Description Usage Arguments Value Note See Also Examples

This function fits a joint generalized estimating equation model to multivariate longitudinal data with mono-type or mixed responses where the regression coefficients are response-specific.

1 2 3 4 5 | ```
JGee2(formula, id, data, nr, na.action = NULL,
family = list(gaussian(link = "identity"), gaussian(link = "identity")),
corstr1 = "independence", Mv = NULL, corstr2 = "independence",
beta_int = NULL, R1 = NULL, R2 = NULL, scale.fix = FALSE, scale.value = 1,
maxiter = 25, tol = 10^-3, silent = FALSE)
``` |

`formula` |
A formula expression in the form of |

`id` |
A vector for identifying subjects. |

`data` |
A data frame which stores the variables in |

`nr` |
Number of multiple responses. |

`na.action` |
A function to remove missing values from the data. Only |

`family` |
A |

`corstr1` |
A character string, which specifies the type of within-subject correlation structure.
Structures supported in |

`Mv` |
If either |

`corstr2` |
A character string, which specifies the type of multivariate response correlation structure.
Structures supported in |

`beta_int` |
User specified initial values for regression parameters. The default value is |

`R1` |
If |

`R2` |
If |

`scale.fix` |
A logical variable; if true, the scale parameter is fixed at the value of |

`scale.value` |
If |

`maxiter` |
The number of iterations that is used in the estimation algorithm. The default value is |

`tol` |
The tolerance level that is used in the estimation algorithm. The default value is |

`silent` |
A logical variable; if true, the regression parameter estimates at each iteration are
printed. The default value is |

An object class of `JGee2`

representing the fit.

The structures `"non_stat_M_dep"`

and `"unstructured"`

are valid only when the data is balanced.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ```
## Not run:
#check the data
data(MSCMsub)
#rename it
mydata=MSCMsub
#check the column labels for formula object
head(mydata)
#prepare formula object before model fitting
formulaj2=cbind(stress,illness)~chlth+csex+education+employed+housize+married+mhlth+race
#prepare family object before model fitting
familyj2=list(binomial(link="logit"),binomial(link="logit"))
#fit the model
fitjgee2=JGee2(formula=formulaj2,id=mydata[,1],data=mydata,nr=2,na.action=NULL,
family=familyj2, corstr1="exchangeable", Mv=NULL, corstr2="unstructured",
beta_int=rep(0,18), R1=NULL, R2=NULL, scale.fix=FALSE, scale.value=1, maxiter=30,
tol=10^-3, silent=FALSE)
#check the object names returned by fitjgee2
names(fitjgee2)
#check the object names returned by summary(fitjgee2)
names(summary(fitjgee2))
#get the coefficients
summary(fitjgee2)$coefficients
#get the within-subject correlation matrix
summary(fitjgee2)$working.correlation1
#get the multivariate response correlation matrix
summary(fitjgee2)$working.correlation2
#get the overall working correlation matrix
summary(fitjgee2)$working.correlation
## End(Not run)
``` |

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