Package 'bdots' reference manual

Title:	Bootstrapped Differences of Time Series
Description:	Analyze differences among time series curves with p-value adjustment for multiple comparisons introduced in Oleson et al (2015) <DOI:10.1177/0962280215607411>.
Authors:	Collin Nolte, Michael Seedorff, Jacob Oleson, Grant Brown, Joseph Cavanaugh, and Bob McMurray
Maintainer:	Collin Nolte <[email protected]>
License:	GPL-3 \| file LICENSE
Version:	1.2.0
Built:	2025-02-01 04:44:32 UTC
Source:	https://github.com/collinn/bdots

Compute AR1 correlation coefficient

Description

Computes value for AR1 correlation coefficient for use in p_adjust

Usage

ar1Solver(t)
ar1Solver(t)

Arguments

`t`	A numeric vector of t-statistics

Value

Estimated AR1 correlation coefficient

Examples

t <- rt(1e3, df = 1)
rho <- ar1Solver(t)


t <- rt(1e3, df = 1)
rho <- ar1Solver(t)

Create bootstrapped curves from bdotsObj

Description

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

Usage

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

Arguments

`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 `p.adjust` function, but for now, just "oleson"
`permutation`	Boolean indicating whether to use permutation testing rather thank adjusting alpha to control FWER. WARNING: This option is very much in beta testing and not recommended for general use. Also not available for paired tests or difference of difference
`cores`	Number of cores to use in parallel. Default is zero, which uses half of what is available.
`...`	not used

Details

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 red 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 outer 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.

Value

Object of class 'bdotsBootObj'

Examples

## 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)

## 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)

Correlation with fixed value in bdots

Description

Find the correlation of a fixed value with the bdots fitted curves at each time point

Usage

bdotsCorr(bdObj, val, ciBands = FALSE, method = "pearson")
bdotsCorr(bdObj, val, ciBands = FALSE, method = "pearson")

Arguments

`bdObj`	Object of class 'bdotsObj'
`val`	Character string of fixed value for correlation in dataset from 'bdotsFit'
`ciBands`	Boolean for including confidence intervals
`method`	Arguments for 'cor' or 'cor.test'. The default option us 'method = "pearson"'

Fit nlme curves to grouped observations

Description

Creates observation level curves to use in bdotsBoot

Usage

bdotsFit(
  data,
  subject,
  time,
  y,
  group,
  curveType = doubleGauss(concave = TRUE),
  cor = TRUE,
  numRefits = 0,
  cores = 0,
  verbose = FALSE,
  ...
)
bdotsFit(
  data,
  subject,
  time,
  y,
  group,
  curveType = doubleGauss(concave = TRUE),
  cor = TRUE,
  numRefits = 0,
  cores = 0,
  verbose = FALSE,
  ...
)

Arguments

`data`	Dataset used
`subject`	Column name of dataset containing subject identifiers
`time`	Column name containing time variable
`y`	Column name containing outcome of interest
`group`	Character vector containing column names of groups. Can be greater than one
`curveType`	See details/vignette
`cor`	Boolean. Autocorrelation?
`numRefits`	Integer indicating number of attempts to fit an observation if the first attempt fails
`cores`	number of cores. Default is `0`, indicating half cores available
`verbose`	currently not used
`...`	Secret

Details

This is step one of the three step bdots process. Things should be more or less straight forward. The only tricky part involves curveType. For now know that one can use doubleGauss(concave = TRUE/FALSE) or logistic(). Should be passed in as a call. See the vignette on customizing this

Value

Object of class 'bdotsObj', inherits from data.table

Examples

## Not run: 
res <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)

## End(Not run)

## Not run: 
res <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)

## End(Not run)

Fits Individual Subject Curve

Description

The one subject version of bdotsFit

Usage

bdotsFitter(
  dat,
  curveType,
  rho,
  numRefits = 0,
  verbose,
  getCovOnly = NULL,
  params = NULL,
  splitVars = NULL,
  datVarNames = NULL,
  ...
)
bdotsFitter(
  dat,
  curveType,
  rho,
  numRefits = 0,
  verbose,
  getCovOnly = NULL,
  params = NULL,
  splitVars = NULL,
  datVarNames = NULL,
  ...
)

