I am currently trying to run a Known Fates Model in RMark with individual time
varying covariates. However, for animals that died early in the study or were
not captured at one capture period I, of course, do not have data for all of
their time points. I thought that NAs would not matter when
Caroline - the phidot forum is an *excellent* spot to post this question.
There is an entire RMark subforum.
www.phidot.org/forum/index.php
Even just searching this forum will probably give you some answers.
Also, this book has a whole section on individual covariates and approaches
for dealing
Hello Mr. FeldesmanI am a master student in biostatistic
my thesis about missing values in microarray data, but � can't create any
values.
� want to create %10, %20,...%90 missing values for all colums in microarray
data set .
Can you help me any code?
thank you for your attention.
Asena Ay�a
Hi Asena,
If you already have microarray data, you can simply change some of the
existing values to NA (datum Not Available). Say you have a toy 10x10
array containing absolute (initial) values:
array_values-matrix(sample(0:400,100,TRUE),nrow=10)
# create a 10% missing array
Dear all,
I've got a bit of a challenge on my hands. I've got survey data produced by
a government agency for which I want to use the person-weights in my
analyses. This is best accomplished by specifying weights in {survey} and
then calculating descriptive statistics/models through functions in
the mitools package is compatible with the survey package.. asdfree.com
has complete step-by-step R code examples to work with govt microdata.
here are the ones with multiply imputed survey data. :)
national health interview survey
national survey of children's health
consumer expenditure
Hello all,
Trying to get this piece of code to work on my data set. It is from
http://www.itc.nl/personal/rossiter.
logit.roc - function(model, steps=100)
{
field.name - attr(attr(terms(formula(model)), factors),
dimnames)[[1]][1]
On Jul 1, 2013, at 10:57 AM, tfj24 wrote:
Hello all,
Trying to get this piece of code to work on my data set. It is from
http://www.itc.nl/personal/rossiter.
logit.roc - function(model, steps=100)
{
field.name - attr(attr(terms(formula(model)), factors),
Dear r-users,
I would like to investigate about how to fill in missing data. I started with
a complete data and try to introduce missing data into the data series. Then I
would use some method to fill in the missing data and then compare with the
original data how good it is. My question
I read your data into a dataframe
x - read.table( clipboard )
and renamed the only column
colnames( x )[1] - orig
With a loop, I created a 2nd column miss where in every 10th row the
observation is set to NA:
for( i in 1 : length( x$orig ) )
{
if( as.integer( rownames( x )[ i ] ) %% 10
Hello,
Something like this?
x - scan(text =
125
130.3
327.2
252.2
33.8
6.1
5.1
0.5
0.5
0
2.3
0
0
0
0
0
0
0
0
0
0.8
5.1
0
0.3
0
0
0
0
0
0
45.7
43.4
0
0
0
0
0
)
putMissing - function(x, by){
idx - by*seq_along(x)
idx - idx[which(idx = length(x))]
x[idx] - NA
x
}
-4352
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Rainer Schuermann
Sent: Thursday, April 25, 2013 5:45 AM
To: r-help@r-project.org
Cc: Roslina Zakaria
Subject: Re: [R] Missing data
I read your data into a dataframe
x
Dear Rui and David
Thank you very much for taking your time to look at my problem.
However, I still cannot seem to figure it out.
I think that you David are corect in your assumption of how my data is
structured. The data in the two columns that I need to cross-table is either
1 or 0.
I made a
On Oct 8, 2012, at 9:06 AM, Rerda wrote:
Dear Rui and David
Thank you very much for taking your time to look at my problem.
However, I still cannot seem to figure it out.
I think that you David are corect in your assumption of how my data is
structured. The data in the two columns that I
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
project.org] On Behalf Of David Winsemius
Sent: Monday, October 08, 2012 4:37 PM
To: Rerda
Cc: r-help@r-project.org
Subject: Re: [R] Missing data (Na) and chi-square tests
On Oct 8, 2012, at 9:06 AM
Hello,
Also, since the values are always 0/1, this should also do it.
with( dat, table( Rash, Hypotension ) )
Hope this helps,
Rui Barradas
Em 09-10-2012 00:36, David Winsemius escreveu:
On Oct 8, 2012, at 9:06 AM, Rerda wrote:
Dear Rui and David
Thank you very much for taking your time
Dear everyone
I am a bit of a computer imbecile and are having problems with R.
