Description Usage Arguments Value See Also Examples
View source: R/model_difference_map.R
this function creates a map of Austria with the relative difference of return levels for two given models. the relative difference is calculated like:
(rl_model_1 - rl_model_2)/rl_model_1 .
1 2 3 4 5 | model_difference_map(covariables, rl_model_1, rl_model_2,
name_model_1 = "model_1",
name_model_2 = "model_2",
plottitle = NULL, save_name = NULL,
save_dir = getwd(), printPlot = TRUE)
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covariables |
a named matrix with the covariables. each row corresponds to one location, columns should include at least lon and lat |
rl_model_1 |
a vector with the return level for every location and the first model |
rl_model_2 |
a vector with the return level for every location and the second model |
name_model_1 |
a character string defining the name of the first model. |
name_model_2 |
a character string defining the name of the second model. |
plottitle |
a character string defining the title of the plot. default is |
save_name |
a character string defining the saving name of the map. |
save_dir |
a character string defining the directory for the map to be saved. |
printPlot |
logical value; if |
a map of Austria with the relative difference of return levels for two given models
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # get covariables
lon = get(data(lon.at))
lon = as.vector(t(lon))
lat = get(data(lat.at))
lat = as.vector(t(lat))
alt = get(data(alt.at))
alt = as.vector(t(alt))
x = get(data(sample_grid_data))
mdday = x$mdday
sd_mmax = x$mmsd
swe_mmax = x$mmswe
# take only locations of the (Austrian) domain
lon = lon[which(!is.na(mdday))]
lat = lat[which(!is.na(mdday))]
alt = alt[which(!is.na(mdday))]
mdday = mdday[which(!is.na(mdday))]
sd_mmax = sd_mmax[which(!is.na(sd_mmax))]
swe_mmax = swe_mmax[which(!is.na(swe_mmax))]
# define matrix 'covariables'
covariables = cbind("lon" = lon, "lat" = lat, "alt" = alt,
"mdday" = mdday, "sd_mmax" = sd_mmax,
"swe_mmax" = swe_mmax)
# load function output from model_selection
sd_m_select = get(data("sd_m_select"))
swe_m_select = get(data("swe_m_select"))
# perform optimization with Ext-Gaussian model
optim_gauss =
optimizer_biv_ext_gauss_model(sd_m_select = sd_m_select,
swe_m_select = swe_m_select,
method = "bobyqa")
sd_coeff_gauss = optim_gauss$coefficients$sd_coeff
# load optimization results from HR model
data(optim_hr)
sd_coeff_hr = optim_hr$coefficients$sd_coeff
# calculate GEV parameters from linear models
sd_GEVparam_gauss =
GEVparameters_from_models(covariables = covariables,
coefficients = sd_coeff_gauss)
sd_GEVparam_hr =
GEVparameters_from_models(covariables = covariables,
coefficients = sd_coeff_hr)
# calculate return levels
q = 100
sd_rl_gauss =
returnlevels(GEVparam = sd_GEVparam_gauss, q = q)
sd_rl_hr =
returnlevels(GEVparam = sd_GEVparam_hr, q = q)
# create relative difference map
model_difference_map(covariables = covariables,
rl_model_1 = sd_rl_hr,
rl_model_2 = sd_rl_gauss,
printPlot = FALSE)
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