[1] "Created: Thu Jun 19 13:57:22 2014"
load("lcdb.rda")
source("funcs.R")
GPR <- TRUE

Set the kernel type to squared exponential and set the inverse width:

kerntype <- "exponential"
wvec <- 2e-4

Definition of measures

Measures can be classified into groups.

  1. Whole curve measures
  1. Fitted curve measures (first smooth data then compute)
  1. Residual from fit measures
  1. Cluster measures (based on groups of up to 4 measures in 30mins)
  1. Other (can’t use these for discriminant purposes)

We have not used all these measures for classification purposes. Some we have not used, like nobs, because they would distort classification performance as explained in the article. Other measures were failed experiments in that they failed to incrementally improve classification performance. We have not reported these in the article but keep them here for future reference.

firstdate <- 53464
daterange <- 2764
detection.limit <- 20.5

Compute all the measures

uids <- lcdbi$id
nuids <- length(uids)
statmat <- NULL
types <- NULL
ids <- NULL
for(i in 1:nuids){
  olc <- lcdb[[i]]
  type <- as.character(lcdbi$type[i])
  id <- as.character(lcdbi$id[i])
  aa <- lcstats(olc,GPR,kerntype,wvec,daterange=daterange,firstdate=firstdate,detection.limit=detection.limit)
  if(is.na(aa[1])) next
  statmat <- rbind(statmat,aa)
  types <- c(types,type)
  ids <- c(ids,id)
}
colnames(statmat) <- c("meds", "iqr","shov", "maxdiff", "dscore","wander", "moveloc","nobs", "totvar", "quadvar", "famp", "fslope","trend",
"outl", "skewres", "std", "shapwilk", "lsd", "gscore", "mdev", "gtvar", "rm.amplitude", "rm.mean",
"rm.stddev", "rm.beyond1std", "rm.fpr20", "rm.fpr35", "rm.fpr50",
"rm.fpr65", "rm.fpr80", "rm.lintrend", "rm.maxslope", "rm.mad",
"rm.medbuf", "rm.pairslope", "rm.peramp", "rm.pdfp", "rm.skew",
"rm.kurtosis", "rm.std", "rm.rcorbor")
cmdb <- data.frame(statmat,type=types,id=ids,row.names=ids)
cmdb$lsd[is.na(cmdb$lsd) | is.infinite(cmdb$lsd)] <- min(cmdb$lsd[!is.infinite(cmdb$lsd)],na.rm=TRUE)-1
cmdb$fslope[is.infinite(cmdb$fslope)] <- 1

Numerical summary of the measures

summary(cmdb)
      meds           iqr             shov          maxdiff      
 Min.   :10.3   Min.   :0.010   Min.   :0.010   Min.   : 0.040  
 1st Qu.:17.4   1st Qu.:0.220   1st Qu.:0.136   1st Qu.: 0.729  
 Median :18.8   Median :0.430   Median :0.297   Median : 1.190  
 Mean   :18.3   Mean   :0.552   Mean   :0.327   Mean   : 1.547  
 3rd Qu.:19.8   3rd Qu.:0.685   3rd Qu.:0.452   3rd Qu.: 1.930  
 Max.   :22.3   Max.   :5.188   Max.   :3.685   Max.   :11.780  
                                                                
     dscore          wander            moveloc              nobs    
 Min.   :0.000   Min.   :1.88e-05   Min.   :0.000095   Min.   :  5  
 1st Qu.:0.129   1st Qu.:1.14e-04   1st Qu.:0.000462   1st Qu.: 21  
 Median :0.215   Median :2.14e-04   Median :0.000725   Median : 57  
 Mean   :0.208   Mean   :3.13e-04   Mean   :0.001196   Mean   :103  
 3rd Qu.:0.284   3rd Qu.:3.85e-04   3rd Qu.:0.001644   3rd Qu.:167  
 Max.   :0.387   Max.   :2.95e-03   Max.   :0.005611   Max.   :641  
                                                                    
     totvar          quadvar             famp            fslope      
 Min.   :0.0003   Min.   :0.00000   Min.   : 0.032   Min.   :0.0025  
 1st Qu.:0.0054   1st Qu.:0.00008   1st Qu.: 0.393   1st Qu.:0.0295  
 Median :0.0120   Median :0.00040   Median : 0.897   Median :0.0675  
 Mean   :0.0234   Mean   :0.00319   Mean   : 1.448   Mean   :0.1300  
 3rd Qu.:0.0287   3rd Qu.:0.00247   3rd Qu.: 2.042   3rd Qu.:0.1817  
 Max.   :0.3257   Max.   :0.17403   Max.   :14.429   Max.   :1.7435  
                                                                     
     trend                outl          skewres            std        
 Min.   :-1.17e-03   Min.   : 1.04   Min.   :-6.872   Min.   :0.0085  
 1st Qu.:-5.30e-05   1st Qu.: 2.19   1st Qu.:-0.426   1st Qu.:0.1512  
 Median :-1.00e-05   Median : 2.88   Median :-0.010   Median :0.2704  
 Mean   :-2.14e-05   Mean   : 3.22   Mean   : 0.045   Mean   :0.3169  
 3rd Qu.: 1.67e-05   3rd Qu.: 3.81   3rd Qu.: 0.433   3rd Qu.:0.4108  
 Max.   : 9.03e-04   Max.   :15.63   Max.   :12.692   Max.   :2.4160  
                                                                      
