Analysis - raw vals test4
Paloma
2025-04-28
Last updated: 2025-04-28
Checks: 6 1
Knit directory:
HairCort-Evaluation-Nist2020/
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Introduction
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)Summary of results (all experiments)
| Version | Author | Date |
|---|---|---|
| 16ce91c | Paloma | 2025-04-10 |
Experiment C: normal sample dilution, and then each sample is spiked
TC5, 6 and 7 were not spiked (run out of spike)
| Version | Author | Date |
|---|---|---|
| e97ccaf | Paloma | 2025-04-01 |
Experiment E: testing precision
different weights: 6, 9 and 12 mg, 3 samples for each weight, 2 duplicates for each sample
Mean_BP SD_BP CV_BP
1 19.2 4.344783 22.62908
# A tibble: 3 × 4
Group CV_BP mean_BP sd_BP
<chr> <dbl> <dbl> <dbl>
1 TP1 21.7 22.6 4.90
2 TP2 10.6 18.8 2.00
3 TP3 28.4 17.5 4.97
# A tibble: 3 × 4
Group CV_BP mean_BP sd_BP
<chr> <dbl> <dbl> <dbl>
1 TP1 21.7 22.6 4.90
2 TP2 10.6 18.8 2.00
3 TP3 7.7 14.7 1.13Using raw Conc_pg.ml vals estimated by MyAssays
I.e. not controlling for weight or dilution
TP3A was diluted using 220uL buffer, while all others used 60 uL
# A tibble: 3 × 4
Group CV_BP mean_BP sd_BP
<chr> <dbl> <dbl> <dbl>
1 TP1 30.9 3016 930.
2 TP2 9.6 3509 337.
3 TP3 23.1 3888. 900.TP1C has a lower value = I lost sample during extraction, probably tube was broken or cap was not tight enough
Below I removed a duplicate for sample TP1B as it is a clear outlier, and TP3A as it has a different dilution
# A tibble: 3 × 4
Group CV_BP mean_BP sd_BP
<chr> <dbl> <dbl> <dbl>
1 TP1 14.7 2664 391.
2 TP2 9.6 3509 337.
3 TP3 8.7 4436. 386.
R version 4.5.0 (2025-04-11)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Detroit
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggpmisc_0.6.1 ggpp_0.5.8-1 dplyr_1.1.4 plyr_1.8.9
[5] bbmle_1.0.25.1 arm_1.14-4 lme4_1.1-37 Matrix_1.7-3
[9] MASS_7.3-65 coefplot_1.2.8 ggplot2_3.5.2 RColorBrewer_1.1-3
[13] knitr_1.50
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.52 bslib_0.9.0
[4] lattice_0.22-6 numDeriv_2016.8-1.1 vctrs_0.6.5
[7] tools_4.5.0 Rdpack_2.6.4 generics_0.1.3
[10] tibble_3.2.1 pkgconfig_2.0.3 lifecycle_1.0.4
[13] farver_2.1.2 compiler_4.5.0 stringr_1.5.1
[16] git2r_0.36.2 MatrixModels_0.5-4 munsell_0.5.1
[19] SparseM_1.84-2 httpuv_1.6.16 quantreg_6.1
[22] htmltools_0.5.8.1 sass_0.4.10 yaml_2.3.10
[25] later_1.4.2 pillar_1.10.2 nloptr_2.2.1
[28] jquerylib_0.1.4 whisker_0.4.1 cachem_1.1.0
[31] reformulas_0.4.0 boot_1.3-31 abind_1.4-8
[34] useful_1.2.6.1 nlme_3.1-168 tidyselect_1.2.1
[37] bdsmatrix_1.3-7 digest_0.6.37 mvtnorm_1.3-3
[40] stringi_1.8.7 reshape2_1.4.4 labeling_0.4.3
[43] splines_4.5.0 rprojroot_2.0.4 fastmap_1.2.0
[46] grid_4.5.0 colorspace_2.1-1 cli_3.6.4
[49] magrittr_2.0.3 utf8_1.2.4 survival_3.8-3
[52] withr_3.0.2 scales_1.3.0 promises_1.3.2
[55] confintr_1.0.2 rmarkdown_2.29 workflowr_1.7.1
[58] coda_0.19-4.1 evaluate_1.0.3 rbibutils_2.3
[61] mgcv_1.9-1 rlang_1.1.6 Rcpp_1.0.14
[64] glue_1.8.0 polynom_1.4-1 rstudioapi_0.17.1
[67] minqa_1.2.8 jsonlite_2.0.0 R6_2.6.1
[70] fs_1.6.6