Issues when obtaining RMSE and R2 from predictions on test data - r
I have a training data (train.dat) and test data (test.dat). I would like to run my linear regression, elastic, tree and random forest model on the test data after training it on the training data.
From there, I would like to get the RMSE and R2 to observe the predictive accuracy of the model. However, I have 2 issues:
When I train my random forest model on test data, I get the error: Error in predict.randomForest(modelFit, newdata) : missing values in newdata. Other models are fine
I can only extract the RMSE and R2 values for my tree model, while the other models return NAs. A screenshot is below
Can anyone tell me what has gone wrong with my code?
My code:
library(caret)
set.seed(10345678)
tr.Control <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5,
classProbs = FALSE,
)
lm7 <- train(Lifeexp ~ . + Govthealth*Privhealth,
data = train.dat,
method = 'lm',
trControl = tr.Control,
preProc = c("center", "scale")
)
alpha.vec <- seq(0, 1, by = 0.1)
lambda.vec2 <- seq(0, 30, length.out = 50)
elastic.grid <- expand.grid(alpha = alpha.vec, lambda = lambda.vec2)
elastic4 <- train(Lifeexp ~ ., data = train.dat,
method = 'glmnet',
trControl = tr.Control,
verbose = FALSE,
tuneGrid = elastic.grid,
preProc = c("center", "scale")
)
cp.vec <- seq(10^-4, 0.1, length.out = 50 )
tree2 <- train(Lifeexp ~ ., data = train.dat
, trControl = tr.Control
, method = "rpart"
, preProcess = c("center", "scale")
, tuneGrid = expand.grid(cp = cp.vec)
)
rf1 <- train(
Lifeexp ~ .,
data = train.dat,
method = "rf",
trControl = tr.Control ,
preProc = c("center", "scale"),
ntree = 1000,
tuneGrid = expand.grid(mtry = seq(1, ncol(train.dat)-1)
)
)
regression.pred <- predict(lm7, newdata = test.dat, na.action = na.pass, type = "raw")
elastic.pred <- predict(elastic4, newdata = test.dat, na.action = na.pass, type = "raw")
tree.pred <- predict(tree2, newdata = test.dat, na.action = na.pass, type = "raw")
rf.pred <- predict(rf1, newdata = test.dat, na.action = na.pass, type = "raw")
test.statistics <- data.frame(
RMSE.regression = RMSE(regression.pred, test.dat$Lifeexp),
Rsquare.regression = R2(regression.pred, test.dat$Lifeexp),
RMSE.el = RMSE(elastic.pred, test.dat$Lifeexp),
Rsquare.el = R2(elastic.pred, test.dat$Lifeexp),
RMSE.tree = RMSE(tree.pred, test.dat$Lifeexp),
Rsquare.tree = R2(tree.pred, test.dat$Lifeexp)
)
test.statistics
train.dat:
structure(list(GDP = c(402.1030419, 442.2030419, 543.3030419,
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622.4988457, 642.7767443, 317.3893069, 269.8711377, 709.5819646,
585.07655, 780.190201, 3122.362815, 3893.596078, 1166.610276,
1674.825261, 3690.113268, 4241.788782, 2441.741991, 4043.662051,
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33994.40657, 21290.86038, 50950.03434, 53024.05921, 13663.02162,
13641.10272, 41945.33167, 1731.209509, 4492.727604, 11861.75616,
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36.44474948, 45.58774948, 46.38774948, 3.333203815, 5.359203815,
16.69390488, 19.46990488, 33.22835541, 5.300580788, 29.97179604,
33.59179604, 5.971383095, 62.66848373, 67.22848373, 8.23568,
14.98141193, 32.6487999, 10.22661548, 16.19961548, 92.18703461,
98.65987461, 143.7665911, 159.7515106, 308.6578979, 402.5568979,
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4201.3698, 122.02145, 238.0044861, 712.2356, 1513.565918, 2015.18042,
2985.23, 8021.80957), Govthealth = c(1.25689, 2.032658, 2.495758057,
2.965478, 1.985478, 2.209019899, 2.882325411, 3.21458, 7.3134408,
1.032568, 5.433434963, 7.235478, 1.239725351, 8.535984039, 10.323589,
1.236589, 3.562868595, 4.673761368, 2.32547, 4.648055553, 23.70949936,
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990.58795, 4125.25658, 998.25998, 1827.566895, 3482.541016, 4800.3256,
989.325, 1254.325, 1756.99939, 1998.23569, 1104.429321, 2521.927002,
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32.95304871, 70.28269196, 90.3256, 15.021457, 27.89465141, 44.9021492,
60.32568, 43.03323364, 60.325845, 8.325698, 11.45799065, 60.32568,
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301.23565, 469.9864197, 990.9689331, 1200.36987, 542.5964966,
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507.0849609, 1114.720093, 2001.23548, 401.14567, 662.03214, 816.2644653,
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713.3709106, 998.32658, 302.0214, 793.8995972, 111.2757187, 162.9289398,
212.3657, 442.32598, 1698.060913, 2226.32568, 145.2365, 268.8859863,
902.32568, 42.36587, 75.64861298, 332.65478, 970.5014648, 556.8964233,
700.32658, 5526.447266), Population = c(12412308L, 20779953L,
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5515525L, 79433029L, 82211508L, 81776930L, 82905782L, 10196792L,
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4841000L, 38110782L, 38258629L, 3047132L, 5076732L, 2048583L,
2073894L, 7824909L), Lifeexp = c(50.331, 55.841, 61.028, 64.486,
47.099, 51.941, 61.627, 66.24, 55.564, 54.404, 67.611, 70.478,
61.974, 57.099, 62.973, 45.746, 48.069, 55.251, 62.32, 65.772,
69.205, 71.509, 69.872, 71.73, 73.428, 74.405, 70.865, 72.594,
74.493, 61.529, 70.173, 78.627, 61.608, 52.192, 45.9, 46.267,
50.896, 54.332, 60.1, 62.82, 65.264, 67.114, 66.165, 71.111,
76.516, 68.793, 71.095, 63.307, 56.048, 57.669, 71.333, 75.439,
71.836, 73.955, 76.562, 73.576, 75.278, 76.52, 60.884, 71.46,
66.343, 73.619, 75.672, 53.595, 66.56, 69.57, 75.654, 78.769,
80.095, 74.619, 77.672, 71.46, 57.865, 66.693, 62.764, 65.095,
70.248, 70.623, 74.184, 76.931, 50.64, 61.195, 70.551, 73.025,
75.317, 81.69512195, 82.74878049, 75.8804878, 77.74146341, 80.40243902,
76.05195122, 77.72195122, 80.18292683, 81.59512195, 79.13658537,
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73.74878049, 75.29512195, 81.54146341, 79.42195122, 81.02926829,
