Updated dat files.

Author: dhiginbotham

Power-Series/data/PRAD.dat

@@ -0,0 +1,45 @@
+Q2(GeV2/c2) GE       dGE          
+0.00790435  0.999295 0.000226169
+0.012875    0.998054 0.000327208
+0.0190242   0.997979 0.000440119
+0.0263684   0.99773  0.000564046
+0.0316128   0.995386 0.000344603
+0.0348699   0.997223 0.000698296
+0.0445541   0.994514 0.000842377
+0.0514877   0.993587 0.000507705
+0.0554199   0.993713 0.000995877
+0.0674657   0.992831 0.00115846
+0.0760697   0.990565 0.000694686
+0.0806893   0.987654 0.00132984
+0.0950589   0.98856  0.00150973
+0.105421    0.987664 0.000904872
+0.11063     0.98374  0.00169804
+0.127338    0.980631 0.00189448
+0.139387    0.982618 0.00113732
+0.154068    0.980602 0.00162444
+0.178067    0.978698 0.00139172
+0.194474    0.969711 0.00195821
+0.221449    0.971109 0.00166763
+0.239514    0.970108 0.00231671
+0.26952     0.964009 0.0019645
+0.289057    0.96136  0.00269927
+0.322267    0.958946 0.00228196
+0.343273    0.960918 0.00310608
+0.379556    0.950858 0.00261942
+0.402031    0.946984 0.0035361
+0.4416      0.945822 0.00297667
+0.465169    0.946496 0.00398828
+0.508132    0.935678 0.00335294
+0.532762    0.935251 0.00446375
+0.604917    0.935283 0.00496115
+0.614183    0.92707  0.00332218
+0.6813      0.915125 0.00548047
+0.775052    0.90622  0.00407501
+0.953374    0.881387 0.00488426
+1.14996     0.868514 0.00575053
+1.36426     0.840808 0.00666317
+1.59555     0.822354 0.00761786
+1.8445      0.801601 0.00860429
+2.10985     0.795157 0.00961182
+2.39179     0.765816 0.0106459
+2.69001     0.749816 0.0116946

Power-Series/data/park.dat

@@ -0,0 +1,19 @@
+# Test Data From Kijun Park
+1.2500e-02   4.20800753e-01   1.15049633e-02
+1.7500e-02   3.93666571e-01   1.30981921e-02
+2.2500e-02   3.81220854e-01   1.34126011e-02
+2.7500e-02   3.68224314e-01   1.37290240e-02
+3.2500e-02   3.44132413e-01   1.37151250e-02
+3.7500e-02   3.32971286e-01   1.44902500e-02
+4.2500e-02   3.18970437e-01   1.53267661e-02
+4.7500e-02   3.04855619e-01   1.63178134e-02
+5.2500e-02   2.89261062e-01   1.71898353e-02
+5.7500e-02   2.75038111e-01   1.81024761e-02
+6.2500e-02   2.72934994e-01   1.94750503e-02
+6.7500e-02   2.59238019e-01   2.03586253e-02
+7.2500e-02   2.46670965e-01   2.12533274e-02
+7.7500e-02   2.33314422e-01   2.20380721e-02
+8.2500e-02   2.27192643e-01   2.31022357e-02
+8.7500e-02   2.16559797e-01   2.38960303e-02
+9.2500e-02   2.10059491e-01   2.48646248e-02
+

Power-Series/data/park2.dat

@@ -0,0 +1,10 @@
+# Test Data From Kijun Park
+1.2500e-02   4.20800753e-01   1.15049633e-02
+1.7500e-02   3.93666571e-01   1.30981921e-02
+2.2500e-02   3.81220854e-01   1.34126011e-02
+2.7500e-02   3.68224314e-01   1.37290240e-02
+3.2500e-02   3.44132413e-01   1.37151250e-02
+3.7500e-02   3.32971286e-01   1.44902500e-02
+4.2500e-02   3.18970437e-01   1.53267661e-02
+4.7500e-02   3.04855619e-01   1.63178134e-02
+

Power-Series/maclaurin.R

@@ -21,34 +21,22 @@
   # of statistical models for a given set of data.  Since AIC estimates the quality 
   # of each model, AIC provides a tool for model selection even for non-nested 
   # models.
-  #
-  # In this code,  we make use of the Akaike information criterion to decide 
-  # the Maclaurin series to use for describing a given set of data.   
-  #
-  # Pick the data to read
-  #
-  #  readinteger <- function()
-  #{ 
-  #  n <- readline(prompt="Enter an integer: ")
-  #  if(!grepl("^[0-9]+$",n))
-  #  {
-  #    return(readinteger())
-  #  }
   # 
-  # return(as.integer(n))
-  #}
-  #print(readinteger())
-  #print(n)
-  #
   # Read the data
   #
   #data <- read.table("data/ge-mainz14.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
-  data <- read.table("data/ge-lowQ2.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
+  #data <- read.table("data/ge-lowQ2.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
+  #data <- read.table("data/park2.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
   #data <- read.table("data/default.dat", header=TRUE,  col.names = c("x", "y", "dy"))
   #data <- read.table("data/PRAD-projected.dat", header=TRUE,  col.names = c("x", "y", "dy"))
+  data <- read.table("data/PRAD.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
   #data <- read.table("data/ge.dat", header=FALSE ,  col.names = c("x", "y", "dy"))
   #data <- read.table("data/Bevington.dat", header=TRUE ,  col.names = c("x", "y", "dy"))
   #
+  message("")
+  message("Data has been read.")
+  message("")
+  
   # Define the mininum model.
   #
   min.model <- lm(data$y ~ data$x, weight=1/data$dy^2)