我想将多层模型中的回归线添加到带有gglot的多面图中.我以为代码应该是这样的:
ggplot(merged_data,
aes(x = log_wealth_per_capita,
y = log_total_deaths_per_million)) +
geom_text(aes(label = iso3), size = 3) +
geom_abline(data = beta_mean, aes(intercept = Intercept, slope = Slope),
color = "red") + # Add fitted lines
facet_wrap(~ Continent) + # Facet by continent
labs(x = "Log(Wealth per Capita)",
y = "Log(Total Deaths per Million)") +
ggtitle("Scatterplots of Wealth per Capita vs. Total Deaths per Million,
by Continent") +
theme_minimal()
结果相当滑稽，每个地块上有六条回归线，但我只想要每一块地一条回归线--以及与各自大陆相关的那条线.
Merded_data对象在下面是dput()，但首先我应该解释如何创建回归系数的数据框.下面的代码从对象Fit in Stan中提取系数.
beta_samples <- extract(fit_var_int_var_slope_rob, "beta_c")
beta_mean <- data.frame(apply(beta_samples$beta, c(2, 3), mean))
colnames(beta_mean) <- c("Intercept", "Slope")
这会产生以下结果:
structure(list(Intercept = c(9.64732954916488, 7.13774770517295,
7.42222605208451, 8.00996749651911, 6.66528812477884, 8.12939250271545
), Slope = c(0.792835920084033, 0.217933401272108, -0.183173179979844,
0.15706373664177, -0.0123708482256735, 0.064920576319393)), class = "data.frame", row.names = c(NA,
-6L))
我可以提取一个包含unique(merged_data$Continent)个大陆的列表，返回:
[1] Europe Asia South America Oceania North America Africa
Levels: Africa Asia Europe North America Oceania South America
所以我的问题是，我如何才能为每一块地块添加一条拟合线，将合适的线与各自大陆的数据相匹配.
For reproducibility the full dataset is:
structure(list(iso3 = c("ALB", "ARE", "ARG", "ARM", "AUS", "AUT",
"AZE", "BEL", "BGD", "BGR", "BHR", "BHS", "BIH", "BLR", "BLZ",
"BOL", "BRA", "BRB", "BWA", "CAF", "CAN", "CHE", "CHL", "CHN",
"CMR", "COD", "COG", "COL", "COM", "CRI", "CYP", "DEU", "DJI",
"DNK", "DZA", "ECU", "EGY", "ESP", "EST", "ETH", "FIN", "FJI",
"FRA", "GAB", "GBR", "GEO", "GMB", "GNB", "GNQ", "GRC", "GUY",
"HRV", "HUN", "IDN", "IND", "IRL", "IRN", "ISL", "ISR", "ITA",
"JAM", "JOR", "JPN", "KAZ", "KEN", "KGZ", "KHM", "KOR", "KWT",
"LAO", "LBN", "LBR", "LBY", "LKA", "LSO", "LTU", "LUX", "LVA",
"MAR", "MDG", "MEX", "MLI", "MLT", "MNE", "MNG", "MOZ", "MUS",
"MWI", "MYS", "NAM", "NIC", "NLD", "NOR", "NPL", "NZL", "OMN",
"PAK", "PAN", "PER", "PHL", "POL", "PRT", "QAT", "ROU", "RUS",
"SAU", "SEN", "SGP", "SLE", "SLV", "SRB", "SVK", "SVN", "SWE",
"SYC", "SYR", "TCD", "THA", "TJK", "TTO", "TUN", "TUR", "UKR",
"URY", "USA", "VNM", "ZAF", "ZMB"), Continent = structure(c(3L,
2L, 6L, 2L, 5L, 3L, 2L, 3L, 2L, 3L, 2L, 4L, 3L, 3L, 4L, 6L, 6L,
4L, 1L, 1L, 4L, 3L, 6L, 2L, 1L, 1L, 1L, 6L, 1L, 4L, 3L, 3L, 1L,
3L, 1L, 6L, 1L, 3L, 3L, 1L, 3L, 5L, 3L, 1L, 3L, 2L, 1L, 1L, 1L,
3L, 6L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 4L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 3L, 3L, 3L, 1L, 1L, 4L,
1L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 4L, 3L, 3L, 2L, 5L, 2L, 2L,
4L, 6L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 1L, 4L, 3L, 3L, 3L,
3L, 1L, 2L, 1L, 2L, 2L, 4L, 1L, 2L, 3L, 6L, 4L, 2L, 1L, 1L), levels = c("Africa",
"Asia", "Europe", "North America", "Oceania", "South America"
), class = "factor"), log_total_deaths_per_million = c(7.14517957334583,
5.51666945712612, 7.96268245052562, 8.05704135351535, 6.83514372509745,
7.83228795255029, 6.90726515889291, 7.98822260566744, 5.14861013330701,
8.64751910196815, 6.95015363039875, 7.6297775329487, 8.53024188569712,
6.61541705010521, 7.43695368477318, 7.51273605212519, 8.08975881654283,
7.6522987486255, 6.97027645414495, 3.00835230491009, 7.24680490556415,
7.39148243340391, 8.06245453732848, 4.44835261984536, 4.25867185564378,
2.69644984303589, 4.17675369846282, 7.91987060378204, 5.25336184074617,
7.50008826037499, 7.32871702487983, 7.64913484913657, 5.1276595234134,
7.37928649087916, 5.03201184552246, 7.60137434818512, 5.41036845035541,
7.84859929666797, 7.71477519325648, 4.11721437631285, 7.62994122367463,
6.85840640876554, 7.86300361247569, 4.85597559585221, 8.14271528238069,
8.42844590861355, 4.9234275341884, 4.43155462182308, 4.69372121044537,
8.21599971634834, 7.38241368293539, 8.44172734392078, 8.50008182343079,
6.37658578147253, 5.93053550396008, 7.53707195436871, 7.41295953241003,
