update news and readme

NEWS.md

@@ -4,6 +4,7 @@
 
 - Maintenance release to fix minor issues with function documentation
 - Fix #130 by avoiding conflict with `vcov` in the `merDeriv` package
+- Upgrade package test infrastructure to 3e testthat specification
 
 ## merTools 0.6.1
 

README.Rmd

@@ -47,6 +47,13 @@ install.packages("merTools")
 
 ## Recent Updates
 
+
+## merTools 0.6.2 (Early 2024)
+
+- Maintenance release to fix minor issues with function documentation
+- Fix #130 by avoiding conflict with `vcov` in the `merDeriv` package
+- Upgrade package test infrastructure to 3e testthat specification
+
 ## merTools 0.6.1 (Spring 2023)
 
 - Maintenance release to keep package listed on CRAN

README.md

@@ -30,6 +30,12 @@ install.packages("merTools")
 
 ## Recent Updates
 
+## merTools 0.6.2 (Early 2024)
+
+- Maintenance release to fix minor issues with function documentation
+- Fix \#130 by avoiding conflict with `vcov` in the `merDeriv` package
+- Upgrade package test infrastructure to 3e testthat specification
+
 ## merTools 0.6.1 (Spring 2023)
 
 - Maintenance release to keep package listed on CRAN
@@ -133,16 +139,16 @@ standard objects produced by `lm` and `glm`:
 predictInterval(m1, newdata = InstEval[1:10, ], n.sims = 500, level = 0.9, 
                 stat = 'median')
 #>         fit      upr       lwr
-#> 1  3.107632 5.190402 0.9368052
-#> 2  3.097741 4.879558 1.2738180
-#> 3  3.401938 5.503266 1.3132856
-#> 4  3.121414 5.060498 1.1299338
-#> 5  3.290234 5.422217 1.2647681
-#> 6  3.146418 5.023299 1.4372531
-#> 7  4.086394 6.158931 1.9473926
-#> 8  3.738121 5.631336 1.7886288
-#> 9  3.763437 5.734384 1.6697661
-#> 10 3.352128 5.337015 1.2505294
+#> 1  3.215698 5.302545 1.4367495
+#> 2  3.155941 5.327796 1.2210140
+#> 3  3.374129 5.287901 1.4875231
+#> 4  3.101672 5.183841 0.9248584
+#> 5  3.299367 5.298370 1.3287058
+#> 6  3.147238 5.368311 1.1132248
+#> 7  4.155194 6.273147 2.2167207
+#> 8  3.873493 5.705669 1.9152401
+#> 9  3.740978 5.737517 2.0454222
+#> 10 3.291242 5.297614 1.2375007
 ```
 
 Note that `predictInterval` is slower because it is computing
@@ -164,53 +170,53 @@ interval.
 predictInterval(m1, newdata = InstEval[1:10, ], n.sims = 200, level = 0.9, 
                 stat = 'median', which = "all")
 #>      effect         fit      upr       lwr obs
-#> 1  combined  3.25969862 5.093473  1.088987   1
-#> 2  combined  3.25185416 5.195660  1.212398   2
-#> 3  combined  3.48995449 5.513208  1.432098   3
-#> 4  combined  3.33674443 4.943025  1.220998   4
-#> 5  combined  3.29898222 5.594381  1.301636   5
-#> 6  combined  3.22542573 5.121188  1.285540   6
-#> 7  combined  4.35693740 6.456601  2.095860   7
-#> 8  combined  3.77082296 5.755422  1.540146   8
-#> 9  combined  3.77734423 6.131939  2.157273   9
-#> 10 combined  3.28954595 5.079436  1.221874  10
-#> 11        s -0.08603730 2.203910 -1.704490   1
-#> 12        s  0.22623596 1.850055 -1.624032   2
-#> 13        s  0.30822074 1.894273 -1.964165   3
-#> 14        s  0.22535767 2.270494 -1.856316   4
-#> 15        s -0.14243952 1.787597 -2.050745   5
-#> 16        s -0.30163547 1.817632 -2.103720   6
-#> 17        s  0.38506882 2.098101 -1.341332   7
