modelsCompare            package:accuracy            R Documentation

_F_u_n_c_t_i_o_n_s _f_o_r _c_o_m_p_a_r_i_n_g _r_e_s_u_l_t_s: _L_R_E, _c_o_m_p_a_r_e, _a_g_r_e_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Use these functions to compare a set of results from an analysis,
     to check if they  agree to a certain number of significant digits.

_U_s_a_g_e:

             # compare numeric values against correct values
             LRE(x,correct, use.LAE=TRUE, cutoff=16)

             # compare the results from two possibly identical models
             modelsAgree(model, model2=NULL, digits=3, ...)
             modelsCompare(model, model2=NULL, param.function=modelBetas,
                                      similarity.function=LRE, summary.function=min)
                                                             
             # extract betas or entire coefficient summaries
             modelBetas(model)
             modelSummary(model)

_A_r_g_u_m_e_n_t_s:

       x: vector of computed values

 correct: vector of correct values

 use.LAE: use log absolute error if $c_i$==0. If false, returns NA
          where $c_i$==0

  cutoff: In LRE results, convert INF to cutoff. Since perfect
          agreement yields an infinite LRE otherwise. Default value of
          16 is based on the number of significant digits for doubles.

   model: first model for comparison, or if model2=NULL, supply a list 
          of models for comparison

  model2: second model for comparison, if 

  digits: number of digits to use for comparison

param.function: function used to extract model parameters for
          comparison

similarity.function: function used to compute similarity between sets
          of model parameters

summary.function: function used to summarize differences

     ...: parameters to pass from modelsAgree to modelsCompare

_D_e_t_a_i_l_s:

     modelsAgree is a convenienence function that calls modelsCompare
     to summarize similarities between models results.

_V_a_l_u_e:

     Returns a new vector of log-relative-errors (log absolute error
     where $c_i$ ==0). The resulting values are roughly interpretable
     as the number of significant digits of agreement between c and x.
     Larger numbers indicate that x is a relatively more accurate
     approximation of c. Numbers less than or equal to zero indicate
     complete disagreement between x and c.

_A_u_t_h_o_r(_s):

     Micah Altman Micah_Altman@harvard.edu <URL:
     http://www.hmdc.harvard.edu/micah_altman/>, Michael McDonald

_R_e_f_e_r_e_n_c_e_s:

     Altman, M., J. Gill and M. P. McDonald.  2003.  _Numerical Issues
     in Statistical Computing for the Social Scientist_.  John Wiley &
     Sons. <URL: http://www.hmdc.harvard.edu/numerical_issues/>

_S_e_e _A_l_s_o:

     See Also as 'ttst'

_E_x_a_m_p_l_e_s:

     # simple LRE examples
     LRE(1.001,1) # roughly 3 significant digits agreement
     LRE(1,1) # complete agreement
     LRE(20,1) # complete disagreement

     # compare two models

     if (is.R()) {
      hasNmle=require(nlme,quietly=TRUE) 
     }  else {
       hasNmle=require(nlme3,quietly=TRUE)
     }

     if (hasNmle) { 
       O2<-Orthodont
       O3<-Orthodont
       
       O2[,2]<-O2[,2]+.1                                    
       O3[,2]<-O2[,2]+.11 
       if (is.R()) {                                   
         fm1<- lmList(Orthodont)
         fm2<- lmList(O2)
         fm3<- lmList(O2)
       } else {
         # workaround bug in lmlist in S-Plus
         fm1<- eval(substitute(lmList(Orthodont)))
         fm2<- eval(substitute(lmList(O2)))
         fm3<- eval(substitute(lmList(O2)))
       }

       # do the three models agree? 
       print(modelsAgree(list(fm1,fm2,fm2)))

       # show details of diagreement between first 2 models
       print(modelsCompare(fm1,fm2))

       #compare betas at 2 significant digits
       print(modelsAgree(fm1,fm2,digits=2,param.function=modelBetas))
       #compare betas at 1 significant digit
       print(modelsAgree(fm1,fm2,digits=1,param.function=modelBetas))
     }

