R Under development (unstable) (2024-10-21 r87258 ucrt) -- "Unsuffered Consequences"
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> ## This is just from ?anova.rq (extended)
> library(quantreg)
Loading required package: SparseM
> data(barro)
> fit0 <- rq(y.net ~  lgdp2 + fse2 + gedy2 , data = barro)
> fit1 <- rq(y.net ~  lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro)
> 
> a01 <- anova(fit1,fit0)
> a01
Quantile Regression Analysis of Deviance Table

Model 1: y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2
Model 2: y.net ~ lgdp2 + fse2 + gedy2
  Df Resid Df F value    Pr(>F)    
1  2      155  18.879 4.596e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
> fit2 <- rq(y.net ~  lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro,
+ 	   tau = 0.75)
> fit3 <- rq(y.net ~  lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro,
+ 	   tau = 0.25)
> 
> a123  <- anova(fit1,fit2, fit3)
Warning message:
In summary.rq(x, se = se, R = R, covariance = TRUE) : 1 non-positive fis
> a.123 <- anova(fit1,fit2, fit3, joint=FALSE)
Warning message:
In summary.rq(x, se = se, R = R, covariance = TRUE) : 1 non-positive fis
> a.123
Quantile Regression Analysis of Deviance Table

Model: y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2
Tests of Equality of Distinct Slopes: tau in {  0.5 0.75 0.25  }

       Df Resid Df F value  Pr(>F)  
lgdp2   2      481  1.0656 0.34535  
fse2    2      481  2.6398 0.07241 .
gedy2   2      481  0.7862 0.45614  
Iy2     2      481  0.0447 0.95632  
gcony2  2      481  0.0653 0.93675  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> 
> AE <- function(x,y) all.equal(x, y, tol = 1e-5)
> ##                                  ---------- {giving a bit more digits below}
> stopifnot(
+ 	  AE(100 * unname(coef(fit0)),
+ 	     c(-0.74679759, 0.46539963, 0.15902838, -36.619915))
+ 	  ,
+ 	  AE(unlist(a01$table),
+ 	     c(ndf=2, ddf=155, Tn = 18.878717, pvalue= 4.6e-08))
+ 	  ,
+ 	  AE(100* unname(coef( fit2 )),
+ 	     c(13.103018, -1.4885239, -0.026452369,
+ 	       0.3999839, 14.526663, -13.504643))
+ 	  ,
+ 	  AE(100* unname(coef( fit3 )),
+ 	     c(6.0860719, -0.88350554, 0.24596781,
+ 	       -14.962498, 15.592489, -15.861804))
+ 	  ,
+ 	  AE(unlist(a123$table),
+ 	     c(ndf = 10, ddf = 473, Tn = 1.80385526, pvalue=0.0575117558))
+ 	  ,
+ 	  AE(a.123$table[,"Tn"],
+ 	     c(1.0655561, 2.6398508, 0.78623238, 0.04467014, 0.065344348))
+ 	  )
> 
> proc.time()
   user  system elapsed 
   1.51    0.26    1.75