I am using R in my research project to do chi-square tests on data imported
from excel .
However I have som missing data in one of my variables (columns) and I need
R to exclude these and make chi-square test on the
Hello,
There are two ways,
1.
?sum # see argument na.rm
sum(whatever, na.rm = TRUE)
2.
?table # produces the 2x2 contingency table, if there are only 2 values
Also, you should provide us with a data example, especially since your
code clearly doesn't work.
Use ?dput like this
dput(
On Oct 5, 2012, at 6:26 AM, Rerda wrote:
Dear everyone
I am a bit of a computer imbecile and are having problems with R.
I am using R in my research project to do chi-square tests on data imported
from excel .
However I have som missing data in one of my variables (columns) and I need
R
At 07:02 24/02/2012, Kawthar Alajmi wrote:
Hi all,
I am running Stepwise logistic regression and i have :
1- Multiple covatiates included in each model (No missing data)
So there is no missing data on any covariate?
2- Genotype data (SNPs) about 500,000 .
I partitioned the data to multiple
Hi all,
I am running Stepwise logistic regression and i have :
1- Multiple covatiates included in each model (No missing data)
2- Genotype data (SNPs) about 500,000 .
I partitioned the data to multiple files (there are missing data)
I run the step by including all the covariates and one SNP at
I admit it isnt reality but I was hoping through judicious use of these
functions I could approximate reality. For example in the years where there are
more than 53 weeks in a year I would be happy if there were a way to recognize
this and drop the last week of data. If there were less than 53
I was just trying to be complete. Why is the frequency argument and
attribute available?
-Original Message-
From: R. Michael Weylandt [mailto:michael.weyla...@gmail.com]
Sent: Saturday, November 26, 2011 2:40 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data
: Saturday, November 26, 2011 2:40 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data?
Why do you need to use a frequency attribute for these data? The point of
the zoo/xts line of time series implementations is that the time stamps are
carried through for each
On Sun, Nov 27, 2011 at 4:08 PM, Kevin Burton rkevinbur...@charter.net wrote:
I admit it isnt reality but I was hoping through judicious use of these
functions I could approximate reality. For example in the years where there
are more than 53 weeks in a year I would be happy if there were a
...@gmail.com]
Sent: Sunday, November 27, 2011 4:24 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data?
On Sun, Nov 27, 2011 at 4:08 PM, Kevin Burton rkevinbur...@charter.net
wrote:
I admit it isnt reality but I was hoping through judicious use of these
functions I could
On Sun, Nov 27, 2011 at 8:10 PM, Kevin Burton rkevinbur...@charter.net wrote:
This has been very helpful. Thank you.
At the risk of further confirming my ignorance and taxing your patience I
would like to add another question. How would I modify this code so that
each week starts with the
, name = name, ...) :
missing values removed from data
-Original Message-
From: R. Michael Weylandt michael.weyla...@gmail.com
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, November 22, 2011 3:10 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data
On Tue, Nov 22, 2011 at 6:50 PM, Kevin Burton rkevinbur...@charter.net wrote:
Void of any other suggestions this approach makes sense but for my case I
think I need to use zoo objects rather than xts. If I sequence the data
generally I don't know if there will be 365 days in the year or 366. So
I was wondering what the best approach is for missing data in a time series.
I give an example using xts but I would like to know what seems to be the
best method. Say I have
library(xts)
xts.ts - xts(1:4,as.Date(c(1970-01-01, 1970-1-3, 1980-10-10,
2007-8-19)), frequency=52)
I would like
Couldn't you use seq.Date() to set up the time index and then just fill as
appropriate?
Alternatively, to.weekly if you are starting with a daily series.
Michael
On Nov 22, 2011, at 4:00 PM, Kevin Burton rkevinbur...@charter.net wrote:
I was wondering what the best approach is for missing
. Michael Weylandt michael.weyla...@gmail.com
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, November 22, 2011 3:10 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data?
Couldn't you use seq.Date() to set up the time index and then just fill as
appropriate?
Alternatively
is a Sunday and the 10th is a Monday (the beginning of the week).
