Plots and tables summarizing results of Poisson simulations
Zhengrong Xing, Peter Carbonetto and Matthew Stephens
Last updated: 2020-06-25
Checks: 7 0
Knit directory: smash-paper/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6fd9dd8 | Peter Carbonetto | 2020-06-25 | wflow_publish(“poisson.Rmd”) |
| html | a40b191 | Peter Carbonetto | 2020-06-25 | Added fitted curves to Spikes plot in poisson demo. |
| Rmd | 24bf5c4 | Peter Carbonetto | 2020-06-25 | Added example run of smash and HF methods to poisson demo. |
| html | 124e2e5 | Peter Carbonetto | 2020-06-23 | Slight edit to plotting code in “poisson” workflowr analysis. |
| Rmd | bfb662b | Peter Carbonetto | 2020-06-23 | A few small revisions to the workflowr analyses. |
| html | fe1c0ea | Peter Carbonetto | 2019-12-20 | Build site. |
| Rmd | fe98ca6 | Peter Carbonetto | 2019-12-20 | wflow_publish(“poisson.Rmd”) |
| html | b369c4d | Peter Carbonetto | 2019-12-16 | Re-built poisson analysis after fixing create.signal.plot(). |
| Rmd | 0f4dc2e | Peter Carbonetto | 2019-12-16 | wflow_publish(“poisson.Rmd”) |
| html | 8398464 | Peter Carbonetto | 2019-12-16 | Added set.seed to poisson.Rmd. |
| Rmd | a10397e | Peter Carbonetto | 2019-12-16 | wflow_publish(“poisson.Rmd”) |
| html | ddc42e3 | Peter Carbonetto | 2019-12-16 | Added data points to plots to illustrate Poisson simulations. |
| Rmd | 323d535 | Peter Carbonetto | 2019-12-16 | wflow_publish(“poisson.Rmd”) |
| Rmd | 00cf92a | Peter Carbonetto | 2019-12-16 | Implemented function create.signal.plot in poisson.functions.R. |
| html | 00cf92a | Peter Carbonetto | 2019-12-16 | Implemented function create.signal.plot in poisson.functions.R. |
| Rmd | aca0299 | Peter Carbonetto | 2019-11-14 | Small edit to code in poisson.Rmd. |
| html | b013a9f | Peter Carbonetto | 2019-11-14 | Adjusted bar plots in Poisson simulations analysis. |
| html | 3a62540 | Peter Carbonetto | 2019-11-14 | Added bar charts summarizing results of Poisson simulations. |
| Rmd | c143a8f | Peter Carbonetto | 2019-11-14 | wflow_publish(“poisson.Rmd”) |
| html | d7735d5 | Peter Carbonetto | 2019-11-13 | Made some improvements to the code for creating the violin plots in |
| Rmd | f6a2109 | Peter Carbonetto | 2019-11-13 | wflow_publish(“poisson.Rmd”) |
| Rmd | dded357 | Peter Carbonetto | 2019-11-13 | Some small adjustments to the plotting code in poisson.Rmd. |
| html | 248e31e | Peter Carbonetto | 2019-11-13 | Re-built poisson analysis after simplifying creation of a violin plot. |
| Rmd | 06f113f | Peter Carbonetto | 2019-11-13 | wflow_publish(“poisson.Rmd”) |
| html | 8925b13 | Peter Carbonetto | 2019-11-12 | Re-built poisson analysis after implementing Bursts and Heavisine functions. |
| Rmd | 4576145 | Peter Carbonetto | 2019-11-12 | wflow_publish(“poisson.Rmd”) |
| html | c890a40 | Peter Carbonetto | 2019-11-12 | Re-ran poisson and gaussian_signals analyses with new code in signals.R. |
| Rmd | 1f76230 | Peter Carbonetto | 2019-11-12 | Restructured some of the code in signals.R. |
| html | 1f76230 | Peter Carbonetto | 2019-11-12 | Restructured some of the code in signals.R. |
| Rmd | 35a397a | Peter Carbonetto | 2019-11-12 | Simplifying some of the code in poisson.Rmd. |
| html | 187ec0c | Peter Carbonetto | 2019-11-12 | Fixed formatting of table in poisson analysis. |
| Rmd | 05e491f | Peter Carbonetto | 2019-11-12 | wflow_publish(“poisson.Rmd”) |
| html | a334fd2 | Peter Carbonetto | 2019-11-12 | More adjustments to plots in poisson analysis. |
| Rmd | 224a580 | Peter Carbonetto | 2019-11-12 | wflow_publish(“poisson.Rmd”) |