-#> 18        s  0.44294445 2.415432 -1.500022   8
-#> 19        s  0.40448327 2.399836 -1.913328   9
-#> 20        s  0.22839660 2.570579 -1.745981  10
-#> 21        d -0.31888163 1.606477 -2.297953   1
-#> 22        d -0.37366911 1.555461 -2.075957   2
-#> 23        d -0.16054175 1.715221 -2.203618   3
-#> 24        d -0.20694151 1.876587 -2.340907   4
-#> 25        d  0.11129869 2.016248 -1.774396   5
-#> 26        d -0.05587943 1.782312 -2.069027   6
-#> 27        d  0.56077534 2.799003 -1.288401   7
-#> 28        d  0.19538590 2.276306 -1.843640   8
-#> 29        d  0.26885661 2.163566 -1.712320   9
-#> 30        d -0.26743035 1.520186 -2.388651  10
-#> 31    fixed  3.51054377 5.293941  1.341126   1
-#> 32    fixed  3.26108728 4.927388  1.169438   2
-#> 33    fixed  2.97127880 4.866901  1.215838   3
-#> 34    fixed  3.03712179 5.036753  1.154742   4
-#> 35    fixed  3.39117433 5.284877  1.437624   5
-#> 36    fixed  3.32728732 5.126942  1.577967   6
-#> 37    fixed  3.34239946 5.172533  1.513171   7
-#> 38    fixed  3.35180632 5.118299  1.431649   8
-#> 39    fixed  3.58880671 5.105028  1.554977   9
-#> 40    fixed  3.27099171 5.169194  1.489591  10
+#> 1  combined  3.35554348 5.217964  1.615782   1
+#> 2  combined  3.21487934 5.327824  1.114338   2
+#> 3  combined  3.44493242 5.474256  1.809136   3
+#> 4  combined  3.24123655 4.838427  1.272174   4
+#> 5  combined  3.20539661 5.367651  1.068128   5
+#> 6  combined  3.54335144 5.481756  1.585809   6
+#> 7  combined  4.23212790 6.267669  2.284923   7
+#> 8  combined  4.05055116 5.684968  1.931558   8
+#> 9  combined  3.84266853 5.492163  2.091312   9
+#> 10 combined  3.24121727 5.183680  1.196101  10
+#> 11        s -0.02342248 1.948494 -1.691035   1
+#> 12        s  0.04148408 2.091467 -1.782386   2
+#> 13        s  0.04477028 2.087629 -2.144621   3
+#> 14        s  0.26160482 2.114509 -1.733429   4
+#> 15        s -0.10803386 1.714535 -1.982283   5
+#> 16        s -0.04962613 1.916212 -1.909187   6
+#> 17        s  0.24916111 2.001528 -1.628554   7
+#> 18        s  0.19640074 2.070513 -1.473660   8
+#> 19        s  0.27031215 2.119763 -1.643120   9
+#> 20        s  0.13772544 2.313012 -1.855489  10
+#> 21        d -0.32196201 1.357316 -2.397083   1
+#> 22        d -0.29691477 1.422141 -2.662141   2
+#> 23        d  0.24828667 1.782181 -1.987563   3
+#> 24        d -0.37893052 1.471225 -2.350781   4
+#> 25        d  0.02142086 2.172075 -2.148417   5
+#> 26        d  0.07926221 2.003462 -1.677765   6
+#> 27        d  0.76480967 2.767889 -1.274501   7
+#> 28        d  0.08757337 2.374201 -1.958689   8
+#> 29        d  0.25289032 2.083732 -1.376630   9
+#> 30        d -0.17775160 1.601744 -2.115104  10
+#> 31    fixed  3.16750528 5.010517  1.371678   1
+#> 32    fixed  3.21493166 5.246672  1.074857   2
+#> 33    fixed  3.36233628 5.581696  1.474776   3
+#> 34    fixed  3.17926915 5.107315  1.621278   4
+#> 35    fixed  3.16562882 5.136197  1.156010   5
+#> 36    fixed  3.15944014 5.114967  1.506315   6
+#> 37    fixed  3.32101367 5.149819  1.407884   7
+#> 38    fixed  3.34020282 5.189215  1.651446   8
+#> 39    fixed  3.17901802 5.000429  1.132874   9
+#> 40    fixed  3.41100236 5.207451  1.555844  10
 ```
 
