bdotsBoot | R Documentation |

Creates bootstrapped curves and performs alpha adjustment. Can perform "difference of difference" for nested comparisons

bdotsBoot( formula, bdObj, Niter = 1000, alpha = 0.05, padj = "oleson", cores = 0, ... )

`formula` |
See details. |

`bdObj` |
An object of class 'bdotsObj' |

`Niter` |
Number of iterations of bootstrap to draw |

`alpha` |
Significance level |

`padj` |
Adjustment to make to pvalues for significance. Will be able to
use anything from |

`cores` |
Number of cores to use in parallel. Default is zero, which uses half of what is available. |

`...` |
not used |

The formula is the only tricky part of this. There will be a minor
update to how it works in the future. The three parts we will examine here
are Groups, the LHS, and the RHS. For all variable names, special characters
should be included with backticks, i.e., ``my-var``

## Groups

The Groups are the values input in `group`

in the `bdotsFit`

function,
which are columns of the dataset used. These will be denoted G_i
Within each group, we will designate the unique values within each group as v_j, ..., whereby
G_i(v_1, v_2) will designate unique two unique values within G_i. The possible
values of v_i will be implied by the group with which they are associated.

For example, if we have groups `vehicle`

and `color`

, we could specify
that we are interested in all blue cars and trucks with the expression
`vehicle(car, truck) + color(red)`

.

## Formula

### Bootstrapped difference of curves

This illustrates the case in which we are taking a simple bootstraped difference between two curves within a single group

If only one group was provided in `bdotsFit`

, we can take the bootstrapped
difference between two values within the group with

`y ~ Group1(val1, val2)`

If more than two groups were provided, we must specify within which values of the other groups we would like to compare the differences from Group1 in order to uniquely identify the observations. This would be

`y ~ Group1(val1, val2) + Group2(val1)`

For example, bootstrapping the differences between cars and trucks when `color`

was provided as a second group, we would need `y ~ vehicle(car, truck) + color(red)`

.

### Bootstrapped difference of difference curves

This next portion illustrates the case in which we are interested in studying the difference between the differences between two groups, which we will call the innerGroup and the outerGroup following a nested container metaphor. Here, we must use caution as the order of these differences matter. Using again the vehicle example, we can describe this in two ways:

We may be interested in comparing the difference between red trucks and cars (d_red) with the difference between blue trucks and cars (d_blue). In this case, we will be finding the difference between cars and trucks twice (one for blue, one for red). The vehicle type is the innerGroup, nested within the outerGroup, in this case, color.

We may also be interested in comparing the difference between red trucks and blue trucks (d_truck) with the difference between red and blue cars (d_car). Here, innerGroup is the color and outerGroup is the vehicle

As our primary object of interest here is not the difference in outcome itself, but the difference
of the outcome within two groups, the LHS of the formula is written
`diffs(y, Group1(val1, val2))`

, where Group1 is the innerGroup. The RHS
is then used to specify the groups of which we want to take the inner difference of. The
syntax here is the same as above. Together, then, the formula looks like

`diffs(y, Group1(val1, val2)) ~ Group2(val1, val2)`

in the case in which only two grouping variables were provided to `bdotsFit`

and

`diffs(y, Group1(val1, val2)) ~ Group2(val1, val2) + Group3(val1) + ...`

is used to uniquely identify the sets of differences when three or more groups were provided.

Object of class 'bdotsBootObj'

## Not run: ## fit <- bdotsFit(cohort_unrelated, ...) boot1 <- bdotsBoot(formula = diffs(Fixations, LookType(Cohort, Unrelated_Cohort)) ~ Group(50, 65), bdObj = fit, N.iter = 1000, alpha = 0.05, p.adj = "oleson", cores = 4) boot2 <- bdotsBoot(formula = Fixations ~ Group(50, 65) + LookType(Cohort), bdObj = fit, N.iter = 1000, alpha = 0.05, p.adj = "oleson", cores = 4) ## End(Not run)

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