Description Usage Arguments Author(s) See Also Examples
View source: R/BI_cox.optimized2.R
cox.optimized2 use glmnet::cv.glmnet to do optimized selection of lasso cox models based on random seeds
1 2 3 4 5 6 | cox.optimized2(expr.matrix, design, select,
event.status = c("TTR.status", "DFS.status", "OS.status"),
event.time = c("TTR.time", "DFS.time", "OS.time"),
event.lower = c(89, 89, 89), k = 10, seed = 2018,
seed.range = 1:2000, R = 100, optimize.method = "min",
show.music = T)
|
expr.matrix |
gene expression with sample cols and genes/markers rows. |
design |
design object with characters cols and sample rows. |
select |
intersting genes/markers |
event.status |
a vector of event status names |
event.time |
a vector of event time names |
event.lower |
the cutoff(>) ofevent.time |
k |
number of folds.Default is 10. |
seed |
a number for randomization |
seed.range |
the range of randomization |
R |
the round of randomization |
optimize.method |
Default is "min".You can also use "1se" |
show.music |
whether to show music at the end of the process |
Weibin Huang<654751191@qq.com>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | ## This is a simulative process and available only with CORRECT VARIABLES
# Get lots of optimized model
model <- cox.optimized2(expr.matrix = x,
design = y,
select = cluster,
event.status="DFSoutcome",
event.time="DFS",
event.lower =0,
k=10,
seed = 2018,
seed.range = 1:2000,R=100,
optimize.method = "min",
show.music = F)
# select models of given status and time
model1 <- model$DFS$modeldata;View(model1)
# models statistics
model2 <- uniqueModel(model1) ;View(model2)
# Visualize the best model
res <- exampleLassoCox(expr.matrix = x,
design = y,
select = cluster,
event.status="DFSoutcome",
event.time="DFS",
event.lower =0,
k=10,
seed = 1601,#某个seed.来自model2
optimize.method = "min",
verbose = T,
save.file = T,
names = "总GIST")
# select best model
mod <- oneModel(model2[1,],dig=5)
# KM1
th1 <- cox.threshold(expr.matrix = tpm,
design = design.train,
model = list(mod),
event.status="DFS.status",
event.time="DFS.time",
event.lower = 89,
smethod="LogRank",
pmethod="HL",
dig = 5,
cancertype.cv="STAD",
file.name = project)
cf <- th1$DFS$cunoff
# KM2: Giving a specified cut off
th2 <- cox.threshold2(expr.matrix = tpm,
design = design.train,
model = rep(list(mod),3),
cut.off = c(cf,cf,cf),
event.status=c("TTR.status","DFS.status","OS.status"),
event.time=c("TTR.time","DFS.time","OS.time"),
event.lower =c(89,89,89),
dig = 5,
cancertype.cv="STAD",
file.name = project)
|
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