Plot Data
Enjoy this brief demonstration of the plot data module
First we simulate some data and estimate means and standard deviations
# Create normal distributed data with mean = 0 and standard deviation = 1
Sigma <- matrix(0.25,3,3)
diag(Sigma) <- 1
set.seed(100)
data <- MASS::mvrnorm(n=1000,mu=c(0,5,10), Sigma=Sigma, empirical=TRUE)
colnames(data) <- c("Before","During","After")
mcmc <- bfw::bfw(project.data = data,
y = "Before,During,After",
saved.steps = 50000,
jags.model = "mean",
job.title = "Stages of Cheese",
jags.seed = 100,
silent = TRUE)
# Print output
round(mcmc$summary.MCMC,3)
#> Mean Median Mode ESS HDIlo HDIhi n
#> mu[1]: Before 0 0 0.002 51201 -0.062 0.063 1000
#> mu[2]: During 5 5 5.000 50000 4.939 5.064 1000
#> mu[3]: After 10 10 10.000 50000 9.938 10.062 1000
#> sigma[1]: Before 1 1 1.000 49354 0.958 1.046 1000
#> sigma[2]: During 1 1 1.000 50000 0.957 1.045 1000
#> sigma[3]: After 1 1 0.997 50000 0.957 1.045 1000
Plot <- bfw::PlotMean(mcmc,
run.repeated = TRUE)
ParsePlot(Plot)Plot the data as repeated measures
plot1
Lets add some noise
set.seed(101)
noise <- apply(data,2, function (x) x + rbinom(length(x),1,0.7))
noise.mcmc <- bfw::bfw(project.data = noise,
y = "Before,During,After",
saved.steps = 50000,
jags.model = "mean",
job.title = "Stages of Cheese",
jags.seed = 101,
silent = TRUE)
# Print output
round(noise.mcmc$summary.MCMC,3)
#> Mean Median Mode ESS HDIlo HDIhi n
#> mu[1]: Before 0.713 0.713 0.713 50000 0.641 0.781 1000
#> mu[2]: During 5.686 5.686 5.690 48350 5.618 5.756 1000
#> mu[3]: After 10.686 10.686 10.685 50648 10.617 10.753 1000
#> sigma[1]: Before 1.120 1.119 1.116 50000 1.072 1.170 1000
#> sigma[2]: During 1.116 1.116 1.112 50000 1.068 1.166 1000
#> sigma[3]: After 1.101 1.100 1.097 49233 1.054 1.151 1000
Plot <- bfw::PlotMean(noise.mcmc,
run.repeated = TRUE)
ParsePlot(Plot)Plot the noise as repeated measures
plot2
Let’s add a group
combined.data <- as.data.frame(rbind(cbind(data,"Y"), cbind(noise,"X") ), stringsAsFactors=FALSE)
combined.data[,1:3] <- lapply(combined.data[,1:3] , as.numeric)
combined.data[,4] <- as.factor(combined.data[,4])
colnames(combined.data) <- c(colnames(data), "Groups")
combined.data <- bfw::bfw(project.data = combined.data,
y = "Before,During,After",
x = "Groups",
job.title = "Stages of Cheese",
saved.steps = 50000,
jags.model = "mean",
jags.seed = 102,
silent = TRUE)
# Print output
round(combined.data$summary.MCMC[, 3:7],3)
#> Mode ESS HDIlo HDIhi n
#> mu[1]: Before 0.359 50000 0.309 0.407 2000
#> mu[2]: Before vs. Groups @ X 0.713 50000 0.641 0.779 1000
#> mu[3]: Before vs. Groups @ Y -0.002 50000 -0.063 0.062 1000
#> mu[4]: During 5.342 50000 5.293 5.391 2000
#> mu[5]: During vs. Groups @ X 5.683 49103 5.616 5.754 1000
#> mu[6]: During vs. Groups @ Y 4.998 50000 4.938 5.062 1000
#> mu[7]: After 10.344 50000 10.293 10.390 2000
#> mu[8]: After vs. Groups @ X 10.688 49245 10.618 10.754 1000
#> mu[9]: After vs. Groups @ Y 9.999 50000 9.938 10.063 1000
#> sigma[1]: Before 1.119 50000 1.086 1.155 2000
#> sigma[2]: Before vs. Groups @ X 1.119 48125 1.071 1.169 1000
#> sigma[3]: Before vs. Groups @ Y 0.998 50000 0.958 1.045 1000
#> sigma[4]: During 1.112 50000 1.079 1.148 2000
#> sigma[5]: During vs. Groups @ X 1.115 49356 1.068 1.165 1000
#> sigma[6]: During vs. Groups @ Y 0.999 50000 0.957 1.045 1000
#> sigma[7]: After 1.105 50000 1.072 1.140 2000
#> sigma[8]: After vs. Groups @ X 1.098 50000 1.054 1.150 1000
#> sigma[9]: After vs. Groups @ Y 1.000 50000 0.957 1.045 1000
# Let's also add some colors!
Plot <- bfw::PlotMean(combined.data,
run.split = TRUE,
run.repeated = TRUE,
monochrome = FALSE)
ParsePlot(Plot)Plot the split data
plot3