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4.35239, 4.25224, 6.44717, 6.97848, 7.235689, 5.43073, 5.54157,
2.985467, 3.124578, 3.32652, 5.22879, 5.48909, 4.236587, 5.321457,
6.323658, 7.5698745, 3.26587, 4.9936, 2.325647, 3.08044, 5.56251,
5.965871, 4.92605)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
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Test.dat:
structure(list(GDP = c(199.9863423, 156.3857186, 389.3980332,
229.4902871, 497.6320261, 749.552711, 826.6215305, 248.0293672,
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64581.94402, 9107.477079, 10201.30354, 38428.3855, 37868.296,
82796.54716), Health = c(6.22435541, 8.909747124, 39.22274712,
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35.25698, 47.90106964, 292.54782, 18.56432343, 70.5685123, 10.56888,
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4099.587891, 1125.365, 4400.325, 1496.87854, 3000.23568, 336.2356,
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1989.235, 446.3265, 796.6470337, 2985.12, 3737.802979, 9658.23
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2517.370117, 577.3640747, 662.32589, 298.1834717, 702.369, 456.325,
568.7339478, 889.36547, 1045.900513, 3987.3654), Privhealth = c(1.36589,
1.832908154, 7.325698, 5.431494236, 2.36589, 29.85413742, 35.3698,
4.23568, 8.9836483, 22.3658, 152.36589, 263.3545532, 225.5363922,
301.325478, 111.575592, 10.23568, 60.89479446, 336.02145, 12.36587,
75.36987, 34.3265, 223.02145, 2.0215478, 11.81901455, 180.9026947,
15.41190529, 85.28456879, 45.321478, 86.49634552, 25.36987, 39.00668716,
220.32145, 14.22738075, 49.326545, 7.02145, 11.50323391, 20.36587,
33.021456, 45.45627975, 400.23568, 516.1798096, NA, 400.32547,
1322.338745, 421.6481018, 969.076416, 426.0691833, 931.8737793,
302.1245, 886.02154, 517.4750366, 889.32547, 90.3256, 626.4102173,
698.8658447, 231.8352966, 301.0324, 522.5147705, 1236.021458,
117.3658, 227.9130707, 1965.3256, 2691.985107, 6600.3256), Population = c(9404500L,
11148758L, 18143315L, 23941110L, 5283814L, 7527394L, 9100837L,
17354392L, 23650172L, 19077690L, 31528585L, 365734L, 2118874L,
2448255L, 29027674L, 61895160L, 93966780L, 57779622L, 17325773L,
21670000L, 2866376L, 32618651L, 530804L, 685503L, 174790340L,
12155239L, 3962372L, 70419L, 69650L, 4802000L, 4077131L, 3726549L,
1056575549L, 1352617328L, 20147590L, 27275015L, 10432421L, 11881477L,
87967651L, 17065100L, 19153000L, 66460344L, 27691138L, 34004889L,
5339616L, 5547683L, 5176209L, 5363352L, 56719240L, 60421760L,
6289000L, 8882800L, 2095344L, 15925513L, 4350700L, 38042794L,
37974750L, 4027887L, 5638676L, 1998161L, 1988925L, 6715519L,
7184250L, 8513227L), Lifeexp = c(46.096, 45.09, 63.798, 62.288,
58.824, 68.736, 70.879, 45.853, 46.229, 58.893, 75.997, 75.905,
56.665, 63.373, 74.41, 66.366, 69.823, 63.857, 69.509, 76.812,
78.458, 71.594, 52.878, 68.384, 70.116, 58.432, 77.452, 66.843,
71.116, 70.386, 69.902, 73.6, 62.505, 69.416, 55.5, 58.472, 58.1,
44.649, 74.837, 76.99463415, 79.23414634, 81.35609756, 77.42195122,
81.24634146, 76.59268293, 79.1, 77.46585366, 79.87073171, 76.97073171,
82.94634146, 78.95365854, 82.80243902, 72.15, 77.98780488, 80.70243902,
76.24634146, 77.75365854, 77.95121951, 83.14634146, 73.20487805,
75.41219512, 77.24243902, 79.6804878, 83.55121951), Govted = c(3.27054,
5.24797, 4.71484, 2.97515, 1.36587, 4.00675, 6.32547, 1.023658,
2.46167, 4.32658, 4.53477, 4.11747, 8.34961, 10.23547, 2.8673,
2.36587, 5.326545, 6.15899, 2.41093, 2.11189, 2.46866, 1.06738,
3.21547, 4.02447, 3.94893, 1.65599, 4.68696, 1.856231, 2.032145,
1.56897, 2.18109, 4.236587, 4.32479, 5.326587, 0.36589, 1.01218,
1.45426, 2.36589, 5.13722, 4.6764, 4.89147, 7.3265, 5.99199,
5.36993, 8.08434, 8.55955, 5.71688, 6.54071, 3.325687, 5.32658,
6.12262, 6.32658, 1.326587, 4.58512, 7.00241, 5.06843, 6.32547,
3.3213, 5.32658, 3.32365, 4.32657, 4.52294, 4.7814, 5.9658745
)), row.names = c(9L, 10L, 12L, 14L, 17L, 19L, 20L, 21L, 22L,
28L, 40L, 43L, 47L, 48L, 59L, 61L, 63L, 68L, 69L, 72L, 76L, 77L,
81L, 83L, 86L, 90L, 94L, 97L, 98L, 101L, 102L, 104L, 106L, 108L,
109L, 110L, 117L, 118L, 123L, 125L, 126L, 132L, 137L, 139L, 142L,
143L, 146L, 147L, 157L, 160L, 166L, 168L, 169L, 174L, 179L, 183L,
184L, 186L, 188L, 189L, 190L, 193L, 194L, 196L), class = "data.frame")
You can use the following code
#Remove the NA from the data freme
test.dat <- na.omit(test.dat)
regression.pred <- predict(lm7, newdata = test.dat, type = "raw")
elastic.pred <- predict(elastic4, newdata = test.dat, type = "raw")
tree.pred <- predict(tree2, newdata = test.dat, type = "raw")
rf.pred <- predict(rf1, newdata = test.dat, type = "raw")
test.statistics <- data.frame(
RMSE.regression = RMSE(regression.pred, test.dat$Lifeexp),
Rsquare.regression = R2(regression.pred, test.dat$Lifeexp),
RMSE.el = RMSE(elastic.pred, test.dat$Lifeexp),
Rsquare.el = R2(elastic.pred, test.dat$Lifeexp),
RMSE.tree = RMSE(tree.pred, test.dat$Lifeexp),
Rsquare.tree = R2(tree.pred, test.dat$Lifeexp)
)
test.statistics
# RMSE.regression Rsquare.regression RMSE.el Rsquare.el RMSE.tree Rsquare.tree
# 1 8.201072 0.3910896 8.115006 0.3984145 6.369994 0.6446954
Related
Replace row names with the column value of another data frame based on column matches
I want to replace the row names of meth.kirp.cpg with anno$V1 if the existing row names matches anno$V2.
library(tidyverse)
rownames(meth.kirp.cpg) <- meth.kirp.cpg %>%
rowwise() %>%
mutate(out = anno$V1[str_which(colnames(meth.kirp.cpg), anno$V2)])
Traceback:
Error in `mutate()`:
ℹ In argument: `out = anno$V1[str_which(rownames(meth.kirp.cpg),
anno$V2)]`.
ℹ In row 1.
Caused by error in `str_detect()`:
! Can't recycle `string` (size 142513) to match `pattern` (size 365860).
Run `rlang::last_error()` to see where the error occurred.