| html | 34f42ac | Peter Carbonetto | 2019-11-12 | Adjusted violin plots in poisson analysis. |
| Rmd | 8333706 | Peter Carbonetto | 2019-11-12 | wflow_publish(“poisson.Rmd”) |
| html | eda1295 | Peter Carbonetto | 2019-11-12 | Re-built poisson analysis using workflowr 1.5.0. |
| Rmd | a0ea389 | Peter Carbonetto | 2019-11-12 | wflow_publish(“poisson.Rmd”) |
| html | f0221c5 | Zhengrong Xing | 2019-10-27 | address some reviewer comments |
| html | bf8b368 | Peter Carbonetto | 2018-12-21 | Fixed plots for Bursts simulations. |
| Rmd | b9f5cd3 | Peter Carbonetto | 2018-12-21 | wflow_publish(c(“index.Rmd”, “poisson.Rmd”)) |
| html | 3441667 | Peter Carbonetto | 2018-12-21 | Revised Bumps results in poisson.Rmd. |
| Rmd | 7db6b71 | Peter Carbonetto | 2018-12-21 | wflow_publish(“poisson.Rmd”) |
| html | ee06399 | Peter Carbonetto | 2018-12-21 | Added plots & tables for Clipped Blocks and Bumps simulations. |
| Rmd | e8bc009 | Peter Carbonetto | 2018-12-21 | wflow_publish(“poisson.Rmd”) |
| html | 5adee34 | Peter Carbonetto | 2018-12-21 | Addd plots and table for Bursts simulations. |
| Rmd | 3776af8 | Peter Carbonetto | 2018-12-21 | wflow_publish(“poisson.Rmd”) |
| html | c333197 | Peter Carbonetto | 2018-12-21 | A few small revsions to the code at the beginning of poisson.Rmd. |
| Rmd | b42715c | Peter Carbonetto | 2018-12-21 | wflow_publish(“poisson.Rmd”) |
| html | 8e3900b | Peter Carbonetto | 2018-12-21 | Updated text at the beginning of poisson.Rmd. |
| Rmd | d351039 | Peter Carbonetto | 2018-12-21 | wflow_publish(“poisson.Rmd”) |
| html | bd84eae | Peter Carbonetto | 2018-12-20 | Build site. |
| Rmd | 51620b3 | Peter Carbonetto | 2018-12-20 | wflow_publish(“poisson.Rmd”) |
| html | bda4fbc | Peter Carbonetto | 2018-12-20 | Wrote function create.violin.plots in poisson.Rmd. |
| Rmd | ed77c12 | Peter Carbonetto | 2018-12-20 | Fixed some merging issues. |
| html | ed961b1 | Peter Carbonetto | 2018-12-20 | Added violin plots for the Spikes and Angles Poisson simulation results. |
| Rmd | 68ce493 | Peter Carbonetto | 2018-12-20 | wflow_publish(“poisson.Rmd”) |
| html | 2acee22 | Peter Carbonetto | 2018-12-20 | Added plots for all test functions. |
| html | 78afaca | Peter Carbonetto | 2018-12-20 | Added plots for all test functions. |
| Rmd | 987a861 | Peter Carbonetto | 2018-12-20 | wflow_publish(“poisson.Rmd”) |
| html | 7908551 | Peter Carbonetto | 2018-12-20 | Built very first draft of the “poisson” workflowr page. |
| Rmd | b3f5b57 | Peter Carbonetto | 2018-12-20 | wflow_publish(“poisson.Rmd”) |
| Rmd | d36bfca | Peter Carbonetto | 2018-12-20 | Misc. revisions to READMEs and documentation. |
| Rmd | 7aa0b11 | Peter Carbonetto | 2018-12-20 | Working on poisson analysis. |
| Rmd | 3c562ea | Peter Carbonetto | 2018-12-19 | Moved poisson_tables.Rmd to poisson.Rmd. |
| Rmd | 190c855 | Peter Carbonetto | 2018-12-19 | Working on R Markdown for Poisson results. |
| Rmd | 25ff9c3 | Peter Carbonetto | 2018-12-19 | Re-organized some of the files used in the Poisson numerical comparisons. |
Here we create plots and tables to compare various methods, including SMASH, for reconstructing a spatially structured signal from Poisson-distributed data. Similar to the Gaussian simulations, we generated data sets using a variety of test functions and intensity ranges. Specifically, we considered 6 test functions, rescaling the test function so that the smallest intensity was \(x\) and the largest intensity was \(y\), with \((x,y)\) set to either \((1/100, 3)\), \((1/8, 8)\) or \((1/128, 128)\).
