Automated build.

.setup/latex/bib/bib.bib

@@ -4934,13 +4934,16 @@ @Article{Orth-Clark-Donnellan-etal-2021
   publisher = {American Psychological Association (APA)},
 }
 
-@Article{Park-Chow-Epskamp-etal-2023,
+@Article{Park-Chow-Epskamp-etal-2024,
   author = {Jonathan J. Park and Sy-Miin Chow and Sacha Epskamp and Peter C. M. Molenaar},
-  date = {2023},
+  date = {2024-02},
   journaltitle = {Multivariate Behavioral Research},
   title = {Subgrouping with chain graphical {VAR} models},
   doi = {10.1080/00273171.2023.2289058},
-  pages = {1--23},
+  issn = {1532-7906},
+  number = {3},
+  pages = {543--565},
+  volume = {59},
   abstract = {Recent years have seen the emergence of an ``idio-thetic'' class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel ``idio-thetic'' model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach–the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.},
   publisher = {Informa UK Limited},
 }

.setup/latex/pdf/.gitignore

@@ -0,0 +1,5 @@
+*
+*/
+!*.pdf
+!.gitignore
+!bib.bib

R/semmcci-mc-ci-dot.R

@@ -24,12 +24,6 @@
 #' @noRd
 .MCCI <- function(object,
                   alpha = NULL) {
-  stopifnot(
-    inherits(
-      object,
-      "semmcci"
-    )
-  )
   thetahatstar <- object$thetahatstar
   thetahat <- object$thetahat
   if (is.null(alpha)) {

R/semmcci-mc-generic.R

@@ -131,8 +131,8 @@ MCGeneric <- function(object,
   }
   if (
     inherits(
-      object,
-      "lavaan"
+      x = object,
+      what = "lavaan"
     )
   ) {
     location <- lavaan::coef(object)[def_vars]

R/semmcci-mc-mi.R

@@ -88,8 +88,8 @@ MCMI <- function(lav,
                  seed = NULL) {
   stopifnot(
     inherits(
-      lav,
-      "lavaan"
+      x = lav,
+      what = "lavaan"
     )
   )
   if (lav@Data@data.type == "moment") {
@@ -107,8 +107,8 @@ MCMI <- function(lav,
   }
   if (
     inherits(
-      mi,
-      "mids"
+      x = mi,
+      what = "mids"
     )
   ) {
     imp <- mice::complete(
@@ -117,15 +117,15 @@ MCMI <- function(lav,
     )
   } else if (
     inherits(
-      mi,
-      "amelia"
+      x = mi,
+      what = "amelia"
     )
   ) {
     imp <- mi$imputations
   } else if (
     inherits(
-      mi,
-      "list"
+      x = mi,
+      what = "list"
     )
   ) {
     imp <- mi

R/semmcci-mc-std.R

@@ -92,8 +92,8 @@ MCStd <- function(mc,
                   alpha = c(0.001, 0.01, 0.05)) {
   stopifnot(
     inherits(
-      mc,
-      "semmcci"
+      x = mc,
+      what = "semmcci"
     )
   )
   stopifnot(

R/semmcci-mc.R

@@ -100,8 +100,8 @@ MC <- function(lav,
                seed = NULL) {
   stopifnot(
     inherits(
-      lav,
-      "lavaan"
+      x = lav,
+      what = "lavaan"
     )
   )
   args <- list(

R/semmcci-methods.R

@@ -8,7 +8,7 @@
 #'   use the argument `alpha` used in `x`.
 #' @param digits Integer indicating the number of decimal places to display.
 #' @param ... further arguments.
-#' @return Returns a matrix of estimates, standard errors,
+#' @return Prints a matrix of estimates, standard errors,
 #'   number of Monte Carlo replications, and confidence intervals.
 #'
 #' @examples

README.md

@@ -1,7 +1,7 @@
 semmcci
 ================
 Ivan Jacob Agaloos Pesigan
-2024-10-01
+2024-10-08
 
 <!-- README.md is generated from .setup/readme/README.Rmd. Please edit that file -->
 
