R Under development (unstable) (2023-08-08 r84908 ucrt) -- "Unsuffered Consequences"
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> cat("# basics test\n")
# basics test
> # se_comb
> COV = matrix(c(.1, .2, .2, .1), nrow=2)
> colnames(COV) <- c("x1", "x2")
> stopifnot(sqrt(sum(COV))==qgcomp::se_comb(covmat=COV, expnms = c("x1", "x2")))
> stopifnot(sqrt(sum(COV))==qgcomp::se_comb(covmat=COV, expnms = c("x1", "x2"), grad=c(1,1)))
> 
> 
> #vc_comb
> colnames(COV)[1] <- c("(Intercept)")
> stopifnot(COV==qgcomp:::vc_comb(aname="(Intercept)", c("x2"), covmat=COV, grad=1.0))
> 
> 
> # grad.poly # anything better here?
> for(deg in 1:3){
+   stopifnot(all(!is.na(qgcomp:::grad.poly(intvals=c(1,2,3), degree=deg))))
+ }
> 
> 
> # stats
> set.seed(50)
> # linear model
> dat = qgcomp::simdata_quantized()
> ft = qgcomp::qgcomp.noboot(f=y ~ x1 + x2 + x3 + x4, expnms = c('x1', 'x2'), data=dat, q=2, family=gaussian(), bayes=TRUE)
> summary(ft)
Mixture slope parameters (Delta method CI):

$coefficients
             Estimate Std. Error    Lower CI Upper CI     Pr(>|t|)
(Intercept) 0.5672164  0.3030633 -0.02677679  1.16121 6.427391e-02
psi1        2.0087597  0.3297667  1.36242892  2.65509 2.253427e-08

> df.residual(ft)
Warning message:
In df.residual.qgcompfit(ft) : df.residual not implemented for this model
> vcov(ft)
            (intercept)        psi1
(intercept)  0.09184738 -0.05825273
psi1        -0.05825273  0.10874605
> AIC(ft)
[1] 320.3829
> BIC(ft)
[1] 336.0139
> logLik(ft)
'log Lik.' -154.1914 (df=6)
> anova(ft)
Analysis of Deviance Table

Model: gaussian, link: identity

Response: y

Terms added sequentially (first to last)


     Df Deviance Resid. Df Resid. Dev       F    Pr(>F)    
NULL                    99     249.70                      
x1    1  118.561        98     131.14 88.0745 3.413e-15 ***
x2    1    0.787        97     130.35  0.5848    0.4463    
x3    1    0.003        96     130.35  0.0022    0.9626    
x4    1    2.466        95     127.88  1.8321    0.1791    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> confint(ft) # not working currently
                  lower   upper
(intercept) -0.02677679 1.16121
psi1         1.36242892 2.65509
> 
> predict(ft)
          1           2           3           4           5           6 
 2.43515792  2.15352149  2.45666393  0.12962390  0.14603541  2.57724976 
          7           8           9          10          11          12 
 2.73957399  2.59748215  0.26662124  2.43770517  0.12580302  2.57597614 
         13          14          15          16          17          18 
 2.57597614  2.59875577  0.40743946  2.45411668  2.73957399  0.14603541 
         19          20          21          22          23          24 
 0.56721643  0.12962390  2.45793756  0.14603541  0.43021909  2.15352149 
         25          26          27          28          29          30 
 2.45539030  2.15606874  2.73702674  2.57597614  0.14858266  2.31711934 
         31          32          33          34          35          36 
 0.40998671  2.59748215 -0.01374156  2.45666393  0.28940088  0.28812725 
         37          38          39          40          41          42 
 2.29816058  2.15479511  0.12835028  0.12580302  0.12707665 -0.01374156 
         43          44          45          46          47          48 
 0.28940088  2.73957399  2.57724976  2.59748215  2.31457209  0.43021909 
         49          50          51          52          53          54 
 2.31329846  0.14603541  2.73575311  2.45793756  2.15479511  2.43897880 
         55          56          57          58          59          60 
 0.26789487  2.43897880  0.28685362 -0.01119431  0.28940088  2.15606874 
         61          62          63          64          65          66 
 2.57852339  0.14730903  0.26916849  2.15606874  0.40998671  0.26789487 
         67          68          69          70          71          72 
-0.01119431 -0.01501519  2.59748215  0.42767184  2.15479511  2.43643155 
         73          74          75          76          77          78 
 0.12707665  0.26662124  2.15479511  2.73702674  0.26789487  0.40743946 
         79          80          81          82          83          84 
 2.59748215  2.29816058  0.57103731  0.56721643  2.29688696  2.43770517 
         85          86          87          88          89          90 
 2.45793756  0.14730903  2.45411668  2.59493489  2.43515792  0.56721643 
         91          92          93          94          95          96 
 2.45666393  0.28685362  0.57103731  0.56976368  0.41126034 -0.01501519 
         97          98          99         100 
 0.42639821  0.56976368  0.40998671  0.42639821 
> predict(ft, newdata = dat[1,])
       1 
2.435158 
> 
> proc.time()
   user  system elapsed 
   2.45    0.34    2.76