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> ###-- Linear Models, basic functionality -- weights included.
> 
> ## From John Maindonald :
> roller <- data.frame(
+  weight     = c(1.9,  3.1,  3.3,  4.8,  5.3,  6.1,  6.4,  7.6,  9.8, 12.4),
+  depression = c(  2,  1,    5,    5,   20,   20,   23,   10,   30,   25))
> 
> roller.lmu  <-  lm(weight~depression, data=roller)
> roller.lsfu <- lsfit(roller$depression, roller$weight)
> 
> roller.lsf  <- lsfit(roller$depression, roller$weight, wt = 1:10)
> roller.lsf0 <- lsfit(roller$depression, roller$weight, wt = 0:9)
> roller.lm  <-  lm(weight~depression, data=roller, weights= 1:10)
> roller.lm0 <-  lm(weight~depression, data=roller, weights= 0:9)
> roller.lm9 <-  lm(weight~depression, data=roller[-1,],weights= 1:9)
> roller.glm <- glm(weight~depression, data=roller, weights= 1:10)
> roller.glm0<- glm(weight~depression, data=roller, weights= 0:9)
> 
> all.equal(deviance(roller.lm),
+           deviance(roller.glm))
[1] TRUE
> all.equal(weighted.residuals(roller.lm),
+           residuals         (roller.glm))
[1] TRUE
> 
> all.equal(deviance(roller.lm0),
+           deviance(roller.glm0))
[1] TRUE
> all.equal(weighted.residuals(roller.lm0, drop=FALSE),
+           residuals         (roller.glm0))
[1] TRUE
> 
> (im.lm0 <- influence.measures(roller.lm0))
Influence measures of
     lm(formula = weight ~ depression, data = roller, weights = 0:9) :

    dfb.1. dfb.dprs   dffit cov.r   cook.d    hat inf
2  -0.0530   0.0482 -0.0530 1.551 0.001635 0.1277    
3  -0.1769   0.1538 -0.1782 1.569 0.018065 0.1742    
4   0.0130  -0.0113  0.0131 1.842 0.000100 0.2613    
5  -0.1174  -0.0185 -0.3370 1.049 0.055523 0.0892    
6  -0.1031  -0.0162 -0.2960 1.229 0.045771 0.1114    
7  -0.0118  -0.1891 -0.5010 1.070 0.119319 0.1555    
8   0.7572  -0.5948  0.7972 1.467 0.309976 0.3510    
9   0.1225  -0.2067 -0.2664 2.391 0.040794 0.4470   *
10 -0.3348   1.1321  2.0937 0.233 0.895835 0.2826   *
> 
> all.equal(unname(im.lm0 $ infmat),
+           unname(cbind(  dfbetas      (roller.lm0)
+                        , dffits       (roller.lm0)
+                        , covratio     (roller.lm0)
+                        ,cooks.distance(roller.lm0)
+                        ,lm.influence  (roller.lm0)$hat)
+                  ))
[1] TRUE
> 
> all.equal(rstandard(roller.lm9),
+           rstandard(roller.lm0),tol=1e-15)
[1] TRUE
> all.equal(rstudent(roller.lm9),
+           rstudent(roller.lm0),tol=1e-15)
[1] TRUE
> 
> all.equal(summary(roller.lm0)$coef,
+           summary(roller.lm9)$coef, tol=1e-15)
[1] TRUE
> all.equal(print(anova(roller.lm0), signif.st=FALSE),
+                 anova(roller.lm9), tol=1e-15)
Analysis of Variance Table

Response: weight
           Df  Sum Sq Mean Sq F value  Pr(>F)
depression  1 158.408 158.408  5.4302 0.05259
Residuals   7 204.202  29.172                
[1] TRUE
>