R Under development (unstable) (2024-09-10 r87115 ucrt) -- "Unsuffered Consequences"
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> options(digits = 4)
> suppressWarnings(RNGversion("3.5.0"))
> 
> ## package and data
> library("betareg")
> data("ReadingSkills", package = "betareg")
> 
> ## beta regression mixture model
> set.seed(1071)
> rs_mix <- betamix(accuracy ~ iq, data = ReadingSkills, k = 3,
+   extra_components = extraComponent(type = "uniform",
+     coef = 0.99, delta = 0.01), nstart = 10)
> 
> ## fitted model
> print(rs_mix)

Call:
betamix(formula = accuracy ~ iq, data = ReadingSkills, k = 3, 
    nstart = 10, extra_components = extraComponent(type = "uniform", 
        coef = 0.99, delta = 0.01))

Cluster sizes:
 1  2  3 
10 20 14 

convergence after 17 iterations
> summary(rs_mix)
$Comp.1
$Comp.1$mean
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)    1.403      0.263    5.33  9.9e-08 ***
iq             0.825      0.216    3.81  0.00014 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

$Comp.1$precision
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)    2.685      0.454    5.91  3.4e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


$Comp.2
$Comp.2$mean
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)   0.5025     0.0825    6.09  1.1e-09 ***
iq           -0.0484     0.1129   -0.43     0.67    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

$Comp.2$precision
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)    4.251      0.747    5.69  1.3e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


> 
> ## further methods
> table(clusters(rs_mix), ReadingSkills$dyslexia)
   
    no yes
  1  7   3
  2  4  16
  3 14   0
> round(posterior(rs_mix), digits = 4)
        [,1]   [,2]   [,3]
 [1,] 1.0000 0.0000 0.0000
 [2,] 0.8720 0.1280 0.0000
 [3,] 1.0000 0.0000 0.0000
 [4,] 0.1521 0.0000 0.8479
 [5,] 1.0000 0.0000 0.0000
 [6,] 0.4674 0.5326 0.0000
 [7,] 0.9276 0.0724 0.0000
 [8,] 0.0000 0.0000 1.0000
 [9,] 0.0022 0.0000 0.9978
[10,] 0.0938 0.0000 0.9062
[11,] 0.0305 0.0000 0.9695
[12,] 0.1621 0.0000 0.8379
[13,] 0.0399 0.0000 0.9601
[14,] 0.2680 0.0000 0.7320
[15,] 0.0002 0.0000 0.9998
[16,] 0.0399 0.0000 0.9601
[17,] 0.3641 0.6359 0.0000
[18,] 0.2545 0.0000 0.7455
[19,] 0.0806 0.9194 0.0000
[20,] 0.2377 0.0000 0.7623
[21,] 1.0000 0.0000 0.0000
[22,] 0.0008 0.0000 0.9992
[23,] 0.6760 0.3240 0.0000
[24,] 0.0415 0.9585 0.0000
[25,] 0.2593 0.0000 0.7407
[26,] 0.0827 0.9173 0.0000
[27,] 0.1375 0.8625 0.0000
[28,] 0.8438 0.1562 0.0000
[29,] 0.1856 0.8144 0.0000
[30,] 0.2568 0.7432 0.0000
[31,] 0.0397 0.9603 0.0000
[32,] 0.0101 0.9899 0.0000
[33,] 0.0029 0.9971 0.0000
[34,] 0.3365 0.6635 0.0000
[35,] 0.1013 0.8987 0.0000
[36,] 0.5090 0.4910 0.0000
[37,] 0.3690 0.6310 0.0000
[38,] 0.1338 0.8662 0.0000
[39,] 0.5589 0.4411 0.0000
[40,] 0.3227 0.6773 0.0000
[41,] 0.3087 0.6913 0.0000
[42,] 0.2423 0.7577 0.0000
[43,] 0.2825 0.7175 0.0000
[44,] 0.2558 0.7442 0.0000
> 
> proc.time()
   user  system elapsed 
   5.35    0.14    5.48