Last updated: 2020-09-07

Checks: 2 0

Knit directory: smash-gen/

This reproducible R Markdown analysis was created with workflowr (version 1.5.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: 57da145

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/.DS_Store

Untracked files:
    Untracked:  analysis/pln_smooth.Rmd
    Untracked:  analysis/pre0221.Rmd
    Untracked:  analysis/smashadditive.Rmd
    Untracked:  analysis/talk1011.Rmd
    Untracked:  data/chipexo_examples/
    Untracked:  data/chipseq_examples/
    Untracked:  talk.Rmd
    Untracked:  talk.html
    Untracked:  talk.pdf

Unstaged changes:
    Modified:   analysis/binomial.Rmd
    Modified:   analysis/fda.Rmd
    Modified:   analysis/protein.Rmd
    Modified:   analysis/r2.Rmd
    Modified:   analysis/sigma.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 57da145 Dongyue Xie 2020-09-07 wflow_publish(“analysis/efsd.Rmd”)

Some collections of literature:

Nonparametric regression for exponential family:

Brown, Lawrence D., T. Tony Cai, and Harrison H. Zhou. “Nonparametric regression in exponential families.” The annals of statistics 38.4 (2010): 2005-2046.

Cleveland, W. S., Mallows, C. L., & McRae, J. E. (1993). ATS methods: Nonparametric regression for non-Gaussian data. Journal of the American Statistical Association, 88(423), 821-835.

Zhang, H. H., & Lin, Y. (2006). Component selection and smoothing for nonparametric regression in exponential families. Statistica Sinica, 1021-1041.

Bianco, A. M., Boente, G., & Sombielle, S. (2011). Robust estimation for nonparametric generalized regression. Statistics & Probability Letters, 81(12), 1986-1994.

Fryzlewicz, P. (2017). Likelihood ratio Haar variance stabilization and normalization for Poisson and other non-Gaussian noise removal. arXiv preprint arXiv:1701.07263.

Local Likelihood Estimation

Generalized addtive model

O’sullivan, F., Yandell, B. S., & Raynor Jr, W. J. (1986). Automatic smoothing of regression functions in generalized linear models. Journal of the American Statistical Association, 81(393), 96-103.