hierarchical
Last updated: 2020-04-06
Checks: 7 0
Knit directory: research/
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| Rmd | a3a9fc4 | DBomber60 | 2020-04-06 | para |
| html | daeaf75 | DBomber60 | 2020-04-06 | Build site. |
| Rmd | f139f32 | DBomber60 | 2020-04-06 | init |
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| Rmd | e28a21b | DBomber60 | 2020-04-06 | test |
Introduction/ Motivation
Suppose we have results from several experiments on the effect of the same drug. That is, for each of \(J\) experiments, we have an estimated effect and standard error, \((y_j, \sigma_j)\). How should we combine these data into an overall effect estimate and what is the error of the overall effect?
summary(cars) speed dist
Min. : 4.0 Min. : 2.00
1st Qu.:12.0 1st Qu.: 26.00
Median :15.0 Median : 36.00
Mean :15.4 Mean : 42.98
3rd Qu.:19.0 3rd Qu.: 56.00
Max. :25.0 Max. :120.00 Including Plots
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| Version | Author | Date |
|---|---|---|
| befc64c | DBomber60 | 2020-04-06 |
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sessionInfo()R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] workflowr_1.6.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.3 rprojroot_1.3-2 digest_0.6.23 later_1.0.0
[5] R6_2.4.1 backports_1.1.5 git2r_0.26.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.5 rlang_0.4.4 fs_1.3.1
[13] promises_1.1.0 whisker_0.4 rmarkdown_2.1 tools_3.6.2
[17] stringr_1.4.0 glue_1.3.1 httpuv_1.5.2 xfun_0.12
[21] yaml_2.2.0 compiler_3.6.2 htmltools_0.4.0 knitr_1.27