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Hurricanes.html
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Hurricanes.html
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<h1>Hurricanes: the female of the species in no more deadly than the male</h1>
<p>(update: the colours on the plots have been fixed so you can actually see them.)</p>
<p>This is a re-analysis of the Jung <em>et al</em> paper <a href="http://dx.doi.org/10.1073/pnas.1402786111">Female hurricanes are deadlier than male hurricanes</a>, complete with the R code. The output is <a href="http://rpubs.com/oharar/19171">available on RPubs</a>, and the code on <a href="https://github.com/oharar/Hurricanes">GitHub</a> (thanks to RStudio for RPubs, and RStudio, which integrates everything so nicely)</p>
<p>First, read in the data, and do some simple arrangements, in particular selecting an ironic colour scheme, and noting which hurricanes killed more than 100 people.</p>
<pre><code class="r">library(gdata)
library(mgcv)
# Read in the data
Data = read.xls("http://www.pnas.org/content/suppl/2014/05/30/1402786111.DCSupplemental/pnas.1402786111.sd01.xlsx",
nrows = 92, as.is = TRUE)
Data$Category = factor(Data$Category)
Data$Gender_MF = factor(Data$Gender_MF)
# Data$ColourMF=c('lightblue', 'pink')[as.numeric(Data$Gender_MF)]
Data$ColourMF = c("blue", "red")[as.numeric(Data$Gender_MF)]
BigH = which(Data$alldeaths > 100) # Select hurricanes with > 100 deaths
# scale the covariates
Data$Minpressure.2014.sc = scale(Data$Minpressure_Updated.2014)
Data$NDAM.sc = scale(Data$NDAM)
Data$MasFem.sc = scale(Data$MasFem)
</code></pre>
<p>Then, plot the data</p>
<pre><code class="r">plot(Data$Year, Data$alldeaths, col = Data$ColourMF, type = "p", pch = 15, xlab = "Year",
ylab = "Number of Deaths", main = "Deaths due to hurricanes in the US")
text(Data$Year[BigH], Data$alldeaths[BigH], Data$Name[BigH], adj = c(0.8, 1.5))
legend(1984, 200, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
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" alt="plot of chunk unnamed-chunk-2"/> </p>
<p>We can see that the four most deadly hurricanes all had female names Given that 67% of hurricanes had female names, the probability that the top 4 were all female is 0.2. So, on its own this is not remarkable.</p>
<p>So, now fit the model that was used in the paper. Results might differ slightly, because I'm probably using a different package for the fitting (mgcv: for those wondering about this, I was using it to smooth the year effects):</p>
<pre><code class="r"># Fit the model used in paper
modJSVH = gam(alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc), data = Data,
family = negbin(theta = c(0.2, 10)))
summary(modJSVH)
</code></pre>
<pre><code>##
## Family: Negative Binomial(0.786)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.503 0.124 20.20 < 2e-16 ***
## MasFem.sc 0.124 0.125 0.99 0.3239
## Minpressure.2014.sc -0.543 0.153 -3.54 0.0004 ***
## NDAM.sc 0.899 0.147 6.11 1.0e-09 ***
## MasFem.sc:Minpressure.2014.sc 0.376 0.156 2.42 0.0157 *
## MasFem.sc:NDAM.sc 0.663 0.152 4.37 1.3e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = -3.51e+03 Deviance explained = 42.8%
## UBRE score = 0.24794 Scale est. = 1 n = 92
</code></pre>
<p>From this we can see no main effect of gender, i.e. at average minimum perssure and normalised damage there is no difference if the name is really masculine or feminine. But there are interactions: at higher minimum pressure and normalised damage, feminine names are associated with more deaths.</p>
<p>Now we be good little statisticians, and look at how well the model fits. Because I did the 2S2 stats course at Leeds Uni (um, about a quarter of a century ago), I'll plot some residuals:</p>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(log(fitted(modJSVH)), resid(modJSVH), col = Data$ColourMF, ylim = c(min(resid(modJSVH)),
0.5 + max(resid(modJSVH))), pch = 15, xlab = "Fitted values", ylab = "Residuals",
main = "Residual plot against log-transformed fitted values")
text(log(fitted(modJSVH)[BigH]), resid(modJSVH)[BigH], Data$Name[BigH], adj = c(0.7,
-0.7))
legend(1984, 200, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>(if I don't log-transform the fitted values, Sandy sits out on the right side of the plot, and the rest line up on the left side)
This looks OK, except Sandy is sticking out a bit, but removing her doesn't change things much. Now let's look at how well the covariates are fitting…</p>
<pre><code class="r">par(mfrow = c(2, 1), mar = c(4.1, 4.1, 3, 1))
plot(Data$Minpressure_Updated.2014, resid(modJSVH), col = Data$ColourMF, ylim = c(min(resid(modJSVH)),
0.5 + max(resid(modJSVH))), pch = 15, xlab = "Minimum pressure", ylab = "Residuals",
main = "Model fit of minimum pressure")
text(Data$Minpressure_Updated.2014[BigH], resid(modJSVH)[BigH], Data$Name[BigH],
adj = c(0.2, -0.7))
legend(910, 2.8, c("Male", "Female"), fill = c("blue", "red"))
plot((Data$NDAM), resid(modJSVH), col = Data$ColourMF, ylim = c(min(resid(modJSVH)),
0.5 + max(resid(modJSVH))), pch = 15, xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of normalized damage")
text((Data$NDAM[BigH]), resid(modJSVH)[BigH], Data$Name[BigH], adj = c(0.8,
-0.7))
legend(40000, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-5"/> </p>
<p>Minimum pressure looks OK, but does normalized damage look like it's curved? The skew in the normalized damage makes it a bit harder to see, but we can transform the x-axis to take a better look…</p>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH) ~ s(sqrt(Data$NDAM)), data = Data), ylim = c(min(resid(modJSVH)),
0.5 + max(resid(modJSVH))), xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of (transformed) normalized damage", rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM), resid(modJSVH), col = Data$ColourMF, pch = 15)
text(sqrt(Data$NDAM[BigH]), resid(modJSVH)[BigH], Data$Name[BigH], adj = c(0.8,
-0.7))
legend(200, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-6"/> </p>
<p>The fitted line is a spline, to help visualise the relationship. It definitely looks curved downwards, and is shouting “fit a quadratic!” at me.</p>
<p>So, let's shut it up and fit a quadratic…</p>
<pre><code class="r"># Fit model with NDAM and NDAM squared
Data$NDAM.sq = Data$NDAM.sc^2
modJSVH.sq = gam(alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc + NDAM.sq),
data = Data, family = negbin(theta = c(0.2, 10)))
summary(modJSVH.sq)
</code></pre>
<pre><code>##
## Family: Negative Binomial(0.964)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc + NDAM.sq)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6449 0.1277 20.71 < 2e-16 ***
