
 ########
> str(MC)
'data.frame':   21130 obs. of  10 variables:
 $ male  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ pop   : int  2 2 2 2 2 2 2 2 2 2 ...
 $ lma   : num  4.9 4.9 4.9 4.9 4.9 4.9 4.9 4.9 4.9 4.9 ...
 $ poids : num  3.03 3.03 3.03 3.03 3.03 3.03 3.03 3.03 3.03 3.03 ...
 $ ventre: int  4 4 4 4 4 4 4 4 4 4 ...
 $ dos   : int  3 3 3 3 3 3 3 3 3 3 ...
 $ date  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ mom   : int  1 1 1 1 1 2 2 2 2 2 ...
 $ c     : chr  "no" "no" "no" "no" ...
 $ N     : Factor w/ 5 levels "explo","stress",..: 3 1 4 5 2 3 1 4 5 2 ...

########

"your inputs have redundant/overlapping information."

yes, thas the case, as you can see :

> cor.test(MC$lma,MC$poids)

        Pearson's product-moment correlation

data:  MC$lma and MC$poids
t = 168.5517, df = 21128, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7514854 0.7629881
sample estimates:
      cor
0.7572954
########
"If your system is exactly singular, it means that at least one of the
columns of your 'model matrix'á is some linear combination of the others, which

essentially means your inputs have redundant/overlapping information."

I think that my variables have some redundant information but it seems me that type-II analysis-of-variance permit to bypass this issue. 

 Are your input variables supposed to be factors (categorical) or numeric? 

In the model I show, there is only continues numeric independent variables (lma and poids). The dependent variable is an categorical and unordered.

########
BUT, when that's will work, I'd like add some independent categorical and ordered  variable (it's possible to considerer them as unordered)
Does it imply some issues ??
########


This can also happen if one of the response categories only occurs with a single combination of your
inputs. Runá some tables on the variables in your model; that might help

clarify things.





But, lma and poids are continues variables
> table(MC$lma,MC$poids)
                        
      1.97 2.17 2.35 2.37 2.41 2.46 2.47 2.48 2.54 2.59 2.71  2.8 2.83 2.85
  4    560    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.4    0    0    0    0    0    0    0  475    0    0    0    0    0    0
  4.5    0  520    0    0    0    0    0    0    0    0    0    0    0    0
  4.6    0    0  570    0  560    0  520    0    0    0    0    0    0  475
  4.7    0    0    0    0    0  565    0    0    0    0  570    0    0    0
  4.8    0    0    0    0    0    0 1090    0    0  570    0  560    0    0
  4.9    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  5      0    0    0  560    0    0    0    0    0    0    0    0    0    0
  5.1    0    0    0    0    0    0    0    0    0    0    0    0  570    0
  5.2    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  5.4    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  5.5    0    0    0    0    0    0    0    0  515    0    0    0    0    0
                                                                           
      2.88 2.91    3 3.02 3.03 3.09 3.11 3.15 3.16 3.18  3.2 3.24 3.35  3.4
  4      0    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.4    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.5    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.6    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.7    0    0    0    0    0  475  515    0    0    0    0    0    0    0
  4.8  570    0    0    0    0    0    0    0    0    0    0    0    0    0
  4.9    0  560  515    0 1550    0    0    0    0    0  570  560    0    0
  5      0    0    0  520    0    0    0    0    0    0  570    0  560    0
  5.1    0    0    0    0    0    0    0  520    0    0    0    0    0  520
  5.2    0    0    0    0    0    0    0    0  570  570    0    0    0    0
  5.4    0    0    0    0    0    0    0    0    0    0    0    0    0    0
  5.5    0    0    0    0    0    0    0    0    0    0    0    0    0    0

      3.43 3.67 3.71 3.95    4 4.06
  4      0    0    0    0    0    0
  4.4    0    0    0    0    0    0
  4.5    0    0    0    0    0    0
  4.6    0    0    0    0    0    0
  4.7    0    0    0    0    0    0
  4.8    0    0    0    0    0    0
  4.9    0    0    0    0    0    0
  5      0    0    0    0    0    0
  5.1    0    0    0    0    0    0
  5.2  570  560    0  520    0    0
  5.4    0    0  565    0    0  520
  5.5    0    0    0    0  570    0

