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Christoph Pahmeyer
Agricultural economics · Thünen Institute of Farm Economics / University Bonn
Published
Edited
May 29, 2022
1 fork
Importers
30 stars
3
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freeny = FileAttachment('freeny.csv').csv({ typed: true })
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summary(freeny)
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pairplot(freeny, { width: 600, height: 450, spacing: -10 })
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linear_model = lm('y ~ income.level', freeny)
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summary(linear_model)
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multiple_regression = lm('market.potential ~ price.index + income.level', freeny)
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summary(multiple_regression)
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multiple_regression_scaled = lm(
'scale(market.potential) ~ scale(price.index) + scale(income.level) - 1',
freeny
)
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summary(multiple_regression_scaled)
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multiple_regression_interaction = lm(
'market.potential ~ price.index + income.level + income.level * price.index',
freeny
)
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summary(multiple_regression_interaction)
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multiple_regression_arithmetic = lm(
'log(market.potential) ~ price.index + log(income.level)',
freeny
)
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summary(multiple_regression_arithmetic)
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regression_wo_intercept = lm(
'market.potential ~ price.index + income.level - 1',
freeny
)
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summary(regression_wo_intercept)
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rlm = lm('weight ~ depression', roller, {
weights: roller.map(r => r.w)
})
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summaryStatisticsReg = summary(rlm)
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polyReg = lm('y ~ poly(income.level, 6)', freeny)
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summary(polyReg)
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summary(lm("y ~ income.level + I(income.level^2)", freeny))
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hsb2 = FileAttachment("hsb2.csv").csv({ typed: true })
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hsb_with_factor = hsb2.map(r => {
const factor = 'race';
// get uniqe factor levels
const factorLevels = [...new Set(hsb2.map(r => r[factor]))];
const sortedLevels = factorLevels.sort();
// for each factor level, add a new column to our dataframe
// e.g. race.f1, race.f2 ...
sortedLevels.forEach(
level => (r[factor + '.f' + level] = r[factor] === level ? 1 : 0)
);
return r;
})
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summary(lm('write ~ race.f2 + race.f3 + race.f4', hsb_with_factor))
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vifValues = vif(multiple_regression)
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View(vifValues)
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publicationTable = stargazer(
[multiple_regression, multiple_regression_scaled],
{
labels: {
"market.potential": "Market potential",
"scale(market.potential)": "Market potential, scaled",
"price.index": "Price index",
"income.level": "Income level",
"scale(price.index,2)": "Price index, scaled",
"scale(income.level,1)": "Income level, scaled"
},
modelNames: ["OLS", "Scaled OLS"],
modelTypes: {} // { OLS: 2}
}
)
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rCodeString = multiple_regression.toR(true)
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summary(multiple_regression)
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import { ocpu } from "@bryangingechen/hello-r-on-opencpu"
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summary([{ m: 1, n: 2, c: 'red' }])
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summary([{ c: 'red', m: 1, n: 2 }], { ignoreNonNumeric: true })
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lm = (await import(await FileAttachment("linear-models.esm@50.js").url()))
.default
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