add1.speedlm | R Documentation |

`add1`

and `drop1`

methods for speedlm and speedglm objectsThese are adviced to be used for `speedlm`

and `speedglm`

models fitted on moderately large data sets. It is also possible to use stepAIC function from package `MASS`

.

```
## S3 method for class 'speedlm'
## S3 method for class 'speedlm'
add1(object, scope, scale = 0, test = c("none", "Chisq","F"),
x = NULL, k = 2, data, ...)
## S3 method for class 'speedlm'
drop1(object, scope, scale = 0, all.cols = TRUE,
test = c("none","Chisq", "F"), k = 2, data, ...)
## S3 method for class 'speedlm'
extractAIC(fit, scale = 0, k=2,...)
## S3 method for class 'speedlm'
nobs(object, use.fallback = FALSE, ...)
## S3 method for class 'speedglm'
## S3 method for class 'speedglm'
add1(object, scope, scale = 0, test = c("none", "LRT",
"Chisq", "F"), x = NULL, k = 2, ...)
## S3 method for class 'speedglm'
drop1(object, scope, scale = 0, test = c("none", "LRT",
"Chisq", "F"), k = 2, ...)
## S3 method for class 'speedglm'
extractAIC(fit, scale = 0, k=2,...)
## S3 method for class 'speedglm'
nobs(object, use.fallback = FALSE, ...)
```

`object` |
a |

`fit` |
a |

`scope` |
see add1 from package |

`scale` |
see add1 from package |

`all.cols` |
see drop1 from package |

`test` |
see add1 from package |

`x` |
see add1 from package |

`k` |
see add1 from package |

`data` |
the data that the model was previously fitted to. If not provided, these will be searched in the parent environment. |

`use.fallback` |
logical. Should fallback methods be used to try to guess the value? |

`...` |
further optional arguments. |

It is possible to use functions step() and stepAIC() for both speedlm and speedglm objects but objects fitted using updateWithMoreData().

An object of classes "`anova`

" and "`data.frame`

" summarizing the differences in fit between the models.

Note that these functions have been poorly tested and need to be checked out more carefully.

Ronen Meiri and Marco Enea

```
set.seed(10)
n <- 50
k <- 3
x <- round(matrix(rnorm(n * k), n, k), digits = 3)
beta <- c(0.05,0.5,0.8,1.3,1.8)
y <- drop(tcrossprod(cbind(1,x,x[,2]*x[,3]),t(beta))) + rnorm(n,,0.2)
colnames(x) <- c("s1", "s2", "s3")
da <- data.frame(y, x)
m0 <- speedlm(y ~ 1, data = da,model=TRUE, y=TRUE)
m0.1 <- add1(m0,scope=~s1+s2+s3, data = da)
m1 <- step(m0,scope=~s1+s2+s3)
m1
```

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