Wald Test

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Description

waldTest allows to perform a Wald test of certain hypotheses on the parameters of models fitted under a freedom equation formulation for both complete and missing data.

Usage

1
waldTest(obj, C, C0)

Arguments

obj

object of the class linML, loglinML or funlinWLS.

C

full rank matrix with number of columns equal to the number of parameters of obj.

C0

a vector of constants with dimension equal to the number of rows of C; by default, it is a vector of zeros.

Details

waldTest performs a test for hypotheses of the type H:C%*%Beta=C0, where Beta are the parameters of the model in obj. The generic function print is used to print the results.

Author(s)

Frederico Zanqueta Poleto(frederico@poleto.com)
Julio da Motta Singer (jmsinger@ime.usp.br)
Carlos Daniel Paulino (daniel.paulino@math.ist.utl.pt)
with the collaboration of
Fabio Mathias Correa (fmcorrea@uesc.br)
Enio Galinkin Jelihovschi (eniojelihovs@gmail.com)

References

Paulino, C.D. e Singer, J.M. (2006). Analise de dados categorizados (in Portuguese). Sao Paulo: Edgard Blucher.

Poleto, F.Z. (2006). Analise de dados categorizados com omissao (in Portuguese). Dissertacao de mestrado. IME-USP. http://www.poleto.com/missing.html.

Poleto, F.Z., Singer, J.M. e Paulino, C.D. (2007). Analyzing categorical data with complete or missing responses using the Catdata package. Unpublished vignette. http://www.poleto.com/missing.html.

Poleto, F.Z., Singer, J.M. e Paulino, C.D. (2012). A product-multinomial framework for categorical data analysis with missing responses. To appear in Brazilian Journal of Probability and Statistics. http://imstat.org/bjps/papers/BJPS198.pdf.

Singer, J. M., Poleto, F. Z. and Paulino, C. D. (2007). Catdata: software for analysis of categorical data with complete or missing responses. Actas de la XII Reunion Cientifica del Grupo Argentino de Biometria y I Encuentro Argentino-Chileno de Biometria. http://www.poleto.com/SingerPoletoPaulino2007GAB.pdf.

Examples

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#Example 11.7 of Paulino and Singer (2006)
e117.TF<-rbind(c(28,40,68),
               c( 5,21,49),
   	           c( 1, 4,15))

e117.catdata<-readCatdata(TF=e117.TF)
e117.A<-kronecker(diag(3),cbind(diag(2),rep(-1,2)))
e117.X1<-rbind(c(1,0,0,0),
               c(0,1,0,0),
   	           c(1,0,2,0),
       	       c(0,1,1,0),
           	   c(1,0,0,2),
               c(0,1,0,1))

e117.loglinwls1<-funlinWLS(model=c("lin","log"), obj=e117.catdata, 
	A1=e117.A,XL=e117.X1)
waldTest(obj=e117.loglinwls1,C=cbind(0*diag(2),diag(2)))