title: Compute AUC of a random uniform forest in regression
body: <p>I have a dataframe <code>df</code></p>
  
  <pre><code>df&lt;-structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 
  3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L), .Label = c("AU-Tum", 
  "AU-Wac", "BE-Bra", "BE-Jal", "BR-Cax", "BR-Sa3", "CA-Ca1", "CA-Ca2", 
  "CA-Ca3", "CA-Gro", "Ca-Man", "CA-NS1", "CA-NS2", "CA-NS3", "CA-NS4", 
  "CA-NS5", "CA-NS6", "CA-NS7", "CA-Oas", "CA-Obs", "CA-Ojp", "CA-Qcu", 
  "CA-Qfo", "CA-SF1", "CA-SF2", "CA-SF3", "CA-SJ1", "CA-SJ2", "CA-SJ3", 
  "CA-TP1", "CA-TP2", "CA-TP4", "CN-Cha", "CN-Ku1", "CZ-Bk1", "De-Bay", 
  "DE-Har", "DE-Tha", "DE-Wet", "DK-Sor", "FI-Hyy", "FI-Sod", "FR-Hes", 
  "FR-Pue", "GF-Guy", "ID-Pag", "IL-Yat", "IT-Col", "IT-Lav", "IT-Non", 
  "IT-Ro1", "IT-Ro2", "IT-Sro", "JP-Tak", "JP-Tef", "JP-Tom", "NL-Loo", 
  "PT-Esp", "RU-Fyo", "SE-Abi", "SE-Fla", "SE-Nor", "SE-Sk1", "SE-Sk2", 
  "SE-St1", "UK-Gri", "UK-Ham", "US-Blo", "US-Bn1", "US-Bn2", "Us-Bn3", 
  "US-Dk3", "US-Fmf", "US-Fwf", "US-Ha1", "US-Ha2", "US-Ho1", "US-Ho2", 
  "US-Lph", "US-Me1", "US-Me3", "US-Nc2", "US-NR1", "US-Oho", "US-Sp1", 
  "US-Sp2", "US-Sp3", "US-Syv", "US-Umb", "US-Wcr", "US-Wi0", "US-Wi1", 
  "US-Wi2", "US-Wi4", "US-Wi8", "VU-Coc", "Austin", "Caxiuana", 
  "Mae Klong", "Niwot Ridge", "Sky Oaks old", "Sky Oaks young", 
  "Sodankylä", "Tomakomai", "Yenisey Abies", "Yenisey Betula", 
  "Yenisey Mixed"), class = "factor"), P = c(1241.59999960661, 
  1282.40000277758, 0, 895, 0, 960.399999260902, 988.300011262298, 
  778.211069688201, 0, 676.725008800626, 1750.51986303926, 1614.11541634798, 
  951.847023338079, 1119.3682884872, 1112.38984390156, 1270.65773075982, 
  1234.72262170166, 1338.46096616983, 1136.69287367165, 1265.46480803983
  ), Te = c(9.20406423444821, 9.58323018294185, NaN, 12.1362462834136, 
  NaN, 10.6474634506736, 10.2948508957069, 11.3363996068107, NaN, 
  11.9457507949986, 9.10006221322572, 7.65505467142961, 8.21480062559674, 
  8.09251754304318, 8.466220758789, 8.48094407814006, 8.77304120569444, 
  8.31727518543397, 8.80921738865237, 9.04091478341757), Y = c(2172.34112930298, 
  2479.44521586597, 1027.63470042497, 2342.35314202309, 868.4010617733, 
  1157.13594430499, 1118.60130960867, 1100.47051284742, 1072.57190890331, 
  1228.25697739795, 2268.14043972082, 2147.62290922552, 2269.1387550775, 
  2247.31983098201, 1903.39138268307, 2174.78291538358, 2359.51909126411, 
  2488.39004804939, 461.398994384333, 567.150629704352)), .Names = c("ID", 
  "P", "Te", "Y"), row.names = 307:326, class = "data.frame")
  </code></pre>
  
  <p>I have implemented a random uniform forest procedure in a LOO Id CV mode. </p>
  
  <pre><code>library (randomUniformForest)
  df$prediction &lt;- NA
  for(id in unique(df$ID)){
    train.df &lt;- df[df$ID != id,]
    test.df &lt;- df[df$ID == id,c("P", "Te")]
    svmFit &lt;- randomUniformForest(Y ~ P+Te, 
                                  data = train.df,
                                  importance = T,
                                  ntree = 100)
    ruf_pred = predict(object = svmFit, X = test.df)
    df$prediction[df$ID == id] &lt;- ruf_pred
  }
  </code></pre>
  
  <p>I would like to compute the AUC value of my model. Somehow, it does not show up in the below command line.</p>
  
  <pre><code>statsModel = model.stats(df$prediction, df$Y, regression = TRUE)
  </code></pre>
  
  <p>Does anyone know how I can compute the AUC value for my model?</p>