R Under development (unstable) (2025-10-16 r88927 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(lqmix)
> 
> # build a small dataset ----
> set.seed(123)
> n_subj = 4
> times = 1:3
> df = expand.grid(id = 1:n_subj, time = times)
> df$meas = rnorm(nrow(df))
> df$trt = sample(c(0,1), nrow(df), replace = TRUE)
> 
> 
> 
> # test errors in the lqmix function -----
> test_that("errors for missing or wrong data arg", {
+   expect_error(lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = NULL),
+                "No input dataset has been given\\.")
+   expect_error(lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = list()),
+                "`data' must be a data frame\\.")
+ })
Test passed 😸
> 
> test_that("error when start=2 but parInit empty", {
+   expect_error(lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = df, start=2, parInit = list()),
+                "No input parameters have been given with start = 2\\.")
+ })
Test passed 🎊
> 
> test_that("quantile out of range triggers error", {
+   expect_error(lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = df, qtl = 0),
+                "Quantile level out of range")
+   expect_error(lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = df, qtl = 1),
+                "Quantile level out of range")
+ })
Test passed 🥇
> 
> 
> test_that("TC branch: errors for missing arguments", {
+   # randomTC specified but G missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTC = ~1,
+           group = "id", time = "time", data = df),
+     "Argument 'G' must be specified to fit a linear quantile mixture with TC random coefficients\\."
+   )
+ 
+   # G provided but randomTC missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTV = ~1, m = 2, G = 2,
+           group = "id", time = "time", data = df),
+     "Argument 'G' is provided, but no 'randomTC' formula is specified\\. To include TC random ciefficients, define 'randomTC' as well\\."
+   )
+ 
+   # m provided but randomTV missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTC = ~1, G = 2, m = 2,
+           group = "id", time = "time", data = df),
+     "Argument 'm' is provided, but no 'randomTV' formula is specified\\. To include TV random coefficients, define 'randomTV' as well\\."
+   )
+ })
Test passed 🌈
> 
> 
> test_that("TV branch: errors for missing arguments", {
+   # randomTV specified but m missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTV = ~1,
+           group = "id", time = "time", data = df),
+     "Argument 'm' must be specified to fit a linear quantile mixture with TV random coefficients\\."
+   )
+ 
+   # m provided but randomTV missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTC = ~1, G = 2, m = 2,
+           group = "id", time = "time", data = df),
+     "Argument 'm' is provided, but no 'randomTV' formula is specified\\. To include TV random coefficients, define 'randomTV' as well\\."
+   )
+ 
+   # G provided but randomTC missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTV = ~1, m = 2, G = 2,
+           group = "id", time = "time", data = df),
+     "Argument 'G' is provided, but no 'randomTC' formula is specified\\. To include TC random ciefficients, define 'randomTC' as well\\."
+   )
+ })
Test passed 🎊
> 
> 
> 
> test_that("TCTV branch: errors for missing arguments", {
+   # --- TCTV branch errors ---
+   # both randomTC and randomTV provided, but G or m missing
+   expect_error(
+     lqmix(formula = meas ~ trt, randomTC = ~1, randomTV = ~1, m = NULL,
+           group = "id", time = "time", data = df),
+     "Both 'G' and 'm' must be specified to fit a linear quantile mixture with TC and TV random coefficients\\."
+   )
+ })
Test passed 🥳
> 
> test_that("No random-coefficients model", {
+   expect_error(
+     lqmix(formula = meas ~ trt,
+           group = "id", time = "time", data = df),
+     "No random-coefficients formulas \\('randomTC' or 'randomTV'\\) have been specified\\. The model corresponds to a standard linear quantile regression without random coefficients\\. Please use the function lqr\\(\\)\\."