Arguments

`dat`	data for single subject/group combo
`curveType`	this is actually a function. Should rename
`rho`	correlation coefficient
`numRefits`	number of refit attempts
`verbose`	not used
`getCovOnly`	only find covariance matrix from starting parameter values
`params`	starting parameters, if wanting to add manually
`splitVars`	variables used to identify group. Might combine with datVarNames
`datVarNames`	character vector indicating reponse and time values from parent call
`...`	not used

Refit Observations Returned from bdotsFit

Description

Refit Observations Returned from bdotsFit

Usage

bdotsRefit(
  bdObj,
  fitCode = 1L,
  quickRefit = FALSE,
  numRefits = 2L,
  paramDT = NULL,
  ...
)
bdotsRefit(
  bdObj,
  fitCode = 1L,
  quickRefit = FALSE,
  numRefits = 2L,
  paramDT = NULL,
  ...
)

Arguments

`bdObj`	An object of class 'bdotsObj' returned from `bdotsFit`
`fitCode`	A length one integer indicating observations to refit. See Details
`quickRefit`	Boolean indicating if a quick refit should be used. If TRUE, rather than prompting the user for adjustments for each observation, `bdotsReft` will jitter the parameters of all observations indicated by `fitCode` and attempt to refit. Between the original and the refitted curve, this will place priority on the higher `fitCode`. If these are equal, R2 will take precedence. Otherwise, the original fit will be kept.
`numRefits`	Integer indicating the number of refit attempts after jittering parameters, either with quickRefit or when done individually
`paramDT`	A `data.table` or `data.frame` that matches the what is returned by `coefWriteout(bdObj)`. That is, it should have columns uniquely identifying observations with subjects and groups, as well as named columns for the paramters. NA parameters are OK. Can also be a subset of the original rows. Note, if this argument is not `NULL`, the remaining arguments will be ignored.
`...`	not used

Details

fitCode indicates lower bound on observations to refit. For example, if fitCode = 4, bdotsRefit will prompt user to refit all observations with fitCode = 4, 5, 6. The quickRit option will attempt to jitter and refit all observations selected by fitCode. Otherwise, the user will be prompted through a menu to individually refit observations

Value

Returns bdObj with updated fits

bdots Remove Function

Description

Remove observations with a specified fitCode and optionally all pairs

Usage

bdRemove(bdObj, fitCode = 6L, removePairs = TRUE)
bdRemove(bdObj, fitCode = 6L, removePairs = TRUE)

Arguments

`bdObj`	bdots object
`fitCode`	min fitCode to remove. Default is 6, which removes all subjects with NULL fits (fitCode = 5 would remove 5 and 6)
`removePairs`	Boolean. Remove subject pairs is one of pair is removed. Default is TRUE to retain paired t-test

Details

This function is used to remove all bdots observations with a fit code equal to or larger than the argument passed to fitCode without refitting. If removePairs = TRUE, all entries for a subject will be removed if their fit failed in any of the groups in which they were a member

ci dataset

Description

ci dataset - need to include details

Usage

ci
ci

Format

An object of class data.frame with 108216 rows and 5 columns.

Extract bdotsFit Moedel Coefficients

Description

Returns coefficient matrix for bdotsFit object

Usage

## S3 method for class 'bdotsObj'
coef(object, ...)
## S3 method for class 'bdotsObj'
coef(object, ...)

Arguments

`object`	A bdotsObj
`...`	not used

Value

Returns matrix of model coefficients for observations in object

Create `data.table` with `bdotsObj` parameters

Description

Creates an object of class data.table that matches parameter values for each observation. This can then be passed to the bdotsRefit function

Usage

coefWriteout(bdObj)
coefWriteout(bdObj)

Arguments

bdObj

An object returned from bdotsFit or bdotsRefit

Value

A data.table matching parameter values to observations

Examples

## Not run: 
fit <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)
parDT <- coefWriteout(fit)

## End(Not run)
## Not run: 
fit <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)
parDT <- coefWriteout(fit)

## End(Not run)

cohort_unrelated dataset

Description

cohort_unrelated dataset - need to include details

Usage

cohort_unrelated
cohort_unrelated

Format

An object of class data.frame with 50100 rows and 6 columns.

