exporting results

[alice2016]

Hi,

I am doing a very simple two-levels models using R2MLwiN.
I was using the "coef" command to save the coefficient as a csv file.
e.g.:
fixedmodel.coeff <- coef(fixedmodel)
write.csv(fixedmodel.coeff , file="modelX.csv")

However, I would also like to save the number of observation, std. error, deviance statistics, and the p-values.

Can anyone help me with that?

Cheers,

Alice

[ChrisCharlton]

We don't currently provide an automated way to extract this information together so you will need to extract it from the returned model object. If for example you had run the following:

Code: Select all

> require(R2MLwiN)
> data(tutorial)
> (mymodel <- runMLwiN(normexam ~ 1 + (1 | student), data = tutorial))


-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 
MLwiN (version: unknown or >2.35)  multilevel model (Normal) 
Estimation algorithm:  IGLS        Elapsed time : 0.55s 
Number of obs:  4059 (from total 4059)        The model converged after 3 iterations.
Log likelihood:      -5754.7 
Deviance statistic:  11509.4 
--------------------------------------------------------------------------------------------------- 
The model formula:
normexam ~ 1 + (1 | student)
Level 1: student      
--------------------------------------------------------------------------------------------------- 
The fixed part estimates:  
               Coef.   Std. Err.       z   Pr(>|z|)         [95% Conf.   Interval] 
Intercept   -0.00011     0.01568   -0.01     0.9942           -0.03084     0.03061 
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  
--------------------------------------------------------------------------------------------------- 
The random part estimates at the student level: 
                  Coef.   Std. Err. 
var_Intercept   0.99764     0.02215 
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*- 

You can extract the number of observations with:

Code: Select all

> nobs(mymodel)
[1] 4059

You can obtain the standard errors with:

Code: Select all

> diag(sqrt(vcov(mymodel)))
     FP_Intercept RP1_var_Intercept 
       0.01567755        0.02214525 

and the deviance statistics with:

Code: Select all

> logLik(mymodel)
'log Lik.' -5754.683 (df=2)
> deviance(mymodel)
[1] 11509.37

Finally you can obtain the p-values with:

Code: Select all

> 2 * pnorm(abs(coef(mymodel) / diag(sqrt(vcov(mymodel)))), lower.tail = FALSE)
     FP_Intercept RP1_var_Intercept 
        0.9942026         0.0000000

If you would like to extract any of the other quantities that are reported you can find how to do this by looking at the functions in the following file: https://github.com/rforge/r2mlwin/blob/ ... nfitIGLS.R. You can see what information related to a model is stored with the following:

Code: Select all

> str(mymodel)
Formal class 'mlwinfitIGLS' [package "R2MLwiN"] with 20 slots
  ..@ version     : chr "MLwiN (version: unknown or >2.35)"
  ..@ Nobs        : num 4059
  ..@ DataLength  : num 4059
  ..@ Hierarchy   : NULL
  ..@ D           : chr "Normal"
  ..@ Formula     :Class 'formula' length 3 normexam ~ 1 + (1 | student)
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  ..@ levID       : chr "student"
  ..@ FP          : Named num -0.000114
  .. ..- attr(*, "names")= chr "FP_Intercept"
  ..@ RP          : Named num 0.998
  .. ..- attr(*, "names")= chr "RP1_var_Intercept"
  ..@ RP.cov      : num [1, 1] 0.00049
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr "RP1_var_Intercept"
  .. .. ..$ : chr "RP1_var_Intercept"
  ..@ FP.cov      : num [1, 1] 0.000246
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr "FP_Intercept"
  .. .. ..$ : chr "FP_Intercept"
  ..@ LIKE        : num 11509
  ..@ elapsed.time: Named num 0.55
  .. ..- attr(*, "names")= chr "elapsed"
  ..@ call        : language runMLwiN(Formula = normexam ~ 1 + (1 | student), data = tutorial)
  ..@ residual    : list()
  ..@ Converged   : logi TRUE
  ..@ Iterations  : num 3
  ..@ Meth        : num 1
  ..@ nonlinear   : num [1:2] 0 1
  ..@ data        :'data.frame':        4059 obs. of  3 variables:
  .. ..$ normexam : num [1:4059] 0.261 0.134 -1.724 0.968 0.544 ...
  .. ..$ student  : Factor w/ 198 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
  .. ..$ Intercept: num [1:4059] 1 1 1 1 1 1 1 1 1 1 ...

[alice2016]

Thank you very much!
This is already very helpful.