Description Usage Arguments Details Value Examples
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.
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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 |
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.
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 |
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