hurricanes: Hurricanes

In DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

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': 92 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 categorization based on MasFem (male = 0, female = 1).

Category

Strength of the hurricane in categories (1:7). (1 = not at all, 7 = very intense).

alldeaths

Human 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 years 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)

DHARMa documentation built on Oct. 18, 2024, 5:09 p.m.