I have a matrix of the following form:
time
id0 2 4 6 9 12 14
3 9 8 NA NA NA NA NA
7 3 NA 3 NA 3 NA 4
13 11 6 7 NA 5 NA 6
.
I hope for each row to select the last observation which is not 'NA'.
For example, for the first row, id=3, the value I want to select
Dear r-helpers,
I have a very simple question. Suppose my data is like
id=c(rep(1,2),rep(2,2))
b=c(2,3,4,5)
m=cbind(id,b)
m
id b
[1,] 1 2
[2,] 1 3
[3,] 2 4
[4,] 2 5
I wish to select the first observation for each id. That is, I want to
quickly select two rows:
id b
1 2
2 4
only. how
PROC MIXED is used to fit mixed effects model for correlated data.
Usually we can use either a REPEATED statment or a RANDOM statement.
The random statement is corresponding to lme function in R -- specifying a
random effect term.
The repeated statement actually directly specifies the
i am trying to fit a survival regression model (cox model or parametric
model) in R by including the covariate effects as a function m(x) instead of
just beta*x. is it possible to fit such a model? can someone recommend some
reference? I searched but only found a package called addreg where
the
I've figured it out by repeatedly testing. It is to use a type='term'
statement, just as used in gam.
sorry to bother.
On 2/19/08, gallon li [EMAIL PROTECTED] wrote:
Thanks a lot, Prof Lumley.
Now I can fit a model like
coxfit=coxpy((time,censor)~pspline(x1)+x2+x3)
but I am not sure how
, then the example in help manule can be used
as
plot(x1, predict(coxfit))
but with more than 1 predictor, i am not sure how to select the one i want.
On 2/19/08, Thomas Lumley [EMAIL PROTECTED] wrote:
On Mon, 18 Feb 2008, gallon li wrote:
i am trying to fit a survival regression model (cox model
I know how to compute the ROC curve and the empirical AUC from the logistic
regression after fitting the model.
But here is my question, how can I compute the standard error for the AUC
estimator resulting form logistic regression? The variance should be more
complicated than AUC based on known
Suppose i want to compute a 95% highest density for a beta distribution
beta(a,b)
the two end points x1 and x2 shoudl satisfy the following two equations:
pbeta(x1,a,b)-pbeta(x2,a,b)=95%
dbeta(x1,a,b)=dbeta(x2,a,b)
Is there any fast way to compute x1 and x2 in R?
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I have the following list of observations of calendar time:
[1] 03-Nov-1997 09-Oct-1991 27-Aug-1992 01-Jul-1994 19-Jan-1990 12-Nov-1993
[7] 08-Oct-1993 10-Nov-1982 08-Dec-1986 23-Dec-1987 02-Aug-1995 20-Oct-1998
[13] 29-Apr-1991 16-Mar-1994 20-May-1991 28-Dec-1987 14-Jul-1999 27-Nov-1998
[19]
I have a sample of observations:
yy
[1] 0. 2.3972 4.3500 -4.1972 0.6361
[6] 1.0806 5.9056 -1.8722 2.1333 -1.1806
[11] 3.6167 0.8778 8.3389 3.8417 1.
[16] -3.7611 -11.6778 -2.0306
Does anybody know if there is such a function to estimate the distribution
for interval censored data?
survfit doesn't work for this type of data as I tried various references.
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__
R-help@r-project.org
Suppose I have a long format for a longitudinal data
id time x
1 1 10
1 2 11
1 3 23
1 4 23
2 2 12
2 3 13
2 4 14
3 1 11
3 3 15
3 4 18
3 5 21
4 2 22
4 3 27
4 6 29
I want to select the x values for each ID when time is equal to 3. When that
observation is not observed, then I want to replace it
I want to print the following multiple boxes of output from R.
-
1st stage |2nd stage | 3rd stage |
x1|x2 | x3|
| |
I have a list of {0,1} values, say
y-c(0,0,0,1,1,0,0,1,0,1,1,1,1)
I want to compute the first few zeros and the last few ones. So the output I
expect is 3 and 4 for this vector. Is there a fast way to match the numbers
easily?