Example data:
meth.kirp.cpg
> dput(meth.kirp.cpg[1:100,1:2])
structure(list(TCGA.2K.A9WE.01A = c(0.461440642939772, 0.143910373119058,
0.847164847154162, 0.737361955793681, 0.716794733144112, 0.351877113536983,
0.248986769373366, 0.0121360989202765, 0.876303885229884, 0.944311384947134,
0.0490407302658151, 0.0200484962577958, 0.0623434271852525, 0.489865398138095,
0.920994933496615, 0.92663932531651, 0.0149191766670711, 0.884749685210921,
0.446591784140497, 0.91113228700911, 0.912199953863369, 0.908167409366654,
0.386721526377863, 0.0386737340626713, 0.0347492896507038, 0.98309370597552,
0.0176080612232509, 0.91878387167279, 0.743683318738873, 0.939148492241393,
0.722471943330892, 0.613143449419421, 0.0111202783577944, 0.843823786705695,
0.836431557867031, 0.390282953982417, 0.027408710286304, 0.0222349236137297,
0.657221610108816, 0.861848830221141, 0.0433751011272091, 0.0281247935879252,
0.938960776959358, 0.919825831744144, 0.922071582222369, 0.874732275907705,
0.0287898761495033, 0.0266947996996682, 0.922915821025777, 0.95009866012662,
0.964858875373814, 0.106451342824246, 0.406100902807456, 0.0421684244823044,
0.0341277368595181, 0.805451068725895, 0.147595746750675, 0.602617067494429,
0.90660866745333, 0.922313274809095, 0.462291286891102, 0.502857899902497,
0.0292904155423265, 0.835117565787527, 0.146789494933407, 0.06805696389495,
0.970563583145203, 0.0379479981289824, 0.058526761439653, 0.938993650169269,
0.44761099556807, 0.558961729061086, 0.939778576056268, 0.0728795533192928,
0.812084345787681, 0.899377654465699, 0.940111049552295, 0.838186810388758,
0.715121288990262, 0.897506380407565, 0.0929678061732199, 0.99024632582796,
0.055583745670494, 0.835146654988372, 0.973309086845447, 0.651216797099359,
0.0218535991986461, 0.0999671036378156, 0.790540668893094, 0.980591855409854,
0.567883806155822, 0.774816434396113, 0.904434807209845, 0.16641097147085,
0.0102686285230525, 0.65243489007093, 0.917594420539083, 0.0147831247626457,
0.844679485594683, 0.65566679452182), TCGA.2Z.A9J1.01A = c(0.595894468074615,
0.0807243779293262, 0.867305510246114, 0.70680600651273, 0.217862460492399,
0.169408257004071, 0.173115013795265, 0.0108902025634162, 0.813866558997356,
0.938576461648791, 0.0426568318037534, 0.0133187057875756, 0.0540543120983417,
0.317547629906197, 0.89911570032979, 0.525131175543627, 0.0152198596492253,
0.586968687135673, 0.49896100615873, 0.946718072906056, 0.859306039060091,
0.91185524112895, 0.28077646371254, 0.0413484993379312, 0.169193526857136,
0.941230054689418, 0.0164701153466769, 0.928402415411224, 0.736184540407898,
0.946288965623826, 0.312150292032857, 0.403171876971832, 0.0091246246912222,
0.535149883791691, 0.801041308364712, 0.171664264695538, 0.022737572168221,
0.0164834707992085, 0.34399568227201, 0.690016503202975, 0.0390842331750004,
0.0270854886242561, 0.888936631403145, 0.911902815624012, 0.858247513475469,
0.877113632682254, 0.0342892379505875, 0.0387268488822914, 0.922299785913074,
0.926130065834329, 0.975692332236198, 0.105415153493416, 0.127593519059119,
0.0540003798276299, 0.030980833881057, 0.914299941557146, 0.0512267439881511,
0.307325891435045, 0.941037265659174, 0.927078967007025, 0.48873418258592,
0.259006924115841, 0.0278764868641079, 0.87768067729952, 0.302640875302654,
0.0706384569300761, 0.968762634771395, 0.0364352674378962, 0.0441231506131831,
0.8307385629478, 0.242575477196221, 0.513439830376976, 0.932449172188782,
0.0526229004254996, 0.81314353054328, 0.778591104943176, 0.95668645045373,
0.453172059602829, 0.250129171963381, 0.863470213940097, 0.0994627135023581,
0.989489689575077, 0.0472116225581592, 0.911407225108748, 0.825189076107663,
0.578029414148402, 0.018058167343065, 0.0855852777154159, 0.819733395638372,
0.988287891473147, 0.255899615791521, 0.643359326354994, 0.491979154678761,
0.0978562004864199, 0.0105671614378101, 0.48897100984416, 0.9024550858788,
0.0131702158217202, 0.81328537816321, 0.85890307119103)), row.names = c("cg00000029",
"cg00000165", "cg00000236", "cg00000289", "cg00000292", "cg00000321",
"cg00000363", "cg00000622", "cg00000658", "cg00000721", "cg00000734",
"cg00000769", "cg00000905", "cg00000924", "cg00000948", "cg00000957",
"cg00001245", "cg00001249", "cg00001261", "cg00001349", "cg00001364",
"cg00001446", "cg00001510", "cg00001582", "cg00001583", "cg00001687",
"cg00001747", "cg00001791", "cg00001809", "cg00001854", "cg00001874",
"cg00002033", "cg00002116", "cg00002145", "cg00002190", "cg00002224",
"cg00002236", "cg00002406", "cg00002426", "cg00002449", "cg00002464",
"cg00002490", "cg00002531", "cg00002591", "cg00002593", "cg00002597",
"cg00002660", "cg00002719", "cg00002769", "cg00002808", "cg00002809",
"cg00002810", "cg00002837", "cg00003091", "cg00003173", "cg00003181",
"cg00003287", "cg00003345", "cg00003513", "cg00003529", "cg00003578",
"cg00003625", "cg00003784", "cg00003969", "cg00003994", "cg00004055",
"cg00004067", "cg00004072", "cg00004082", "cg00004089", "cg00004105",
"cg00004121", "cg00004192", "cg00004207", "cg00004209", "cg00004429",
"cg00004533", "cg00004562", "cg00004608", "cg00004773", "cg00004818",
"cg00004883", "cg00004939", "cg00004963", "cg00004979", "cg00004996",
"cg00005010", "cg00005040", "cg00005072", "cg00005083", "cg00005112",
"cg00005166", "cg00005215", "cg00005297", "cg00005306", "cg00005390",
"cg00005437", "cg00005543", "cg00005617", "cg00005619"), class = "data.frame")
anno
> dput(anno[1:100,])
structure(list(V1 = c("TSPY4", "TTTY14", "TMSB4Y", "TBL1Y", "TMSB4Y",
"TSPY4", "RPS4Y2", "EIF1AY", "PCDH11Y", "TBL1Y", "ZFY", "FAM197Y2",
"TTTY14", "TSPY4", "ZFY", "NLGN4Y", "EIF1AY", "TSPY4", "TBL1Y",
"UTY", "PRKY", "ZFY", "CD24", "PRKY", "TSPY1", "CYorf15A", "TSPY2",
"TTTY15", "RPS4Y2", "UTY", "CYorf15A", "RPS4Y2", "TSPY2", "TBL1Y",
"TSPY3", "DDX3Y", "CYorf15A", "ZFY", "RBMY1F", "DDX3Y", "RPS4Y2",
"ZFY", "DDX3Y", "TTTY15", "BCORL2", "PCDH11Y", "KDM5D", "TTTY14",
"EIF1AY", "DDX3Y", "LOC100101121", "CYorf15A", "TTTY15", "TSPY1",
"TSPY1", "FAM197Y2", "TSPY4", "TMSB4Y", "DDX3Y", "TTTY15", "TTTY20",
"NLGN4Y", "TSPY4", "CYorf15A", "RPS4Y2", "KDM5D", "RBMY1J", "EIF1AY",
"KDM5D", "ZFY", "TGIF2LY", "HMGN5", "EBP", "UBL4A", "WDR13",
"MTM1", "BCOR", "ZCCHC12", "FTHL17", "PORCN", "NAA10", "PCDH11X",
"ARSE", "DOCK11", "PDK3", "LONRF3", "MAGIX", "PCYT1B", "SLC6A8",
"UBE2A", "TAF9B", "STARD8", "BCOR", "ZIC3", "IL1RAPL2", "TMSB4X",
"CLCN5", "LOC100133957", "SCML1", "GNL3L"), V2 = c("cg00050873",
"cg00212031", "cg00214611", "cg01707559", "cg02004872", "cg02011394",
"cg02050847", "cg02233190", "cg02494853", "cg02839557", "cg02842889",
"cg03052502", "cg03244189", "cg03443143", "cg03683899", "cg03706273",
"cg03750315", "cg04016144", "cg04042030", "cg04448376", "cg04689676",
"cg04840163", "cg05230942", "cg05480730", "cg05544622", "cg05621349",
"cg05865243", "cg05890011", "cg06322277", "cg06479204", "cg07731488",
"cg07747963", "cg08242338", "cg08921682", "cg09350919", "cg09856092",
"cg10076560", "cg10213302", "cg10267609", "cg10698069", "cg10841270",
"cg11131351", "cg14180491", "cg14741114", "cg15027426", "cg15295597",
"cg15329860", "cg15345074", "cg15422579", "cg15429127", "cg15682806",
"cg15682993", "cg15746461", "cg15810474", "cg15935877", "cg17834650",
"cg17837162", "cg18032798", "cg18077436", "cg25032547", "cg25071634",
"cg25518695", "cg25705492", "cg25756647", "cg26058907", "cg26517491",
"cg26983430", "cg26983535", "cg27049643", "cg27433982", "cg27539833",
"cg00008945", "cg00011200", "cg00011891", "cg00014152", "cg00016522",
"cg00016934", "cg00018261", "cg00021786", "cg00026186", "cg00072288",
"cg00072839", "cg00074638", "cg00112256", "cg00114625", "cg00114913",
"cg00116709", "cg00139317", "cg00140085", "cg00142683", "cg00192980",
"cg00200463", "cg00206414", "cg00240113", "cg00241296", "cg00241907",
"cg00264378", "cg00265812", "cg00266918", "cg00360365")), row.names = c(1L,
2L, 4L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L,
20L, 21L, 23L, 25L, 27L, 29L, 31L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 45L, 47L, 48L, 50L, 51L, 52L, 54L, 56L, 57L,
58L, 61L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 72L, 73L, 74L, 75L,
76L, 77L, 78L, 79L, 80L, 82L, 83L, 85L, 86L, 87L, 88L, 89L, 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, 122L, 123L, 124L, 125L), class = "data.frame")
There is no match between your cpg dataframe and annotation dataframe: table(rownames(meth.kirp.cpg) %in% anno$V2) # FALSE # 100 Below should work with your full data, assuming there is are no duplicates and all rownames are in annotation dataframe: rownames(meth.kirp.cpg) <- anno$V2[ match(rownames(meth.kirp.cpg), anno$V2) ]
Event Analysis around Earnings Announcement Dates with multiple firms and multiple dates
I am trying to compute Cumulative Abnormal Returns per firm around the EPS announcement dates by using the EvReturn function from the ererpackage. I have a dataframe (df) with column Date (time-series), 5 columns with daily firms returns, and last column with the market index returns. The second dataframe (Dates_clean), consists of 2 columns, one referring to the firms and the second with the announcement dates. This dataframe has been tidied and cleaned from NAs and it is possible that a firm is not present due to missing observations regarding EPS dates.