+   )
+ })
Test passed 🥇
> 
> 
> # ----- test that lqmix retuns proper objects and estimates ----
> 
> test_that("TC branch calls lqmixTC() and returns an lqmix object.", {
+ 
+   res = lqmix(formula = meas ~ trt, randomTC = ~1, group="id", time="time", G=2, data = df, qtl=0.5, se=FALSE)
+   expect_s3_class(res, "lqmix")
+   expect_named(res, c("betaf","betarTC", "pg", "sigma.e",
+                       "npar", "AIC", "BIC", "qtl", "G", "nsbjs", "nobs", "postTC", "miss",
+                       "model", "mmf", "mmrTC", "y",
+                       "lk","scale","call","formula","randomTC","group","time"), ignore.order = TRUE, ignore.case = TRUE)
+   expect_equal(res$formula, meas ~ trt)
+   expect_equal(res$randomTC, ~1)
+   expect_equal(c(round(res$betarTC,3)), c(-0.490, 0.663))
+   expect_null(res$randomTV)
+   expect_equal(res$group, "id")
+   expect_equal(res$time, "time")
+ })
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TC |   0.5 |     2 |      0 |     -16.455 |          NA | 
     TC |   0.5 |     2 |      6 |    -14.1891 | 7.44091e-07 | 
--------|-------|-------|--------|-------------|-------------|
Test passed 🎊
> 
> 
> test_that("TV branch dispatches to lqmixTV and returns lqmix object", {
+ 
+   res = lqmix(formula = meas ~ trt, randomTV = ~1, group="id", time="time", m=2, data = df, qtl=0.5, se=FALSE)
+   expect_s3_class(res, "lqmix")
+   expect_named(res, c("betaf","betarTV", "delta","Gamma", "sigma.e",
+                       "npar", "AIC", "BIC", "qtl", "m", "nsbjs", "nobs", "postTV", "miss",
+                       "model", "mmf", "mmrTV", "y",
+                       "lk","scale","call","formula","randomTV","group","time"), ignore.order = TRUE)
+   expect_equal(res$randomTV, ~1)
+   expect_null(res$randomTC)
+   expect_equal(c(round(res$betarTV,3)), c(-0.490, 0.663))
+   expect_equal(res$group, "id")
+   expect_equal(res$time, "time")
+ })
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TV |   0.5 |     2 |      0 |    -16.3348 |          NA | 
     TV |   0.5 |     2 |     10 |    -14.2394 |   0.0109862 | 
     TV |   0.5 |     2 |     20 |     -14.194 |  0.00123772 | 
     TV |   0.5 |     2 |     30 |    -14.1897 | 0.000138095 | 
     TV |   0.5 |     2 |     40 |    -14.1891 | 1.84027e-05 | 
     TV |   0.5 |     2 |     44 |    -14.1891 | 8.26667e-06 | 
--------|-------|-------|--------|-------------|-------------|
Test passed 😀
> 
> 
> test_that("TCTV branch dispatches to lqmixTCTV and returns lqmix object", {
+ 
+   res = lqmix(formula = meas ~ trt, randomTC = ~trt, randomTV = ~1, group="id", time="time", G=2, m=2, data = df, qtl=0.5, se=FALSE)
+   expect_named(res, c("betarTV","betarTC", "pg","delta","Gamma", "sigma.e",
+                       "npar", "AIC", "BIC", "qtl", "m", "G", "nsbjs", "nobs", "postTC", "postTV", "miss",
+                       "model", "mmrTC", "mmrTV", "y",
+                       "lk","scale","call","formula", "randomTC", "randomTV","group","time"), ignore.order = TRUE)
+   expect_null(res$beta)
+   expect_null(res$mmf)
+   expect_s3_class(res, "lqmix")
+   expect_equal(c(round(res$betarTC,3)), c(-0.289, 1.199))
+   expect_equal(res$randomTC, ~trt)
+   expect_equal(res$randomTV, ~1)
+ })
--------|-------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   m   |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|-------|--------|-------------|-------------|
   TCTV |   0.5 |     2 |     2 |      0 |    -18.6194 |          NA | 
   TCTV |   0.5 |     2 |     2 |     10 |    -13.6478 |  0.00168698 | 
   TCTV |   0.5 |     2 |     2 |     20 |    -13.6355 | 0.000964199 | 
   TCTV |   0.5 |     2 |     2 |     30 |    -13.6286 |  0.00048556 | 
   TCTV |   0.5 |     2 |     2 |     40 |    -13.6254 | 0.000226023 | 
   TCTV |   0.5 |     2 |     2 |     50 |    -13.6238 | 0.000124414 | 
   TCTV |   0.5 |     2 |     2 |     60 |    -13.6228 |  7.9931e-05 | 
   TCTV |   0.5 |     2 |     2 |     70 |    -13.6221 |  5.5466e-05 | 
   TCTV |   0.5 |     2 |     2 |     80 |    -13.6217 | 4.00627e-05 | 
   TCTV |   0.5 |     2 |     2 |     90 |    -13.6213 |  2.9709e-05 | 
   TCTV |   0.5 |     2 |     2 |    100 |    -13.6211 | 2.24682e-05 | 
   TCTV |   0.5 |     2 |     2 |    110 |    -13.6209 | 1.72536e-05 | 
   TCTV |   0.5 |     2 |     2 |    120 |    -13.6207 | 1.34099e-05 | 
   TCTV |   0.5 |     2 |     2 |    130 |    -13.6206 | 1.05227e-05 | 
   TCTV |   0.5 |     2 |     2 |    133 |    -13.6206 | 9.79981e-06 | 
--------|-------|-------|-------|--------|-------------|-------------|
Test passed 😀
> 
> 
> 
> # ----- test that search_lqmix retuns proper objects and estimates ----
> 
> test_that("search_lqmix: errors for missing Gv and mv", {
+   expect_error(search_lqmix(formula = y ~ x, data = df),
+                "No values for both Gv and mv are provided\\.")
