R Under development (unstable) (2024-06-25 r86831 ucrt) -- "Unsuffered Consequences"
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> #
> # Tests of Thiel-Sen regresion
> #
> library(deming)
> 
> # Data from Sen, JASA 1968
> # Note that his confidence interval is incorrect in the paper, since
> #  his list of values for (y_i - y_j)/(t_i - t_j) has mistakes
> # He also does not use the large sample formula, but rather a table from
> #  Kendall for the width of the interval
> sen <- data.frame(t=c(1,  2,  3,  4, 10, 12, 18),
+                   y=c(9, 15, 19, 20, 45, 55, 78))
> fit <- theilsen(y ~ t, data=sen, conf=.93)
> all.equal(coef(fit), c("(Intercept)"=6, "t"=4))
[1] TRUE
> all.equal(fit$ci[2,], c(26/7, 35/8))
[1] TRUE
> 
> # The data is strictly monotone, so the symmetric solution = usual one
> fit2 <- theilsen(y ~ t, data=sen, symmetric=TRUE)
> all.equal(coef(fit2), coef(fit))
[1] TRUE
> 
> 
> # How do the symmetric and regular compare?
> fcoef <- matrix(0, 7, 3)
> for (i in 1:7) {
+     tfit1 <- theilsen(new.lot ~ old.lot, ferritin, subset=(period==i))
+     tfit2 <- theilsen(new.lot ~ old.lot, ferritin, subset=(period==i), 
+                       symmetric=TRUE)
+     tfit3 <- pbreg(new.lot ~ old.lot, ferritin, subset=(period==i))
+     fcoef[i,] <- c(tfit1$coef[2], tfit2$coef[2], tfit3$coef[2])
+ }
> # For this set Passing-Bablock and STS agree
> # (When the slope is exactly 1 they must be identical, and these are close to 1).
> all.equal(fcoef[,2], fcoef[,3])
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   0.28    0.10    0.34