## MasFem.sc 0.1670 0.1280 1.31 0.1917
## Minpressure.2014.sc -0.4473 0.1524 -2.93 0.0033 **
## NDAM.sc 1.5406 0.2726 5.65 1.6e-08 ***
## NDAM.sq -0.2642 0.0621 -4.26 2.1e-05 ***
## MasFem.sc:Minpressure.2014.sc 0.2822 0.1529 1.84 0.0651 .
## MasFem.sc:NDAM.sc 0.7457 0.2763 2.70 0.0070 **
## MasFem.sc:NDAM.sq -0.1042 0.0631 -1.65 0.0985 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = -1.76 Deviance explained = 52.3%
## UBRE score = 0.28544 Scale est. = 1 n = 92
</code></pre>
<p>and look at the plot…</p>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH.sq) ~ s(sqrt(Data$NDAM)), data = Data), ylim = c(min(resid(modJSVH.sq)),
0.5 + max(resid(modJSVH.sq))), xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of (transformed) normalized damage", rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM), resid(modJSVH.sq), col = Data$ColourMF, pch = 15)
text(sqrt(Data$NDAM[BigH]), resid(modJSVH.sq)[BigH], Data$Name[BigH], adj = c(0.8,
-0.7))
legend(200, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-8"/> </p>
<p>This still looks a bit bendy. But the nice curve above was on the square root scale, so let's use that…</p>
<pre><code class="r"># Fit model with sqrt(NDAM) and sqrt(NDAM)^2. The latter is also
# abs(NDAM.sc)
Data$NDAM.sqrt.sc = scale(sqrt(Data$NDAM))
Data$NDAM.abs = Data$NDAM.sqrt.sc^2
modJSVH.sqrt = gam(alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sqrt.sc +
NDAM.abs), data = Data, family = negbin(theta = c(0.2, 10)))
</code></pre>
<p>and look at the plot…</p>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH.sqrt) ~ s(sqrt(Data$NDAM)), data = Data), ylim = c(min(resid(modJSVH.sqrt)),
0.5 + max(resid(modJSVH.sqrt))), xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of (transformed) normalized damage", rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM), resid(modJSVH.sqrt), col = Data$ColourMF, pch = 15)
text(sqrt(Data$NDAM[BigH]), resid(modJSVH.sqrt)[BigH], Data$Name[BigH], adj = c(0.8,
-0.7))
legend(200, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p>
<p>All of which looks so much better. So, this is a nicer model: the only difference from the model used in the paper is that we are assuming that any effect of normalized damage is non-linear.</p>
<p>And now what does the model look like?</p>
<pre><code class="r">summary(modJSVH.sqrt)
</code></pre>
<pre><code>##
## Family: Negative Binomial(1.119)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sqrt.sc +
## NDAM.abs)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5737 0.1288 19.98 < 2e-16 ***
## MasFem.sc 0.0828 0.1300 0.64 0.52377
## Minpressure.2014.sc -0.1427 0.1610 -0.89 0.37543
## NDAM.sqrt.sc 1.4810 0.2091 7.08 1.4e-12 ***
## NDAM.abs -0.2961 0.0780 -3.80 0.00015 ***
## MasFem.sc:Minpressure.2014.sc 0.0737 0.1633 0.45 0.65171
## MasFem.sc:NDAM.sqrt.sc 0.2723 0.2116 1.29 0.19814
## MasFem.sc:NDAM.abs -0.0271 0.0771 -0.35 0.72554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.333 Deviance explained = 58.3%
## UBRE score = 0.27618 Scale est. = 1 n = 92
</code></pre>
<p>Oh look! The gender effects - any term with MasFem.sc in it - have totally disappeared! The only effect is of normalised damage.</p>
<h2>Effects of Year</h2>
<p>Finally, I added Year as a effect in various ways (linear, spline, random effect):</p>
<pre><code class="r">modJSVH.year.linear = gam(alldeaths ~ Year + MasFem.sc * (Minpressure.2014.sc +
NDAM.sqrt.sc + NDAM.abs), data = Data, family = negbin(theta = c(0.2, 10)))