For each combination of test function and intensity range, we simulated 100 data sets. In the plots and tables below, we summarize the error (MISE) in the estimates computed in the 100 data sets from each simulation setting. A final summary of all the simulations is shown at the end.
The plots shown below for the “Bursts” simulations, as well as the bar plots summarizing all the Poisson simulations, were included in the manuscript.
Analysis settings
We will view the results from these methods:
methods <- c("ash","BMSM","haarfisz_R")These variables specify the row and column names for the tables:
These are settings used in plotting the test functions:
n <- 1024
t <- 1:n/nSet up environment
Load the plyr, knitr, kableExtra, ggplot2 and cowplot packages.
library(smashr)
library(haarfisz)
library(plyr)
library(knitr)
library(kableExtra)
library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())Some of the test functions are defined in signals.R, so we load them here. Some additional functions are defined in file poisson.functions.R.
source("../code/signals.R")
source("../code/poisson.functions.R")Initialize the sequence of pseudorandom numbers.
set.seed(1)Load results
Load the results of the simulation experiments.
load("../output/pois.RData")Spikes data
This plot shows the function used to simulate the “Spikes” data sets at the \((1/100, 3)\) intensity range. We also show an example data set simulated from the Spikes intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.s <- 0.01 + spike.f(t)
x <- rpois(n,mu.s)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p1 <- create.signal.plot(t,x,mu.s) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p1)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 00cf92a | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Spikes simulations:
mise.s.table <- cbind(mise.s.1[methods],
mise.s.8[methods],
mise.s.128[methods])
create.results.table(mise.s.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 690.0116 | 329.2584 | 48.86856 |
| BMSM | 1007.3414 | 397.7901 | 41.87725 |
| Haar-Fisz | 722.1856 | 287.4406 | 18.05736 |
Each column shows the results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a more detailed summary of the same results:
mise.hf.ti.r.s.1 <- colMeans(rbind(mise.hf.ti.r.4.s.1,
mise.hf.ti.r.5.s.1,
mise.hf.ti.r.6.s.1,
mise.hf.ti.r.7.s.1))
mise.hf.ti.r.s.8 <- colMeans(rbind(mise.hf.ti.r.4.s.8,
mise.hf.ti.r.5.s.8,
mise.hf.ti.r.6.s.8,
mise.hf.ti.r.7.s.8))
mise.hf.ti.r.s.128 <- colMeans(rbind(mise.hf.ti.r.4.s.128,
mise.hf.ti.r.5.s.128,
mise.hf.ti.r.6.s.128,
mise.hf.ti.r.7.s.128))
pdat.s1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.s.1,
"BMSM" = mise.BMSM.s.1,
"SMASH" = mise.ash.s.1))
pdat.s8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.s.8,
"BMSM" = mise.BMSM.s.8,
"SMASH" = mise.ash.s.8))
pdat.s128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.s.128,
"BMSM" = mise.BMSM.s.128,
"SMASH" = mise.ash.s.128))
create.violin.plots(pdat.s1,pdat.s8,pdat.s128)
| Version | Author | Date |
|---|---|---|
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 187ec0c | Peter Carbonetto | 2019-11-12 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| 34f42ac | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
Angles data
This is the function used to simulate the “Angles” data sets at different ranges of intensities. We also show an example data set simulated from the Angles intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.ang <- 0.01 + angles.f(t)
