stmodelGLM-class | R Documentation |

`"stmodelGLM"`

This is the stepp model for data arising from the following Generalized Linear Models: 1. gaussian with identity link, 2. binomial with logit link, and 3. Poisson with log link.

One can specify additional covariates in the model using `model.matrix`

in R.

The new method returns the stmodelGLM object.

The estimate method returns a list with the following fields:

`model` |
the stepp model - "GLMGe" - Gaussian model, "GLMBe" - binomial model, and "GLMPe" - Poisson model |

`sObs1` |
a vector of effect estimates of all subpopulations based on the first treatment |

`sSE1` |
a vector of standard errors of effect estimates of all subpopulations based on the first treatment |

`oObs1` |
effect estimate of the entire population based on the first treatment |

`oSE1` |
the standard error of the effect estimate of the entire population based on the first treatment |

`sObs2` |
a vector of effect estimates of all subpopulations based on the first treatment |

`sSE2` |
a vector of standard errors of effect estimates of all subpopulations based on the first treatment |

`oObs2` |
effect estimate of the entire population based on the first treatment |

`oSE2` |
the standard error of the effect estimate of the entire population based on the first treatment |

`sglmw` |
Wald's statistics for the effect estimate differences between the two treatments |

`RD` |
a vector of effect estimates difference of all subpopulations between the two treatments |

`RDSE` |
a vector of the standard error effect estimates difference of all subpopulations between the two treatments |

`ORD` |
overall difference of effect estimates of the entire population between the two treatments |

`ORDSE` |
the standard error of the overall difference of the effect estimates of the entire population between the two treatments |

`logR` |
a vector of log ratio of effect estimates of all subpopulations between the two treatments |

`logRSE` |
a vector of standard error of log ratio of effect estimates of all subpopulations between the two treatments |

`ologR` |
log ratio of effect estimates of the entire population between the two treatments |

`ologRSE` |
the standard error of the log ratio of effect estimates of the entire population between the two treatments |

`sglmlogrw` |
Wald's statistics for the log ratio of effect estimates between the two treatments |

The test method returns a list with the following fields:

`model` |
the stepp model - "GLMt" |

`sigma` |
the covariance matrix for subpopulations based on effect differences |

`hasigma` |
the homogeneous association covariance matrix for subpopulations based on effect differences |

`HRsigma` |
the covariance matrix for the subpopulations based on hazard ratio |

`haHRsigma` |
the homogeneous association covariance matrix for subpopulations based on hazard ratio |

`pvalue` |
the supremum pvalue based on effect difference |

`chi2pvalue` |
the chisquare pvalue based on effect difference |

`hapvalue` |
the homogeneous association pvalue based on effect difference |

`HRpvalue` |
the supremum pvalue based on hazard ratio |

`haHRpvalue` |
the homogeneous association pvalue based on hazard ratio |

Objects can be created by calls of the form `new("stmodelGLM", ...)`

or by

the construction function stmodel.GLM.

`coltrt`

:Object of class

`"numeric"`

the treatment variable`colY`

:Object of class

`"numeric"`

a vector containing the outcome`trts`

:Object of class

`"numeric"`

a vector containing the codes for the 2 treatment groups, first and second treatment groups, respectively`MM`

:Object of class

`"ANY"`

a model matrix for extra adjustment covariates; default is NULL

currently, stepp can only support covariates with numeric values, no logical values or factors allowed

these values need to be converted to numeric with some encoding schemes`glm`

:Object of class

`"character"`

the glm to be used for analysis: "gaussian", "binomial", "poisson"`link`

:Object of class

`"character"`

the link function; reserved for future use

Class `"stmodel"`

, directly.

- estimate
`signature(.Object = "stmodelGLM")`

:

estimate the effect in absolute and relative scale of the overall and each subpopulation`signature(.Object = "stmodelGLM")`

:

print the estimate, covariance matrices and statistics- test
`signature(.Object = "stmodelGLM")`

:

perform the permutation tests or GEE and obtain various statistics

Wai-Ki Yip

`stwin`

, `stsubpop`

, `stmodelKM`

,
`stmodelCI`

,
`steppes`

, `stmodel`

,
`stepp.win`

, `stepp.subpop`

, `stepp.KM`

,
`stepp.CI`

, `stepp.GLM`

,
`stepp.test`

, `estimate`

, `generate`

```
showClass("stmodelGLM")
```

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