round(summary(modJSVH.year.linear)$p.table, 2)
</code></pre>
<pre><code>## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.02 12.01 -0.34 0.74
## Year 0.00 0.01 0.55 0.58
## MasFem.sc 0.12 0.14 0.85 0.39
## Minpressure.2014.sc -0.13 0.16 -0.84 0.40
## NDAM.sqrt.sc 1.49 0.21 7.14 0.00
## NDAM.abs -0.30 0.08 -3.87 0.00
## MasFem.sc:Minpressure.2014.sc 0.07 0.16 0.44 0.66
## MasFem.sc:NDAM.sqrt.sc 0.28 0.21 1.33 0.18
## MasFem.sc:NDAM.abs -0.03 0.08 -0.44 0.66
</code></pre>
<pre><code class="r">modJSVH.year.spline = gam(alldeaths ~ s(Year) + MasFem.sc * (Minpressure.2014.sc +
NDAM.sqrt.sc + NDAM.abs), data = Data, family = negbin(theta = c(0.2, 10)))
summary(modJSVH.year.spline)$s.table
</code></pre>
<pre><code>## edf Ref.df Chi.sq p-value
## s(Year) 1 1 0.3019 0.5827
</code></pre>
<pre><code class="r">modJSVH.year.rf = gamm(alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sqrt.sc +
NDAM.abs), random = list(Year = ~1), data = Data, family = negbin(theta = c(0.2,
10)))
</code></pre>
<pre><code>##
## Maximum number of PQL iterations: 20
</code></pre>
<pre><code class="r">VarCorr(modJSVH.year.rf$lme)
</code></pre>
<pre><code>## Year = pdLogChol(1)
## Variance StdDev
## (Intercept) 2.370e-09 4.868e-05
## Residual 2.189e-01 4.679e-01
</code></pre>
<p>There was no effect anywhere:</p>
<ul>
<li>in the linear model, the Year effect is pretty much zero, </li>
<li>in the smoothed fit the effect gets shrunk away so there is only one degree of freedom (i.e. a straight line): it revets to the linear model</li>
<li>as a random effect, the standard deviation of the year effect is 0.01% of the residual standard deviation</li>
</ul>
<p>So the fact that only female names were used until 1979 turns out to be a red herring.</p>
<h2>Just Gender</h2>
<p>One can also look simply at the effect of gender of hurricane name, which avoids the issues of how feminine Sandy is. TL;DR version: the results are qualitatively the same.</p>
<pre><code class="r">modJSVH.gender = gam(alldeaths ~ Gender_MF * (Minpressure.2014.sc + NDAM.sc),
data = Data, family = negbin(theta = c(0.2, 10)))
summary(modJSVH)
</code></pre>
<pre><code>##
## Family: Negative Binomial(0.786)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.503 0.124 20.20 < 2e-16 ***
## MasFem.sc 0.124 0.125 0.99 0.3239
## Minpressure.2014.sc -0.543 0.153 -3.54 0.0004 ***
## NDAM.sc 0.899 0.147 6.11 1.0e-09 ***
## MasFem.sc:Minpressure.2014.sc 0.376 0.156 2.42 0.0157 *
## MasFem.sc:NDAM.sc 0.663 0.152 4.37 1.3e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = -3.51e+03 Deviance explained = 42.8%
## UBRE score = 0.24794 Scale est. = 1 n = 92
</code></pre>
<pre><code class="r">
par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH.gender) ~ s(sqrt(Data$NDAM)), data = Data), ylim = c(min(resid(modJSVH.gender)),
0.5 + max(resid(modJSVH.gender))), xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of (transformed) normalized damage", rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM), resid(modJSVH.gender), col = Data$ColourMF, pch = 15)
text(sqrt(Data$NDAM[BigH]), resid(modJSVH.gender)[BigH], Data$Name[BigH], adj = c(0.8,
-0.7))
legend(200, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-13"/> </p>
<p>With the quadratic on sqrt(NDAM)…</p>
<pre><code class="r"># Fit model with sqrt(NDAM) and sqrt(NDAM)^2. The latter is also
# abs(NDAM.sc)
modJSVH.sqrt.gender = gam(alldeaths ~ Gender_MF * (Minpressure.2014.sc + NDAM.sqrt.sc +