Create old bdots curve list

Description

Create old bdots curve list

Usage

createCurveList(x, prs, splitGroups)
createCurveList(x, prs, splitGroups)

Arguments

`x`	list of group distributions
`prs`	named list of parsed call
`splitGroups`	splitGroups

Curve Fitter

Description

Used in bdotsFit

Usage

curveFitter(dat, ff, params, rho, numRefits = 0, getCovOnly = NULL, ...)
curveFitter(dat, ff, params, rho, numRefits = 0, getCovOnly = NULL, ...)

Arguments

`dat`	data used in building curve
`ff`	formula used in buildilng curve
`params`	starting parameters
`rho`	correlation coefficient
`numRefits`	number of refit attempts
`getCovOnly`	only find covariance matrix from starting parameter values
`...`	don't know that this is used, can maybe get rid of it

df_cohort_unrelated dataset

Description

df_cohort_unrelated dataset - need to include details

Usage

df_cohort_unrelated
df_cohort_unrelated

Format

An object of class data.frame with 78156 rows and 5 columns.

df_target dataset

Description

df_target dataset - need to include details

Usage

df_target
df_target

Format

An object of class data.frame with 37575 rows and 4 columns.

Double Gauss curve function for nlme

Description

Double Gauss function used in fitting nlme curve for observations

Usage

doubleGauss(dat, y, time, params = NULL, concave = TRUE, ...)
doubleGauss(dat, y, time, params = NULL, concave = TRUE, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable, character vector
`time`	time variable, character vector
`params`	`NULL` unless user wants to specify starting parameters for gnls
`concave`	Boolean
`...`	just in case

Details

User should only have to worry about setting concavity of this function

y ~ (time < mu) * (exp(-1 * (time - mu) ^ 2 / (2 * sig1 ^ 2)) * (ht - base1) + base1) + (mu <= time) * (exp(-1 * (time - mu) ^ 2 / (2 * sig2 ^ 2)) * (ht - base2) + base2)

DoubleGauss2 curve function for nlme

Description

DoubleGauss2 function used in fitting nlme curve for observations

Usage

doubleGauss2(dat, y, time, params = NULL, concave = TRUE, ...)
doubleGauss2(dat, y, time, params = NULL, concave = TRUE, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable, character vector
`time`	time variable, character vector
`params`	`NULL` unless user wants to specify starting parameters for gnls
`concave`	Boolean
`...`	just in case

Details

User should only have to worry about setting concavity of this function. Presently only work for time series scaled out to 2000ms

y ~ (time < mu) * (exp(-1 * (time - mu) ^ 2 / (2 * sig1 ^ 2)) * (ht - base1) + base1) + (mu <= time) * (exp(-1 * (time - mu) ^ 2 / (2 * sig2 ^ 2)) * (ht - base2) + base2)

Effective Alpha Functional

Description

Functional that returns function for computing effective alpha for given parameters and distribution

Usage

effectiveAlpha_f(rho, n = 10, df = NULL, method = "norm")
effectiveAlpha_f(rho, n = 10, df = NULL, method = "norm")

Arguments

`rho`	Correlation coefficient
`n`	Number of observations
`df`	Degrees of freedom if `method = "t"`
`method`	Character string. Determines distribution for adjusted alpha can be either `"norm"` for normal distribution or `"t"` for t-dist

Exponential curve function

Description

Exponential function used in fitting nlme curve for observations

Usage

expCurve(dat, y, time, params = NULL, ...)
expCurve(dat, y, time, params = NULL, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable
`time`	time variable
`params`	`NULL` unless user wants to specify starting parameters for gnls
`...`	just in case

Details

Remove any values of zero, or jitter, before using with bdotsFit

y ~ x_0 exp(k beta)

