[R] bug in Windows implementation of nlme::groupedData

[Key, Melissa]

I am trying to replicate a semi-parametric analysis described in Harezlak, Jaroslaw, David Ruppert, and Matt P. Wand. Semiparametric regression with R. New York, NY: Springer, 2018. (https://link.springer.com/book/10.1007%2F978-1-4939-8853-2).

I can successfully run the analysis, but now I'm trying to move it into my workflow, which requires that the analysis be conducted within a function (using targets package), and the `groupedData` function now fails with an error that it cannot find the `X` matrix (see reprex below).  I've tried the reprex on both my personal Mac (where it works??) and on windows machines (where it does not) - so the problem is likely specific to Windows computers (yes, this seems weird to me too).
All packages have been updated, and I'm running the latest version of R on all machines.

Reprex:

library(HRW) # contains example data and ZOSull function
library(nlme)

data(growthIndiana)


analyze_this <- function(df) {

  mean.x <- mean(df$age)
  mean.y <- mean(df$height)
  sd.x <- sd(df$age)
  sd.y <- sd(df$height)

  df$x <- (df$age - mean.x) / sd.x
  df$y <- (df$height - mean.y) / sd.y

  X <- model.matrix(~ x * male * black, data = df)
  dummyID <- rep(1, length(nrow(X)))

  grouped_data <- groupedData(y ~ X[,-1]|rep(1, length = nrow(X)), data = data.frame(y = df$y, X, dummyID))

}


# doesn't work on Windows machine, does work on the Mac
analyze_this(growthIndiana)
#> Error in eval(aux[[2]], object): object 'X' not found

# does work

df <- growthIndiana

mean.x <- mean(df$age)
mean.y <- mean(df$height)
sd.x <- sd(df$age)
sd.y <- sd(df$height)

df$x <- (df$age - mean.x) / sd.x
df$y <- (df$height - mean.y) / sd.y

X <- model.matrix(~ x * male * black, data = df)
dummyID <- rep(1, length(nrow(X)))

grouped_data <- groupedData(y ~ X[,-1]|rep(1, length = nrow(X)), data = data.frame(y = df$y, X, dummyID))


# attempted work-around.

analyze_this2 <- function(df) {
  num.global.knots = 20
  num.subject.knots = 10

  mean.x <- mean(df$age)
  mean.y <- mean(df$height)
  sd.x <- sd(df$age)
  sd.y <- sd(df$height)

  df$x <- (df$age - mean.x) / sd.x
  df$y <- (df$height - mean.y) / sd.y

  X <- model.matrix(~ x * male * black, data = df)
  dummyID <- rep(1, length(nrow(X)))

  # grouped_data <- groupedData(y ~ X[,-1]|rep(1, length = nrow(X)), data = data.frame(y = df$y, X, dummyID))

  global.knots = quantile(unique(df$x), seq(0, 1, length = num.global.knots + 2)[-c(1, num.global.knots + 2)])
  subject.knots = quantile(unique(df$x), seq(0, 1, length = num.subject.knots + 2)[-c(1, num.subject.knots + 2)])

  Z.global <- ZOSull(df$x, range.x = range(df$x), global.knots)
  Z.group <- df$black * Z.global
  Z.subject <- ZOSull(df$x, range.x = range(df$x), subject.knots)

  Zblock <- list(
    dummyID = pdIdent(~ 0 + Z.global),
    dummyID = pdIdent(~ 0 + Z.group),
    idnum = pdSymm(~ x),
    idnum = pdIdent(~ 0 + Z.subject)
  )

  df$dummyID <- dummyID
  tmp_data <- c(
    df,
    X = list(X),
    Z.global = list(Z.global),
    Z.group = list(Z.global),
    Z.subject = list(Z.subject)
  )

  fit <- lme(y ~ 0 + X,
    data = tmp_data,
    random = Zblock
  )

}

# this works (warning - lme takes awhile to fit)
analyze_this2(growthIndiana)

sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22000)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
#>
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base
#>
#> other attached packages:
#> [1] nlme_3.1-153 HRW_1.0-5
#>
#> loaded via a namespace (and not attached):
#>  [1] lattice_0.20-45    digest_0.6.29      withr_2.4.3        grid_4.1.2
#>  [5] magrittr_2.0.1     reprex_2.0.1       evaluate_0.14      KernSmooth_2.23-20
#>  [9] highr_0.9          stringi_1.7.6      rlang_0.4.12       cli_3.1.0
#> [13] rstudioapi_0.13    fs_1.5.2           rmarkdown_2.11     splines_4.1.2
#> [17] tools_4.1.2        stringr_1.4.0      glue_1.6.0         xfun_0.29
#> [21] yaml_2.2.1         fastmap_1.1.0      compiler_4.1.2     htmltools_0.5.2
#> [25] knitr_1.37

Created on 2022-01-07 by the [reprex package](https://reprex.tidyverse.org) (v2.0.1)

 Here's the session Info for the Mac:

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] nlme_3.1-153  targets_0.8.1 HRW_1.0-5

loaded via a namespace (and not attached):
 [1] compiler_4.1.2     pillar_1.6.4       tools_4.1.2        digest_0.6.28      lattice_0.20-45    jsonlite_1.7.2     evaluate_0.14
 [8] lifecycle_1.0.1    tibble_3.1.6       pkgconfig_2.0.3    rlang_0.4.12       igraph_1.2.9       cli_3.1.0          yaml_2.2.1
[15] xfun_0.28          fastmap_1.1.0      withr_2.4.2        knitr_1.36         htmlwidgets_1.5.4  vctrs_0.3.8        grid_4.1.2
[22] tidyselect_1.1.1   glue_1.5.0         data.table_1.14.2  R6_2.5.1           processx_3.5.2     fansi_0.5.0        rmarkdown_2.11
[29] callr_3.7.0        purrr_0.3.4        magrittr_2.0.1     scales_1.1.1       codetools_0.2-18   ps_1.6.0           htmltools_0.5.2
[36] ellipsis_0.3.2     splines_4.1.2      colorspace_2.0-2   KernSmooth_2.23-20 utf8_1.2.2         visNetwork_2.1.0   munsell_0.5.0
[43] crayon_1.4.2


Melissa Key, Ph.D.
Statistician
Non-Invasive Brain Stimulation Team
Infoscitex

Cell: 937-550-4981
Email: mkey using infoscitex.com<mailto:mkey using infoscitex.com>
Base email: melissa.key.ctr using us.af.mil<mailto:melissa.key.ctr using us.af.mil>


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