@@ -84,12 +84,12 @@ library(lavaan)
 ``` r
 summary(df)
 #>        X                  M                  Y           
-#>  Min.   :-2.83590   Min.   :-3.39632   Min.   :-3.10445  
-#>  1st Qu.:-0.67337   1st Qu.:-0.71509   1st Qu.:-0.72742  
-#>  Median :-0.00771   Median :-0.10422   Median :-0.02411  
-#>  Mean   :-0.02052   Mean   :-0.05667   Mean   :-0.02289  
-#>  3rd Qu.: 0.64278   3rd Qu.: 0.59442   3rd Qu.: 0.68659  
-#>  Max.   : 3.02822   Max.   : 3.55209   Max.   : 3.51176  
+#>  Min.   :-3.50967   Min.   :-3.00067   Min.   :-4.18793  
+#>  1st Qu.:-0.71504   1st Qu.:-0.71011   1st Qu.:-0.68311  
+#>  Median :-0.01692   Median :-0.04121   Median :-0.05995  
+#>  Mean   :-0.00513   Mean   :-0.03631   Mean   :-0.01269  
+#>  3rd Qu.: 0.66690   3rd Qu.: 0.65676   3rd Qu.: 0.67217  
+#>  Max.   : 2.97956   Max.   : 3.13662   Max.   : 3.07920  
 #>  NA's   :100        NA's   :100        NA's   :100
 ```
 
@@ -129,18 +129,18 @@ mc <- MC(fit, R = 20000L, alpha = 0.05)
 mc
 #> Monte Carlo Confidence Intervals
 #>              est     se     R    2.5%  97.5%
-#> cp        0.2437 0.0317 20000  0.1818 0.3064
-#> b         0.5041 0.0304 20000  0.4440 0.5626
-#> a         0.5330 0.0299 20000  0.4742 0.5911
-#> X~~X      0.9810 0.0460 20000  0.8918 1.0720
-#> Y~~Y      0.5520 0.0271 20000  0.4985 0.6061
-#> M~~M      0.7483 0.0360 20000  0.6777 0.8191
-#> Y~1       0.0059 0.0253 20000 -0.0444 0.0558
-#> M~1      -0.0339 0.0290 20000 -0.0915 0.0232
-#> X~1      -0.0221 0.0325 20000 -0.0853 0.0418
-#> indirect  0.2687 0.0222 20000  0.2263 0.3127
-#> direct    0.2437 0.0317 20000  0.1818 0.3064
-#> total     0.5124 0.0296 20000  0.4544 0.5699
+#> cp        0.2627 0.0305 20000  0.2030 0.3224
+#> b         0.5249 0.0311 20000  0.4638 0.5860
+#> a         0.5304 0.0280 20000  0.4751 0.5851
+#> X~~X      1.0645 0.0500 20000  0.9645 1.1612
+#> Y~~Y      0.5478 0.0266 20000  0.4954 0.5996
+#> M~~M      0.7227 0.0347 20000  0.6546 0.7900
+#> Y~1       0.0109 0.0251 20000 -0.0383 0.0610
+#> M~1      -0.0365 0.0282 20000 -0.0921 0.0186
+#> X~1      -0.0078 0.0340 20000 -0.0742 0.0598
+#> indirect  0.2784 0.0218 20000  0.2367 0.3220
+#> direct    0.2627 0.0305 20000  0.2030 0.3224
+#> total     0.5411 0.0286 20000  0.4851 0.5969
 ```
 
 ### Monte Carlo Confidence Intervals - Multiple Imputation
@@ -174,15 +174,15 @@ mcmi <- MCMI(fit, mi = mi, R = 20000L, alpha = 0.05, seed = 42)
 mcmi
 #> Monte Carlo Confidence Intervals (Multiple Imputation Estimates)
 #>             est     se     R   2.5%  97.5%
-#> cp       0.2436 0.0322 20000 0.1806 0.3065
-#> b        0.5029 0.0305 20000 0.4431 0.5621
-#> a        0.5335 0.0303 20000 0.4736 0.5928
-#> X~~X     0.9826 0.0459 20000 0.8931 1.0724
-#> Y~~Y     0.5515 0.0273 20000 0.4986 0.6049
-#> M~~M     0.7489 0.0355 20000 0.6787 0.8181
-#> indirect 0.2683 0.0220 20000 0.2264 0.3128
-#> direct   0.2436 0.0322 20000 0.1806 0.3065
-#> total    0.5118 0.0299 20000 0.4533 0.5700
+#> cp       0.2604 0.0303 20000 0.2009 0.3199
+#> b        0.5266 0.0309 20000 0.4668 0.5863
+#> a        0.5316 0.0281 20000 0.4766 0.5869
+#> X~~X     1.0672 0.0506 20000 0.9685 1.1666
+#> Y~~Y     0.5466 0.0268 20000 0.4940 0.5985
+#> M~~M     0.7231 0.0347 20000 0.6548 0.7911
+#> indirect 0.2799 0.0218 20000 0.2379 0.3235
+#> direct   0.2604 0.0303 20000 0.2009 0.3199
+#> total    0.5403 0.0284 20000 0.4852 0.5961
 ```
 