NDAM.abs), data = Data, family = negbin(theta = c(0.2, 10)))
# summary(modJSVH.sqrt)
</code></pre>
<p>The residual plot…</p>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH.sqrt.gender) ~ s(sqrt(Data$NDAM)), data = Data), ylim = c(min(resid(modJSVH.sqrt.gender)),
0.5 + max(resid(modJSVH.sqrt.gender))), xlab = "Normalized Damage", ylab = "Residuals",
main = "Model fit of (transformed) normalized damage", rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM), resid(modJSVH.sqrt.gender), col = Data$ColourMF, pch = 15)
text(sqrt(Data$NDAM[BigH]), resid(modJSVH.sqrt.gender)[BigH], Data$Name[BigH],
adj = c(0.8, -0.7))
legend(200, 2.8, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-15"/> </p>
<p>And the model:</p>
<pre><code class="r">summary(modJSVH.sqrt)
</code></pre>
<pre><code>##
## Family: Negative Binomial(1.119)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sqrt.sc +
## NDAM.abs)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5737 0.1288 19.98 < 2e-16 ***
## MasFem.sc 0.0828 0.1300 0.64 0.52377
## Minpressure.2014.sc -0.1427 0.1610 -0.89 0.37543
## NDAM.sqrt.sc 1.4810 0.2091 7.08 1.4e-12 ***
## NDAM.abs -0.2961 0.0780 -3.80 0.00015 ***
## MasFem.sc:Minpressure.2014.sc 0.0737 0.1633 0.45 0.65171
## MasFem.sc:NDAM.sqrt.sc 0.2723 0.2116 1.29 0.19814
## MasFem.sc:NDAM.abs -0.0271 0.0771 -0.35 0.72554
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.333 Deviance explained = 58.3%
## UBRE score = 0.27618 Scale est. = 1 n = 92
</code></pre>
<p>Again, like a magician I have magicked away the gender effect!</p>
<hr/>
<h2>Update: Following up from comments</h2>
<h3>Influential points?</h3>
<p>On the <a href="http://discussion.theguardian.com/comment-permalink/36569266">blog post</a>, msulzer suggested the curvature could just be a result of a couple of points with laege damage. So I removed the three largest points, and re-ran the analysis:</p>
<pre><code class="r">NoBigNDAM = which(Data$NDAM < 200^2)
modJSVH.nobig = gam(alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc),
data = Data[NoBigNDAM, ], family = negbin(theta = c(0.2, 10)))
summary(modJSVH.nobig)
</code></pre>
<pre><code>##
## Family: Negative Binomial(0.923)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * (Minpressure.2014.sc + NDAM.sc)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.567 0.122 21.12 < 2e-16 ***
## MasFem.sc 0.147 0.122 1.20 0.2285
## Minpressure.2014.sc -0.497 0.152 -3.26 0.0011 **
## NDAM.sc 1.419 0.250 5.69 1.3e-08 ***
## MasFem.sc:Minpressure.2014.sc 0.300 0.153 1.96 0.0497 *
## MasFem.sc:NDAM.sc 0.788 0.250 3.15 0.0016 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = -0.782 Deviance explained = 49.2%
## UBRE score = 0.24538 Scale est. = 1 n = 89
</code></pre>
<pre><code class="r">
par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1))
plot(gam(resid(modJSVH.nobig) ~ s(sqrt(Data$NDAM[NoBigNDAM])), data = Data),
ylim = c(min(resid(modJSVH.nobig)), 0.5 + max(resid(modJSVH.nobig))), xlab = "Normalized Damage",
ylab = "Residuals", main = "Model fit of (transformed) normalized damage",
rug = FALSE, shade = TRUE)
points(sqrt(Data$NDAM[NoBigNDAM]), resid(modJSVH.nobig), col = Data$ColourMF,
pch = 15)
legend(0, 2.5, c("Male", "Female"), fill = c("blue", "red"))
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-17"/> </p>
<p>And the curvature is still there. Whew!</p>
<h3>GAM rather than quadratic?</h3>
<p>On twitter, Gavin Simpson <a href="https://twitter.com/ucfagls/status/474317573111046144">sugested using a GAM rather than a quadratic curve</a>. So, let's try it…</p>
