Cannot replicate the "w" produced by estimateLogicle() using w = (m - log10(t / abs(r))) / 2

[saheedb]

I could also ask 'How does estimateLogicle() calculate w?', but not sure that's an -issue-

I calculate w using estimateLogicle():

fcsfiles <- list.files(pattern = "CytoTrol",
                       system.file("extdata",package = "flowWorkspaceData"),
                       full = TRUE)
x <- read.FCS(fcsfiles[1])

transList <- estimateLogicle(x, "V450-A")

environment(transList@transforms[["V450-A"]]@f)[["w"]]

w is calculated to be 0.4552218

Next I calculate w using w = (m - log10(t / abs(r))) / 2 per the flowCore doc

t <- environment(transList@transforms[["V450-A"]]@f)[["t"]]
r <- min(x@exprs[, "V450-A"])
m <- 4.5

w = (m - log(t / abs(r))) / 2
w

In this case, w is calculated to be -1.417491. I gather the issue is that estimateLogicle() is not using the minimum channel value for r but the t object doesn't include a r parameter, so what is it using for r?

sessionInfo():
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: RHEL

Matrix products: default
BLAS: /data/apps/R/4.0.3/lib64/R/lib/libRblas.so
LAPACK: /data/apps/R/4.0.3/lib64/R/lib/libRlapack.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] gridExtra_2.3 ggcyto_1.18.0 flowWorkspace_4.5.3 ncdfFlow_2.36.0
[5] BH_1.75.0-0 RcppArmadillo_0.10.1.2.0 flowAI_1.20.1 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[17] flowCore_2.5.0

loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1 rio_0.5.16 cytolib_2.5.3
[5] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.13 farver_2.0.3
[9] hexbin_1.28.1 IDPmisc_1.1.20 fansi_0.4.1 lubridate_1.7.9.2
[13] xml2_1.3.2 knitr_1.30 spam_2.6-0 jsonlite_1.7.2
[17] broom_0.7.3 dbplyr_2.0.0 png_0.1-7 RPMG_2.2-3
[21] rgeos_0.5-5 graph_1.68.0 shiny_1.5.0 compiler_4.0.3
[25] httr_1.4.2 backports_1.2.1 assertthat_0.2.1 fastmap_1.1.0
[29] cli_2.2.0 later_1.1.0.1 htmltools_0.5.0 tools_4.0.3
[33] dotCall64_1.0-0 gtable_0.3.0 glue_1.4.2 Rwave_2.4-8
[37] reshape2_1.4.4 maps_3.3.0 Rcpp_1.0.5 carData_3.0-4
[41] Biobase_2.50.0 cellranger_1.1.0 vctrs_0.3.6 RFOC_3.4-6
[45] changepoint_2.2.2 xfun_0.19 openxlsx_4.2.3 rvest_0.3.6
[49] mime_0.9 lifecycle_0.2.0 flowDensity_1.24.0 gtools_3.8.2
[53] XML_3.99-0.5 zoo_1.8-8 zlibbioc_1.36.0 MASS_7.3-53
[57] scales_1.1.1 RProtoBufLib_2.5.1 hms_0.5.3 promises_1.1.1
[61] parallel_4.0.3 RColorBrewer_1.1-2 fields_11.6 curl_4.3.2
[65] aws.signature_0.6.0 MBA_0.0-9 latticeExtra_0.6-29 stringi_1.5.3
[69] S4Vectors_0.28.1 caTools_1.18.0 BiocGenerics_0.36.1 zip_2.1.1
[73] flowViz_1.54.0 rlang_0.4.10 pkgconfig_2.0.3 matrixStats_0.61.0-9001
[77] bitops_1.0-6 evaluate_0.14 lattice_0.20-41 labeling_0.4.2
[81] tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[85] gplots_3.1.1 generics_0.1.0 DBI_1.1.0 withr_2.3.0
[89] pillar_1.4.7 haven_2.3.1 foreign_0.8-80 abind_1.4-5
[93] sp_1.4-4 modelr_0.1.8 crayon_1.3.4 car_3.0-10
[97] GEOmap_2.4-4 KernSmooth_2.23-17 rmarkdown_2.6 aws.s3_0.3.21
[101] jpeg_0.1-8.1 grid_4.0.3 readxl_1.3.1 data.table_1.13.6
[105] Rgraphviz_2.34.0 splancs_2.01-40 reprex_0.3.0 digest_0.6.28
[109] xtable_1.8-4 httpuv_1.5.4 RcppParallel_5.0.2 stats4_4.0.3
[113] munsell_0.5.0 RSEIS_3.9-3