salbmM: Sensitivity Analysis for Binary Missing Data

Description Usage Arguments Details Value Examples

View source: R/salbmM.R

Description

For a list of dataframes, where each frame is of the form (Y_1,Y_2, ..., Y_K) and Y_t takes the values 0, 1, or 2 (missing), salbmM estimates E[ Y_t | alpha ] where alpha is one of a number of sensitivity paramaters under a Markovian assumption of order m.

Usage

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salbmM( data, Narm = length(data), m, K, ntree, 
        EmpEst=FALSE, NEst=0,
        seeds = 1:length(data), seeds2 = -1 - 1:length(data), 
        alphas, NBootstraps = 0, bBS = 1, 
        returnJP = TRUE, returnSamples = FALSE )

Arguments

data

a list of dataframes

Narm

the number of dataframes to process

m

order of the Markov assumption, note 2m+2 < K

K

The number of time-points

ntree

The number of trees in the random forest passed to randomForestSRC

EmpEst

logical, indicating if empirical estimation should be used when calculating the mean value of Yt.

NEst

The number of values of Yt to use in calculating the mean of Yt.

seeds

vector of positive numbers used as seeds in producing bootstrap samples. There should be at least one seed for each treatment arm.

seeds2

vector of negative numbers passed to randomForestSRC. There should be at least one seed for each treatment arm.

alphas

vector of sensitivity parameters

NBootstraps

number of bootstrap samples to be created and analyzed

bBS

Start Bootstrap number. Bootstrap IDs are given as bBS:eBS where eBS = bBs + NBootstraps - 1. Setting bBS and eBS is useful when running salbmM in parallel.

returnJP

Logical indicating if the list of joint probability distributions returned by random forest for each treatment group should be returned. This is used by addSamples to create Bootstrap samples.

returnSamples

Logical indicating if generated bootstrap samples should be returned

Details

For each dataframe separately, randomForestSRC is used to create a set of joint distributions f(Yn-m, Yn-m+1, ..., Yn-1, Yn, Yn+1, ... Yn+m+1) where Yi can take three possible values, 0, 1, or missing (represented by the value 2). The Markovian assumption of order m can be summarized as f( Y_n | Y_i, i = 1, 2, ..., n-1, n+1, ..., K) = f( Y_n | Y_i, i = max(1,n-m), ..., n-1, n+1, ..., min(n+m+1,K)) for n > 1.

RandomForestSRC is used to estimate the joint distributions, f_i( Y_n | Y_n-m, ..., Y_n-1, Y_n+1, ..., Y_n+m+1). For each sensitivity parameter, alpha, these distributions are used to compute the E[ Y_K | alpha ] Bootstraping is carried out using the $f_i$.

Because of the Markov assumption the full distribution f can be replaced by a set of distributions of order no more than 2m+2. This allows estimation in situations where K is large and estimation of the full joint distribution is unfeasable.

Value

salbmM returns a list which contains the following:

Main1R

results for treatment group 1 in wide format

Main1RL

results for treatment group 1 in long format

Main1wts

means and standard deviations for trt1

jps1

joint distribution returned from randomForestRSC, trt 1

Samp1R

results for bootstrap samples trt1 in wide format

Samp1RL

results for bootstrap samples trt1 in long format

Samp1wts

means and standard deviations of bootstrap samples trt1.

Main2R

results for treatment group 2 in wide format

Main2RL

results for treatment group 2 in long format

Main2wts

means and standard deviations for trt2

jps2

joint distribution returned from randomForestRSC trt 2

Samp2R

results for bootstrap samples trt2 in wide format

Samp2RL

results for bootstrap samples trt2 in long format

Samp2wts

means and standard deviations of bootstrap samples trt2.

data

the salbm data object supplied in the call to salbmM

m

the Markov paramater supplied in the call to salbmM

K

the value of K supplied in the call to salbmM

ntree

the value of ntree supplied in the call to salbmM

NEst

the value of NEst supplied in the call to salbmM

alphas

the value of alphas supplied in the call to salbmM

seeds

the value of seeds supplied in the call to salbmM

seeds2

the value of seeds2 supplied in the call to salbmM

bBS

the value of bBS supplied in the call to salbmM

eBS

the value of eBS supplied in the call to salbmM

NBootstraps

the value of NBootstraps supplied in the call to salbmM

Examples

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  # Clinical trial data with two arms.
  data(trt1)
  data(trt2)
  data <- list( trt1 = trt1, trt2 = trt2 )

  R     <-  salbmM( data = data , m = 2, K = 6, ntree = 1000,
              seeds = c(22,18), seeds2 = c(-2,-3),
              alphas = -8:8, NBootstraps=0 )

salbm documentation built on May 25, 2021, 9:07 a.m.

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