@M.Hemanth wrote:
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. Age Gender Income 0 1 2 0 0 0 0 0 1 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.
My model is as follows :-
mdl = oglmx( prcing ~Ann_In1+Edu+Env_imp+rs_imp,data=cpdat, link = “logit”, constantMEAN = F, constantSD = F, delta = 0, threshparam = NULL) summary(mdl)
Estimate Std. error t value Pr(>|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
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.
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