> table(MC$poids,MC$N)                  
                                        
       explo stress SS_cache thermo immo
  1.97   112    112      112    112  112
  2.17   104    104      104    104  104
  2.35   114    114      114    114  114
  2.37   112    112      112    112  112
  2.41   112    112      112    112  112
  2.46   113    113      113    113  113
  2.47   322    322      322    322  322
  2.48    95     95       95     95   95
  2.54   103    103      103    103  103
  2.59   114    114      114    114  114
  2.71   114    114      114    114  114
  2.8    112    112      112    112  112
  2.83   114    114      114    114  114
  2.85    95     95       95     95   95
  2.88   114    114      114    114  114
  2.91   112    112      112    112  112
  3      103    103      103    103  103
  3.02   104    104      104    104  104
  3.03   310    310      310    310  310
  3.09    95     95       95     95   95
  3.11   103    103      103    103  103
  3.15   104    104      104    104  104
  3.16   114    114      114    114  114
  3.18   114    114      114    114  114
  3.2    228    228      228    228  228
  3.24   112    112      112    112  112
  3.35   112    112      112    112  112
  3.4    104    104      104    104  104
  3.43   114    114      114    114  114
  3.67   112    112      112    112  112
  3.71   113    113      113    113  113
  3.95   104    104      104    104  104
  4      114    114      114    114  114
  4.06   104    104      104    104  104


> table(MC$lma,MC$N)

      explo stress SS_cache thermo immo
  4     112    112      112    112  112
  4.4    95     95       95     95   95
  4.5   104    104      104    104  104
  4.6   425    425      425    425  425
  4.7   425    425      425    425  425
  4.8   558    558      558    558  558
  4.9   751    751      751    751  751
  5     442    442      442    442  442
  5.1   322    322      322    322  322
  5.2   558    558      558    558  558
  5.4   217    217      217    217  217
  5.5   217    217      217    217  217




Date: Fri, 5 Feb 2010 05:36:12 -0800
Subject: Re: [R] mlogit
From: djmuser@gmail.com
To: hugomh@hotmail.fr

Hi:

On Fri, Feb 5, 2010 at 5:23 AM, hugo Mathe <hugomh@hotmail.fr> wrote:



Hi,

I'm using the function "mlogit" from the package "mlogit" in order to make a multinomial model, with random and nested effect*. But, currently, even a basic model as



> mlogit(c ~ lma + poids , MC, shape = "long", alt.var = "N")

Erreur dans drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) :

 ásous-programme Lapack dgesv : le systŢme est exactement singulier



which is the same thing than

Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) :

"Lapack routine dgesv: system is exactly singular"

If your system is exactly singular, it means that at least one of the
columns of your 'model matrix'á is some linear combination of the others, which

essentially means your inputs have redundant/overlapping information. Are
your input variables supposed to be factors (categorical) or numeric? Type
str(MC) to view the class of each of the variables in your data frame to see

whether your expectations match reality. This can also happen if one of
the response categories only occurs with a single combination of your
inputs. Runá some tables on the variables in your model; that might help

clarify things.

HTH,
Dennis



I didn't find any explanations on the web ...



my data look like this



> head(MC)

 ámale pop lma poids ventre dos date mom á c á á áN

1 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á1 á á á áno áSS_cache

2 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á1 á á á áno á á á áexplo

3 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á1 á á á áno á á thermo

4 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á1 á á á áno á á á áimmo

5 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á1 á á á áno á á á ástress

6 á á1 á 2 á á4.9 á3.03 á á á4 á á 3 á á á1 á á2 á á á yes á SS_cache



somes data of the Examples (in ?mlogit) look like mine



> head(TravelMode)

 áindividual ámode choice wait vcost travel gcost income size avincome á á time

1 á á á á á1 á air á á no á 69 á á59 á á100 á á70 á á3.5 á á1 á á á 35 2.816667

2 á á á á á1 train á á no á 34 á á31 á á372 á á71 á á3.5 á á1 á á á á0 6.766667

3 á á á á á1 á bus á á no á 35 á á25 á á417 á á70 á á3.5 á á1 á á á á0 7.533333

4 á á á á á1 á car á áyes á á0 á á10 á á180 á á30 á á3.5 á á1 á á á á0 3.000000

5 á á á á á2 á air á á no á 64 á á58 á á 68 á á68 á á3.0 á á2 á á á 30 2.200000

6 á á á á á2 train á á no á 44 á á31 á á354 á á84 á á3.0 á á2 á á á á0 6.633333

 á timeair

1 2.816667

2 0.000000

3 0.000000

4 0.000000

5 2.200000

6 0.000000



and

> mlogit(choice ~ time + timeair , TravelMode, shape = "long", alt.var = "mode")



Call:

mlogit(formula = choice ~ time + timeair, data = TravelMode, á á shape = "long", alt.var = "mode", method = "nr", print.level = 0)



Coefficients:

alttrain á áaltbus á áaltcar á á átime á timeair

á-3.6776 á -4.2339 á -4.5232 á -0.5963 á -2.7048



work prefectly



so, I don't understant any thing, ...

Is anybody understand why this function didn't work on my data ?



MANY THANKS





hugo mathÚ hubertEmail : hugomh@hotmail.fr



PS: * if anybody know how to consider interaction between 2 nested factors, I'm interested ...

(it is "a:b in (A:B)" if a is nested in A and b nested in B ?)



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