Description Usage Arguments Details Value Author(s) Examples
This function allows imputing data sets with a MCAR-devoted algorithm and a MNAR-devoted algorithm using probabilities that missing values are MCAR. If such a probability is superior to 0.5, then the MCAR-devoted algorithm is used, otherwise it is the MNAR-devoted algorithm.
1 2 | impute.mix(tab, prob.MCAR, conditions, repbio=NULL, reptech=NULL, method="slsa", nknn=15,
weight=1, selec="all", ind.comp=1, progress.bar=TRUE, q=0.95)
|
tab |
A data matrix containing numeric and missing values. Each column of this matrix is assumed to correspond to an experimental sample, and each row to an identified peptide. |
prob.MCAR |
A matrix of probabilities that each missing value is MCAR. For instance such a matrix can be obtained from the function |
conditions |
A vector of factors indicating the biological condition to which each column (experimental sample) belongs. |
repbio |
A vector of factors indicating the biological replicate to which each column belongs. Default is NULL (no experimental design is considered). |
reptech |
A vector of factors indicating the technical replicate to which each column belongs. Default is NULL (no experimental design is considered). |
method |
The method used for imputing MCAR data. If |
nknn |
The number of nearest neighbours used in the SLSA algorithm (see |
weight |
The way of weighting in the algorithm (see |
selec |
A parameter to select a part of the dataset to find nearest neighbours between rows. This can be useful for big data sets (see |
ind.comp |
If |
progress.bar |
If |
q |
A quantile value (see |
The missing values for which prob.MCAR
is superior to 0.5 are imputed with either the function impute.slsa
or the MLE algorithm (function impute.wrapper.MLE
of the R package imputeLCMD). The other missing values are considered MNAR and imputed with impute.igcda
.
The input matrix tab
with imputed values instead of missing values.
Quentin Giai Gianetto <[email protected]>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | #Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=0.5,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Fast imputation of missing values with the impute.rand algorithm
dat.rand=impute.rand(tab=res.sim$dat.obs,conditions=res.sim$condition);
#Estimation of the mixture model
res=estim.mix(tab=res.sim$dat.obs, tab.imp=dat.rand, conditions=res.sim$condition);
#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);
proba=prob.mcar.tab(born$tab.lower,born$tab.upper,res);
#Imputation under the assumption of MCAR and MNAR values
tabi=impute.mix(tab=res.sim$dat.obs, prob.MCAR=proba, conditions=res.sim$conditions,
repbio=res.sim$repbio, method="slsa", nknn=15, weight=1, selec="all", ind.comp=1,
progress.bar=TRUE);
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