x <- rpois(n,mu.ang)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p2 <- create.signal.plot(t,x,mu.ang) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p2)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 00cf92a | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Angles simulations:
mise.ang.table <- cbind(mise.ang.1[methods],
mise.ang.8[methods],
mise.ang.128[methods])
create.results.table(mise.ang.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 145.2623 | 68.47102 | 10.249294 |
| BMSM | 147.3973 | 73.86633 | 10.491953 |
| Haar-Fisz | 314.4088 | 122.79203 | 9.082691 |
Each column shows the results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a visualization of the same results:
mise.hf.ti.r.ang.1 <- colMeans(rbind(mise.hf.ti.r.4.ang.1,
mise.hf.ti.r.5.ang.1,
mise.hf.ti.r.6.ang.1,
mise.hf.ti.r.7.ang.1))
mise.hf.ti.r.ang.8 <- colMeans(rbind(mise.hf.ti.r.4.ang.8,
mise.hf.ti.r.5.ang.8,
mise.hf.ti.r.6.ang.8,
mise.hf.ti.r.7.ang.8))
mise.hf.ti.r.ang.128 <- colMeans(rbind(mise.hf.ti.r.4.ang.128,
mise.hf.ti.r.5.ang.128,
mise.hf.ti.r.6.ang.128,
mise.hf.ti.r.7.ang.128))
pdat.ang1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.ang.1,
"BMSM" = mise.BMSM.ang.1,
"SMASH" = mise.ash.ang.1))
pdat.ang8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.ang.8,
"BMSM" = mise.BMSM.ang.8,
"SMASH" = mise.ash.ang.8))
pdat.ang128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.ang.128,
"BMSM" = mise.BMSM.ang.128,
"SMASH" = mise.ash.ang.128))
create.violin.plots(pdat.ang1,pdat.ang8,pdat.ang128)
| Version | Author | Date |
|---|---|---|
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 187ec0c | Peter Carbonetto | 2019-11-12 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| 34f42ac | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
Heavisine data
This is the function used to simulate the “Heavisine” data sets at different ranges of intensities. We also show an example data set simulated from the Heavisine intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.hs <- 0.01 + heavi.f(t)
x <- rpois(n,mu.hs)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p3 <- create.signal.plot(t,x,mu.hs) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p3)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 00cf92a | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Heavisine simulations:
mise.hs.table <- cbind(mise.hs.1[methods],
mise.hs.8[methods],
mise.hs.128[methods])
create.results.table(mise.hs.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 81.41384 | 43.21293 | 7.213658 |
| BMSM | 85.28578 | 44.22229 | 7.353458 |
| Haar-Fisz | 274.25562 | 105.47241 | 9.225844 |
Each column shows results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a visualization of the same results:
mise.hf.ti.r.hs.1 <- colMeans(rbind(mise.hf.ti.r.4.hs.1,
mise.hf.ti.r.5.hs.1,
mise.hf.ti.r.6.hs.1,
mise.hf.ti.r.7.hs.1))
mise.hf.ti.r.hs.8 <- colMeans(rbind(mise.hf.ti.r.4.hs.8,
mise.hf.ti.r.5.hs.8,
mise.hf.ti.r.6.hs.8,
mise.hf.ti.r.7.hs.8))
mise.hf.ti.r.hs.128 <- colMeans(rbind(mise.hf.ti.r.4.hs.128,
mise.hf.ti.r.5.hs.128,
mise.hf.ti.r.6.hs.128,
mise.hf.ti.r.7.hs.128))
pdat.hs1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.hs.1,
"BMSM" = mise.BMSM.hs.1,
"SMASH" = mise.ash.hs.1))
pdat.hs8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.hs.8,
"BMSM" = mise.BMSM.hs.8,
"SMASH" = mise.ash.hs.8))