    shapwilk          lsd            gscore           mdev      
 Min.   :0.245   Min.   :-5.47   Min.   :0.000   Min.   :0.003  
 1st Qu.:0.892   1st Qu.:-2.31   1st Qu.:0.116   1st Qu.:0.278  
 Median :0.949   Median :-1.59   Median :0.266   Median :0.480  
 Mean   :0.913   Mean   :-1.74   Mean   :0.227   Mean   :0.582  
 3rd Qu.:0.976   3rd Qu.:-1.10   3rd Qu.:0.332   3rd Qu.:0.775  
 Max.   :0.998   Max.   : 1.13   Max.   :0.399   Max.   :4.782  
                                                                
     gtvar         rm.amplitude      rm.mean           rm.stddev       
 Min.   :  0.00   Min.   :0.035   Min.   :0.00e+00   Min.   :0.00e+00  
 1st Qu.:  1.58   1st Qu.:0.500   1st Qu.:1.00e-08   1st Qu.:0.00e+00  
 Median :  4.46   Median :0.809   Median :4.00e-08   Median :1.00e-08  
 Mean   : 10.81   Mean   :1.020   Mean   :1.01e-06   Mean   :4.00e-07  
 3rd Qu.: 15.44   3rd Qu.:1.355   3rd Qu.:1.30e-07   3rd Qu.:6.00e-08  
 Max.   :154.34   Max.   :6.225   Max.   :8.57e-05   Max.   :1.24e-04  
                                                                       
 rm.beyond1std      rm.fpr20         rm.fpr35         rm.fpr50     
 Min.   :0.000   Min.   :0.0000   Min.   :0.0001   Min.   :0.0002  
 1st Qu.:0.200   1st Qu.:0.0978   1st Qu.:0.1968   1st Qu.:0.3157  
 Median :0.273   Median :0.1392   Median :0.2581   Median :0.3949  
 Mean   :0.266   Mean   :0.1543   Mean   :0.2715   Mean   :0.4011  
 3rd Qu.:0.333   3rd Qu.:0.1852   3rd Qu.:0.3261   3rd Qu.:0.4788  
 Max.   :0.667   Max.   :0.9532   Max.   :0.9728   Max.   :0.9827  
                                                                   
    rm.fpr65         rm.fpr80       rm.lintrend       rm.maxslope    
 Min.   :0.0003   Min.   :0.0006   Min.   :-0.1758   Min.   :   0.0  
 1st Qu.:0.4669   1st Qu.:0.7067   1st Qu.:-0.0001   1st Qu.:  62.3  
 Median :0.5606   Median :0.7851   Median : 0.0000   Median : 125.0  
 Mean   :0.5515   Mean   :0.7606   Mean   : 0.0022   Mean   : 169.1  
 3rd Qu.:0.6511   3rd Qu.:0.8604   3rd Qu.: 0.0001   3rd Qu.: 225.0  
 Max.   :0.9931   Max.   :1.0000   Max.   : 0.3413   Max.   :1756.1  
                                                                     
     rm.mad        rm.medbuf      rm.pairslope     rm.peramp    
 Min.   :0.004   Min.   :0.000   Min.   :0.000   Min.   :    0  
 1st Qu.:0.105   1st Qu.:1.000   1st Qu.:0.433   1st Qu.:    1  
 Median :0.200   Median :1.000   Median :0.500   Median :    1  
 Mean   :0.249   Mean   :0.977   Mean   :0.496   Mean   :   16  
 3rd Qu.:0.326   3rd Qu.:1.000   3rd Qu.:0.533   3rd Qu.:    2  
 Max.   :2.506   Max.   :1.000   Max.   :1.000   Max.   :34671  
                                                                
    rm.pdfp          rm.skew         rm.kurtosis         rm.std     
 Min.   :   0.0   Min.   :-13.955   Min.   :  0.70   Min.   :0.014  
 1st Qu.:   0.6   1st Qu.: -0.665   1st Qu.:  2.13   1st Qu.:0.222  
 Median :   1.0   Median : -0.002   Median :  3.11   Median :0.375  
 Mean   :   3.7   Mean   : -0.180   Mean   :  6.04   Mean   :0.472  
 3rd Qu.:   1.6   3rd Qu.:  0.516   3rd Qu.:  5.36   3rd Qu.:0.616  
 Max.   :1735.4   Max.   : 11.105   Max.   :208.94   Max.   :3.429  
                                                                    
   rm.rcorbor          type                  id      
 Min.   :0.0000   nv     :1725   1001072061085:   1  
 1st Qu.:0.0000   sn     : 536   1001072061310:   1  
 Median :0.0000   cv     : 461   1001072061380:   1  
 Mean   :0.0141   downes : 376   1001072061435:   1  
 3rd Qu.:0.0000   rrlyr  : 292   1001072061602:   1  
 Max.   :0.5000   agn    : 140   1001072061641:   1  
                  (Other): 190   (Other)      :3714  

Plots of the measures

mnames <- names(cmdb)
for(i in 1:(ncol(cmdb)-2)){
    vname <- mnames[i]
    tranf <- functran[[match(vname,names(functran))]]
    y <- sapply(cmdb[,i], tranf)
    ylab <- ifelse(tranf=="identity",mnames[i],paste0(tranf,"(",mnames[i],")"))
    plot(cmdb[,i] ~ cmdb$type, ylab=ylab, xlab="Type")
   }

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