[R] Running multidimensional regressions

[David Winsemius]

On Jan 13, 2014, at 4:28 PM, andrews Nikolaiv wrote:

> 
> 
> 
> Dear R helpers!,
> 
> I have a question on how to run a regression with many indices.
> To give you a practical example,
> 
> let
> y_{itabp} be an dependent variable (representing   prices) indexed by 
> i=country, t=time,  a=area, b=brand and p=package size.
> 
> In 
> particular, we  collected prices on the product "cereals"  from  i=1...,I countries 
> over a period of t=1,...,T_{i} months. For example, for Italy we  have 
> price information over24 months whereas for Germany we  have  price 
> information over 36 months.
> For each country, we have price 
> information  by area (a=1,...,A_{i}- for example, for Italy we  have 
> price information for 5 areas whereas for Germany we  have  price 
> information for 9  areas). 
> For each area we have  information on prices by brand (b=1,...,4 )
> Finally, for each brand prices are broken down by package size (p=1,2,3)
> 
> I want to run a semiparametric regression to see the effect of package size on   y_{itcabp}  
> I display  a sample of my data
> 
> 
> 
> 
> 
> 
>  Country
>  Area
>  brand
>  packsize
>  dates
>  price
>  Package_size
> 
> 
>  AA
>  A1
>  b1
>  ps1
>  01/11/2008
>  1.760342
>  0.075
> 
> 
>  AA
>  A1
>  b1
>  ps1
>  01/12/2008
>  1.786739
>  0.075
> 
> 
>  AA
>  A1
>  b1
>  ps2
>  01/11/2008
>  1.725466
>  0.075
> 
> 
>  AA
>  A1
>  b1
>  ps2
>  01/12/2008
>  1.678327
>  0.075
> 
> 
>  AA
>  A1
>  b1
>  ps3
>  01/11/2008
>  1.941369
>  0.075
> 
> 
>  AA
>  A1
>  b1
>  ps3
>  01/12/2008
>  1.874848
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps1
>  01/11/2008
>  21.49573
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps1
>  01/12/2008
>  22.40766
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps2
>  01/11/2008
>  23.44514
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps2
>  01/12/2008
>  23.1251
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps3
>  01/11/2008
>  22.14254
>  0.075
> 
> 
>  AA
>  A2
>  b2
>  ps3
>  01/12/2008
>  21.04197
>  0.075
> 
> 
>  BB
>  A1
>  b1
>  ps1
>  01/01/2009
>  17.38787
>  0.05
> 
> 
>  BB
>  A1
>  b1
>  ps1
>  01/02/2009
>  18.45013
>  0.05
> 
> 
>  BB
>  A1
>  b1
>  ps2
>  01/01/2009
>  17.59772
>  0.05
> 
> 
>  BB
>  A1
>  b1
>  ps2
>  01/02/2009
>  18.41634
>  0.05
> 
> 
>  BB
>  A1
>  b1
>  ps3
>  01/01/2009
>  18.55188
>  0.05
> 
> 
>  BB
>  A1
>  b1
>  ps3
>  01/02/2009
>  19.08645
>  0.05
> 
> 
> I also created the variables
> 
> countryN that takes 1 for AA, 2 for BB etc,
> AreaN  that takes 1  for A1, 2 for A2, etc,
> brandN   that takes 1 for b1, 2 for b2 etc,
> packsizeN that takes 1 for ps1, 2 for ps2 etc,
> timeN that takes 1 for 01/11/2008 or 01/01/2009 and  2 for 01/12/2008 or 01/02/2009
> I, then, run
> 
> data<- read.csv("cereals.csv")
> rm(list=ls())

Wouldn't that destroy all your work to read from "cereals.csv"?

> foo <- read.csv("cereals.csv")
> attach(foo)
> require(np)
> model <- npreg(price~factor(Package_size)+factor(timeN)+factor(countryN)+factor(AreaN)+ordered(brandN)+ordered(packsizeN))
> summary(model)
> plot(model,common.scale=FALSE)
> 
> 
> Do you think that these commands serve my goal (to estimate the effect of package size on   y_{itcabp})?
> 
> Any code provided is greatly appreciated.
> 
> Thank you very much in advance,
> 
> andrews
> 
> 
> 		 	   		  
> 	[[alternative HTML version deleted]]

You should post in plain text.

> 
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David Winsemius
Alameda, CA, USA