pdat.hs128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.hs.128,
"BMSM" = mise.BMSM.hs.128,
"SMASH" = mise.ash.hs.128))
create.violin.plots(pdat.hs1,pdat.hs8,pdat.hs128)
Bursts data
This is the function used to simulate the “Bursts” data sets at different ranges of intensities. We also show an example data set simulated from the Bursts intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.bur <- 0.01 + bursts.f(t)
x <- rpois(n,mu.bur)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p4 <- create.signal.plot(t,x,mu.bur) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p4)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| ddc42e3 | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Bursts simulations:
mise.bur.table <- cbind(mise.bur.1[methods],
mise.bur.8[methods],
mise.bur.128[methods])
create.results.table(mise.bur.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 487.3353 | 234.3490 | 33.11112 |
| BMSM | 706.0353 | 301.8645 | 34.42320 |
| Haar-Fisz | 618.3874 | 299.3932 | 25.19659 |
Each column shows results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a visualization of the same results:
mise.hf.ti.r.bur.1 <- colMeans(rbind(mise.hf.ti.r.4.bur.1,
mise.hf.ti.r.5.bur.1,
mise.hf.ti.r.6.bur.1,
mise.hf.ti.r.7.bur.1))
mise.hf.ti.r.bur.8 <- colMeans(rbind(mise.hf.ti.r.4.bur.8,
mise.hf.ti.r.5.bur.8,
mise.hf.ti.r.6.bur.8,
mise.hf.ti.r.7.bur.8))
mise.hf.ti.r.bur.128 <- colMeans(rbind(mise.hf.ti.r.4.bur.128,
mise.hf.ti.r.5.bur.128,
mise.hf.ti.r.6.bur.128,
mise.hf.ti.r.7.bur.128))
pdat.bur1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.bur.1,
"BMSM" = mise.BMSM.bur.1,
"SMASH" = mise.ash.bur.1))
pdat.bur8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.bur.8,
"BMSM" = mise.BMSM.bur.8,
"SMASH" = mise.ash.bur.8))
pdat.bur128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.bur.128,
"BMSM" = mise.BMSM.bur.128,
"SMASH" = mise.ash.bur.128))
create.violin.plots(pdat.bur1,pdat.bur8,pdat.bur128)
Clipped Blocks data
This is the function used to simulate the “Clipped Blocks” data sets at different ranges of intensities. We also show an example data set simulated from the Clipped Blocks intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.cb <- 0.01 + cblocks.f(t)
x <- rpois(n,mu.cb)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p5 <- create.signal.plot(t,x,mu.cb) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p5)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| fe1c0ea | Peter Carbonetto | 2019-12-20 |
| ddc42e3 | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Clipped Blocks simulations:
mise.cb.table <- cbind(mise.cb.1[methods],
mise.cb.8[methods],
mise.cb.128[methods])
create.results.table(mise.cb.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 307.8030 | 137.2787 | 6.822219 |
| BMSM | 355.1542 | 143.0926 | 6.911608 |
| Haar-Fisz | 632.2052 | 338.5486 | 29.719806 |
Each column shows results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a visualization of the same results:
mise.hf.ti.r.cb.1 <- colMeans(rbind(mise.hf.ti.r.4.cb.1,
mise.hf.ti.r.5.cb.1,
mise.hf.ti.r.6.cb.1,
mise.hf.ti.r.7.cb.1))
mise.hf.ti.r.cb.8 <- colMeans(rbind(mise.hf.ti.r.4.cb.8,
mise.hf.ti.r.5.cb.8,
mise.hf.ti.r.6.cb.8,
mise.hf.ti.r.7.cb.8))
mise.hf.ti.r.cb.128 <- colMeans(rbind(mise.hf.ti.r.4.cb.128,
mise.hf.ti.r.5.cb.128,
mise.hf.ti.r.6.cb.128,
mise.hf.ti.r.7.cb.128))
pdat.cb1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.cb.1,