df looks as follow (subset only for the last 2 years, daily observations of returns):
df <-- structure(list(Date = c("2021-12-31", "2022-01-03", "2022-01-04",
"2022-01-05", "2022-01-06", "2022-01-07", "2022-01-10", "2022-01-11",
"2022-01-12", "2022-01-13", "2022-01-14", "2022-01-17", "2022-01-18",
"2022-01-19", "2022-01-20", "2022-01-21", "2022-01-24", "2022-01-25",
"2022-01-26", "2022-01-27", "2022-01-28", "2022-01-31", "2022-02-01",
"2022-02-02", "2022-02-03", "2022-02-04", "2022-02-07", "2022-02-08",
"2022-02-09", "2022-02-10", "2022-02-11", "2022-02-14", "2022-02-15",
"2022-02-16", "2022-02-17", "2022-02-18", "2022-02-21", "2022-02-22",
"2022-02-23", "2022-02-24", "2022-02-25", "2022-02-28", "2022-03-01",
"2022-03-02", "2022-03-03", "2022-03-04", "2022-03-07", "2022-03-08",
"2022-03-09", "2022-03-10", "2022-03-11", "2022-03-14", "2022-03-15",
"2022-03-16", "2022-03-17", "2022-03-18", "2022-03-21", "2022-03-22",
"2022-03-23", "2022-03-24", "2022-03-25", "2022-03-28", "2022-03-29",
"2022-03-30", "2022-03-31", "2022-04-01", "2022-04-04", "2022-04-05",
"2022-04-06", "2022-04-07", "2022-04-08", "2022-04-11", "2022-04-12",
"2022-04-13", "2022-04-14", "2022-04-15", "2022-04-18", "2022-04-19",
"2022-04-20", "2022-04-21", "2022-04-22", "2022-04-25", "2022-04-26",
"2022-04-27", "2022-04-28", "2022-04-29"), NESN = c(NA, 0.0128217636632681,
0.0055390098819148, -0.0303446403908639, -0.00353202700477517,
-0.0148639604729367, -0.0242468464841654, 0.000833755941585634,
0.000566825263230264, -0.00107292453477992, -0.00413304805849857,
0.00944796286389749, 0.00606019163539706, -0.0178671560381302,
0.0102682549157, 0.00238042231089342, -0.0264129023440169, -0.00359737479380895,
-0.00057237456191328, 0.00371815995136426, -0.00208920373247656,
0.00360658333406638, 0.00460158995889248, 0.0124240943672786,
-0.0192088934850052, -0.0116772543954452, 0.00161803666364402,
-0.00947063835830442, 0.00910418738855001, -0.0111621216023017,
0.0149520456912964, -0.00898947983118181, -0.000580331235212728,
-0.00085760228693943, 0.00337970744966198, 0.00873269055978332,
0.0104415116340701, -0.00548153587914391, 0.00615346747892431,
-0.0248914439483823, 0.0173285069170668, 0.0237445174472881,
0.0114592474061839, -0.00044228969975324, -0.0129341888051192,
-0.0039655856135451, -0.0259827935397877, -0.0295994170754248,
0.0307311936266654, -0.013299455406402, -0.0056093783764315,
0.0110647313950434, 0.00334040819250792, 0.00425756006997746,
0.0172069005359381, 0.0251378343192163, -0.0021649080340248,
0.00189626896268957, -0.0135983085238801, 0.0114262007450368,
-0.00381128173575684, 0.0058674672957324, -0.00307865494333059,
0.00175366558880397, 0.00248499184478623, 0.00721501950695935,
0.0140475807883897, 0.0150789811679537, -0.0124887026538493,
0.00613195773358854, 0.0132145840052262, -0.0015996735360132,
-0.0066051386016398, 0.00478929567729036, 0.000712513206063692,
0, -0.00234061707177347, -0.0254790654941593, -0.002887253994175,
0.0031573171246464, 0.0188337863652812, 0.0128193479643504, -0.0131137915005016,
0.0142879572931387, 0.00363369724621165, -0.00110402156089173
), MC = c(NA, 0.00550261575073563, 0.0177834448996657, 0.0188176125853796,
-0.0407649022737596, -0.0155420098162522, -0.0183010694192803,
0.0103886467592562, -0.00183070448319778, -0.0263867607751148,
-0.021159281495444, 0.0162863512416866, -0.0195218616791354,
0.036701342749085, 0.00888605545163479, -0.00951769122788382,
-0.049340634153576, 0.00633645237423841, 0.0248878672659074,
0.0152138699844748, 0.0322766524097042, 0.00865438181820477,
0.0132850798110877, -0.00396012072659391, -0.0241332685589187,
-0.00786778247404551, 0.0141615838200913, -0.0139641131076824,
0.014870269120445, -0.0145131154740279, -0.0300199660152931,
-0.0218975214448938, 0.0358204424148594, -0.00922132830585842,
-0.00509031265552273, 0.00321590363749413, -0.0205452396116511,
-0.00446266058653078, -0.00508069260599331, -0.0449083741501392,
0.0482780235448934, -0.0100514536376457, -0.0516743602589363,
0.0194952092513918, -0.0322887147335422, -0.0641394765595972,
-0.0173068505872533, -0.0311734655958205, 0.0954377559421478,
-0.0243947811500892, 0.0119067888938236, 0.00638796543754672,
-0.0145319835423444, 0.065932734214486, 0.00143122890265102,
0.0152422943022248, -0.0096967499833438, 0.0168987761680872,
-0.0304396645441839, 0.000640877885772717, 0.00208067872578832,
0.0154956918516587, 0.0567879413578825, -0.0168206261325913,
-0.0168057506728613, 0.000308592594485901, 0.0220126129867861,
-0.00451860424097195, -0.0373729875542405, -0.0191766740018862,
0.0123403885706372, -0.0186802220420382, 0.0177444777777833,
0.005072438614252, 0.00930500067891504, 0, -3.12499902288543e-07,
-0.00921874999999994, 0.0129321873521526, 0.0172808575677141,
-0.0211199942209961, -0.0375232958098812, -0.0134827788364557,
0.0153135163323701, 0.000972753811222749, 0.00372650801247842
), ASML = c(NA, -0.00707457495736874, -0.0290725339785473, -0.0152647901460136,
-0.0184823371590401, 0.00971814435287954, -0.0640693722781605,
0.00755262856381633, 0.0307819825808584, 0.0255294675755942,
-0.0310802702293709, 0.0217999099657287, -0.0225541062341073,
-0.0236980117549149, 0.0220375314655912, -0.0167179739743669,
-0.070396438711375, -0.0162393217912211, 0.0253696004759689,
-0.0127093712930013, -0.0283213085062449, 0.0496373256889857,
0.0104341989959946, -0.00316405782212825, -0.0412701616473979,
-0.0167302195887068, 0.014887800465154, -0.0106529882383843,
0.0393645189761693, -0.00798216711956512, -0.024996058038682,
-0.017207904689711, 0.0235835268894051, 0.00174602988969963,
0.00749259452866369, -0.0134901418194397, -0.0343618513323983,
0.0248730936819173, -0.000354295774261049, -0.0108096737888227,
0.0553563040643841, 0.0166350336725456, -0.0362331061055623,
0.010395360497353, -0.0169754801097395, -0.069247700897907, 0.0131186232688818,
-0.0436560986000191, 0.077563666368923, -0.0210021464720349,
-0.00476696090336659, -0.0152907148120854, 0.011037414017806,
0.0712443030593835, 0.0100189998239766, 0.0430985046864196, 0.00459075408780252,
0.0236664062345713, -0.0212058320781517, 0.00293240036892017,
0.00730826701315568, 0.00403160465895103, 0.0165410283377181,
