This document is part of the "DrBats" project whose goal is to implement exploratory statistical analysis on large sets of data with uncertainty. The idea is to visualize the results of the analysis in a way that explicitely illustrates the uncertainty in the data.
The "DrBats" project applies a Bayesian Latent Factor Model.
This project involves the following persons, listed in alphabetical order :
Bénédicte Fontez (aut)
Nadine Hilgert (aut)
Susan Holmes (aut)
Gabrielle Weinrott (cre, aut)
require(DrBats) st_data <- drbats.simul(N = 10, t.range = c(0, 1000), b.range = c(0.2, 0.4), c.range = c(0.6, 0.8), b.sd = 0.5, c.sd = 0.5, y.range = c(-5, 5), sigma2 = 0.2, breaks = 15, data.type = 'sparse.tend')
mycol<-c("#ee204d", "#1f75fe", "#1cac78", "#ff7538", "#b4674d", "#926eae", "#fce883", "#000000", "#78dbe2", "#6e5160", "#ff43a4")
The parameters b.range
and c.range
dictate the location of two peaks, and b.sd
and c.sd
the variance of the peaks. Once the signals have been simulated, the function samples observation times over the range of possible times t.range
. Few times are chosen in b.range
and c.range
, and many are chosen outside these ranges.
The parameter data.type
specifies the type of signal to simulate: sparse
will simulate a bi-modal signal that is flat between the modes. The sparse.tend
option will simulate bi-modal signals with a trend, and the sparse.tend.cos
will simulate periodic bi-modal signals with a trend.
matplot(t(st_data$t), t(st_data$X), type = 'l', lty = 1, lwd = 1, xlab = 'Time', ylab = ' ', col = mycol[1:10]) points(t(st_data$t), t(st_data$X), pch = '.')
The drbats.simul()
function projects the simulated data onto a histogram basis (whose size is determined by the parameter breaks
).
matplot(t(st_data$proj.pca$Xt.proj$X.proj), type = 's', lty = 1, lwd = 1, xlab = 'Time', ylab = ' ', col = mycol[1:10])
We can also use real functional data, like the Canadian Weather data available in the fda
package.
require(fda) Canada.temp <- CanadianWeather$monthlyTemp[ , 1:10]
The data looks like this :
matplot(Canada.temp, type = 'l', xaxt = "n", xlab = "", ylab = "Temp °C", col = mycol[1:12]) axis(side = 1, labels = rownames(Canada.temp), at = 1:12)
To project onto the histogram basis, we use the function histoProj()
where we specify the matrix of observation times, the range of observation times on which to construct the basis, and the number of breaks.
proj.Canada <- histoProj(t(Canada.temp), t = t(matrix(rep(1:12, 10), nrow = 12, ncol = 10)), t.range = c(1, 12), breaks = 13)$X.proj rownames(proj.Canada) = colnames(Canada.temp) colnames(proj.Canada) = rownames(Canada.temp)
The projected data looks like this :
matplot(t(proj.Canada), type = 's', lwd = 2, xaxt = "n", col = mycol[1:12]) axis(side = 1, labels = colnames(proj.Canada), at = 1:12)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.