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).
[[alternative HTML
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
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?
[[alternative HTML version
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 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?
[[alternative HTML version deleted]]
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 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
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 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
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
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
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 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 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 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 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 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?
[[alternative HTML version deleted]]
I want to print the following multiple boxes of output from R.
-
1st stage |2nd stage | 3rd stage |
x1|x2 | x3|
| |
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
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 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
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.
[[alternative HTML version deleted]]
__
R-help@r-project.org
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
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]
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?
[[alternative HTML
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
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