-0.0170617639615278, -0.0196080007058823, -0.00540967213114751,
0.0176357313934843, -0.0294781484344746, -0.0457273107021545,
-0.00769569645056023, 0.00334966691029259, -0.029861744320177,
0.0146653992395438, 0.010171668543006, -0.0130715444394179, 0,
-1.7898690185536e-07, 0.00519062108466084, 0.0530628561253561,
-0.0125130178368247, -0.0219178119722282, -0.0411412887152115,
-0.0334124520723025, -0.00132262939176431, 0.0378286505877248,
-0.00127556088046676), ROG = c(NA, -0.0016563124362825, -0.0167576047110743,
0.00558547220350492, -0.00931186868686873, 0.00929598170989054,
-0.0189468728798304, 0.0102378674073382, -0.0211679240011176,
-0.0090002397780341, 0.00835899311866473, 0.00677858618059668,
-0.004236109928897, 0.00246186811339411, -3.6951028517751e-05,
-0.0085303180179872, -0.0453608483871986, 0.00297013943738533,
0.00575500793484429, 0.0165975968466054, 0.00233697546175771,
-0.000929104641140599, 0.0127388162493018, -0.00699098766109441,
-0.034148341582287, -0.0182875241992058, 0.0008188551362458,
0.0038028755990831, 0.0290255912422577, -0.00518866385230854,
-0.014558705998831, -0.0111877742472386, 0.0114612566678807,
0.00773075782606858, -0.00964711322222001, -0.00537533349502783,
-0.00277687179752917, 0.0100685911308367, 0.00239370555303364,
-0.00492692631648861, 0.0156943530061375, 0.0141076879880042,
0.0095079601184076, -0.00810543484286608, -0.0153314409260049,
-0.00265576875393958, 0.011543968321482, 0.00650858137799504,
0.0172871323248192, -0.013768790740987, 0.00885575961597662,
0.0135187375923218, -5.44893487662845e-05, -0.00846638540982525,
0.00148964049045319, 0.0260458184590857, 0.0049176082464939,
-0.000655460348850556, -0.000953002940294323, 0.0201022930361616,
0.00785770864045432, -0.00585620372821327, -0.0105159715950733,
-0.00101256130850647, -0.00614261872165789, 0.0145685186996185,
0.0366446438948085, 0.00785569119190255, 0.0111315542528179,
0.0105840795630807, 0.0110330061523778, 0.00756508782135668,
-0.0247558224043232, 0.000999685813030116, -0.00142410234860157,
0, -0.00234052114911543, -0.0406737559105963, -0.0119804035990065,
-0.0253254619925773, 0.00693062609568162, -0.00655543146327209,
-0.0173983359583453, 0.0239373293877456, -0.00889126611629498,
-2.8255212379813e-06), DJSTOXX = c(NA, 0.00448956330284256, 0.00822265035062086,
0.000665968984872922, -0.0125074340644002, -0.00391672453668146,
-0.014833902655213, 0.00842939391029529, 0.00646896140565878,
-0.000302344092258267, -0.0100667827031564, 0.00696648315220894,
-0.00975618817245349, 0.00231978423713897, 0.00508630710398661,
-0.0184133486433087, -0.0381245455216094, 0.00708217470094685,
0.0167954411441527, 0.00646040726886765, -0.0101525109126204,
0.00715709523318564, 0.0127472893082297, 0.0045192172025077,
-0.0175615871465199, -0.0138360753686276, 0.0067878981966738,
0.000120356858084092, 0.0171767249252694, -0.00207254104941135,
-0.00589392210453665, -0.0183275230158848, 0.0143092365731592,
0.000447004322467581, -0.0068709568459443, -0.00805937763694919,
-0.0130249474835507, 0.000692602999960323, -0.00277069175010591,
-0.0328295068963999, 0.0331852560597776, -0.000926074963563228,
-0.0237052175314, 0.00896542681851686, -0.020101628384252, -0.0356274007682458,
-0.0110105316066746, -0.00508229969266472, 0.0468249263880254,
-0.0168766270684341, 0.00950095407560014, 0.0120160585194342,
-0.00282797911787214, 0.0306352270637984, 0.00455346192440631,
0.0091122594851849, 0.000435550608561153, 0.00846535252146574,
-0.0100600682445028, -0.00211439307007666, 0.00104840080252311,
0.00137141546833175, 0.0174494900565429, -0.00411821862698636,
-0.00940915708728995, 0.00543807309261601, 0.00839116898191072,
0.00191698129536877, -0.015334581522056, -0.0020812772770139,
0.0130697264521857, -0.00588544107183153, -0.0035024167766855,
0.000289061644585642, 0.00664430735011456, 0, 0, -0.00769436536709167,
0.00837864552170919, 0.00318842343654246, -0.0178911495356058,
-0.0180825084765205, -0.00902690097368308, 0.00728639570524336,
0.00620287637010186, 0.00743961741666777)), class = "data.frame", row.names = c(NA,
-86L))
Whereas, Dates_clean is:
Dates_clean <-- structure(list(stock = c("NESN", "NESN", "MC", "ASML", "ASML",
"ROG", "ROG"), EPS = structure(c(19103, 19040, 19019, 19102,
19011, 19107, 19026), class = "Date")), row.names = c("33", "130",
"260", "293", "390", "423", "520"), class = "data.frame", na.action = structure(c(`NA` = 1L,
NA.1 = 2L, NA.2 = 3L, NA.3 = 4L, NA.4 = 5L, NA.5 = 6L, NA.6 = 7L,
NA.7 = 8L, NA.8 = 9L, NA.9 = 10L, NA.10 = 11L, NA.11 = 12L, NA.12 = 13L,
NA.13 = 14L, NA.14 = 15L, NA.15 = 16L, NA.16 = 17L, NA.17 = 18L,
NA.18 = 19L, NA.19 = 20L, NA.20 = 21L, NA.21 = 22L, NA.22 = 23L,
NA.23 = 24L, NA.24 = 25L, NA.25 = 27L, NA.26 = 28L, NA.27 = 29L,
NA.28 = 30L, NA.29 = 31L, NA.30 = 32L, NA.31 = 33L, NA.32 = 34L,
NA.33 = 35L, NA.34 = 36L, NA.35 = 37L, NA.36 = 38L, NA.37 = 39L,
NA.38 = 40L, NA.39 = 41L, NA.40 = 42L, NA.41 = 43L, NA.42 = 44L,
NA.43 = 45L, NA.44 = 46L, NA.45 = 47L, NA.46 = 48L, NA.47 = 49L,
NA.48 = 50L, NA.49 = 51L, NA.50 = 52L, NA.51 = 53L, NA.52 = 54L,
NA.53 = 55L, NA.54 = 57L, NA.55 = 58L, NA.56 = 59L, NA.57 = 60L,
NA.58 = 61L, NA.59 = 62L, NA.60 = 63L, NA.61 = 64L, NA.62 = 65L,
NA.63 = 66L, NA.64 = 67L, NA.65 = 68L, NA.66 = 69L, NA.67 = 70L,
NA.68 = 71L, NA.69 = 72L, NA.70 = 73L, NA.71 = 74L, NA.72 = 75L,
NA.73 = 76L, NA.74 = 77L, NA.75 = 78L, NA.76 = 79L, NA.77 = 80L,
NA.78 = 81L, NA.79 = 82L, NA.80 = 83L, NA.81 = 84L, NA.82 = 85L,
NA.83 = 86L, NA.84 = 87L, NA.85 = 88L, NA.86 = 89L, NA.87 = 90L,
NA.88 = 91L, NA.89 = 92L, NA.90 = 93L, NA.91 = 94L, NA.92 = 95L,
NA.93 = 96L, NA.94 = 97L, NA.95 = 98L, NA.96 = 99L, NA.97 = 100L,
NA.98 = 101L, NA.99 = 102L, NA.100 = 103L, NA.101 = 104L, NA.102 = 106L,
NA.103 = 107L, NA.104 = 108L, NA.105 = 109L, NA.106 = 110L, NA.107 = 111L,
NA.108 = 112L, NA.109 = 113L, NA.110 = 114L, NA.111 = 115L, NA.112 = 116L,
NA.113 = 117L, NA.114 = 118L, NA.115 = 119L, NA.116 = 120L, NA.117 = 121L,
NA.118 = 122L, NA.119 = 124L, NA.120 = 125L, NA.121 = 126L, NA.122 = 127L,
NA.123 = 128L, NA.124 = 129L, NA.125 = 130L, NA.126 = 131L, NA.127 = 132L,
NA.128 = 133L, NA.129 = 134L, NA.130 = 135L, NA.131 = 136L, NA.132 = 137L,
