Last updated: 2019-11-12

Checks: 7 0

Knit directory: smash-paper/analysis/

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.


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.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(1) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

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:    dsc/code/Wavelab850/MEXSource/CPAnalysis.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/DownDyadHi.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/DownDyadLo.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FAIPT.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FCPSynthesis.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FMIPT.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FWPSynthesis.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FWT2_PO.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FWT_PBS.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FWT_PO.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/FWT_TI.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IAIPT.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IMIPT.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IWT2_PO.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IWT_PBS.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IWT_PO.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/IWT_TI.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/LMIRefineSeq.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/MedRefineSeq.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/UpDyadHi.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/UpDyadLo.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/WPAnalysis.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/dct_ii.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/dct_iii.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/dct_iv.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/dst_ii.mexmac
    Ignored:    dsc/code/Wavelab850/MEXSource/dst_iii.mexmac

Untracked files:
    Untracked:  files.txt

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 44525e8 Peter Carbonetto 2019-11-12 wflow_publish(“gaussian_signals.Rmd”)
html d01177c Peter Carbonetto 2019-11-12 Simplified some of the code used in the gaussian_signals analysis.
Rmd fe0ba95 Peter Carbonetto 2019-11-12 wflow_publish(“gaussian_signals.Rmd”)
html cc557b5 Peter Carbonetto 2019-11-12 Simplified the plotting code in gaussian_signals analysis.
Rmd fdc9258 Peter Carbonetto 2019-11-12 wflow_publish(“gaussian_signals.Rmd”)
html c6e3cd3 Peter Carbonetto 2019-11-12 Build site.
Rmd 70f9238 Peter Carbonetto 2019-11-12 wflow_publish(“gaussian_signals.Rmd”)
html f0221c5 Zhengrong Xing 2019-10-28 address some reviewer comments
html 8caff70 Peter Carbonetto 2018-12-06 Re-built the workflowr pages after several minor changes to the text
Rmd c589dbb Peter Carbonetto 2018-12-06 wflow_publish(c(“index.Rmd”, “gaussian_signals.Rmd”,
html 35f03c0 Peter Carbonetto 2018-12-04 Changed title of gaussian_signals.Rmd.
Rmd 4a35339 Peter Carbonetto 2018-12-04 wflow_publish(c(“gaussian_signals.Rmd”, “index.Rmd”))
html 6897465 Peter Carbonetto 2018-12-04 Added gaussian_signals page to the home.
Rmd 7ebd899 Peter Carbonetto 2018-12-04 wflow_publish(c(“gaussian_signals.Rmd”, “index.Rmd”))
html f35239b Peter Carbonetto 2018-12-04 Completed the gaussian_signals page.
Rmd 53df81d Peter Carbonetto 2018-12-04 wflow_publish(“gaussian_signals.Rmd”)
html abc74e5 Peter Carbonetto 2018-12-04 Added plots for for variance signals.
Rmd c8957e0 Peter Carbonetto 2018-12-04 wflow_publish(“gaussian_signals.Rmd”)
html 1fe523e Peter Carbonetto 2018-12-04 Adjusted the plots of the mean functions.
Rmd 1bddd73 Peter Carbonetto 2018-12-04 wflow_publish(“gaussian_signals.Rmd”)
html dc4c6cd Peter Carbonetto 2018-12-04 I now have plots of all the mean functions in gaussian_signals.Rmd.
Rmd a8b9722 Peter Carbonetto 2018-12-04 wflow_publish(“gaussian_signals.Rmd”)
html 469c32f Peter Carbonetto 2018-12-04 Generated the gaussian_signals webpage for the first time.
Rmd 2ab6ac0 Peter Carbonetto 2018-12-04 wflow_publish(“gaussian_signals.Rmd”)
Rmd ee71f27 Peter Carbonetto 2018-12-04 Made a few small adjustments to the text in the “gaussianmeanest” analysis.