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I have a data set like the following:
subject visit x1
1 1 0.5
1 2 1.2
1 3 0.7
2 1 0.4
2 2 0.6
2 3 1.0
.
where x1 is the interval between the two visits. Now I want to calculate the
cumulative intervals since the beinging, for example
subject visit x1 cum
1 1 0.5 0.5
1 2 1.2 0.5+1.2
1 3
I have the complete data like
id time censor
1 10 0
1 20 0
1 30 0
2 10 0
2 20 1
2 30 0
2 40 0
3 10 0
3 20 0
3 30 1
for id 1, i want to select the last row since all censor indicator is 0; for
id 2, i want to select the row where censor ==1; for id 3, i also want to
select the row where
-- Forwarded message --
From: gallon li [EMAIL PROTECTED]
Date: Tue, Nov 25, 2008 at 1:58 PM
Subject: Re: [R] select a subset
To: Stefan Grosse [EMAIL PROTECTED]
I am sorry but my question is not solvable by using subset alone.
You see, the selection criterion is different
I have the following data
ID x y time
1 10 20 0
1 10 30 1
1 10 40 2
2 12 23 0
2 12 25 1
2 12 28 2
2 12 38 3
3 5 10 0
3 5 15 2
.
x is time invariant, ID is the subject id number, y is changing over time.
I want to find out the difference between the first and last observed y
value for each
I have the following longitudinal data:
id time y
1 1 10
1 2 12
1 3 15
1 6 18
2 1 8
2 3 9
2 4 11
2 5 12
3 1 8
3 4 16
4 1 9
4 5 13
5 1 7
5 2 9
5 6 11
I want to select the observations at time 4. if the observation at time 4 is
missing, then i want to slect the observation at time 3. if the
I have a 5 column matrix like
12 10 8 6 3
10 9 8 7 5
14 NA 4 NA NA NA
15 NA 10 NA 5
...
I want to select the position of the first entry for each row =5
for example, for the first row, I want to select the last element and return
its position as 5;
for th e third row, I want to select the third
Suppose I have a matrix like
A=matrix(0,4,6)
A[1,]=c(16,10,2,4,8,7)
A[2,]=c(16,10,12,14,8,7)
A[3,]=c(16,10,13,15,19,17)
A[4,]=c(16,9,13,15,9,7)
A
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 16 102487
[2,] 16 10 12 1487
[3,] 16 10 13 151917
[4,]
I have a following matrix and wish to define a variable based the variable
A=matrix(0,5,5)
A[1,]=c(30,20,100,120,90)
A[2,]=c(40,30,20,50,100)
A[3,]=c(50,50,40,30,30)
A[4,]=c(30,20,40,50,50)
A[5,]=c(30,50,NA,NA,100)
A
[,1] [,2] [,3] [,4] [,5]
[1,] 30 20 100 120 90
[2,] 40 30
I have a following matrix and wish to define a variable based the variable
A=matrix(0,5,5)
A[1,]=c(30,20,100,120,90)
A[2,]=c(40,30,20,50,100)
A[3,]=c(50,50,40,30,30)
A[4,]=c(30,20,40,50,50)
A[5,]=c(30,50,NA,NA,100)
A
[,1] [,2] [,3] [,4] [,5]
[1,] 30 20 100 120 90
[2,] 40 30
I wonder if there is a simple way of doing this?
My data is very simple, a right censored outcome (T,delta) and the
predictor is simply Z*t, i.e., the cox model is like
h(t)=h0(t)exp(beta*Z*t)
can this be done with coxph?
__
R-help@r-project.org
i wish to change a column of factor variable to multiple columns of
zero-ones
for example, my factor could be
ff=c('a','a','b','b','c','c')
then I want to have two columns (for three levels) that are
0 0
0 0
1 0
1 0
0 1
0 1
how can i do this fast?
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suppose I have the following data
id=c(rep(1,3),rep(2,5),rep(3,4))
time=c(seq(1,3),seq(2,6),seq(1,4))
ds=cbind(id,time)
ds
id time
[1,] 11
[2,] 12
[3,] 13
[4,] 22
[5,] 23
[6,] 24
[7,] 25
[8,] 26
[9,] 31
[10,] 32
[11,] 33
i have converted my data into date format like below:
day=as.Date(originaldate,%m/%d/%Y)
day[1:5]
[1] 2008-04-12 2011-07-02 2011-09-02 2008-04-12 2008-04-12
I wish to select only those observations from 2007 to 2009, how can I
select from this list?
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My data are from 2008 to 2010, with repeated measures for same subjects. I
wish to compute number of months since january 2008.
The data are like the following:
ID date
1 4/12/2008
1 4/13/2008
1 5/11/2008
2 3/21/2009
2 4/22/2009
2 8/05/2009
...
the date column are in the format %m/%d/%y. i wish
x=rchisq(100,1)
density(x)
the density plot will give density for negative part also. of course I can
truncate the plot to only view the non-negative part.
I wonder if there is a program to compute density for a user-specified
range, in this case, only [0, infinity).
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