+ })
Test passed 🥳
> 
> test_that("search_lqmix: errors for unsupported method", {
+   expect_error(search_lqmix(formula = y ~ x, data = df, Gv = 1, method = "unknown"),
+                "The method specified for selecting the optimal model is not supported\\.")
+ })
Test passed 🎊
> 
> 
> test_that("search_lqmix: TC branch calls lqmixTC and returns search_lqmix object", {
+ 
+   res = search_lqmix(formula = meas ~ trt, randomTC = ~1, group = "id", time = "time",
+                       data = df, Gv = 1:2, method = "bic", se = FALSE, nran = 0)
+ 
+   expect_s3_class(res, "search_lqmix")
+   expect_named(res, c("optimal","allmodels","lkv","aicv","bicv",
+                       "qtl","Gv","mv","method","call","formula","randomTC","group","time"),
+                ignore.order = TRUE, ignore.case = TRUE)
+   expect_equal(res$formula, meas ~ trt)
+   expect_equal(round(c(res$optimal$betarTC),3), c(-0.490, 0.663))
+   expect_equal(res$randomTC, ~1)
+   expect_null(res$randomTV)
+ })
Search the optimal linear quantile mixture model 
************************************************* 
--------|-------|--------|-------------|
  model |  qtl  |  iter  |      lk     |
--------|-------|--------|-------------|
    HOM |   0.5 |      0 |    -16.2719 | 
--------|-------|--------|-------------|
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TC |   0.5 |     2 |      0 |     -16.455 |          NA | 
     TC |   0.5 |     2 |      6 |    -14.1891 | 7.44091e-07 | 
--------|-------|-------|--------|-------------|-------------|
Test passed 😸
> 
> 
> test_that("search_lqmix: TV branch calls lqmixTV and returns search_lqmix object", {
+ 
+   res <- search_lqmix(formula = meas ~ trt, randomTV = ~1, group = "id", time = "time",
+                       data = df, mv = 1:2, method = "bic", se = FALSE, nran = 0)
+ 
+   expect_s3_class(res, "search_lqmix")
+   expect_named(res, c("optimal","allmodels","lkv","aicv","bicv",
+                       "qtl","Gv","mv","method","call","formula","randomTV","group","time"),
+                ignore.order = TRUE, ignore.case = TRUE)
+   expect_equal(round(c(res$optimal$betarTV),3), c(-0.490, 0.663))
+   expect_equal(res$formula, meas ~ trt)
+   expect_equal(res$randomTV, ~1)
+   expect_null(res$randomTC)
+ })
Search the optimal linear quantile mixture model 
************************************************* 
--------|-------|--------|-------------|
  model |  qtl  |  iter  |      lk     |
--------|-------|--------|-------------|
    HOM |   0.5 |      0 |    -16.2719 | 
--------|-------|--------|-------------|
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TV |   0.5 |     2 |      0 |    -16.3348 |          NA | 
     TV |   0.5 |     2 |     10 |    -14.2394 |   0.0109862 | 
     TV |   0.5 |     2 |     20 |     -14.194 |  0.00123772 | 
     TV |   0.5 |     2 |     30 |    -14.1897 | 0.000138095 | 
     TV |   0.5 |     2 |     40 |    -14.1891 | 1.84027e-05 | 
     TV |   0.5 |     2 |     44 |    -14.1891 | 8.26667e-06 | 
--------|-------|-------|--------|-------------|-------------|
Test passed 🌈
> 
> 
> test_that("search_lqmix: TCTV branch calls lqmixTCTV and returns search_lqmix object", {
+ 
