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benmarwick committed Jan 30, 2024
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60 changes: 53 additions & 7 deletions README.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,8 @@ knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
out.width = "100%",
dpi=300
)
```

Expand Down Expand Up @@ -117,6 +118,51 @@ Here's the key to interpreting the LRI plot above (from Gingerich 2019:109)

Thus for the example data above, the median slope lies between expectation for directional change and random change, and is significantly different from both. The more negative confidence limit excludes both -1.000 expected for stasis and -0.500 expected for a random time series, and the more positive limit excludes 0.000 expected for a directional time series. Thus the time series is interpreted as directional with a random component (cf. Gingerich 2019:142).

Here's a plot the summarises the slopes from the above analysis, and includes some guide to interpreting the output:

```{r fig.width=6, fig.height=6}
library(ggbeeswarm)
ggplot(data.frame(bootresultd[[2]])) +
aes(0.25,
slope) +
geom_boxplot() +
geom_quasirandom(alpha = 0.1) +
annotate("segment",
y = slope_max,
yend = slope_max,
x = 0,
xend = 0.5,
colour = "red") +
annotate("segment",
y = slope_min,
yend = slope_min,
x = 0,
xend = 0.5,
colour = "red") +
annotate("text",
x = -0.5,
y = 0,
label = "Directional\n(slope at or near 0)") +
annotate("text",
x = -0.5,
y = -0.5,
label = "Random\n(slope at or near -0.5)") +
annotate("text",
x = -0.5,
y = -1,
label = "Stationary\n(slope at or near -1)") +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()) +
scale_x_continuous(limits = c(-0.8, 1)) +
scale_y_continuous(limits = c(-1.5, 0.5))
```

In the figure above we see the distribution of all slopes from the LRI plot. The boxplot shows the median slope, the red lines show the minimum and maximum values for the confidence interval of the slope distribution. On the left of the panel are text labels to assist with the interpretation of the slope distribution. In the example above, we see the distribution of slope values ranges between 0 and -0.5, with the CI excluding -0.5 (the lower red line is at `r slope_min`) which can be interpreted as a time series that is directional with a random component.

## Examples from 'Rates of Evolution: A Quantitative Synthesis'

Here are a few complete case studies from Gingerich's book. The code is almost exactly as he wrote it, taken from the Dryad repository for the book, I have only slightly simplified the code in a few places. Because of the distinctive way that Gingerich builds up the final plot with many different base plot functions, all code has to be in one block to work correctly in an R Markdown document. His original code and data files for all the case studies in the book are included here in the `data-raw` folder.
Expand Down Expand Up @@ -408,7 +454,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LA[(i + k), 6] - LA[i, 6] #mean diff.
poolsd = PoolSD(LA[i + k, 3], LA[i, 3], LA[i + k, 7], LA[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LA[i + k, 3], LA[i, 3], LA[i + k, 7], LA[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -457,7 +503,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LW[(i + k), 6] - LW[i, 6] #mean diff.
poolsd = PoolSD(LW[i + k, 3], LW[i, 3], LW[i + k, 7], LW[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LW[i + k, 3], LW[i, 3], LW[i + k, 7], LW[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -506,7 +552,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LT[(i + k), 6] - LT[i, 6] #mean diff.
poolsd = PoolSD(LT[i + k, 3], LT[i, 3], LT[i + k, 7], LT[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LT[i + k, 3], LT[i, 3], LT[i + k, 7], LT[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -556,7 +602,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LB[(i + k), 6] - LB[i, 6] #mean diff.
poolsd = PoolSD(LB[i + k, 3], LB[i, 3], LB[i + k, 7], LB[i, 7])#n1,n2,sd1,sd2
poolsd = roev::PoolSD(LB[i + k, 3], LB[i, 3], LB[i + k, 7], LB[i, 7])#n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -616,15 +662,15 @@ psize <- 1.5 #2
#assign 'equation' position as "normal","lower","none" at end of each call
#send (1)idrx matrix, (2)mode(diff/rate), (3)panel placement coordinate x,
# (4)panel placement coordinate y, (5)bootn, (6)mode, (7)psize, (8)equation
bootresultd = TriPanelBC(idrcA, "r", 1, 4.5, bootn, mode, psize, "lower")
bootresultd = roev::TriPanelBC(idrcA, "r", 1, 4.5, bootn, mode, psize, "lower")
wrlb.d = bootresultd[[1]]
bootmat.d = bootresultd[[2]]
#====== Plot LRI panel c
#send idrx matrix, n, mode(diff/rate), panel placement coordinates, mode
idrcG = rbind(idrcW, idrcT, idrcB)
bootresultr = TriPanelBC(idrcG, "r", 13, 4.5, bootn, mode, psize, "lower")
bootresultr = roev::TriPanelBC(idrcG, "r", 13, 4.5, bootn, mode, psize, "lower")
wrlb.r = bootresultr[[1]]
bootmat.r = bootresultr[[2]]
Expand Down
76 changes: 66 additions & 10 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -110,8 +110,8 @@ slope_med <- round(bootresultd[[1]][2], 3)
slope_min <- round(bootresultd[[1]][3], 3)
```

In the example above, we see a median slope of -0.261, and a confidence
interval for the slope of -0.422 to -0.123.
In the example above, we see a median slope of -0.264, and a confidence
interval for the slope of -0.423 to -0.119.

Here’s the key to interpreting the LRI plot above (from Gingerich
2019:109)
Expand All @@ -137,6 +137,62 @@ random time series, and the more positive limit excludes 0.000 expected
for a directional time series. Thus the time series is interpreted as
directional with a random component (cf. Gingerich 2019:142).

