120 lines
3.3 KiB
Text
120 lines
3.3 KiB
Text
---
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title: "Chapter 6"
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---
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# Chapter 6
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CV transmission etc..
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Figure 6.4
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kroeber seriation:
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<div class="pottery-gallery" aria-label="Examples of the three pottery types">
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<figure>
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<img src="images/chapter-6/black-on-red.jpg" alt="Tusayan Black-on-Red bowl viewed from above">
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<figcaption>Black-on-Red</figcaption>
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</figure>
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<figure>
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<img src="images/chapter-6/three-color.jpg" alt="Fourmile Polychrome bowl">
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<figcaption>Three-Color</figcaption>
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</figure>
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<figure>
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<img src="images/chapter-6/corrugated.jpg" alt="Corrugated pottery jar from New Mexico">
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<figcaption>Corrugated</figcaption>
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</figure>
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</div>
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<p class="pottery-credit">
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Images from Wikimedia Commons:
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<a href="https://commons.wikimedia.org/wiki/File:Grand_Canyon_Tusayan_Black_on_Red_bowl.jpg">Tusayan Black-on-Red bowl</a>, Grand Canyon National Park, CC BY 2.0;
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<a href="https://commons.wikimedia.org/wiki/File:Fourmile_Polychrome_Bowl,_Anasazi_(Native_American),_1350-1400_C.E.,_02.257.2562.jpg">Fourmile Polychrome bowl</a>, Riggs Pueblo Pottery Fund, no known restrictions;
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<a href="https://commons.wikimedia.org/wiki/File:Corrugated_Jar,_950%E2%80%931200_AD,_New_Mexico.jpg">Corrugated jar</a>, Netherzone, CC BY-SA 4.0.
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</p>
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```{r battleship-seriation, fig.width=7.2, fig.height=4.8, fig.cap=""}
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dt <- data.frame(
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"Black-on-Red" = c(2, 4, 3, 3, 2, 4, 3, 9, 2, 6, 5, 3, 7, 0, 0, 0),
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"Three-Color" = c(14, 10, 9, 9, 5, 5, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0),
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"Corrugated" = c(0, 1, 0, 2, 2, 3, 4, 9, 22, 24, 11, 36, 44, 59, 63, 64),
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check.names = FALSE
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) #these data are not he original but estimated from the image in Lyman and Harpole 2002
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rownames(dt) <-
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rev( c( "Zuni",
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"Towway",
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"Kolliwa",
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"Shunnte",
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"Wimmay",
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"Mattsak",
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"Kyakki",
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"Pinnawa",
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"Site W",
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"Hattsina",
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"Kyakki W",
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"Shoptlu",
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"Hawwik B",
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"Te\'alla",
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"Site X",
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"Tetlnat"
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))
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ship_cols <- c("#8c6bb1", "#1f77b4", "#ff7f0e")
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series_col <- ncol(dt)
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n_phase <- nrow(dt)
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phase_axis <- seq_len(n_phase)
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phase_labels <- rownames(dt)
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series_max <- apply(dt, 2, max)
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ship_gap <- max(dt) * 0.08
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series_left <- c(0, cumsum(series_max[-series_col] + ship_gap))
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series_center <- series_left + series_max / 2
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plot_right <- series_left[series_col] + series_max[series_col]
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par(las = 1, bty = "n", mar = c(1, 7, 3, 1), xpd = NA)
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plot(
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NA,
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xlim = c(0, plot_right),
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ylim = c(0.5, n_phase + 1.5),
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axes = FALSE,
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xlab = "",
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ylab = ""
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)
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for (s in seq_len(series_col)) {
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for (r in phase_axis) {
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chip_bottom <- (n_phase - r) + 0.55
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chip_top <- (n_phase - r) + 1.45
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chip_width <- dt[r, s]
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rect(
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series_center[s] - chip_width / 2,
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chip_bottom,
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series_center[s] + chip_width / 2,
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chip_top,
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col = ship_cols[s],
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border = "white"
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)
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}
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}
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text(
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series_center,
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n_phase + 1.1,
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labels = colnames(dt),
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font = 2,
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cex = 0.9
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)
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text(
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0,
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n_phase - phase_axis + 1,
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labels = phase_labels,
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pos = 2,
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font = 2,
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cex = 0.9
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)
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```
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A more common wa to represent tht would be to plot the freuecny through time, using
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```{r}
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matplot(cbind(1:nrow(dt) ,1:nrow(dt) ,1:nrow(dt)) ,dt,type="l",lwd=3,col=ship_cols,lty=1,ylab="frequency",xlab="time")
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```
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This should remind you of the plot about changes in allele frequency seen in Chapter 3, we will see later how we can use these description of the data to test hypotheses about the cultural transmission process.
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