MissingHelpers | R Documentation |
compute group mean by LOD
compute group mean by LOD
weight lod by nr of NA's $(LOD * nrNas + meanAbundance *nrObs)/(nrMeasured)$
data
data
config
config
prob
quantile of groups with one observed value to estimate LOD
stats
data.frame with group statistics
weighted
should we weight the LOD
new()
initialize
MissingHelpers$new(data, config, prob = 0.5, weighted = TRUE)
data
data
config
config
prob
default 0.5, median of groups with one observed value
weighted
should group average be computed used weighting, default TRUE.
get_stats()
get data.frame with statistics
MissingHelpers$get_stats()
data.frame
get_LOD()
determine limit of detection computes quantile of abundances in groups with a single observation
MissingHelpers$get_LOD()
integer LOD
impute_weighted_lod()
compute group averages using weighted lod
MissingHelpers$impute_weighted_lod()
impute_lod()
if group average absent substitute with LOD
MissingHelpers$impute_lod()
get_poolvar()
compute pooled var per protein
MissingHelpers$get_poolvar(prob = 0.75)
prob
prob of sd from proteins where it can be computed
get_contrast_estimates()
get contrast estimates
MissingHelpers$get_contrast_estimates(Contrasts)
Contrasts
named array with contrasts
get_contrasts()
compute contrasts
MissingHelpers$get_contrasts(Contrasts, confint = 0.95, all = FALSE)
Contrasts
vector with contrasts
confint
compute confint
all
return all columns, default FALSE
clone()
The objects of this class are cloneable with this method.
MissingHelpers$clone(deep = FALSE)
deep
Whether to make a deep clone.
Contrasts <- c("group.b-a" = "group_A - group_B", "group.a-ctrl" = "group_A - group_Ctrl")
dd <- prolfqua::sim_lfq_data_protein_config(Nprot = 100,weight_missing = 2)
mh <- prolfqua::MissingHelpers$new(dd$data, dd$config, prob = 0.8,weighted = TRUE)
xx <- mh$get_stats()
xx <- mh$get_LOD()
xx <- mh$impute_weighted_lod()
xx <- mh$impute_lod()
xx <- mh$get_poolvar()
bb <- mh$get_contrast_estimates(Contrasts)
mh$get_contrasts(Contrasts)
dd <- prolfqua::sim_lfq_data_2Factor_config(Nprot = 100,weight_missing = 0.1)
Contrasts <- c("c1" = "TreatmentA - TreatmentB",
"C2" = "BackgroundX- BackgroundZ",
"c3" = "`TreatmentA:BackgroundX` - `TreatmentA:BackgroundZ`",
"c4" = "`TreatmentB:BackgroundX` - `TreatmentB:BackgroundZ`"
)
mh <- prolfqua::MissingHelpers$new(dd$data, dd$config, prob = 0.8,weighted = TRUE)
mh$get_stats()$interaction |> table()
mh$get_contrast_estimates(Contrasts)
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