以高斯/法线曲线绘制xy图，并带有误差（理想情况下为R）

Question

I have xy data, which both have +/- errors (that are equal on each side). The type of data it is has normal distribution, on in both the xy direction. At the moment we plot it either as typical xy crosses, or using geom_rect(); but both of which have issues in demonstrating what the data represents. I am looking for a solution that would allow the each of the xy data points to be represented as some sort of normal/Gaussian distribution (instead of just as +) as per my rough sketch below.

Below is an example data frame.

structure(list(Age = c(2003L, 1999L, 1995L, 1993L, 1993L, 1990L, 1988L, 1987L, 1985L, 1984L, 1983L, 1975L, 1974L, 1972L, 1963L, 1960L, 1959L, 1957L, 1953L, 1951L, 1951L, 1946L, 1940L, 1936L, 1930L, 1927L, 1919L, 1914L, 1906L, 1885L, 1864L, 1842L, 1830L, 1810L, 1803L, 1783L, 1762L, 1741L, 1720L, 1699L, 1678L, 1657L ), Age_error = c(1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 2L, 2L, 2L, 3L, 5L, 3L, 3L, 4L, 6L, 4L, 8L, 5L, 7L, 5L, 10L, 14L, 17L, 23L, 21L, 20L, 53L, 67L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L), Value = c(0, 0.07, 0, 0.09, 0.02, 0.06, -0.02, 0.154, 0.05, 0.02, -0.03, -0.024, -0.01, -0.06, -0.15, -0.04, 0.065, -0.1, -0.09, -0.02, -0.024, -0.11, -0.081, -0.13, -0.12, -0.07, -0.16, -0.122, -0.057, -0.18, -0.095, -0.105, -0.23, -0.19, -0.178, -0.267, -0.26, -0.158, -0.079, -0.218, -0.148, -0.193), Value_error = c(0.17, 0.143, 0.18, 0.18, 0.17, 0.19, 0.18, 0.163, 0.19, 0.18, 0.18, 0.142, 0.17, 0.18, 0.17, 0.17, 0.152, 0.17, 0.17, 0.17, 0.151, 0.17, 0.154, 0.17, 0.18, 0.26, 0.17, 0.144, 0.145, 0.18, 0.153, 0.153, 0.17, 0.18, 0.144, 0.155, 0.138, 0.141, 0.157, 0.14, 0.147, 0.137)), .Names = c("Age", "Age_error", "Value", "Value_error"), class = "data.frame", row.names = c(NA, -42L))

This is the sort of code I am using to just get a typical xy error plot for this data frame.

ggplot() + geom_linerange(data=mydata, aes(y=Value, x=Age, xmin=Age-Age_error, xmax=Age+Age_error, ymin=Value-Value_error, ymax=Value+Value_error)) + geom_errorbarh(data=mydata, aes(y=Value, x=Age, xmin=Age-Age_error, xmax=Age+Age_error, ymin=Value-Value_error, ymax=Value+Value_error)) 

I haven't found a function yet to do xy normal distribution type plots and there might not be one, but thought someone might have some ideas! Many thanks in advance.

Answer 1

Do you want a contour plot of Age versus Value as a 2d kernel density?

require(MASS)
dens <- with(dat, MASS::kde2d(Age, Value))
str(dens)
#-------------
List of 3
 $ x: num [1:25] 1657 1671 1686 1700 1715 ...
 $ y: num [1:25] -0.267 -0.249 -0.232 -0.214 -0.197 ...
 $ z: num [1:25, 1:25] 0.00152 0.00187 0.00226 0.00267 0.00312 ...
#--------------
# kde2d is designed for contour display: x-vector, y-vector, z-Matrix
 contour(dens)

Added the data points so the connection between the contour plot and the data was more visible:

 points(dat$Age, dat$Value, cex=0.3, col="red")

Answer 2

If you need each Age,Value pair to have a +ve and -ve error, then I think you may be looking for smoothScatter function. This function plots densities of each point using a color scheme that fades as you get farther from the point.

smoothScatter(mydata$Age, mydata$Value)

Results in