https://github.com/dataobservatory-eu/openmusesampling

https://github.com/dataobservatory-eu/openmusesampling

Last synced: 10 days ago
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• Host: GitHub
• URL: https://github.com/dataobservatory-eu/openmusesampling
• Owner: dataobservatory-eu
• License: gpl-3.0
• Created: 2024-05-24T10:04:33.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-12-06T14:59:20.000Z (17 days ago)
• Last Synced: 2024-12-06T15:47:17.140Z (16 days ago)
• Language: R
• Homepage: https://dataobservatory-eu.github.io/openmusesampling/
• Size: 256 KB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# openmusesampling

This package contains functions to sample ISRCs using a Metropolis-Hastings uniform random-walk algorithm. Run the code below to see how it works. We are using a data with 1204 observations as our "population" and estimating the mean of the popularity index with samples of size 100.

```{r}

example <- openmusesampling(x = "popularity", data= sample_data)

example

```

The estimate mean should be close to the true mean in our data.

```{r}

mean(sample_data$popularity)

```

Check the histogram

```{r}

hist(example$samples$sample_mean_popularity,

     xlab = "Values",

     main = "Histogram of 1000 RW sample means with N = 100",

     cex.main = 1.5,

     cex.lab = 1.3,

     cex.axis = 1.3)

box()

points(x = mean(sample_data$popularity), y = 0, pch = 17, col = "red", cex = 2)

points(x = mean(example$samples$sample_mean_popularity), y = 0, pch = 2, col = "blue", cex = 2, lwd = 2)

legend("topright", c("True pop. mean", "Mean RW distr."), col = c("red", "blue"), lwd = 3,

       bty = "n")

```