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February 13, 2020 01:49
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Calculation of M statistic for model comparison
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m.stat <- function(mod, obs, ...) { | |
# model field = x; observed field = y | |
# mse the mean square error between x and y, | |
# V and G are spatial variance and domain mean of the respective fields | |
# https://doi.org/10.1002/(SICI)1097-0088(199604)16:4%3C379::AID-JOC18%3E3.0.CO;2-U | |
obs = na.omit(obs) | |
mod = na.omit(mod) | |
stopifnot(length(obs) == length(mod)) | |
se = (obs - mod)^2 | |
mse = mean(se) | |
## variance and mean of modelled | |
Vx = var(mod) | |
Gx = mean(mod) | |
## variance and mean of observed | |
Vy = var(obs) | |
Gy = mean(obs) | |
## calc M-stat | |
M = (2 / pi) * asin(1 - mse / (Vx + Vy + ((Gx - Gy)^2))) | |
## multiply by 1000 | |
return(round(M*1000, 0)) | |
} | |
## TESTS ## | |
# obs <- c(-37.3, -93.3, -45.9, 101.9, -7.9, 84.6, -29.4, 34.2, -129.2, 46.5, -67, | |
# -177, -1, -28.8, -81.3, -172.2, 196.6, -3.6, -109.8, 17.6, -60.6, -57.1, | |
# 119.1, 151.5, 251.1, 212.1, 108.8, -105.4, -3.7, 152.5, -58.5, -254.3, | |
# -9.5, -164.4, 271.8, 9, -73.6, 124.6, 13.8, -183.1, -138.7, -140, -199.7, | |
# 118.3, -20.7, 245.1, 261.5, -49.3, -13.5, 196.8) | |
# mod <- c(-2.6, -6.5, -3.2, 7.1, -0.5, 5.9, -2, 2.4, -8.9, 3.2, -4.6, -12.3, -0.1, | |
# -2, -5.6, -11.9, 13.6, -0.2, -7.6, 1.2, -4.2, -4, 8.2, 10.5, 17.4, 14.7, | |
# 7.5, -7.3, -0.3, 10.6, -4.1, -17.6, -0.7, -11.4, 18.8, 0.6, -5.1, 8.6, 1, | |
# -12.7, -9.6, -9.7, -13.8, 8.2, -1.4, 17, 18.1, -3.4, -0.9, 13.6) | |
# plot(mod, obs); cor.test(mod, obs) ## perfect correlation, order of mag diff in values | |
# m.stat(mod, obs) ## 99 = low score due to differences | |
# m.stat(mod*10, obs) ## 773 = high score, no order of mag difference | |
# m.stat(obs, obs) ## 1000 = perfect score, modelled = observed | |
# m.stat(obs, obs/2) ## ~ 594, modelled is half of observed | |
# clim <- getData("worldclim", res = 10, var = "tmax") | |
# clim <- resample(crop(clim, extent(112,153,-43,-10)), | |
# y = raster(res = 0.5, ext = extent(112,153,-43,-10))) | |
# plot(clim[[1]][]/10, clim[[2]][]/10) | |
# cor.test(clim[[1]][]/10, clim[[2]][]/10) | |
# m.stat(mod = clim[[2]][]/10, obs = clim[[1]][]/10) ## 859 | |
# futClim <- getData("CMIP5", var="tmax", res=10, rcp = 45, model='AC', year = 50) | |
# futClim <- resample(crop(futClim, extent(112,153,-43,-10)), | |
# y = raster(res = 0.5, ext = extent(112,153,-43,-10))) | |
# plot(futClim[[1]][], clim[[1]][]) | |
# spplot(stack(futClim[[1]], clim[[1]]), main = "Future Vs Obs") | |
# cor.test(futClim[[1]][], clim[[1]][]) ## 0.98 | |
# m.stat(mod = futClim[[1]][], obs = clim[[1]][]) ## 798 |
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