"BMSM" = mise.BMSM.cb.1,
"SMASH" = mise.ash.cb.1))
pdat.cb8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.cb.8,
"BMSM" = mise.BMSM.cb.8,
"SMASH" = mise.ash.cb.8))
pdat.cb128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.cb.128,
"BMSM" = mise.BMSM.cb.128,
"SMASH" = mise.ash.cb.128))
create.violin.plots(pdat.cb1,pdat.cb8,pdat.cb128)
Bumps data
This is the function used to simulate the “Bumps” data sets at different ranges of intensities. We also show an example data set simulated from the Bumps intensity function (black circles), and the fitted curves obtained by applying SMASH (orange) and the Haar-Fisz method (light blue) to these data:
mu.b <- 0.01 + bumps.f(t)
x <- rpois(n,mu.b)
out.hf <- denoise.poisson(x,meth.1 = hf.la10.ti4,cs.1 = 50,hybrid = FALSE)
out.smash <- smash.poiss(x,post.var = TRUE)
p6 <- create.signal.plot(t,x,mu.b) +
geom_line(dat = data.frame(x = t,y = out.smash$est),col = "darkorange") +
geom_line(dat = data.frame(x = t,y = out.hf),col = "dodgerblue")
print(p6)
| Version | Author | Date |
|---|---|---|
| a40b191 | Peter Carbonetto | 2020-06-25 |
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| fe1c0ea | Peter Carbonetto | 2019-12-20 |
| ddc42e3 | Peter Carbonetto | 2019-12-16 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bda4fbc | Peter Carbonetto | 2018-12-20 |
| ed77c12 | Peter Carbonetto | 2018-12-20 |
| ed961b1 | Peter Carbonetto | 2018-12-20 |
| 2acee22 | Peter Carbonetto | 2018-12-20 |
| 78afaca | Peter Carbonetto | 2018-12-20 |
This table summarizes the results from the Bumps simulations:
mise.b.table <- cbind(mise.b.1[methods],
mise.b.8[methods],
mise.b.128[methods])
create.results.table(mise.b.table)| (1/100, 3) | (1/8, 8) | (1/128, 128) | |
|---|---|---|---|
| SMASH | 2597.462 | 1194.624 | 141.2119 |
| BMSM | 4036.773 | 1889.937 | 171.0706 |
| Haar-Fisz | 3113.365 | 1658.742 | 184.6601 |
Each column shows results at a different range of intensities. The individual table entries give the average error (MISE) in the estimates, in which the average is taken over the 100 data sets simulated at the given range of intensities.
The combined violin-boxplots provide a visualization of the same results:
mise.hf.ti.r.b.1 <- colMeans(rbind(mise.hf.ti.r.4.b.1,
mise.hf.ti.r.5.b.1,
mise.hf.ti.r.6.b.1,
mise.hf.ti.r.7.b.1))
mise.hf.ti.r.b.8 <- colMeans(rbind(mise.hf.ti.r.4.b.8,
mise.hf.ti.r.5.b.8,
mise.hf.ti.r.6.b.8,
mise.hf.ti.r.7.b.8))
mise.hf.ti.r.b.128 <- colMeans(rbind(mise.hf.ti.r.4.b.128,
mise.hf.ti.r.5.b.128,
mise.hf.ti.r.6.b.128,
mise.hf.ti.r.7.b.128))
pdat.b1 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.b.1,
"BMSM" = mise.BMSM.b.1,
"SMASH" = mise.ash.b.1))
pdat.b8 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.b.8,
"BMSM" = mise.BMSM.b.8,
"SMASH" = mise.ash.b.8))
pdat.b128 <- get.violin.plot.data(list("Haar-Fisz" = mise.hf.ti.r.b.128,
"BMSM" = mise.BMSM.b.128,
"SMASH" = mise.ash.b.128))
create.violin.plots(pdat.b1,pdat.b8,pdat.b128)
| Version | Author | Date |
|---|---|---|
| 124e2e5 | Peter Carbonetto | 2020-06-23 |
| 187ec0c | Peter Carbonetto | 2019-11-12 |
| a334fd2 | Peter Carbonetto | 2019-11-12 |
| 34f42ac | Peter Carbonetto | 2019-11-12 |
| eda1295 | Peter Carbonetto | 2019-11-12 |
| f0221c5 | Zhengrong Xing | 2019-10-27 |
| bf8b368 | Peter Carbonetto | 2018-12-21 |
| ee06399 | Peter Carbonetto | 2018-12-21 |
Combined results
These plots summarize the results for all 6 simulation scenarios.