<pre><code class="r">modJSVH.gam = gam(alldeaths ~ MasFem.sc * Minpressure.2014.sc + s(sqrt(NDAM),
by = MasFem.sc), data = Data, family = negbin(theta = c(0.2, 10)))
summary(modJSVH.gam)
</code></pre>
<pre><code>##
## Family: Negative Binomial(0.803)
## Link function: log
##
## Formula:
## alldeaths ~ MasFem.sc * Minpressure.2014.sc + s(sqrt(NDAM), by = MasFem.sc)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.552 0.130 19.70 < 2e-16 ***
## MasFem.sc -4.126 4.946 -0.83 0.4
## Minpressure.2014.sc -1.097 0.131 -8.36 < 2e-16 ***
## MasFem.sc:Minpressure.2014.sc 0.909 0.198 4.59 4.4e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(sqrt(NDAM)):MasFem.sc 5.85 7.03 32.2 3.9e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = -0.792 Deviance explained = 44.3%
## UBRE score = 0.32436 Scale est. = 1 n = 92
</code></pre>
<pre><code class="r">par(mfrow = c(1, 1), mar = c(4.1, 4.1, 3, 1), oma = c(0, 0, 0, 0))
plot(modJSVH.gam, resid = TRUE, shade = TRUE, xlab = "Normalized Damage", ylab = "Effect on mortality")
</code></pre>
<p><img 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AS4BGgiHMnfG7sIDHjFzq5du1iyZAlr1qxhwIABHa2b+vpkHOaSSqXOCPwjR46c0cIfPnx4uUtUDzQ2NrJu3TrWrl3LCy+8wLBhw2hoaODKK69k2rRp9OnTk1xLjjgGfBMwHWjszojP0mbgI8CvgDcD9wPpJsty4BbCwX09YcCr7Nra2li3bh1PPvkkW7duZcqUKcybN485c+ZUxf7rVCrFli1bWLt2LWvWrOHw4cPMmDGDK664gjlz5hj4MZZKpdixYwfr169n/fr17Nmzh5EjRzJ79mzmzJnD9OnT3Y/eiTgG/HcJrfd/BY52Z+RnoR24FfhPQpA/AHwdGAD8DdAS9e/KOODiTvp9kPB9393dIg14dUdraytPPfUUjz/+OPv27aOhoYEFCxZw0UUXVX0LJ5VKsXXrVtasWdMR+NOmTevYpD9y5Mhyl1i1Wlpa2Lx5Mxs2bGDDhg20tLQwdepULrvsMi677DImTJhQ7hIrRhwD/gCQXrpOZfXLd/R9d6QD/llgC2HXQHPU703AT4BCdt5dQ7jaXi6LCD9U3tbdIg14FeqVV15h6dKlLFmyhOPHj3PdddexYMGCWBwcF2epVIpt27axZs0a1q5dy6FDh7jgggs6WvijRo0qd4mJ1NzczObNm9m8eTMbN26ksbGRc889l5kzZzJz5kwuu+wyLwXbA3EM+EV5+v2sOx+WRzvhRjb3AxuA+YRjACBsuv844VK5PeEmehVNKpVi48aNPPnkk6xevZq6ujrmzZvHvHnzKuYAuThqb29nx44dPPvss6xfv55du3ZRW1vLrFmzmDVrFhdffHFVHY3dU+3t7bz88su88MILbN26la1bt9LU1NQR5ulTHt1y0rviGPCltItwOdx0PauAucAXgc8AdwPf6OFnGPDqVQcPHmT58uUsW7aMl19+mZkzZ3L99dfT0NBQFfvTyyV9MZ5NmzaxZcsWWltbGTVqVEdATZs2reovlNLW1sbevXvZuXMn27dvZ/v27ezdGw5jGjduHNOnT2f69OlceOGFntlQAuUK+N7e1N5dk4CBwFTgAiC9LegQ8C7gp2WqS+pw7Ngx1qxZw8qVK9m4cSN1dXVcddVV3HXXXe6PLKExY8YwZswY5s+f39HtlVdeYfPmzaxbt44HHniAQ4cOUVNTw8SJE5k8eXLHVfjGjRtHv379yld8L2pubmbfvn3s3buXvXv3smfPHl566SVOnDhBTU0N9fX1TJo0ialTp7Jw4ULGjRtX9cd8VJtq+m/bglfBTp48yW9+8xs2btzIhg0b2LVrF4MHD+ayyy5j7ty5XHzxxR4xHHNtbW3s3r2bnTt3smvXLnbu3MnLL79MKpWib9++jBw5suNyvqNHj2b48OGMGTOGc889l8GDB5el5lQqxeHDhzl8+DBNTU0cOnSIgwcP0tjYyIEDBzh48CCpVIpUKkVtbS1jx45l3Lhx1NfXM2HCBOrr673DWgxVewteKovXXnuNPXv2dITAtm3b2L9/PzU1NUydOpWZM2fyvve9j8mTJ9v6qTA1NTVMmTKFKVOmvK5fKpWisbGRffv20djYyP79+9m4cSMHDhygubmZ48ePd7y3ra2NmpoaBg4cyODBgxk8eDA1NTUMGjSIfv36MWTIkC5rOXXqFK2traRSKVpbWzl69CjHjx/n1VdfPeN9ffv2pa6ujrq6Os477zzOO+88xo8fz6xZszp+hNTUuNpWYfLNKW8D/hEYndX9BeDSolUk9ZK2tjYOHTrE/v37O/4eOHCA/fv309jYSCqVon///kyYMKHjJipvectbGDt2bLlLV5H17duX0aNHM3p09uott5MnT9LS0kJLSwvHjh2jra3tjB8BhXxe+s59Q4YMYciQIQwaNMjWtooqX8B/i7Dv+1eceSU5bzyjkmtvb+fQoUMdmy8PHjzYsRkz/fro0TMv11BTU8Pw4cMZPXo0I0aM6DgQK71Z1ha5CtW/f3+GDx/uhXhUUfIF/GDgU4SLzEhFdfLkyY6DhNKPPXv2cOzYsY593SNGjOjYfJkO7hkzZnRszvQ8XUk6LV/A/zPw54QryRX7SnaqEu3t7bz00ku88MILPP/882zdupUjR44wePBgJk2axMSJE6mvr+fyyy+nvr6+oP2bkqTX6+p+8OOBzwOvES5GkzawiDUpQQ4dOsSzzz7LunXr2LJlC6lUigkTJjBt2jSuvvpqbrvttqo/Z1mSiiFfwP9ZyapQYhw5coTVq1ezcuVKtm3bRl1dHbNnz+bGG29k8eLF9O/fv9wlSlJVyBfwD0Z/xxEuRLOHcCMYzwVXh1OnTvHMM8+wYsUK1q9fz6BBg2hoaODmm29m2rRpnisuSWWSL+DHAj8AFmZ0exS4DdhXzKIUb/v27WP58uUsX76cxsZGLr30Uq699lruuOMOL9EqSTGRL+C/DQwCriVcK34i8DXgXuDm4pemuEjfw3zp0qVs2LCBkSNHcs011/Cxj32MMWPGlLs8SVIO+QL+TYQgXx69folwx7cHOx1CiXHw4EGefvppnnzySfbv38/s2bO54YYb+PCHP5yYa3lLUpLlC/j9hCvWPZLR7bKouxKmtbWVtWvXsmrVKp5//nmGDRvGVVddxeLFi6mvry93eZKks5Qv4L9A2Af/Tk5von8j8N4S1KUiO3LkCM8++yxr167l+eefZ8CAAcyePZuFCxfykY98xFa6JFW4fAF/H7CVEOgTgOeATwIrS1BXHE165JFHqK2tZejQodTW1lJXV8fQoUPLXVeXWltb2bZtG5s3b+b5559n79691NbWMmvWLObPn89dd93l6WuSlDBd3ZZoJdUb6Nlea2pqYvv27Wdc/7y1tbXjDcOGDeu4A1T68qnDhw/veF5XV8egQYOKcqT5iRMnaGxsPOMyr7t37+64Stz06dO58MILufPOOxk/fnyvf74kKV6872Dh9t16661539Dc3ExLS0vH38OHD/PKK6/wwgsvdPwoaG5u5tixY5w6dYpUKty3J32HqaFDh55x16lcXn31VY4fP95xY5X29nb69OnDwIEDGT58OPX19dTX17Nw4UImTJjg9dklqUrlC/hZwBbgZEa3wcBkYFMxi6pUtbW11NbWnvVBael7RR89epT29vzXETrnnHMYNGhQRewakCSVT66Anxb93UA4B/5ARr+ZwI8I58erl/Tr16/jx4EkSb0hV8BvzXi+LEf/nxepFkmS1EtyBfyU6O824BrObMFDOGVOkiTFWK47geyIHkOA3yKcB7+DcLrcHcCA0pQmSZK6K9+tvu4FPgIcil5vIdxo5u+KXZQkSeqZfEfR3wr8IfBA9PpHwDHC1e3+uMh1SZKkHsjXgj8AjMjqNprTLXpJkhRTXV2L/rvA24DdwHjgt4EPlaAuSZLUA/la8D8A5gMHCee/twJvB75T/LIkSVJPdHWp2qcJrffzgbXA8WIXJEmSei5fC34S8Awh4J8ktN6fBaaWoC5JktQD+QL+H4F9wPXR60eBF4FvFbsoSZLUM/k20b+RcFDdluh1E3AP8LNiFyVJknomXwv+ReA6oE9Gt0uAnUWtSJIk9Vi+FvzHCK31P4pe/xqYB/xusYuSJEk9kyvg30E4qG4IMBv4PcKBdfuAuwkH3kmSpBjLtYn+e8D7gf8ALgdWAPcTWvDjgLeWrDpJktQtuVrwXwH+Knp+XyfD9emkuyRJioFcLfgngZHR83GEMM9+SJKkGMvVgn8E+GD0fBYwNsd71hetIkmS1GO5Av7vOb1p/uFOhrMVL0lSjOXaRP8Zwm1iDxLOex+X4yFJkmIsV8AvjLqPJFzFro1witw+YABwR8mqkyRJ3ZIr4B8mnB4H4Up2BzL6TQG+VOyiJElSz+S7VK0kSapQBrwkSQlkwEuSlECd3WzmjUAdcHH0+pbo78W53y5JkuKks4D/y6zX/17sQiRJUu/JFfDnlbyK0/oBo4FRQCOwHzhVxnokSapIuQL+cMmrgEHA/yLcxa4uo/sR4P8AfwGcKENdkiRVpM420Zfad4ArgQ8Dm4CjhPvRzwA+AXwb+EAB45kB3NBJv0uA1h5XKklSBYhLwC8CbgN+ntV9DbA9R/fOnJOnXzteQ1+SVCXiEvA7gLcAD3HmPve+UfcdBY5nQ/TIZSZeR1+SVCXiEvB3Ab8gtOK3EDalDyJscu8HvLV8pUmSVHniEvDLgYnAzYRQH0k4iv47wAOEffKSJKlAcQl4CCH+vXIXIUlSEnipWkmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgWrKXYAkqTo0NDT0ydV99erV7aWupRoY8JKkbusstIs1Dn8MFM6AlyR1qTeCvDe4FaBwBrwk6XXiEuiFylVvtYe+AS9JqrhAL0T2d6q2wDfgJakKJTHQu5L5nash7A14SaoS1RjqnamG1r0BL0kJZaAXLomtewNekhLEUO+5pIS9AS9JFc5QL55KDnsDXpIqkKFeepUW9ga8JFUIQz0+0v+LOAe9AS9JMWaox1ucW/UGvCTFjKFemeIW9ga8JMWAoZ4scdiEb8BLCVOMoIhDaySJDPXkK+f/2ICXKlQpVxzewav3GOoqlbgG/BDgEqAJ2AqkyluOVH5xDAbv4FWYOP7vlHxxCfjNwEeAXwFvBu4HRkT9lgO3AHvLU5pUHpUaCtVwje+uVOr/TskSl4CfAZwL9AfuA34MfB0YAPwN8A3g1rJVJ5VAUkOhWlr5Sf3/qXLFJeDTpgAjgU8AzVG3vwZ+UraKpCKq1lBIQiu/Wv93qhxxCviLgLXAMUKLflXU/RKgpVxFSb3NYHi9uLfy/Z+pEsUl4HcDXwS+FL2+F5gbdfsMcHeB45kLLOikXwOntwpIJWM4dE85jtz3f6UkiUvATwIGAlOBC4DaqPsh4F3ATwscz35gTSf95gL9elCjVDCDonictlJh4hLwACeAjdED4KuEA+0az2IcO6NHLu8AxnW7OikPQ0dS3MQp4LN9EvguZxfwUskY6pLiLM4BL8WGYS6p0sQ54P8RD4pTGRjmkpIgzgF/Z7kLUDIZ4JKqQZwDXuqSYS1JuRnwiiWDW5J6xoBXWRnkklQcBrxKxjCXpNIx4FU0BroklY8Br15joEtSfBjw6jYDXZLiy4DXWTHUJakyGPDKy0CXpMpkwOsMBrokJYMBX+UMdElKJgO+ShjkklRdDPiEMMAlSZkM+G4yUCVJcWbAnwVDXZJUKfqWuwBJktT7DHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISyICXJCmBDHhJkhLIgJckKYEMeEmSEsiAlyQpgQx4SZISqKbcBWTpB4wGRgGNwH7gVFkrkiSpAsWlBT8I+FtCqO8FngFeAg4C9wADy1eaJEmVJy4t+O8AVwIfBjYBR4EhwAzgE8C3gQ+UrTpJkipMXAJ+EXAb8POs7muA7Tm6S5KkPOKyiX4H8BbCPvhMfaPuO0pcjyRJ6gXXAE3AIWA58AjwFGGffBNwVS98xj3Av/Vg+JrocR4wIOqWeWzAQKA/MAKoi7qNA87J6F+X0a8u6pYex5iscfYBhmWMP/v9aUOjuurIL3PY7OeZ3fKNJ/0ds3+I5fqc7GmT63lX/WuyHtndMz8v3/gzpb9f9vuyp0dX9fbkuJBc07+7w5/NMF31624t+V7XEX6oj+6lcffh9PKXyznEp+Gi0+J+HFUx55uefvfHujtgXBaE5cBE4G7Cl/kN8ATwsaj70+UrrcNlwEzgm8BCworm