BiostatR Blog

 Here is to show the prior "student_t(20, 0, 2)" from brms in R: # Parameters df <- 20     # degrees of freedom mu <- 0      # mean (location) sigma <- 2   # scale # Define x range (say, ±4 standard deviations) x <- seq(mu - 5*sigma, mu + 5*sigma, length.out = 1000) # Student-t density y <- dt((x - mu) / sigma, df = df) / sigma # Plot plot(x, y, type = "l", lwd = 2, col = "blue",      main = "Student-t(20, 0, 2)",      xlab = "x", ylab = "Density") For a prior = "lognormal(-0.2, 0.8)" meanlog <- -0.2 sdlog <- 0.8 x <- seq(from=0, to=10, length=100) y <- dlnorm(x, meanlog, sdlog) plot(x, y)

 If you load the .Rdata in an environment with already many data, it can be difficult to know what is new after the loading. The solution is to use: print(load("yourobject.Rdata"))

ERA5 is the latest development in the ERA series, and  improves significantly on its predecessors by: - Offering a higher horizontal resolution of 31 km and 137 vertical levels from the surface up to 0.01 hPa (around 80 km); - Using a more recent and advanced version of the ECMWF IFS model; - Providing hourly estimates of atmospheric variables; - Providing a consistent representation of uncertainties for these variables; - Using more satellite observations in the data assimilation. Here are some information to access this database using python batch Please visit http://climate.copernicus.eu/climate-reanalysis for more information including documentation and guidelines on how to download the data. https://codes.ecmwf.int/grib/param-db/139 Layer 1: 0 - 7cm Layer 2: 7 - 28cm Layer 3: 28 - 100cm Layer 4: 100 - 289cm Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. It is assumed that there is no heat transfer out of th...

 Error because library libintl.h is not found; to solve: brew install gettext cd /opt/R/arm64/include/ sudo ln -s /opt/homebrew/include/libintl.h libintl.h

 atT <- structure(c(1672520400, 1675198800, 1677618000, 1680296400, 1682888400,              1685566800, 1688158800, 1690837200, 1693515600, 1696107600, 1698786000,              1701378000, 1704056400, 1706734800, 1709240400, 1711918800, 1714510800,              1717189200, 1719781200, 1722459600, 1725138000, 1727730000, 1730408400,              1733000400, 1735678800), class = c("POSIXct", "POSIXt"), tzone = "Asia/Riyadh") # Work as expected plot(x = atT, y=rep(1, length(atT)), xaxt="n") axis(side = 1, at=atT, labels = as.character(seq_along(atT)))    # Only first label is shown plot(x = atT, y=rep(1, length(atT)), xaxt="n") axis(1, at=atT, label=c("Jan-2023", "", "", "", "", "", "Jul-2023", "", "", "", "", "",                                  ...

Let X, one observation composed of two values (x1, x2) obtained from Gaussian distributions µ1, s1 and µ2, s2 with rho being a correlation coefficient between Gaussian distributions 1 and 2, the likelihood is: library(mvtnorm) dmvnorm(x=c(x1,x2), mean=c(µ1,µ2), sigma=matrix(c(s1, rho, rho, s2), ncol=2))