 This can lead to some useful plotting:
 
 ``` r
 library(ggplot2)
-#> Warning: package 'ggplot2' was built under R version 4.2.2
+#> Warning: package 'ggplot2' was built under R version 4.3.2
 plotdf <- predictInterval(m1, newdata = InstEval[1:10, ], n.sims = 2000, 
                           level = 0.9, stat = 'median', which = "all", 
                           include.resid.var = FALSE)
@@ -229,6 +235,9 @@ ggplot(plotdf, aes(x = obs, y = fit, ymin = lwr, ymax = upr)) +
   facet_grid(residVar~effect) + theme_bw()
 #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
 #> ℹ Please use `linewidth` instead.
+#> This warning is displayed once every 8 hours.
+#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
+#> generated.
 ```
 
 ![](man/figures/README_unnamed-chunk-7-1.png)<!-- -->
@@ -256,12 +265,12 @@ fixed and random effect parameters.
 feSims <- FEsim(m1, n.sims = 100)
 head(feSims)
 #>          term        mean      median         sd
-#> 1 (Intercept)  3.22302416  3.22328224 0.01893656
-#> 2    service1 -0.07353238 -0.07503196 0.01322848
-#> 3   lectage.L -0.18550746 -0.18689223 0.01757622
-#> 4   lectage.Q  0.02531346  0.02532793 0.01262718
-#> 5   lectage.C -0.02446487 -0.02332242 0.01217267
-#> 6   lectage^4 -0.02074847 -0.02171527 0.01314005
+#> 1 (Intercept)  3.22469489  3.22427807 0.01767782
+#> 2    service1 -0.07112136 -0.07190156 0.01382577
+#> 3   lectage.L -0.18681442 -0.18833173 0.01617233
+#> 4   lectage.Q  0.02208290  0.02252175 0.01259911
+#> 5   lectage.C -0.02656846 -0.02507924 0.01099316
+#> 6   lectage^4 -0.02165334 -0.02073531 0.01355675
 ```
 
 And we can also plot this:
@@ -278,12 +287,12 @@ We can also quickly make caterpillar plots for the random-effect terms:
 reSims <- REsim(m1, n.sims = 100)
 head(reSims)
 #>   groupFctr groupID        term        mean      median        sd
-#> 1         s       1 (Intercept)  0.15555959  0.14798520 0.3359070
-#> 2         s       2 (Intercept) -0.03924940 -0.03158545 0.3216017
-#> 3         s       3 (Intercept)  0.32218754  0.29815012 0.2944466
-#> 4         s       4 (Intercept)  0.22220605  0.19904690 0.2887927
-#> 5         s       5 (Intercept)  0.05738118  0.03711978 0.3373406
-#> 6         s       6 (Intercept)  0.14302324  0.14638548 0.2363066
+#> 1         s       1 (Intercept)  0.21300834  0.22465501 0.3266393
+#> 2         s       2 (Intercept) -0.04278168 -0.08474934 0.3164631
+#> 3         s       3 (Intercept)  0.41998629  0.42229608 0.2865010
+#> 4         s       4 (Intercept)  0.28818926  0.28746523 0.3019460
+#> 5         s       5 (Intercept)  0.05833502  0.03342223 0.3539464
+#> 6         s       6 (Intercept)  0.08108947  0.08478943 0.2049882
 ```
 
 ``` r
@@ -350,11 +359,11 @@ impSim <- REimpact(m1, InstEval[7, ], groupFctr = "d", breaks = 5,
 #> Warning: executing %dopar% sequentially: no parallel backend registered
 impSim
 #>   case bin   AvgFit     AvgFitSE nobs
-#> 1    1   1 2.777249 2.863249e-04  193
-#> 2    1   2 3.247372 6.770648e-05  240
-#> 3    1   3 3.546850 5.480821e-05  254
-#> 4    1   4 3.817535 6.531969e-05  265
-#> 5    1   5 4.216303 2.176350e-04  176
+#> 1    1   1 2.775244 2.889334e-04  193
+#> 2    1   2 3.251612 5.378474e-05  240
+#> 3    1   3 3.535261 5.204943e-05  254
+#> 4    1   4 3.834013 6.310989e-05  265
+#> 5    1   5 4.220246 1.859937e-04  176
 ```
 
 The result of `REimpact` shows the change in the `yhat` as the case we