96J+/YGbCccR/APw9aj7T4AbMt77ZeCrUb+vA78fPQDuBwYTdlcMJfxQ+OS5T4MAAAn/SURBVCynwzT7/RD+fx8FZgGfA0bmqT9z2OznAO8Abgc+CUzOM465wNQCPiezzt8jTAOyupOjW+bz9DSfFT3SZkb9Mj8v1/C5pP83bwdqcwyXPXxn9Xb1OZ0ZCNyU5/MK0Z3Pfydh/upsfBDm4f7dqKWz14OA/wmcD/zLWY4317jfBkwHFuQZ5jrCOkPx0t3lpVSuAqYUadxl++5x2QcP4cC675W7CEmSkiBOAd8bFgK3dtLvGsIpeJIkJV5cAn5RAe/5WQHvWUnYvJ/Luzj7zY+SJFWkuAT8l4DZXbynTxf9AZqjRy4vkX8ftSRJiRGXg+yuAH4IrCMcmJPrIUmSChSXFnw78CAwHjhR5lokSap4cWnBAzwA3FjuIiRJSoI4BXy2r+I+c0mSuqWQA9fKpZ1wUYttvTS+3wXeA6zoxrBvIlx8JkW4GMLLwAGgnnDwXh/CVeuOEy7qcRJ4DriaUH9j9N5hhB9VzxEu3NIUjf8lwgVkVhOu+HWAcIGbC4DNhGmR/X6iz70AeCUa/4vAa518h/qMYbOfvwSMJVzNaRCwK/ouucZxLPqM1qx+dcDhjHFn1lnfyXO66D+MMM3T82n6AMpzCdPxSNZ36Wz8mWYRpv8Ywt0K27KGyx6+s3rJ0b0rdYTvMCaqP9fnFaI7nz+OMJ/kuv1yuobxhHm7/Sxr6Wx69QMuItxP4g2E+bsrdYT/a3uOcY8BWgg3ojrQyfAjCPNorvk3KYYRpkOq3IWche7M5z0xlHDRtGMFvn8KYdk8WIRa5hDuq9JdfwRc2J0BqyngRxCCujv+mnCMAISVVip69OX0QtaXUHNN9LeNEJgno9d9Ob3FpC16X3rYVPTe17LGWcPpAMp+f1q6nn4Z780l/dmpHM8zu/XNM570d4TXh8AtwI85c6tQ5rTJ9Zwu+mfPn+1Z3dPTtbPPyiU9TbPflzkdcnXPrpcc3buSPY1yfV4huvP5+T6ns+/enfFmv05P7/6EZaEr7wQe4vXLQqF1duc7VJq3AksJFwerFKX+v8wnXB10Z4HvT6/bzubHbaH6kfuHdaEOAw/3Ui2x8Q907/rVxdDtawFXEadR1x4l3rvF4uBBzrwHg17v+3g53q7cS9hqVNXichR9LneWuwBJkiqVrQlJkhLIgJckKYEMeEmSEsiAlyQpgfqVu4AKcRjYUu4iYs5p1LXDhOsaqHMtwCaSf6pbT7QS5qN8p8VWu+OE9dGr5S5EkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRVqauAtcAh4HvAwPKWExu7gPaMR+bV2ap5mvUB1gGLsrrnmybVNr06m0bOU8G7CVfyOwo8Clyc0c/5KMg3jZyPVJChQBPwTcLKaDNwT1krioeBhMuIfga4JXq8JepXzdPsZuAHhJVKZnjlmybVNr06m0bOU8E04DXgS8DbgV8COwjTx/koyDeNnI9UsPcTZoj09frfm/W6Wr2BsIKeAlwI9M/oV83T7FFC6yA7vPJNk2qbXp1NI+epYDFwDBgQvZ5BmC5zcD5KyzeNnI+yeDe5zs0g/Mo7Fb3eBNQBo8pWUTxcEP19jnAzh73Am6Nu1TzNbgSuzdE93zSptunV2TRyngoeAd7G6RukzIueb8H5KC3fNHI+ymLAd24k4a5NaUejv6PLUEuc1AKrgAWEBeQnwP3AEJxmueSbJk6vwHkq2Aw8QdjU/Gng28DnCN/Z+SjIN42cj7IY8J07CAzKeD0k+ttYhlri5D5gLrASOAJ8DRgBXILTLJd808TpFThPnXYpYTfGR4Hbgb+KujsfndbZNHI+ymLAd24LYT9OehrNAJqB/WWrKB4+Cnwx43V6X1gTTrNc8k0Tp1fgPBWMI2yCfobwne/L6Od8FOSbRs5HKthQ4DDweeBqwqk9HnUJ7yQcqfph4E3AQ8BywoJT7dNsILmPou9smlTj9Mo1jZyngs8Svuu7gZsyHufhfJSWbxo5H+msXAOsJ/wC/Fc8bzLtbuAlwmawB4DxGf2qeZrlCi/IP02qbXp1No2cp+BBzjyHO/1oiPo7H3U9jZyPJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJElSolwPtANfzOr+cWBHyasJbgEOR88XE+ob20vjbojGV5fVfWTUPf04ASwD3tRLnysp0rfcBUhV5i+Bi3swfB9gUi/Vkmkt8FdAaxHGncvngd8C/gDYBPwXcEOJPluSpF6TbsFvBpYD/aLu2S3490TvOQz8hNMt6puAPcDPgDZgEXAA+CegCVgZDfss0AJ8k/BjAOAuYBdwDFgKvCHqntmCv4nQmgb4Mme2stuBj0b9bgTWA0eBX3D6x0Yf4GPA7uizfkD+FvxNWd1/DDyR8bqzmnvjewP8CWG67wb+b1RTWmffUZKk10kH/BsJQbo46p4Z8HOBVNRtISGU/ivqd1M0/H8Cv00IunbgQ8Ao4AVCWE+J+rcDlwCzouf/PRrnMuDn0Tg7C/hRwEXR4+8IAfkG4HzgVeBzhBB8mBCsfYF3R/0WA++MvtPZBPzt0fD9uqi5N773/Oiz/jgaZg+nAz7fd5Qk6XXSAT8W+AtCa3MSZwb8N4HHM4a5NhpmHKcDfnjULx1050avfwT8e/R8cNRvfvQZv0to1U4G/h+wOnpfZwGfNhd4jRC+EDarbycE68iM7zQBeBC4L2PY93J2Af+OqPuYLmruje/9Q+DfMj77bk4HfL7vKFUUf5VKpfd1Qoh8i9ObkyGEyK6M1zujv+lNxCeBQxn9TxF+KEBo+R/JeJ52CHg70Ehoic4rsMbzCOF5P/CvUbephBbuK4TN5E9G3c8H6oHfZAy/vcDPSRtF+DHRWEDNPf3ekzlzOmc+z/cdpYpiwEuldxK4A3gboZWZtoczW4oTM7rDmQFWqMWEI9RvJIT2vxQwTB/ge4RN83+W0X0f8FjUvw9ha8KthAP09hCCM7v2Qr0dWEEI7+7UnC3fOLKn8/iM5/m+o1RRaspdgFSlVgLfIGweTrfUf0jYV/whYAPhlLpfA3t78DkjCC3jGkJQvS96PSXPMH9O2GT+bsLuAYBmQmv+bkLoPxc9nwY8AHyfcLDaU9H3+XIXdc0i7B44N+Oz0qfKdafmbPnG8X3CAYzvB14GPp0xXL7vKEnS62Tug08bStj/viOj238jHDh2BPgPzjyKPnMf+SLC0fRp/wZ8N3o+kPCj4ArCPu1HCae/rSEE11pCiHe2D34prz+KPj3udwMbCUeY/4qwSRtCa/fjhNbxHkLAPwfUZk2HXOfBLye0tNPy1dwb37sP4ayAvcBW4G8580dUZ99RkiTF2AcIwT2esAXhnzl9oJ4kSapQtYTrCaS3IqzCVrokSYlxLjCs3EVIkiRJBfv/ZgN23csAPQEAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-18"/> </p>