NA.133 = 138L, NA.134 = 139L, NA.135 = 140L, NA.136 = 141L, NA.137 = 142L,
NA.138 = 143L, NA.139 = 144L, NA.140 = 145L, NA.141 = 146L, NA.142 = 147L,
NA.143 = 148L, NA.144 = 149L, NA.145 = 150L, NA.146 = 151L, NA.147 = 152L,
NA.148 = 153L, NA.149 = 155L, NA.150 = 156L, NA.151 = 157L, NA.152 = 158L,
NA.153 = 159L, NA.154 = 160L, NA.155 = 161L, NA.156 = 162L, NA.157 = 163L,
NA.158 = 164L, NA.159 = 165L, NA.160 = 166L, NA.161 = 167L, NA.162 = 168L,
NA.163 = 169L, NA.164 = 170L, NA.165 = 171L, NA.166 = 172L, NA.167 = 173L,
NA.168 = 174L, NA.169 = 175L, NA.170 = 177L, NA.171 = 178L, NA.172 = 179L,
NA.173 = 180L, NA.174 = 181L, NA.175 = 182L, NA.176 = 183L, NA.177 = 184L,
NA.178 = 185L, NA.179 = 186L, NA.180 = 187L, NA.181 = 188L, NA.182 = 189L,
NA.183 = 190L, NA.184 = 191L, NA.185 = 192L, NA.186 = 193L, NA.187 = 194L,
NA.188 = 195L, NA.189 = 196L, NA.190 = 197L, NA.191 = 198L, NA.192 = 199L,
NA.193 = 200L, NA.194 = 201L, NA.195 = 202L, NA.196 = 203L, NA.197 = 204L,
NA.198 = 205L), class = "omit"))
Those are subsets of my original dataframes, encompassing more than 20 years of daily observations and 250 firms.
My goal is to compute automatically cumulative abnormal returns for each firm around each earnings announcement date. I was able to compute the simple 1 firm or multiple firms with 1 event date in common for all. I am struggling in telling R how to match each date and firm with the dataframe containing returns and then compute the cumulative abnormal returns.
Until now I have tried this code:
hh2 <- list()
for(i in Dates_clean[2]){
firms <- colnames(df)[2:6]
hh2[[i]] <- evReturn(y = df, firm = firms, event.date = i, y.date = "Date", index = "DJSTOXX", event.win = 3, est.win = 100, digits = 4)}
Error in xj[i] : only 0's may be mixed with negative subscripts
Any help is highly appreciated.
Boxplot labelling outliers returns an error using data rownames
I am trying to label the outliers in my boxplot using the text function so I can find out from which class the outliers are coming from. I've stored the rownames of my data in variable "rownames" using names(vehData) to get the row names. When I apply this however, I get an error.
ERROR: Error in which(removeOutliers1 == bxpdat$out, arr.ind = TRUE) :
'list' object cannot be coerced to type 'double'
Completely new to R programming. Completely not sure how to fix this or what I am doing wrong
Thanks in advance for any help!
library(reshape2)
vehData <-
structure(
list(
Samples = 1:6,
Comp = c(95L, 91L, 104L, 93L, 85L,
107L),
Circ = c(48L, 41L, 50L, 41L, 44L, 57L),
D.Circ = c(83L,
84L, 106L, 82L, 70L, 106L),
Rad.Ra = c(178L, 141L, 209L, 159L,
205L, 172L),
Pr.Axis.Ra = c(72L, 57L, 66L, 63L, 103L, 50L),
Max.L.Ra = c(10L,
9L, 10L, 9L, 52L, 6L),
Scat.Ra = c(162L, 149L, 207L, 144L, 149L,
255L),
Elong = c(42L, 45L, 32L, 46L, 45L, 26L),
Pr.Axis.Rect = c(20L,
19L, 23L, 19L, 19L, 28L),
Max.L.Rect = c(159L, 143L, 158L, 143L,
144L, 169L),
Sc.Var.Maxis = c(176L, 170L, 223L, 160L, 241L, 280L),
Sc.Var.maxis = c(379L, 330L, 635L, 309L, 325L, 957L),
Ra.Gyr = c(184L,
158L, 220L, 127L, 188L, 264L),
Skew.Maxis = c(70L, 72L, 73L,
63L, 127L, 85L),
Skew.maxis = c(6L, 9L, 14L, 6L, 9L, 5L),
Kurt.maxis = c(16L,
14L, 9L, 10L, 11L, 9L),
Kurt.Maxis = c(187L, 189L, 188L, 199L,
180L, 181L),
Holl.Ra = c(197L, 199L, 196L, 207L, 183L, 183L),
Class = c("van", "van", "saab", "van", "bus", "bus")
),
row.names = c(NA,
6L), class = "data.frame")
#Remove outliers
removeOutliers <- function(data) {
OutVals <- boxplot(data)$out
remOutliers <- sapply(data, function(x) x[!x %in% OutVals])
return (remOutliers)
}
vehDataRemove1 <- vehData[, -1]
vehDataRemove2 <- vehDataRemove1[,-19]
vehData <- vehDataRemove2
vehClass <- vehData$Class
rownames <- names(vehData) #column names
#Begin removing outliers
removeOutliers1 <- removeOutliers(vehData)
bxpdat <- boxplot(removeOutliers1)
#Also tried using vehicles$Class instead of rownames but get the same error
text(bxpdat$group, bxpdat$out,
rownames[which(removeOutliers1 == bxpdat$out, arr.ind = TRUE)[,1]],
pos = 4)
The boxplot looks like this. I am trying to label the outliers based on the x axis e.g. "Comp", "Circ", "D.Circ", "Rad.Ra", "Max.L.Ra" etc.. & by vehicle class "Van", "Bus" ..
Crammed text issue when identifying class
If it is the outliers in the 2nd boxplot, it would be: bxpdat <- boxplot(removeOutliers1) text(bxpdat$group, bxpdat$out, bxpdat$names[bxpdat$group], pos = 4) Maybe looks better like this, if you adjust the margin and flip the labels: par(mar=c(8,3.5,3.5,3.5)) bxpdat = boxplot(removeOutliers1,las=2,cex=0.5) text(bxpdat$group, bxpdat$out, bxpdat$names[bxpdat$group], pos = 4,cex=0.5)
I understood the question differently to #StupidWolf. I thought the goal was to replace points indicating outliers with the text of the vehicle class (bus, van or saab). If you simply print the variable name (e.g. Skew.maxis), then you might as well have simply plotted the outliers as points. Unless I'm missing something.
Here is code to answer the question as I understood it, for what it's worth (beginning after defining removeOutliers):
# CHANGE: Create vehClass vector before removing Class from the dataframe
vehClass <- vehData$Class
vehDataRemove1 <- vehData[, -1]
vehDataRemove2 <- vehDataRemove1[,-19]
vehData <- vehDataRemove2
#Begin removing outliers
removeOutliers1 <- removeOutliers(vehData)
bxpdat <- boxplot(removeOutliers1) # use boxplot(vehData) if you plot all the outliers as points
# loop over columns
n_plot <- 1; set.seed(123) # only plot n_plot randomly-chosen outliers
for(i in 1:ncol(vehData)){
# find out which row indices were removed as outliers
diffInd <- which(vehData[[i]] %in% setdiff(vehData[[i]], removeOutliers1[[i]]))
# if none were, then don't add any outlier text
if(length(diffInd) == 0) next
print(i)
print(paste0("l:", length(diffInd)))
if(length(diffInd) > n_plot){
diffIndPlot <- sample(diffInd, n_plot, replace = FALSE)
} else diffIndPlot <- diffInd
text(x = i, y = vehData[[i]][diffIndPlot],
labels = paste0(vehClass[diffIndPlot], ": ", vehData[[i]][diffIndPlot]))
}
How could I insert a histogram into a `geom_smooth` plot?