Here’s a plot the summarises the slopes from the above analysis, and
includes some guide to interpreting the output:

``` r
library(ggbeeswarm)
#> Loading required package: ggplot2

ggplot(data.frame(bootresultd[[2]])) +
aes(0.25,
slope) +
geom_boxplot() +
geom_quasirandom(alpha = 0.1) +
annotate("segment",
y = slope_max,
yend = slope_max,
x = 0,
xend = 0.5,
colour = "red") +
annotate("segment",
y = slope_min,
yend = slope_min,
x = 0,
xend = 0.5,
colour = "red") +
annotate("text",
x = -0.5,
y = 0,
label = "Directional\n(slope at or near 0)") +
annotate("text",
x = -0.5,
y = -0.5,
label = "Random\n(slope at or near -0.5)") +
annotate("text",
x = -0.5,
y = -1,
label = "Stationary\n(slope at or near -1)") +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank()) +
scale_x_continuous(limits = c(-0.8, 1)) +
scale_y_continuous(limits = c(-1.5, 0.5))
```

<img src="man/figures/README-unnamed-chunk-7-1.png" width="100%" />

In the figure above we see the distribution of all slopes from the LRI
plot. The boxplot shows the median slope, the red lines show the minimum
and maximum values for the confidence interval of the slope
distribution. On the left of the panel are text labels to assist with
the interpretation of the slope distribution. In the example above, we
see the distribution of slope values ranges between 0 and -0.5, with the
CI excluding -0.5 (the lower red line is at -0.423) which can be
interpreted as a time series that is directional with a random
component.

## Examples from ‘Rates of Evolution: A Quantitative Synthesis’

Here are a few complete case studies from Gingerich’s book. The code is
Expand Down Expand Up @@ -496,7 +552,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LA[(i + k), 6] - LA[i, 6] #mean diff.
poolsd = PoolSD(LA[i + k, 3], LA[i, 3], LA[i + k, 7], LA[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LA[i + k, 3], LA[i, 3], LA[i + k, 7], LA[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -545,7 +601,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LW[(i + k), 6] - LW[i, 6] #mean diff.
poolsd = PoolSD(LW[i + k, 3], LW[i, 3], LW[i + k, 7], LW[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LW[i + k, 3], LW[i, 3], LW[i + k, 7], LW[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -594,7 +650,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LT[(i + k), 6] - LT[i, 6] #mean diff.
poolsd = PoolSD(LT[i + k, 3], LT[i, 3], LT[i + k, 7], LT[i, 7]) #n1,n2,sd1,sd2
poolsd = roev::PoolSD(LT[i + k, 3], LT[i, 3], LT[i + k, 7], LT[i, 7]) #n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -644,7 +700,7 @@ for (k in 1:(n - 1)) {
idr[nc, 1] = k / gentime
} #interval
meandiff = LB[(i + k), 6] - LB[i, 6] #mean diff.
poolsd = PoolSD(LB[i + k, 3], LB[i, 3], LB[i + k, 7], LB[i, 7])#n1,n2,sd1,sd2
poolsd = roev::PoolSD(LB[i + k, 3], LB[i, 3], LB[i + k, 7], LB[i, 7])#n1,n2,sd1,sd2
idr[nc, 2] = meandiff / poolsd #diff.sd
idr[nc, 3] = abs(idr[nc, 2]) / idr[nc, 1] #rate.sd.gen
idr[nc, 4] = log10(idr[nc, 1]) #log.i
Expand Down Expand Up @@ -704,15 +760,15 @@ psize <- 1.5 #2
#assign 'equation' position as "normal","lower","none" at end of each call
#send (1)idrx matrix, (2)mode(diff/rate), (3)panel placement coordinate x,
# (4)panel placement coordinate y, (5)bootn, (6)mode, (7)psize, (8)equation
bootresultd = TriPanelBC(idrcA, "r", 1, 4.5, bootn, mode, psize, "lower")
bootresultd = roev::TriPanelBC(idrcA, "r", 1, 4.5, bootn, mode, psize, "lower")
wrlb.d = bootresultd[[1]]
bootmat.d = bootresultd[[2]]


#====== Plot LRI panel c
#send idrx matrix, n, mode(diff/rate), panel placement coordinates, mode
idrcG = rbind(idrcW, idrcT, idrcB)
bootresultr = TriPanelBC(idrcG, "r", 13, 4.5, bootn, mode, psize, "lower")
bootresultr = roev::TriPanelBC(idrcG, "r", 13, 4.5, bootn, mode, psize, "lower")
wrlb.r = bootresultr[[1]]
bootmat.r = bootresultr[[2]]

Expand All @@ -739,7 +795,7 @@ text(
)
```

<img src="man/figures/README-unnamed-chunk-10-1.png" width="100%" />
<img src="man/figures/README-unnamed-chunk-11-1.png" width="100%" />

The original caption for this plot in the book is “Size change in the
snow goose Anser caerulescens studied at La Pérouse Bay, Manitoba, from
Expand Down Expand Up @@ -1239,7 +1295,7 @@ bootresultd = PalPanelBC(idrx[, 1:8], "d", 1, 4.5, bootn, mode, psize, "normal",
bootresultr = PalPanelBC(idrx[, 1:8], "r", 13, 4.5, bootn, mode, psize, "normal", 0)
```

<img src="man/figures/README-unnamed-chunk-12-1.png" width="100%" />
<img src="man/figures/README-unnamed-chunk-13-1.png" width="100%" />

Here is the original caption for this figure from the book: “Rates of
evolution in two Miocene merycoidodont mammal lineages spanning five
Expand Down
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