Description Usage Arguments Details Value Author(s) See Also Examples
The analyzeRep
function analyzes a single replicate of data, possibly at different
interim levels.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | analyzeRep(analysisCode,
replicate,
removeMissing = TRUE,
removeParOmit = TRUE,
removeRespOmit = TRUE,
interimCode,
software = c("R", "SAS"),
seed = .deriveFromMasterSeed(),
parOmitFlag = getEctdColName("ParOmit"),
respOmitFlag = getEctdColName("RespOmit"),
missingFlag = getEctdColName("Missing"),
interimCol = getEctdColName("Interim"),
doseCol = getEctdColName("Dose"),
initialDoses = NULL,
stayDropped = TRUE,
fullAnalysis = TRUE,
workingPath = getwd(),
method = getEctdDataMethod())
|
analysisCode |
(Required) File containing analysis code (for R or SAS) or an R function for analysis (R only) |
replicate |
(Required) Replicate number of data to be analyzed |
removeMissing |
(Optional) Should rows marked as 'Missing' during the data generation step be removed from the data before analysis is performed? TRUE by default |
removeParOmit |
(Optional) Should any rows marked as 'Omitted' during the parameter data generation step (ie. parameters out of range) be removed from the data before analysis is performed? TRUE by default |
removeRespOmit |
(Optional) Should any rows marked as 'Omitted' during the response generation step (ie. responses out of range) be removed from the data before analysis is performed? TRUE by default |
interimCode |
(Optional) An R function to be applied to interim datasets in order to
creation interim decisions. See the help file for the |
software |
(Optional) The software to be used for analysis: either "R" or "SAS". "R" is the default software used |
seed |
(Optional) Random number seed to use for the analysis. Based on the current random seed by default |
parOmitFlag |
(Optional) Parameter omit flag name. "PAROMIT" by default |
respOmitFlag |
(Optional) Response omit flag name. "RESPOMIT" by default |
missingFlag |
(Optional) Missing flag name. "MISSING" by default |
interimCol |
(Optional) Interim variable name. "INTERIM" by default |
doseCol |
(Optional) Dose variable name. "DOSE" by default |
initialDoses |
(Optional) For interim analyses, which doses should be present in interim 1? All are included by default |
stayDropped |
(Optional) For interim analyses, if a dose is dropped, should it stay dropped in following interims (as opposed to allowing the interim step to reopen the dose) |
fullAnalysis |
(Optional) Should a "full" analysis be performed on all doses? Default TRUE |
workingPath |
(Optional) Root directory in which replicate data is stored, and in which we should perform the analysis. Current working directory is used by default |
method |
Data storage method (ie. where the replicate data is stored). Given by getEctdDataMethod by default |
The analyzeRep
function calls the performAnalysis
function in
order to analyze and summarize a single simulated replicate dataset (held in the
"ReplicateData" subdirectory of the specified working path).
The first step of the analysis is to use the removeMissing, removeParOmit and removeRespOmit flags (together with the missingFlag, parOmitFlag and respOmitFlag inputs) in order to subset the data if required. For example, we may wish to remove all observations flagged as "missing" in an earlier simulation of subject dropout. The subset is applied to the data before the analysis.
The analysis code must be either an R function, a reference to an external R script, or a
reference to an external SAS script. If the software is set as "SAS", it is assumed that the
analysisCode is an external SAS script.
If the analysis code is a SAS script, it must accept a single dataset called work.infile, and
create an output dataset called work.outfile. The work.outfile dataset must be a valid
"Micro Evaluation" structure as specified in the help file for checkMicroFormat
.
If the software is "R", the analysis code input must be either an R function or an R script.
The R analysis code must also return a valid "Micro Evaluatoin" structure as specified in
function checkMicroFormat
The first step in analyzeRep
is to perform a full analysis on the data (which
has possibly been subset be the remove* inputs). Following the analysis, the
checkMicroFormat
function is used to ensure the return data is a valid
"Micro Evaluation" data structure.
The return structure is appended with drop and stop flags (set to 0) and with interim
variables (where interim is "FULL").
If the interimCode has been specified, and the "interimCol" variable is found in the data,
interim analyses will be performed iteratively on sections of the data. The interimCode
input must be an R function that returns a suitable list structure as described in the
interimAnalysis
help file.
For each value of "interimCol", the analysis will be performed on a section of data (using a
call to performAnalysis
), and the return from the analysis will be checked
(using a call to checkMicroFormat
). The "Micro Evaluation" output is then
passed to the interimAnalysis
function and the return list checked for
instruction.
If any return interim list includes doses to "DROP", the doses will be removed from future
analyses. If the "STOP" flag in the list is set to "TRUE", the analysis is stopped at this
interim.
Finally, all micro evaluation outputs (with appended interim variables and drop/stop flags) are combined and returned.
A "Micro Evaluation" structure with additional variables (interim column, drop flag and stop flag)
Mike K Smith mstoolkit@googlemail.com
performAnalysis
is called by analyzeRep
to perform
each analysis on the subset of data. interimAnalysis
executes the
interimCode
and updates the data changes accordibgly.
analyzeData
calls analyzeRep
sequentially.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
# Analysis Code
emaxFun <- function(data){
library(DoseResponse)
with( data,
{
uniDoses <- sort( unique(D))
eFit <- emaxalt( RESP, D )
outDf <- data.frame( D = uniDoses,
MEAN = eFit$dm[as.character(uniDoses)],
SE = eFit$dsd[as.character(uniDoses)] )
outDf$LOWER <- outDf$MEAN - 2 * outDf$SE
outDf$UPPER <- outDf$MEAN + 2 * outDf$SE
outDf$N <- table(DOSE)[ as.character(uniDoses) ]
outDf
})
}
analyzeRep(replicate = 1, analysisCode = emaxFun)
## End(Not run)
|
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