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---
Public
Edited
Dec 5, 2023
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maples = d3.csv("https://pasta.lternet.edu/package/data/eml/knb-lter-hbr/157/3/5c415a399b00430d35d13c31312f1e53",d3.autoType)
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maples
Type Table, then Shift-Enter. Ctrl-space for more options.

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maples2003 =maples.map((d) => (
{
year: (d.Year).toString() ,
watershed: d.Watershed,
stemLength: d.StemLength,
leafMass: d.LeafDryMass,
stemMass: d.StemDryMass,
leafArea: d.CorrectedLeafArea
}
)).filter((d) => d.year=="2003")
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PlotMatrix()
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//Untreated Seeds - remove outliers
maple_untreated=maples2003.filter((d)=>d.watershed=="Reference" && d.stemMass<0.04)
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//Untreated seeds parameters (OLS estimates)
mapleUntreatedLM = new ML.SimpleLinearRegression(maple_untreated.map(d => d.stemLength),maple_untreated.map(d => d.stemMass))
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//coefficients of untreated seeds linear model
mapleUntreat_coeff=mapleUntreatedLM.coefficients
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// Pearson corr, R^2, r
maple_untreat_corr = mapleUntreatedLM.score(maple_untreated.map(d => d.stemLength), maple_untreated.map(d => d.stemMass))
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//Treated Seeds data - remove outliers
maaple_treated=maples2003.filter((d)=>d.watershed=="W1" && d.stemLength<132.3)
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maple_treated=maaple_treated.filter((d)=>d.watershed=="W1" && d.stemMass<.057)
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//Calcium treated seeds parameters
mapleTreatedLM = new ML.SimpleLinearRegression(maple_treated.map(d => d.stemLength),maple_treated.map(d => d.stemMass))
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//coefficients of untreated seeds linear model
mapleTreat_coeff=mapleTreatedLM.coefficients
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// Pearson corr, R^2, r
maple_treat_corr = mapleTreatedLM.score(maple_treated.map(d => d.stemLength), maple_treated.map(d => d.stemMass))
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//concat both datasets of treated and untreated groups with outliers removed (dropped 5 data points altogether)
maple2003_adj=maple_untreated.concat(maple_treated)
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Plot.plot({
color: { legend: true },
marks: [
Plot.dot(maple2003_adj, {
x: "stemLength",
y: "stemMass",
stroke: "watershed",
tip: true
}),
Plot.linearRegressionY(maple2003_adj,{ x:"stemLength",y:"stemMass",stroke:"watershed"})
]
})
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import {scale, asMatrix} from "@chrispahm/hierarchical-clustering" //import asMatrix as well for PCA analysis 2D predictions
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//maple2003 = original 2003 data ; maple2003_adj has outliers removed
//mapleScale = scale(maples2003)
mapleScale= scale(maple2003_adj)
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maplesArray = mapleScale.map(
(d) => [d.leafArea, d.leafMass, d.stemMass, d.stemLength]
)
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mapleClusters = ML.KMeans(maplesArray, 2)
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//mapleKmeans = maples2003.map((d,i) => ({...d, clusterNo: mapleClusters.clusters[i]}))
mapleKmeans = maple2003_adj.map((d,i) => ({...d, clusterNo: mapleClusters.clusters[i]}))

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mapleKmeans
Type Table, then Shift-Enter. Ctrl-space for more options.

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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
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