I am trying to mimic some figures from journal papers. Here is an example from Schlenker and Roberts (2009).
I'd like to add a similar histogram to my own plot. Please see below. Is it possible to achieve this task with ggplot? Thanks.
See a dput data below. rh represents x axis and yhat1 indicates the y axis.
> dput(df.m[,c('rh','yhat1')])
structure(list(rh = c(11L, 13L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L,
47L, 48L, 49L, 50L, 51L, 52L, 53L, 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), yhat1 = c(0.0097784, 0.111762325, 0.0887123966666667,
0.24714677, 0.079887235, 0.162714825, 0.24789043, 0.107558165,
0.182885584545455, 0.136690964444444, 0.159203683333333, 0.5156053805,
0.587034213636364, 0.233377613, 0.31531245, 0.4778449572, 0.212574774137931,
0.2274105676, 0.253733041707317, 0.560999839354839, 0.224892959444444,
0.392268151304348, 0.351498776603774, 0.366547010727273, 0.35013903469697,
0.382026272372881, 0.510611202461538, 0.391176294871795, 0.423356474328358,
0.380316089137931, 0.459821489651163, 0.388949226593407, 0.506833284166667,
0.459263999259259, 0.558535709906542, 0.745323656071429, 0.60167464606383,
0.72210854266129, 0.695203745656566, 0.638265557105263, 0.52373110503876,
0.611695133046875, 0.963833986386555, 0.803060819275362, 0.837984669112426,
0.7931166204, 0.870764136976744, 1.21005393820225, 0.862845527777778,
1.028402381125, 1.2077895633526, 1.01176334204082, 1.08139833964706,
0.90346288, 1.05871937863014, 1.27788244930233, 1.16250975336634,
1.1450916525, 1.4412301412, 1.21264826238281, 1.35417930411504,
1.18588206727273, 1.40277204710084, 1.33194569259259, 1.18413544210084,
1.22718163528571, 1.33992107226667, 1.44770425268156, 1.43974964777778,
1.26656031551351, 1.58998655363636, 1.29994566024272, 1.46398530493902,
1.26061274530055, 1.30718501225275, 1.20523443567901, 1.23789593428571,
1.34433582230769, 1.36438752851852, 1.5915544857037, 1.10979387898438,
1.31898147708661, 1.426120105, 1.52075980155738, 1.40629729460177,
0.9048366681, 1.2973945580531, 1.37696154192982)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -88L))
Hopefully this can get you started: library(ggplot2) breaks <- 20 maxcount <- max(table(cut(df.m$rh,breaks = 20))) + 1 ggplot(data = df.m, aes(x = rh)) + stat_smooth(formula = y ~ x, aes(y = yhat1 * 10 + maxcount), method = "loess") + scale_y_continuous(breaks = c(0,5), "Exposure (Days)", sec.axis = sec_axis(~ (. - maxcount) /10, "Log of Daily Confirmed Case Counts")) + geom_histogram(bins = breaks, color = "black", fill = "green") + geom_segment(aes(x = 85, xend = 85, y = 0 + maxcount, yend = Inf), col = "red", linetype = "dashed") + labs(x = "Relative Humidity Percentage") + theme_classic() + theme(axis.line.y.left = element_line(color= "green"), axis.title.y.left = element_text(hjust = 0.05, color = "green"))
Trouble with date column in R (time series)
Hi I am working with a table in r. The first column consists of the date(monthly) and the following columns contain different return data on several portfolios. I downloaded the package PerformanceAnalytics and therefore I need this data to be read a time series.
This is what I tried to do. It has worked with a sheet before. But now I always get this error. But I only changed the return data nothing else. I dont understand why it wont read the date correctly.
> library(PerformanceAnalytics)
Loading required package: xts
Loading required package: zoo
Attaching package: ‘zoo’
The following objects are masked from ‘package:base’:
as.Date, as.Date.numeric
Package PerformanceAnalytics (1.4.3541) loaded.
Copyright (c) 2004-2014 Peter Carl and Brian G. Peterson, GPL-2 | GPL-3
http://r-forge.r-project.org/projects/returnanalytics/
> #load file into R
> FactorR <- read.table("~/Desktop/Rfiles/FactorRegression.csv",header=TRUE,sep=";")
>
> #Time Series (first column date)
> FactorR_xts <- xts(x = FactorR[, -1],order.by = as.Date(FactorR$Date))
Error in charToDate(x) :
character string is not in a standard unambiguous format
I attached the dput function so you can see what kind of data I am talking about.(I did not include all data because it would be too much)
put(FactorR)
structure(list(Date = structure(c(203L, 55L, 5L, 142L, 70L, 35L,
85L, 167L, 178L, 102L, 105L, 116L, 204L, 26L, 2L, 143L, 71L,
9L, 145L, 36L, 157L, 169L, 19L, 181L, 107L, 192L, 122L, 7L,
30L, 60L, 146L, 17L, 158L, 90L, 92L, 182L, 49L, 193L, 123L, 8L,
133L, 61L, 72L, 76L, 159L, 41L, 93L, 183L, 22L, 194L, 53L, 3L,
134L, 62L, 147L, 77L, 87L, 170L, 94L, 46L, 108L, 195L, 124L,
9L, 135L, 32L, 148L, 78L, 39L, 171L, 95L, 184L, 109L, 118L, 125L,
10L, 136L, 16L, 149L, 79L, 160L, 172L, 45L, 185L, 110L, 52L,
126L, 11L, 57L, 63L, 150L, 37L, 161L, 173L, 20L, 186L, 111L,
196L, 127L, 28L, 137L, 64L, 73L, 80L, 162L, 42L, 96L, 187L, 23L,
197L, 54L, 4L, 138L, 65L, 34L, 81L, 163L, 174L, 97L, 104L, 112L,
198L, 25L, 1L, 139L, 66L, 151L, 82L, 88L, 175L, 98L, 47L, 113L,
199L, 128L, 12L, 140L, 33L, 152L, 83L, 40L, 176L, 99L, 188L,
114L, 119L, 129L, 29L, 58L, 67L, 153L, 38L, 164L, 177L, 21L,
189L, 115L, 200L, 130L, 13L, 31L, 68L, 154L, 18L, 165L, 91L,
100L, 190L, 50L, 201L, 131L, 14L, 141L, 69L, 74L, 84L, 166L,
43L, 101L, 191L, 24L, 202L), .Label = c("26.02.10", "26.02.99",
"27.02.04", "27.02.09", "27.02.98", "28.02.01", "28.02.02", "28.02.03",
"28.02.05", "28.02.06", "28.02.07", "28.02.11", "28.02.13", "28.02.14",
"28.04.00", "28.04.06", "28.06.02", "28.06.13", "28.09.01", "28.09.07",
"28.09.12", "28.11.03", "28.11.08", "28.11.14", "29.01.10", "29.01.99",
"29.02.00", "29.02.08", "29.02.12", "29.03.02", "29.03.13", "29.04.05",
"29.04.11", "29.05.09", "29.05.98", "29.06.01", "29.06.07", "29.06.12",
"29.07.05", "29.07.11", "29.08.03", "29.08.08", "29.08.14", "29.09.00",
"29.09.06", "29.10.04", "29.10.10", "29.10.99", "29.11.02", "29.11.13",
"29.12.00", "29.12.06", "30.01.04", "30.01.09", "30.01.98", "30.03.01",
"30.03.07", "30.03.12", "30.04.01", "30.04.02", "30.04.03", "30.04.04",
"30.04.07", "30.04.08", "30.04.09", "30.04.10", "30.04.12", "30.04.13",
"30.04.14", "30.04.98", "30.04.99", "30.05.03", "30.05.08", "30.05.14",
