title: How to interpret oglmx function in R programming?
body: <p>am currently working on a project wherein am supposed to model public acceptance on pricing schemes.
  The independent variables being used for model:- Age, gender,income etc... which are categorical in nature, so I converted them into factored variables using as.factor() function.</p>
  
  <pre><code>Age Gender Income
  0     1      2
  0     0      0 
  0     0      1
  </code></pre>
  
  <p>I have certain other variables like Transit satisfaction, Environment improvement etc... which are ordered factors on scale of 1 to 5 . 1 being extremely dissatisfied and 5 being very satisfied.</p>
  
  <p>My model is as follows :- </p>
  
  <pre><code>mdl = oglmx( prcing ~Ann_In1+Edu+Env_imp+rs_imp,data=cpdat, link = "logit", constantMEAN = F, constantSD = F, delta = 0, threshparam = NULL)
  summary(mdl)
  </code></pre>
  
  <hr>
  
  <pre><code>            Estimate   Std. error   t value      Pr(&gt;|t|)    
  
  Ann_In11   0.1605540    0.3021613   0.5314      0.5951749   
  
  Ann_In12  -0.9556992     0.4218504 -2.2655      0.0234824 *  
  
  Edu1       0.0710699     0.2678081  0.2654       0.7907196
  
  
  Edu2       1.0732587    0.7112519   1.5090       0.1313061         
  
  Env_imp.L -0.8524288    0.4899275  -1.7399      0.0818752 .  
  
  Env_imp.Q  0.0784353     0.3936332      0.1993          0.8420595    
  
  Env_imp.C  0.4589036      0.4498676     1.0201          0.3076878    
  
  Env_imp^4 -0.2219108       0.4423486    -0.5017         0.6159032    
  
  rd_sft.L   2.6335035      0.7362206      3.5771         0.0003475 ***
  
  rd_sft.Q  -0.7064391     0.5773880      -1.2235          0.2211377    
  
  rd_sft.C   0.0130127     0.4408486       0.0295         0.9764519    
  
  rd_sft^4  -0.2886550       0.3582014     -0.8058         0.4203318  
  </code></pre>
  
  <p>I obtained the results as below. Am unable to interpret the results. Any leads in this can be very helpful.
  In case of rd_sft (road safety ) as rd_sft.L (linear) is signiicant than other levels, can we neglect the other levels i.e Q,C,^4 in model formation ??
  please through some light on model formulation and its intepretation as i am new to R.</p>