Find modified alpha

Description

find modified alpha

Usage

findModifiedAlpha(
  rho,
  n,
  df,
  alpha = 0.05,
  errorAcc = 0.001,
  gradDiff = ifelse(cores > 3, 0.5, 0.1),
  cores = 0,
  verbose = FALSE,
  method = "t"
)
findModifiedAlpha(
  rho,
  n,
  df,
  alpha = 0.05,
  errorAcc = 0.001,
  gradDiff = ifelse(cores > 3, 0.5, 0.1),
  cores = 0,
  verbose = FALSE,
  method = "t"
)

Arguments

`rho`	correlation coefficient
`n`	number of observations
`df`	degrees of freedom if method == "t"
`alpha`	starting alpha from which to adjust
`errorAcc`	acceptable error for alphastar
`gradDiff`	gradient steps in algorithm
`cores`	number of cores. Default is zero, or half of what's available
`verbose`	will probably remove this
`method`	either "t" or "norm"

fwerAlpha

Description

Family wise alpha calculation

Usage

fwerAlpha(rho, k, n = 10)
fwerAlpha(rho, k, n = 10)

Arguments

`rho`	Correlation coefficient
`k`	Bounds of non-critical region
`n`	Number of observations

Details

Returns effective alpha, given number of tests and the correlation coefficient. This isn't explicitly checked, but there is no reason this function should take any non-scalar values. Derivation of this can be found on pg 12 of Jake's 'Detecting time-specific differences'. This function performs the expression

$1 - P(I_t)P(I_t \ | \ I_{t-1})^{N-1}$

Create Distribution for Groups

Description

Create Distribution for Groups

Usage

getBootDist(x, b = 1000)
getBootDist(x, b = 1000)

Arguments

`x`	A subset object from bdObj
`b`	Number of bootstraps

Get Fit Correlations

Description

Helper function for finding correlation of fixed value and fitted values within group

Usage

getFitCorforGroups(x, val, ciBands = FALSE, method = "pearson")
getFitCorforGroups(x, val, ciBands = FALSE, method = "pearson")

Arguments

`x`	A split object of class 'bdObj' split by identifiers
`val`	Fixed value from dataset
`ciBands`	boolean for including cibands
`method`	method for correlation function

Return fitted values

Description

Returns fitted values at observed times

Usage

getSubCurveValues(bd, origNames = TRUE, origTime = TRUE)
getSubCurveValues(bd, origNames = TRUE, origTime = TRUE)

Arguments

`bd`	Single row of bdObj
`origNames`	use original names for y and time, or use "y" and "time"
`origTime`	Boolean. Do I actually want fitted values at observed times for that subject, or data.table with fitted values at the union of times

Details

Given a single row of bdObj, this returns fitted values at the observed times to use in conjunction with whatever else

Generate t-distribution for permutation

Description

Creates vector of t-statistics for given permutation returning either entire vector or max values

Usage

getT(x, idx, group, whole = FALSE)
getT(x, idx, group, whole = FALSE)

Arguments

`x`	bdObj
`idx`	permutation to use
`group`	group that we are permuting against
`whole`	return vector of T stats or just the max

Linear curve function

Description

Linear function used in fitting nlme curve for observations

Usage

linear(dat, y, time, params = NULL, ...)
linear(dat, y, time, params = NULL, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable
`time`	time variable
`params`	`NULL` unless user wants to specify starting parameters for gnls
`...`	just in case

Details

Don't use this function please

y ~ slope*time + intercept

Logistic curve function for nlme

Description

Logistic function used in fitting nlme curve for observations

Usage

logistic(dat, y, time, params = NULL, ...)
logistic(dat, y, time, params = NULL, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable
`time`	time variable
`params`	`NULL` unless user wants to specify starting parameters for gnls
`...`	just in case

Details

y ~ mini + (peak - mini) / (1 + exp(4 * slope * (cross - (time)) / (peak - mini)))

Adjust P-values for Multiple Comparisons

Description

Identical to stats::p.adjust, but includes method = "oleson"