+   res <- search_lqmix(formula = meas ~ trt, randomTC = ~trt, randomTV = ~1,
+                       group = "id", time = "time", data = df, Gv = 1:2, mv = 1:2,
+                       method = "bic", se = FALSE, nran = 0)
+ 
+   expect_s3_class(res, "search_lqmix")
+   expect_named(res, c("optimal","allmodels","lkv","aicv","bicv",
+                       "qtl","Gv","mv","method","call","formula","randomTC", "randomTV","group","time"),
+                ignore.order = TRUE, ignore.case = TRUE)
+   expect_equal(round(c(res$optimal$betarTC),3), c(0.138, 1.292))
+   expect_equal(res$randomTC, ~trt)
+   expect_equal(res$randomTV, ~1)
+ })
Search the optimal linear quantile mixture model 
************************************************* 
--------|-------|--------|-------------|
  model |  qtl  |  iter  |      lk     |
--------|-------|--------|-------------|
    HOM |   0.5 |      0 |    -16.2719 | 
--------|-------|--------|-------------|
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TC |   0.5 |     2 |      0 |     -14.581 |          NA | 
     TC |   0.5 |     2 |      7 |    -13.8681 | 3.09715e-06 | 
--------|-------|-------|--------|-------------|-------------|
--------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|--------|-------------|-------------|
     TV |   0.5 |     2 |      0 |    -16.3348 |          NA | 
     TV |   0.5 |     2 |     10 |    -14.2394 |   0.0109862 | 
     TV |   0.5 |     2 |     20 |     -14.194 |  0.00123772 | 
     TV |   0.5 |     2 |     30 |    -14.1897 | 0.000138095 | 
     TV |   0.5 |     2 |     40 |    -14.1891 | 1.84027e-05 | 
     TV |   0.5 |     2 |     44 |    -14.1891 | 8.26667e-06 | 
--------|-------|-------|--------|-------------|-------------|
--------|-------|-------|-------|--------|-------------|-------------|
  model |  qtl  |   m   |   G   |  iter  |      lk     |   (lk-lko)  |
--------|-------|-------|-------|--------|-------------|-------------|
   TCTV |   0.5 |     2 |     2 |      0 |    -18.6194 |          NA | 
   TCTV |   0.5 |     2 |     2 |     10 |    -13.6478 |  0.00168698 | 
   TCTV |   0.5 |     2 |     2 |     20 |    -13.6355 | 0.000964199 | 
   TCTV |   0.5 |     2 |     2 |     30 |    -13.6286 |  0.00048556 | 
   TCTV |   0.5 |     2 |     2 |     40 |    -13.6254 | 0.000226023 | 
   TCTV |   0.5 |     2 |     2 |     50 |    -13.6238 | 0.000124414 | 
   TCTV |   0.5 |     2 |     2 |     60 |    -13.6228 |  7.9931e-05 | 
   TCTV |   0.5 |     2 |     2 |     70 |    -13.6221 |  5.5466e-05 | 
   TCTV |   0.5 |     2 |     2 |     80 |    -13.6217 | 4.00627e-05 | 
   TCTV |   0.5 |     2 |     2 |     90 |    -13.6213 |  2.9709e-05 | 
   TCTV |   0.5 |     2 |     2 |    100 |    -13.6211 | 2.24682e-05 | 
   TCTV |   0.5 |     2 |     2 |    110 |    -13.6209 | 1.72536e-05 | 
   TCTV |   0.5 |     2 |     2 |    120 |    -13.6207 | 1.34099e-05 | 
   TCTV |   0.5 |     2 |     2 |    130 |    -13.6206 | 1.05227e-05 | 
   TCTV |   0.5 |     2 |     2 |    133 |    -13.6206 | 9.79981e-06 | 
--------|-------|-------|-------|--------|-------------|-------------|
Test passed 🥇
> 
> 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
   6.54    0.54    7.07