 ### Standardized Monte Carlo Confidence Intervals
@@ -195,30 +195,30 @@ passing the result of the `MC()` function or the `MCMI()` function to
 MCStd(mc, alpha = 0.05)
 #> Standardized Monte Carlo Confidence Intervals
 #>              est     se     R   2.5%  97.5%
-#> cp        0.2414 0.0309 20000 0.1806 0.3019
-#> b         0.5109 0.0284 20000 0.4535 0.5644
-#> a         0.5209 0.0250 20000 0.4706 0.5684
+#> cp        0.2635 0.0302 20000 0.2041 0.3219
+#> b         0.5157 0.0280 20000 0.4603 0.5697
+#> a         0.5413 0.0243 20000 0.4920 0.5869
 #> X~~X      1.0000 0.0000 20000 1.0000 1.0000
-#> Y~~Y      0.5522 0.0255 20000 0.5019 0.6020
-#> M~~M      0.7286 0.0260 20000 0.6769 0.7785
-#> indirect  0.0059 0.0200 20000 0.2270 0.3053
-#> direct   -0.0335 0.0309 20000 0.1806 0.3019
-#> total    -0.0223 0.0253 20000 0.4557 0.5555
+#> Y~~Y      0.5175 0.0246 20000 0.4697 0.5659
+#> M~~M      0.7070 0.0263 20000 0.6555 0.7579
+#> indirect  0.0106 0.0196 20000 0.2409 0.3173
+#> direct   -0.0361 0.0302 20000 0.2041 0.3219
+#> total    -0.0075 0.0243 20000 0.4928 0.5879
 ```
 
 ``` r
 MCStd(mcmi, alpha = 0.05)
 #> Standardized Monte Carlo Confidence Intervals
 #>             est     se     R   2.5%  97.5%
-#> cp       0.2426 0.0316 20000 0.1794 0.3029
-#> b        0.5228 0.0283 20000 0.4543 0.5646
-#> a        0.5172 0.0250 20000 0.4709 0.5697
+#> cp       0.2737 0.0302 20000 0.2021 0.3199
+#> b        0.4989 0.0278 20000 0.4630 0.5707
+#> a        0.5429 0.0244 20000 0.4938 0.5894
 #> X~~X     1.0000 0.0000 20000 1.0000 1.0000
-#> Y~~Y     0.5366 0.0256 20000 0.5018 0.6025
-#> M~~M     0.7325 0.0261 20000 0.6755 0.7782
-#> indirect 0.2704 0.0198 20000 0.2279 0.3056
-#> direct   0.2426 0.0316 20000 0.1794 0.3029
-#> total    0.5130 0.0260 20000 0.4550 0.5563
+#> Y~~Y     0.5279 0.0247 20000 0.4674 0.5648
+#> M~~M     0.7053 0.0265 20000 0.6526 0.7561
+#> indirect 0.2709 0.0197 20000 0.2425 0.3195
+#> direct   0.2737 0.0302 20000 0.2021 0.3199
+#> total    0.5446 0.0245 20000 0.4932 0.5889
 ```
 
 ## Documentation

vignettes/benchmark-complete.Rmd

@@ -1,7 +1,7 @@
 ---
 title: "Benchmark: Comparing the Monte Carlo Method with Nonparametric Bootstrapping"
 author: "Ivan Jacob Agaloos Pesigan"
-date: "2024-10-01"
+date: "2024-10-08"
 bibliography: "vignettes.bib"
 csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
 nocite: |
@@ -94,9 +94,9 @@ MC(fit, R = 100L, alpha = 0.05)
 #> cp       0.2333 0.0296 100 0.1806 0.2903
 #> b        0.5082 0.0279 100 0.4555 0.5527
 #> a        0.4820 0.0280 100 0.4220 0.5301
-#> X~~X     1.0590 0.0426 100 0.9751 1.1296
+#> X~~X     1.0590 0.0426 100 0.9883 1.1428
 #> Y~~Y     0.5462 0.0231 100 0.5064 0.5959
-#> M~~M     0.7527 0.0337 100 0.7024 0.8208
+#> M~~M     0.7527 0.0337 100 0.6846 0.8029
 #> indirect 0.2449 0.0179 100 0.2058 0.2738
 #> direct   0.2333 0.0296 100 0.1806 0.2903
 #> total    0.4782 0.0295 100 0.4162 0.5283
@@ -175,22 +175,19 @@ benchmark_complete_01 <- microbenchmark(
 