Usage

p_adjust(p, method = "oleson", n = length(p), alpha = 0.05, df, rho, cores = 0)
p_adjust(p, method = "oleson", n = length(p), alpha = 0.05, df, rho, cores = 0)

Arguments

`p`	numeric vector of p-values (possibly with NAs).
`method`	correction method, a character string. Can be any of the methods in p.adjust.methods, with the additional value `method = "oleson"`
`n`	number of comparisons, must be at least `length(p)`; only set this (to non-default) when you know what you are doing!
`alpha`	adjustment to be made with method oleson
`df`	degrees of freedom, if using `method = "oleson"`
`rho`	AR1 correlation coefficient, if using `method = "oleson"`
`cores`	number of cores for use in parallel, only valid for `method = "oleson"`. Default is zero, using half of the available cores

Details

This function works identically to the function p.adjust, with the additional option to use method = "oleson". For this option, user must include a value for df, alpha. If method = "oleson" and no value is given for rho, 0.9 will be used. To compute a value for rho from t-statistics, use ar1Solver.

Value

Returns a vector of adjusted p-values just as in p.adjust, but with additional attributes for alphastar and rho.

Parameter t-test

Description

Perform t-test on curve parameters of bdotsFit object

Usage

parTest2(bdObj, group, vals = NULL)
parTest2(bdObj, group, vals = NULL)

Arguments

`bdObj`	Object of class `bdObj`
`group`	Length one character of grouping column in which to perform t-test
`vals`	Character vector of values within grouping column in which to perform the test. If `NULL`, it will do all pairwise tests

Details

Performs pairwise t-test. Currently only tests at alpha = 0.95. Also currently only allows t-test within single grouping column. Ability to test across grouping columns to come later

Value

List of t-test results of class bdotsPars_ttest

Examples

## Not run: 
res <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)
tstats <- parTest(res, group = "LookType", vals = c("Cohort", "Unrelated_Cohort"))

## End(Not run)
## Not run: 
res <- bdotsFit(data = cohort_unrelated,
                subject = "Subject",
                time = "Time",
                y = "Fixations",
                group = c("Group", "LookType"),
                curveType = doubleGauss(concave = TRUE),
                cor = TRUE,
                numRefits = 2,
                cores = 0,
                verbose = FALSE)
tstats <- parTest(res, group = "LookType", vals = c("Cohort", "Unrelated_Cohort"))

## End(Not run)

Permutation testing for bboot

Description

Provides alternative for controlling FWER

Usage

permTest(x, prs, alpha, P)
permTest(x, prs, alpha, P)

Arguments

`x`	this is splitGroups (each item of list bdotsObj)
`prs`	from parser, indicates inner/outer groups
`alpha`	alpha for fwer
`P`	number of permutations

Plot for object of class bdotsBootObj

Description

Allows a number of different but also unstable option for plotting an object of class bdotsBoot

Usage

## S3 method for class 'bdotsBootObj'
plot(x, alpha = NULL, ciBands = TRUE, plotDiffs = TRUE, group = NULL, ...)
## S3 method for class 'bdotsBootObj'
plot(x, alpha = NULL, ciBands = TRUE, plotDiffs = TRUE, group = NULL, ...)

Arguments

`x`	An object of class bdotsBootObj
`alpha`	Significance level for plotting confidence intervals.
`ciBands`	Boolean indicating whether or not to include confidence intervals around fitted curves
`plotDiffs`	Boolean to plot difference curve
`group`	Specify group to plot if difference of difference was used. The user can also subset the bdotsBootObj prior to plotting. Currently not used
`...`	ignore for now, but will eventually allow plot parameters

Details

This plot function is also a bit unstable and is expected to change

Value

List of ggplot objects, which may be helpful if the margins are weird

Plots for bdotsCorr

Description

Plots correlation of fixed value wtih fitted curves over time

Usage

## S3 method for class 'bdotsCorrObj'
plot(x, ciBands = FALSE, window = NULL, ...)
## S3 method for class 'bdotsCorrObj'
plot(x, ciBands = FALSE, window = NULL, ...)