<p>The gender effect effect is still present, and the smoothed effect doesn't look that different from a straight line, even though the model is giving the curve more than 1 degree of freedom (as it would do if we had a simple straight line).</p>
<p>We can also look at Gender as a binary variable…</p>
<pre><code class="r">modJSVH.gamMF = gam(alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM),
by = Gender_MF), data = Data, family = negbin(theta = c(0.2, 10)))
summary(modJSVH.gamMF)
</code></pre>
<pre><code>##
## Family: Negative Binomial(1.131)
## Link function: log
##
## Formula:
## alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM), by = Gender_MF)
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.1859 0.1894 11.54 <2e-16 ***
## Gender_MF1 0.1339 0.2306 0.58 0.56
## Minpressure.2014.sc -0.0608 0.3211 -0.19 0.85
## Gender_MF1:Minpressure.2014.sc -0.0498 0.3676 -0.14 0.89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(sqrt(NDAM)):Gender_MF0 2.36 2.84 9.79 0.018 *
## s(sqrt(NDAM)):Gender_MF1 2.26 2.81 53.78 2.2e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.344 Deviance explained = 58.7%
## UBRE score = 0.28926 Scale est. = 1 n = 92
</code></pre>
<pre><code class="r">par(mfrow = c(1, 2), mar = c(2.6, 2.6, 3, 0.5), oma = c(2, 2, 0, 0))
plot(modJSVH.gamMF, xlab = "", ylab = "", resid = TRUE, shade = TRUE, cex = 3,
select = 1, main = "Male")
plot(modJSVH.gamMF, xlab = "", ylab = "", resid = TRUE, shade = TRUE, cex = 3,
select = 1, main = "Female")
mtext("Normalized Damage", 1, outer = TRUE)
mtext("Effect on mortality", 2, outer = TRUE)
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-19"/> </p>
<p>which takes us back to the model with no gender effects, and indeed a formal comparison suggests little difference: the difference in deviance is actually negative, so the simpler model fits better, and the gender effect is gone (again):</p>
<pre><code class="r">modJSVH.gamMF2 = gam(alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM)),
data = Data, family = negbin(theta = c(0.2, 10)))
anova(modJSVH.gamMF2, modJSVH.gamMF)
</code></pre>
<pre><code>## Analysis of Deviance Table
##
## Model 1: alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM))
## Model 2: alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM), by = Gender_MF)
## Resid. Df Resid. Dev Df Deviance
## 1 85.4 101
## 2 83.4 101 1.99 -0.436
</code></pre>
<pre><code class="r">summary(modJSVH.gamMF2)
</code></pre>
<pre><code>##
## Family: Negative Binomial(1.114)
## Link function: log
##
## Formula:
## alldeaths ~ Gender_MF * Minpressure.2014.sc + s(sqrt(NDAM))
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.179 0.190 11.47 <2e-16 ***
## Gender_MF1 0.132 0.232 0.57 0.57
## Minpressure.2014.sc 0.216 0.227 0.95 0.34
## Gender_MF1:Minpressure.2014.sc -0.331 0.230 -1.44 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(sqrt(NDAM)) 2.63 3.23 65.5 1.2e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.376 Deviance explained = 58.4%
## UBRE score = 0.24136 Scale est. = 1 n = 92
</code></pre>
<p>So, this demonstrates the dilemma we have: either there is a gender effect, or thre is a non-linear effect of normalised damage. The choice of which one to use would depend as much on expert knowledge. I would still use normalised damage, because I think it is more plausible.</p>
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