"30.06.00", "30.06.03", "30.06.04", "30.06.05", "30.06.06", "30.06.08",
"30.06.09", "30.06.10", "30.06.11", "30.06.14", "30.06.98", "30.06.99",
"30.07.04", "30.07.10", "30.07.99", "30.08.02", "30.08.13", "30.09.02",
"30.09.03", "30.09.04", "30.09.05", "30.09.08", "30.09.09", "30.09.10",
"30.09.11", "30.09.13", "30.09.14", "30.09.98", "30.09.99", "30.10.09",
"30.10.98", "30.11.00", "30.11.01", "30.11.04", "30.11.05", "30.11.06",
"30.11.07", "30.11.09", "30.11.10", "30.11.11", "30.11.12", "30.11.98",
"30.11.99", "30.12.05", "30.12.11", "31.01.00", "31.01.01", "31.01.02",
"31.01.03", "31.01.05", "31.01.06", "31.01.07", "31.01.08", "31.01.11",
"31.01.12", "31.01.13", "31.01.14", "31.03.00", "31.03.03", "31.03.04",
"31.03.05", "31.03.06", "31.03.08", "31.03.09", "31.03.10", "31.03.11",
"31.03.14", "31.03.98", "31.03.99", "31.05.00", "31.05.01", "31.05.02",
"31.05.04", "31.05.05", "31.05.06", "31.05.07", "31.05.10", "31.05.11",
"31.05.12", "31.05.13", "31.05.99", "31.07.00", "31.07.01", "31.07.02",
"31.07.03", "31.07.06", "31.07.07", "31.07.08", "31.07.09", "31.07.12",
"31.07.13", "31.07.14", "31.07.98", "31.08.00", "31.08.01", "31.08.04",
"31.08.05", "31.08.06", "31.08.07", "31.08.09", "31.08.10", "31.08.11",
"31.08.12", "31.08.98", "31.08.99", "31.10.00", "31.10.01", "31.10.02",
"31.10.03", "31.10.05", "31.10.06", "31.10.07", "31.10.08", "31.10.11",
"31.10.12", "31.10.13", "31.10.14", "31.12.01", "31.12.02", "31.12.03",
"31.12.04", "31.12.07", "31.12.08", "31.12.09", "31.12.10", "31.12.12",
"31.12.13", "31.12.14", "31.12.97", "31.12.98", "31.12.99"), class = "factor"),
T1V = c(2.647778077, 2.210168532, 5.184543047, 8.040141376,
1.375197787, 5.254693278, 0.238583717, -0.897572167, -6.812178155,
-4.904778447, 1.445454477, 4.362544312, 0.577758687, -1.049345994,
-0.862978469, 1.496311077, 1.535298083, 0.288034989, 1.002503645,
-0.677737904, 1.148733333, -0.068879397, -0.933636437, 1.952957927,
0.864593373, 0.69587105, 1.566383785, 0.201725025, 0.108433102,
1.121251221, 0.697840536, -0.341798507, 1.750353464, -0.336236355,
-0.173630687, 0.405227621, 0.407442779, 0.301534209, -0.252288427,
-2.197112455, 0.4182172, 2.417270431, -1.777693712, 0.333608117,
-0.963997684, -6.639419411, 0.258711011, 0.186660625, 1.075364953,
-0.260546877, -0.144517713, 2.614703924, 1.592532166, 0.247679225,
-2.45731793, -4.605964615, -0.051317674, -2.162348318, -2.094287999,
1.053871887, 0.775032852, -2.409925349, -1.24731202, 0.20137383,
2.9796142, 1.18379607, 0.530516718, 0.687770774, 2.425813597,
1.070508498, 1.594988715, 2.577337728, 1.735724627, 4.753962343,
1.817757107, 0.287317513, 2.122250222, 0.509726992, 1.623651005,
-0.629218412, 1.413071621, 1.466153048, -0.032322501, 1.570878067,
2.495539535, 4.669928369, 2.540314459, 1.351671444, -0.511289999,
.
..
....
....
1.637709345, 0.949670725, -0.380310863, -1.434786801, 0.546588731,
-1.680930574, -1.497671033, 2.134405674, 0.189844698), T3R = c(0.440505512,
5.325647834, 8.837385281, 21.10071908, 4.5326005, 6.606732343,
-4.488433652, -1.304513421, -27.57526532, -19.22941607, 13.12560656,
10.95535151, -2.960696646, -1.282931055, -4.047714673, 4.325802659,
13.34806221, -3.940632325, 2.668465326, -2.035239493, 2.265868534,
2.901646772, 1.555938816, 8.725598107, 11.1111256, 15.10307892,
10.71764649, -1.860936247, -3.235221339, -0.718662895, 2.928862379,
1.567574208, 0.098434872, -2.639317291, -4.334738565, -7.662240412,
-1.392672778, -0.249440069, -7.519374824, -12.54244192, 3.211494367,
-1.798924417, -9.750103402, -17.47336517, -13.59092267, -30.85835803,
6.627120118, 13.84521564, 1.224167247, -4.282226202, -3.879824851,
11.0002882, -1.633862571, 0.728697276, -15.20216478, -21.43439457,
-9.173494124, -27.72510655, -1.643806123, 15.30080078, -11.42185815,
-10.86780424, -10.08529262, 0.158622664, 19.07560852, 4.410459583,
6.983702045, 9.726738752, 11.96532368, 0.865241128, 10.52710826,
1.824183803, 0.051281172, 7.643560265, 3.857934445, -4.269747269,
0.193491252, -1.127403274, -1.145642636, -4.336023223, -4.750288798,
1.386568693, -3.058304715, 3.87811701, 6.007778471, 6.972611825,
7.139746344, 4.366307305, -4.231872029, 0.465995363, 3.370806119,
6.055047349, 1.589337466, 6.641594709, -5.834167246, 0.500189653,
3.001936466, 5.665564573, 6.219235151, 4.696735739, 3.597032279,
-6.95415108, -2.658694701, 0.700309545, 3.870252718, 4.059903633,
4.129877722, 2.850231626, 6.026897131, 11.42913672, -1.40600749,
4.68987461, 6.138984252, 0.859683472, -0.783511946, -2.061859604,
-7.537614888, -3.971992672, 2.743416779, -13.26388813, 1.902781239,
-21.73358064, 5.433251961, -6.426065721, 5.500056238, 1.813441355,
-11.11515726, -5.234823589, 2.582946217, -16.67855167, -36.66711169,
-12.46637364, -5.211445441, -8.572591139, -17.88276043, 2.956958358,
25.59635755, 9.043196394, -1.052072638, 8.698101054, 11.55426061,
6.544403365, -4.495701412, -3.156245124, -1.293693294, 5.803543849,
-0.762197087, 8.000348105, 2.646959488, -12.09434448, -2.563082034,
1.466128125, -1.863374559, 4.699135454, 3.622459782, -1.706221195,
4.038651722, 2.817603386, 1.027156327, -1.486388335, 0.168641413,
-3.888501653, -9.915080583, -11.88374941, -13.56634471, -10.51374661,
3.846951996, -11.50943308, 2.074359943, 7.548294859, 6.711539857,
1.806850477, -0.576496993, -9.21065397, -4.154519223, 3.525193617,
-0.24777096, 3.601168094, 0.143557195, -6.368196817, 5.231960646,
6.810400741, 3.672507394, -2.556477674, -2.869519924, 4.479135652,
-5.380429829, 1.713023169, 3.396652152, 4.922622663, 4.040155598,
1.512006061, 0.24907751, 4.496251525, 0.92375895, -0.774870584,
-3.784012139, 5.614058853, 5.327086162, -0.706470295, 0.771043886,
-4.377376587, -2.491251246, 3.172560156, -2.082216546)), .Names = c("Date",
"T1V", "T2V", "T3V", "T1MV", "T2MV", "T3MV", "T1BTM", "T2BTM",
"T3BTM", "T1MOM", "T2MOM", "T3MOM", "Rm", "SMB", "HML", "MOM",
"T1R", "T2R", "T3R"), class = "data.frame", row.names = c(NA,
-205L))
I would be very happy if anyone could help me.
You need to specify your date format (see ?as.Date):
dates <- c("26.02.10", "26.02.99", "27.02.04", "27.02.09", "27.02.98", "28.02.01", "28.02.02", "28.02.03")
as.Date(dates, "%d.%m.%y")