Arguments

`x`	object of class 'bdotsCorrObj'
`ciBands`	boolean. Whether or not to include confidence intervals in plots. Must have been selected in 'bdotsCorr'
`window`	A length 2 numeric vector with start and end points for the plotting window
`...`	super secret, don't use

Plot a bdotsFit object

Description

Plot individual fits or model fit parameters from an object of class 'bdotsObj'. These functions are not very stable

Usage

## S3 method for class 'bdotsObj'
plot(x, fitCode = NULL, gridSize = NULL, plotfun = "fits", ...)
## S3 method for class 'bdotsObj'
plot(x, fitCode = NULL, gridSize = NULL, plotfun = "fits", ...)

Arguments

`x`	An object of class 'bdotsObj' returned from `bdotsFit`
`fitCode`	Currently not used
`gridSize`	Length one numeric indicating size of plot grid. Default is 2x2. For right now, they are square
`plotfun`	Plot either subject fits or model parameters with "fits" or "pars"
`...`	ignore for now (other args to plot.generic)

Details

Right now, these functions are a bit unstable and expected to change. The largest current issue is with the placement of the legend, which cannot be adjusted. If you are running into issues with seeing things correctly, try making the "Plots" window in RStudio larger before running this function

Value

This will return a list of all of the plots rendered.

Polynomial curve function for nlme

Description

Polynomial function used in fitting nlme curve for observations

Usage

polynomial(dat, y, time, degree, raw = TRUE, params = NULL, ...)
polynomial(dat, y, time, degree, raw = TRUE, params = NULL, ...)

Arguments

`dat`	subject data to be used
`y`	outcome variable
`time`	time variable
`degree`	degree of polynomial
`raw`	Boolean, use raw polynomials?
`params`	`NULL` unless user wants to specify starting parameters for gnls
`...`	just in case

Details

It's recommended that one uses raw polynomials for this function for numerical stability. As inference is not performed on the parameters themselves, this should have minimial consequences

y ~ mini + (peak - mini) / (1 + exp(4 * slope * (cross - (time)) / (peak - mini)))

Print 'bdotsBootObj'

Description

Prints argument. Really, just the summary function

Usage

## S3 method for class 'bdotsBootObj'
print(x, ...)
## S3 method for class 'bdotsBootObj'
print(x, ...)

Arguments

`x`	An object of class 'bdotsBootObj'
`...`	Top secret alpha one code red

Details

Generic for printing 'bdotsBootObj'

Print bdotsBoot Summary

Description

That's pretty much it. This is a print method, so there is likely not much need to call it directly

Usage

## S3 method for class 'bdotsBootSummary'
print(x, ...)
## S3 method for class 'bdotsBootSummary'
print(x, ...)

Arguments

`x`	generic name, but this will be an object of bdotsBootSummary
`...`	ignored for now

Print Parameter Test Summary

Description

Print Parameter Test Summary

Usage

## S3 method for class 'bdotsPars_ttest2'
print(x, ...)
## S3 method for class 'bdotsPars_ttest2'
print(x, ...)

Arguments

`x`	object to be printed
`...`	not used

Details

That's pretty much it. This is a print method, so there is likely not much need to call it directly

Print bdotsObj Summary

Description

Print bdotsObj Summary

Usage

## S3 method for class 'bdotsSummary'
print(x, ...)
## S3 method for class 'bdotsSummary'
print(x, ...)

Arguments

`x`	object to be printed
`...`	not used

Details

That's pretty much it. This is a print method, so there is likely not much need to call it directly

rbindlist for bdotsObjects

Description

Similar to data.table::rbindlist, but preserves botsObjects attributes

Usage

## S3 method for class 'bdObjList'
rbindlist(x, ...)
## S3 method for class 'bdObjList'
rbindlist(x, ...)

Arguments

`x`	bdotsObject
`...`	for compatability with data.table

Split object of class bdotsObj

Description

Analogous to other splitting functions, but retains necessary attributes across the split object. As of now, it can only be unsplit with bdots::rbindlist

Usage

## S3 method for class 'bdotsObj'
split(x, f, drop = FALSE, by, ...)
## S3 method for class 'bdotsObj'
split(x, f, drop = FALSE, by, ...)