pdat.s1$simulation <- "Spikes"
pdat.s8$simulation <- "Spikes"
pdat.s128$simulation <- "Spikes"
pdat.s1$intensity <- "(1/100, 3)"
pdat.s8$intensity <- "(1/8, 8)"
pdat.s128$intensity <- "(1/128, 128)"
pdat.ang1$simulation <- "Angles"
pdat.ang8$simulation <- "Angles"
pdat.ang128$simulation <- "Angles"
pdat.ang1$intensity <- "(1/100, 3)"
pdat.ang8$intensity <- "(1/8, 8)"
pdat.ang128$intensity <- "(1/128, 128)"
pdat.hs1$simulation <- "Heavisine"
pdat.hs8$simulation <- "Heavisine"
pdat.hs128$simulation <- "Heavisine"
pdat.hs1$intensity <- "(1/100, 3)"
pdat.hs8$intensity <- "(1/8, 8)"
pdat.hs128$intensity <- "(1/128, 128)"
pdat.bur1$simulation <- "Bursts"
pdat.bur8$simulation <- "Bursts"
pdat.bur128$simulation <- "Bursts"
pdat.bur1$intensity <- "(1/100, 3)"
pdat.bur8$intensity <- "(1/8, 8)"
pdat.bur128$intensity <- "(1/128, 128)"
pdat.cb1$simulation <- "Clipped"
pdat.cb8$simulation <- "Clipped"
pdat.cb128$simulation <- "Clipped"
pdat.cb1$intensity <- "(1/100, 3)"
pdat.cb8$intensity <- "(1/8, 8)"
pdat.cb128$intensity <- "(1/128, 128)"
pdat.b1$simulation <- "Bumps"
pdat.b8$simulation <- "Bumps"
pdat.b128$simulation <- "Bumps"
pdat.b1$intensity <- "(1/100, 3)"
pdat.b8$intensity <- "(1/8, 8)"
pdat.b128$intensity <- "(1/128, 128)"
pdat <- rbind(pdat.s1,pdat.s8,pdat.s128,
pdat.ang1,pdat.ang8,pdat.ang128,
pdat.hs1,pdat.hs8,pdat.hs128,
pdat.bur1,pdat.bur8,pdat.bur128,
pdat.cb1,pdat.cb8,pdat.cb128)
pA <- create.bar.plots(pdat,show.legend = FALSE)
pdat <- rbind(pdat.b1,pdat.b8,pdat.b128)
pB <- create.bar.plots(pdat)
plot_grid(pA,pB,nrow = 1,rel_widths = c(7,4))
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.5
#
# 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
#
# Random number generation:
# RNG: Mersenne-Twister
# Normal: Inversion
# Sample: Rounding
#
# 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_1.0.0 ggplot2_3.3.0 kableExtra_1.1.0 knitr_1.26
# [5] plyr_1.8.5 haarfisz_4.5 wavethresh_4.6.8 MASS_7.3-51.4
# [9] smashr_1.2-5
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.3 invgamma_1.1 lattice_0.20-38 assertthat_0.2.1
# [5] zeallot_0.1.0 rprojroot_1.3-2 digest_0.6.23 truncnorm_1.0-8
# [9] R6_2.4.1 backports_1.1.5 evaluate_0.14 highr_0.8
# [13] httr_1.4.1 pillar_1.4.3 rlang_0.4.5 rstudioapi_0.10
# [17] data.table_1.12.8 irlba_2.3.3 whisker_0.4 Matrix_1.2-18
# [21] rmarkdown_2.0 labeling_0.3 webshot_0.5.2 readr_1.3.1
# [25] stringr_1.4.0 munsell_0.5.0 mixsqp_0.3-44 compiler_3.6.2
# [29] httpuv_1.5.2 xfun_0.11 pkgconfig_2.0.3 SQUAREM_2017.10-1
# [33] htmltools_0.4.0 tidyselect_0.2.5 tibble_2.1.3 workflowr_1.6.2
# [37] viridisLite_0.3.0 crayon_1.3.4 dplyr_0.8.3 withr_2.1.2
# [41] later_1.0.0 bitops_1.0-6 grid_3.6.2 gtable_0.3.0
# [45] lifecycle_0.1.0 git2r_0.26.1 magrittr_1.5 scales_1.1.0
# [49] stringi_1.4.3 farver_2.0.1 fs_1.3.1 promises_1.1.0
# [53] xml2_1.2.2 vctrs_0.2.1 tools_3.6.2 glue_1.3.1
# [57] purrr_0.3.3 hms_0.5.2 yaml_2.2.0 colorspace_1.4-1
# [61] ashr_2.2-50 caTools_1.17.1.3 rvest_0.3.5