Changes between Version 22 and Version 23 of OfficialTolArchiveNetworkQltvRespModel

Timestamp:

Jan 18, 2011, 3:41:33 PM (14 years ago)

Author:

Víctor de Buen Remiro

Comment:

--

OfficialTolArchiveNetworkQltvRespModel

@@ -15 +15 @@
 example, it can be very usefull to handle with data extrated from an stratified 
 sample.
-
-This class implements max-likelihood estimation by means of package 
-[wiki:OfficialTolArchiveNetworkNonLinGloOpt NonLinGloOpt] and bayesian simulation
-using [wiki:OfficialTolArchiveNetworkBysSampler BysSampler].
 
 Let be 
@@ -61 +57 @@
 are compatible with lower and upper bounds [[BR]] [[BR]]
 [[LatexEquation( A \beta \le a )]] [[BR]] [[BR]]
+
+This class implements max-likelihood estimation by means of package 
+[wiki:OfficialTolArchiveNetworkNonLinGloOpt NonLinGloOpt] and bayesian simulation
+using [wiki:OfficialTolArchiveNetworkBysSampler BysSampler].
+
+The only mandatory members are the matrices of output and input of the regression
+{{{
+#!cpp
+  //Output vector 0 o 1 (mx1)
+  VMatrix y;
+  //Input matrix (mxn)
+  VMatrix X;
+}}}
+You can also specify these other members:
+{{{
+#!cpp
+  //Weights  vector (mx1), default values are 1
+  VMatrix w=Rand(0,0,0,0);
+  //Name of output
+  Text output.name = "";
+  //Names of input variables
+  Set input.name = Copy(Empty);
+  //Set of BysMcmc::@Bsr.TruncatedNormal
+  Set prior = Copy(Empty);
+  //Constraining matrices A*b<=a
+  //Constraining coefficient matrix
+  VMatrix A=Rand(0,0,0,0); 
+  //Constraining border vector
+  VMatrix a=Rand(0,0,0,0); 
+
+
+}}}
 
 === Weighted Logit Regression ===