Arguments

`x`	Object of class bdotsObj
`f`	For consistency with generic, but is not used
`drop`	logical. Default FALSE will not drop empty list elements caused by factor levels not referred by that factor. Analagous to data.table::split
`by`	Character vector of column names on which to split. Usually will be Subject or one of the fitted groups
`...`	not used

Subset a nested group bdotsBoot objects

Description

Subset a nested group bdotsBoot objects

Usage

## S3 method for class 'bdotsBootObj'
subset(x, group, adjustAlpha = NULL, ...)
## S3 method for class 'bdotsBootObj'
subset(x, group, adjustAlpha = NULL, ...)

Arguments

`x`	An object returned from `bdotsBoot`
`group`	A group to subset. Must be an outer group
`adjustAlpha`	currently not used. Will give option to recompute adjusted alpha
`...`	Not used

Details

This function is used to subset a bdotsBootObject that was fit to compute the difference of differences. This allows the user to subset out the outer group in the comparison for plotting and investigation

Summary for bdotsBootObj

Description

Provides summary information for bdotsBootObj

Usage

## S3 method for class 'bdotsBootObj'
summary(object, ...)
## S3 method for class 'bdotsBootObj'
summary(object, ...)

Arguments

`object`	An object of class bdotsObj
`...`	Ignored for now

Value

Returns an object of class "bdotsBootSummary". There is some summarized information included if assigned to an object, i.e., 'summ <- summary(bdBootObj)' then 'str(summ)'

Summary for bdotsObj

Description

Provides summary information for bdotsObj

Usage

## S3 method for class 'bdotsObj'
summary(object, ...)
## S3 method for class 'bdotsObj'
summary(object, ...)

Arguments

`object`	An object of class bdotsObj
`...`	not used

Value

Returns an object of class "bdotsSummary". There is some summarized information included if assigned to an object, i.e., 'summ <- summary(bdObj)' then 'str(summ)'

target dataset

Description

target dataset - need to include details

Usage

target
target

Format

An object of class data.frame with 25050 rows and 4 columns.

Write fits from `bdotsBoot` to csv file

Description

The function is used to write out columns for each group for which a curve was bootstrapped

Usage

writeCSV(bootObj, file, alpha = 0.05, ...)
writeCSV(bootObj, file, alpha = 0.05, ...)

Arguments

`bootObj`	An object of class `bdotsBootObj`
`file`	file name to write out csv
`alpha`	alpha level for upper/lower CI
`...`	Other arguments passed to `data.table::fread`

Details

This is potentially useful for constructing plots in a separate application. There is an additional column, Significant indicating if a particular time point was considered significant between the difference curves. For difference of difference objects, this only indicates significance for the outer difference.

Package 'bdots'

Help Index

Compute AR1 correlation coefficient

Description

Usage

Arguments

Value

See Also

Examples

Create bootstrapped curves from bdotsObj

Description

Usage

Arguments

Details

Value

Examples

Correlation with fixed value in bdots

Description

Usage

Arguments

Fit nlme curves to grouped observations

Description

Usage

Arguments

Details

Value

Examples

Fits Individual Subject Curve

Description

Usage

Arguments

Refit Observations Returned from bdotsFit

Description

Usage

Arguments

Details

Value

bdots Remove Function

Description

Usage

Arguments

Details

ci dataset

Description

Usage

Format

Extract bdotsFit Moedel Coefficients

Description

Usage

Arguments

Value

Create data.table with bdotsObj parameters

Description

Usage

Arguments

Value

Examples

cohort_unrelated dataset

Description

Usage

Format

Create old bdots curve list

Description

Usage

Arguments

Curve Fitter

Description

Usage

Arguments

df_cohort_unrelated dataset

Description

Usage

Format

df_target dataset

Description

Usage

Format

Double Gauss curve function for nlme

Description

Usage

Create `data.table` with `bdotsObj` parameters