Displaying 9 results from an estimated 9 matches for "celltype".
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calltype
2001 Oct 15
0
predict.fda difficulties
...-5020. Home: (650) 497 1135
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Toy Example
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# Dummy cross-validation routine
set.seed(7771)
TotalN<-162
TestN<-80
TrainN<-TotalN-TestN
# Make a dummy frame with 11 kinds of "CellType" labels and 17 normal
variables
DummyCellTypes<-floor(runif(TotalN,0,11)+1)
DummyParams<-data.frame(CellType=DummyCellTypes,
matrix(rnorm(TotalN*17),TotalN,17))
DummyParams$CellType<-factor(DummyParams$CellType)
table(DummyParams$CellType)
TrainFDAs<-list(100)
TestConfusions&l...
2007 Jun 06
2
Multiple color schemes for barchart (lattice)
Hello R-help.
I am trying to make a stacked barplot where the color of the sections of
each bar depend on another variable.
> myData[1:11,]
score percent marker cellType Malignant
1 0 100.00000 ESR1 (ER) Bladder.M(5) TRUE
2 0 80.00000 PAX8 Bladder.M(5) TRUE
3 1 20.00000 PAX8 Bladder.M(5) TRUE
4 0 100.00000 ESR1 (ER) Brain.N(3) FALSE
5 0 100.00000 PAX8 Brain.N(3) FALSE
6 3 100.00000 ESR1 (ER) Breast.M(11) TRUE
7...
2004 May 14
2
NLME model question
...ey are done 6 times each. Therefore, I do not want to ask if
there are significant differences in r & k between 24 sets of data
("Runs")- rather I want to be able to say that there are differences
between the four cell types occurring 6 times each. So how do
i incorporate "CellType" explicitly into my model structure??
i.e. If i was lust looking at say linear growth and was using lme I would
have written something like
lme(NoofCells~Time*CellType,random=~1|Runs,data=CellData)
Any thoughts/suggestions gratefully received
Darren Shaw
---------------------------------...
2012 Jul 18
0
Building a web risk calculator based on Cox, PH--definitive method for calculating probability?
Here is an example of how to do it.
> library(survival)
> vfit <- coxph(Surv(time, status) ~ celltype + trt, data=veteran)
> userinput <- data.frame(celltype="smallcell", trt = 1)
> usercurve <- survfit(vfit, newdata=userinput) #the entire predicted
survival curve
> user2 <- summary(usercurve, time= 2*365.25) # 2 year time point
> user2$surv
[1] 0.0007438084...
2012 Jul 17
0
Building a web risk calculator based on Cox PH--definitive method for calculating probability?
...rstand what he meant in his last sentence.
Here is some code for you to quickly illustrate what you suggest.
library(rms)
library(survival)
library(Hmisc)
data(veteran)
dd=datadist(veteran)
options(datadist='dd')
options(digits=4)
obj=with(veteran,Surv(time,status))
vetcoxph=coxph(obj~celltype+trt,data=veteran) #I will fit models from
both the survival and rms packages so you can
#use what you like
vetcph=cph(obj~celltype+trt,data=veteran,surv=TRUE,time.inc=5*365,x=T,y=T)
#let's say the user inputted that their cell type was smallcell and their
treatment was "1".
userin...
2008 Mar 02
0
coxpath() in package glmpath
...have difficult time understanding the output.
Therefore, I would greatly appreciate if anyone can
help me understand how to use the function.
> data(lung.data)
> attach(lung.data)
> fit.a <- coxpath(lung.data)
> print(fit.a)
Call:
coxpath(data = lung.data)
Step 1 : karno
Step 2 : celltype
Step 5 : trt
Step 6 : prior
Step 7 : age
Step 8 : diagtime
> summary(fit.a)
Call:
coxpath(data = lung.data)
Df Log.p.lik AIC BIC
Step 1 0 -505.8840 1011.7679 1011.7679
Step 2 1 -486.0691 974.1382 977.0581
Step 5 2 -484.8520 973.7040 979.5440
Step 6 3 -483.4018 97...
2010 Jan 21
1
Merging and extracting data from list
...ike 9 ;; [Source:UniProtKB/TrEMBL;Acc:B7ZGX0] gene
ENSG00000227232 1 -1 14363 29570 17551 29343
WASH5P WAS protein family homolog 5 pseudogene (WASH5P), non-coding
RNA [Source:RefSeq DNA;Acc:NR_024540] gene
.....
Chers list (chersList)
name chr start end cellType antibody features
maxLevel score
chr1.cher1 1 859132 859732 human AB ENSG00000223764
ENSG00000231958 ENSG00000187634 1.25736038968316 0.664381383074449
chr1.cher2 1 889564 890464 human AB ENSG00000188976
1.47884233632064 2.8883913144686...
2002 Jan 18
2
length of dimnames???
...y idea?
Thanks,
jp
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> frame1 <- read.table("/Users/gattuso/zRdata/coca/oceanic.txt",
>header=TRUE,sep=",")
> frame2 <- read.table("/Users/gattuso/zRdata/coca/cellids.txt",
>header=TRUE,sep=",")
> frame1$celltype <- frame2[match(frame1$ID,frame2$ID),2]
> frame1$cellid <- frame2[match(frame1$ID,frame2$ID),3]
> frame1$zone <- frame2[match(frame1$ID,frame2$ID),6]
> frame1$area <- frame2[match(frame1$ID,frame2$ID),7]
> frame1$perimeter <- frame2[match(frame1$ID,frame2$ID),8]
>...
2012 May 09
12
Matrix heatmap
I would like to organize my data as follows:
I have a table that contains various data, and the numbers represent a level
of similarity between these data,
eg RF00013 has 100% similarity with the data RF00014.
I would leave my table as a heatmap where darker colors represent higher
similarity, and the lighter colors represent less level of similarity.
I'm using version 2.11 of R.
these