hurricanes {DHARMa} | R Documentation |
Hurricanes
Description
A data set on hurricane strength and fatalities in the US between 1950 and 2012. The data originates from the study by Jung et al., PNAS, 2014, who claim that the masculinity / femininity of a hurricane name has a causal effect on fatalities, presumably through a different perception of danger caused by the names.
Format
A 'data.frame': 93 obs. of 14 variables
- Year
Year of the hurricane (1950-2012)
- Name
Name of the hurricane
- MasFem
Masculinity-femininity rating of the hurricane's name in the range 1 = very masculine, 11 = very feminine
- MinPressure_before
Minimum air pressure (909-1002)
- Minpressure_Updated_2014
Updated minimum air pressure (909-1003)
- Gender_MF
Binary gender (male/female) based on MasFem (male = 0, female = 1)
- Category
Strength of the hurricane in categories (1:7). (1 = not at all, 7 = very intense)
- alldeaths
Deaths occured (1:256)
- NDAM
normalized damage in millions (1:75.000). The raw (dollar) amounts of property damage caused by hurricanes were obtained, and the unadjusted dollar amounts were normalized to 2013 monetary values by adjusting them to inflation, wealth and population density
- Elapsed_Yrs
elapsed since the occurrence of hurricanes (1:63)
- Source
MWR/wikipedia ()
- ZMasFem
scaled (MasFem)
- ZMinPressure_A
scaled (Minpressure_Updated_2014)
- ZNDAM
scaled (NDAM)
...
References
Jung, K., Shavitt, S., Viswanathan, M., & Hilbe, J. M. (2014). Female hurricanes are deadlier than male hurricanes. Proceedings of the National Academy of Sciences, 111(24), 8782-8787.
Examples
## Not run:
# Loading hurricanes dataset
library(DHARMa)
data(hurricanes)
str(hurricanes)
# this is the model fit by Jung et al.
library(glmmTMB)
originalModelGAM = glmmTMB(alldeaths ~ scale(MasFem) *
(scale(Minpressure_Updated_2014) + scale(NDAM)),
data = hurricanes, family = nbinom2)
# no significant deviation in the general DHARMa plot
res <- simulateResiduals(originalModelGAM)
plot(res)
# but residuals ~ NDAM looks funny, which was pointed
# out by Bob O'Hara in a blog post after publication of the paper
plotResiduals(res, hurricanes$NDAM)
# we also find temporal autocorrelation
res2 = recalculateResiduals(res, group = hurricanes$Year)
testTemporalAutocorrelation(res2, time = unique(hurricanes$Year))
# task: try to address these issues - in many instances, this will
# make the MasFem predictor n.s.
## End(Not run)