      13
9   729      15
10 1000      17
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.88 0.03 0.92 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')