6.21218316063569, 7.20399909488331, 8.10421180673984, 7.1468351695947,
7.13193749070183, 6.40126358350027, 6.89080645571083, 4.65679442560723,
5.03977329583958, 5.20540131852172, 6.54174402992356, 6.40030893899961,
4.48993889186828, 7.59793907304635, 4.01537350235111, 6.85108121841244,
6.6510937231183, 5.72841654894787, 8.175756693213, 7.3422359997599,
8.30244157907528, 6.07569999142584, 3.87446650670151, 7.8736118740934,
3.49301662675331, 7.42102850492459, 8.3505010957726, 6.51038630852537,
4.22310417130816, 6.70217287413362, 4.88001015042657, 7.00201945737222,
7.37672650047835, 3.56274917696972, 7.17678802882614, 6.96108464915987,
5.97595743088023, 6.5492314841142, 6.91898894442494, 4.86749598817746,
7.5822235905929, 8.780734608921, 6.35960154995015, 8.01192675337012,
7.90811028272797, 5.54524384852477, 8.15793605935459, 7.92785704669004,
5.57948828738416, 4.73463582462489, 5.84827812288232, 2.67587130273798,
6.50364724962591, 7.87389960937892, 8.23159002321146, 8.44469647433975,
7.83586785665906, 7.38115999297662, 4.96255787586308, 2.39297409272173,
6.17675876449841, 2.53043754003599, 7.96113516712754, 7.77538083887501,
7.08035809477059, 7.92609488909099, 7.7133092915014, 8.14330756105566,
6.08686335934797, 7.44596997418451, 5.31453514457087), log_wealth_per_capita = c(-3.75898035360504,
-2.30513018407809, -5.15826474719596, -4.13704891468614, -1.01815206669121,
-1.56998235269109, -4.63158176044283, -1.18050664725151, -5.2270293779777,
-3.4695614743071, -2.55643252841621, -3.30866733261863, -3.79008962183494,
-4.04530785241544, -5.04430577861233, -4.91974126137638, -4.2520217105767,
-2.89272421693947, -4.68474649927825, -7.92285291901668, -1.27905696134074,
-0.614474094883125, -3.26474959140575, -2.94887236699916, -6.48256023935423,
-7.5666038756891, -7.25859957831427, -4.46477625706788, -5.7859018934777,
-3.43352741677342, -2.5013673515235, -1.56489315157883, -6.31647917483157,
-1.18541971461973, -5.14110219738108, -4.44117978341142, -4.35986153060899,
-1.7200350530719, -2.8384891497519, -6.2716115443935, -1.88859060820926,
-4.53978745885536, -1.42309330171415, -4.89232991805667, -1.4537706667511,
-4.45613846126653, -6.79811831800763, -7.11218217128348, -4.68824594167466,
-2.40801457277519, -4.8745396859623, -2.98084579253421, -3.07468943312031,
-4.47821640025086, -4.70001102996997, -1.73184554135212, -4.10271593112113,
-1.30912002570409, -1.95269182439728, -1.58547855882824, -4.2276029333948,
-4.08318652004516, -1.53290984434818, -3.6936200969551, -5.06516163946559,
-5.74974884455749, -5.68925590637949, -1.68871303977341, -2.35561281223366,
-5.44495224256278, -2.91323997827936, -6.11029672207666, -4.54655264066805,
-4.08646388905808, -7.07601986150314, -3.05933449608398, -0.723129242676512,
-2.80938485340546, -4.68640604945897, -6.89095712714412, -3.50788258121201,
-6.92556251317859, -2.19411235662953, -2.98369142842271, -5.55221071457427,
-7.7634924031241, -3.05625388797155, -6.95272293617992, -4.07220443835686,
-4.58367105990774, -4.87696387678156, -1.16571436245702, -1.38575808421678,
-6.02149477878732, -1.3740669528581, -3.44658647343914, -5.83599304580156,
-3.499200966496, -4.35443174428065, -4.78934795119787, -3.2533381999935,
-2.04128340377559, -2.07273121905011, -3.4953591405128, -3.81441211796923,
-2.93881687051817, -6.02998268532188, -1.24521086128058, -7.95558745543141,
-3.76923203445355, -3.75622774643608, -2.96645530217171, -2.45787631881079,
-1.32880245983023, -3.41916126243044, -7.22385160271581, -7.56655989697871,
-3.93796775546548, -6.0919169937804, -3.47608347554203, -4.32895976893165,
-4.0212869307674, -4.44617158748751, -3.12670371412107, -0.964967847085259,
-4.70477623876113, -4.1761629901758, -6.76433802721084)), row.names = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L,
70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L,
83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L,
107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L,
118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L,
129L, 130L), na.action = structure(c(`38` = 38L, `53` = 53L), class = "omit"), class = "data.frame")