 ``` r
 summary(benchmark_complete_01, unit = "ms")
-#>   expr        min          lq        mean      median        uq        max
-#> 1   MC    57.4866    65.34062    89.34902    90.13031   109.495   111.8187
-#> 2   NB 21120.0967 21183.25395 21307.35418 21297.32431 21383.171 21561.8921
-#>   neval
-#> 1    10
-#> 2    10
+#>   expr        min         lq       mean     median         uq        max neval
+#> 1   MC   107.0472   110.6029   130.1915   134.4885   139.3162   163.0922    10
+#> 2   NB 30389.4674 30447.2342 31251.4541 30565.6150 31203.4065 34893.3142    10
 ```
 
 ### Summary of Benchmark Results Relative to the Faster Method
 
 
 ``` r
 summary(benchmark_complete_01, unit = "relative")
-#>   expr      min       lq     mean   median      uq      max neval
-#> 1   MC   1.0000   1.0000   1.0000   1.0000   1.000   1.0000    10
-#> 2   NB 367.3916 324.1973 238.4733 236.2948 195.289 192.8291    10
+#>   expr      min       lq     mean   median       uq      max neval
+#> 1   MC   1.0000   1.0000   1.0000   1.0000   1.0000   1.0000    10
+#> 2   NB 283.8885 275.2843 240.0422 227.2731 223.9755 213.9484    10
 ```
 
 ## Plot
@@ -230,8 +227,8 @@ benchmark_complete_02 <- microbenchmark(
 ``` r
 summary(benchmark_complete_02, unit = "ms")
 #>   expr         min          lq        mean      median          uq         max
-#> 1   MC    18.40792    18.45909    19.81844    19.16909    21.20621    21.85372
-#> 2   NB 21206.01690 21254.05895 21355.69727 21344.33830 21418.80005 21677.68715
+#> 1   MC    34.71615    35.97336    41.09723    38.48091    44.26897    52.48712
+#> 2   NB 30351.94804 30593.86637 31340.43989 30758.84186 30859.84223 35055.47333
 #>   neval
 #> 1    10
 #> 2    10
@@ -242,9 +239,9 @@ summary(benchmark_complete_02, unit = "ms")
 
 ``` r
 summary(benchmark_complete_02, unit = "relative")
-#>   expr      min       lq     mean   median       uq     max neval
-#> 1   MC    1.000    1.000    1.000    1.000    1.000   1.000    10
-#> 2   NB 1152.005 1151.414 1077.567 1113.477 1010.025 991.945    10
+#>   expr      min      lq     mean   median       uq      max neval
+#> 1   MC   1.0000   1.000   1.0000   1.0000   1.0000   1.0000    10
+#> 2   NB 874.2891 850.459 762.5925 799.3274 697.0987 667.8871    10
 ```
 
 ## Plot