Set up environment

Load the ggplot2 and cowplot packages, and the functions definining the mean and variances used to simulate the data.

library(ggplot2)
library(cowplot)
source("../code/signals.R")

Generate the ground-truth signals

Here, n specifies the length of the signals.

n <- 1024
t <- 1:n/n

Define the mean functions.

mu.sp   <- spike.fn(t,"mean")
mu.bump <- bumps.fn(t,"mean")
mu.blk  <- blocks.fn(t,"mean")
mu.ang  <- angles.fn(t,"mean")
mu.dop  <- doppler.fn(t,"mean")
mu.blip <- blip.fn(t,"mean")
mu.cor  <- cor.fn(t,"mean")

Define the variance functions.

var1 <- cons.fn(t,"var")
var2 <- texp.fn(t,"var")
var3 <- doppler.fn(t,"var")
var4 <- bumps.fn(t,"var")
var5 <- cblocks.fn(t,"var")

Plot the signal means

This function is used to draw the mean and variance functions.

plot.signal <- function (t, y, label)
  quickplot(t,y,geom = "line",color = I("darkorange"),
            xlab = "",ylab = "",main = label)

These plots show each of the mean functions used in generating the Gaussian data sets.

theme_set(theme_cowplot())
plot_grid(plot.signal(t,mu.sp,"Spikes (sp)"),
          plot.signal(t,mu.bump,"Bumps (bump)"),
          plot.signal(t,mu.blk,"Blocks (blk)"),
          plot.signal(t,mu.ang,"Angles (ang)"),
          plot.signal(t,mu.dop,"Doppler (dop)"),
          plot.signal(t,mu.blip,"Blip (blip)"),
          plot.signal(t,mu.cor,"Corner (cor)"),
          nrow = 4,ncol = 2)

Version Author Date
cc557b5 Peter Carbonetto 2019-11-12
c6e3cd3 Peter Carbonetto 2019-11-12
f0221c5 Zhengrong Xing 2019-10-28
1fe523e Peter Carbonetto 2018-12-04
dc4c6cd Peter Carbonetto 2018-12-04
469c32f Peter Carbonetto 2018-12-04

Plot the signal variances

These plots show the variance functions used in generating the Gaussian data sets. In practice, these functions are rescaled in the simulations to achieve the desired signal-to-noise ratios (see the paper for a more detailed explanation).

plot_grid(plot.signal(t,var1,"Constant variance (v1)"),
          plot.signal(t,var2,"Triple exponential (v2)"),
          plot.signal(t,var3,"Doppler (v3)"),
          plot.signal(t,var4,"Bumps (v4)"),
          plot.signal(t,var5,"Clipped (v5)"),
          nrow = 3,ncol = 2)

Version Author Date
d01177c Peter Carbonetto 2019-11-12
cc557b5 Peter Carbonetto 2019-11-12
c6e3cd3 Peter Carbonetto 2019-11-12
f0221c5 Zhengrong Xing 2019-10-28
abc74e5 Peter Carbonetto 2018-12-04
469c32f Peter Carbonetto 2018-12-04

sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.6
# 
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] cowplot_0.9.4 ggplot2_3.2.0
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.1       compiler_3.4.3   pillar_1.3.1     later_0.8.0     
#  [5] git2r_0.26.1     plyr_1.8.4       workflowr_1.5.0  tools_3.4.3     
#  [9] digest_0.6.18    evaluate_0.13    tibble_2.1.1     gtable_0.2.0    
# [13] pkgconfig_2.0.2  rlang_0.3.1      yaml_2.2.0       xfun_0.7        
# [17] withr_2.1.2.9000 stringr_1.4.0    dplyr_0.8.0.1    knitr_1.23      
# [21] fs_1.2.7         rprojroot_1.3-2  grid_3.4.3       tidyselect_0.2.5
# [25] glue_1.3.1       R6_2.4.0         rmarkdown_1.16   purrr_0.2.5     
# [29] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_0.5.0    
# [33] promises_1.0.1   htmltools_0.3.6  assertthat_0.2.1 colorspace_1.4-0
# [37] httpuv_1.5.0     labeling_0.3     stringi_1.4.3    lazyeval_0.2.1  
# [41] munsell_0.4.3    crayon_1.3.4