-Original Message-
From: R. Michael Weylandt michael.weyla...@gmail.com
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, November 22, 2011 3:10 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data
, weeks, name = name, ...) :
missing values removed from data
-Original Message-
From: R. Michael Weylandt michael.weyla...@gmail.com
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, November 22, 2011 3:10 PM
To: Kevin Burton
Cc: r-help@r-project.org
Subject: Re: [R] Missing data
Hi readers,
I'm new to the R package and have a question about handling missing data in
R. I have a dataset from a longitudinal study where we are are testing a
series of models, some in which lagged variables are used to predict an
outcome and others in which concurrent variables are used. Due
Hello
Running R2.9.2 on Windows XP
I am puzzled by the performance of LME in situations where there are missing
data. As I understand it, one of the strengths of this sort of model is how
well it deals with missing data, yet lme requires nonmissing data.
Thus,
m1.mod1 - lme(fixed =
Peter Flom wrote:
I am puzzled by the performance of LME in situations where there are
missing data. As I
understand it, one of the strengths of this sort of model is how well it
deals with missing
data, yet lme requires nonmissing data.
You are confusing missing data with an
I wrote
I am puzzled by the performance of LME in situations where there are
missing data. As I
understand it, one of the strengths of this sort of model is how well it
deals with missing
data, yet lme requires nonmissing data.
Mark Difford replied
You are confusing missing data with
Mixed models based on likelihood methods can often handle missing
observations within subjects, but they not do well with missing
individual elements in the design matrices (think unit nonresponse vs
item nonresponse in the survey world). Continuing with the example I
recently sent to you
Hi Peter,
See e.g. Hedeker and Gibbons, Longitudinal Data Analysis, which
repeatedly stresses that
mixed models provide good estimates if the data are missing at random.
This may be true. However, one of the real strengths of LME is that it
handles unbalanced designs, which is a different
hi
I would like to know how I can complete those missing data from these
programs:
program number one
DATOS2 - sin(seq(1,20,0.1))
DATOS2[103] - NA
DATOS2[65] - NA
DATOS2[134] - NA
this is the other one
data(pressure)
DATOS3 - pressure
DATOS3[4,1] - NA
DATOS3[14,1:2] -
Hi All,
Newbie question that i'm sure is easy, but i can't seem to apply properly
I read in a datafram from a CSV file and i want to tell R that from coloum
n_0 to n_32 the value -1 is missing data
i was looking at the
is.na(xx) - c(..,...,) idea but i can't seem to apply it properly, can
anyone
This might help the first question:
da - (-1):1
x - data.frame(a1=sample(da,10,TRUE), a2=sample(da,10,TRUE),
a3=sample(da,10,TRUE))
x
a1 a2 a3
1 0 1 0
2 0 0 1
3 0 1 0
4 -1 0 -1
5 1 0 -1
6 1 1 -1
7 1 -1 -1
8 -1 0 0
9 1 1 0
10 0 1 0
is.na(x[1:3]) - x[1:3] ==
My dataset contains missing data and I would like to do something like an EM
algorithm or a Markov Chain Monte Carlo approach to get rid of the missing
data.
Is there a function for imputation or simulation of missing data apart from
those in the randomForest library?
Thanks in advance
Birgit
On 6/4/2008 5:32 AM, Birgitle wrote:
My dataset contains missing data and I would like to do something like an EM
algorithm or a Markov Chain Monte Carlo approach to get rid of the missing
data.
Is there a function for imputation or simulation of missing data apart from
those in the
Birgit,
not knowing your data, I would recommend R-package mice or function
aregImpute from R-package Hmisc as good multi-purpose tools.
Regards, Ulrike
--
View this message in context:
http://www.nabble.com/missing-data-imputation---simulation-tp17642736p17643601.html
Sent from the R help
Many thenks to both of you:
Will have a look.
Birgit
Chuck Cleland wrote:
On 6/4/2008 5:32 AM, Birgitle wrote:
My dataset contains missing data and I would like to do something like an
EM
algorithm or a Markov Chain Monte Carlo approach to get rid of the
missing
data.
Is there a
Hi There,
Will somebody know if there is a function in R which can compute the
proximity from an incomplete data matrix?
Or any other software which can do this?
Thank you.
Van
__
R-help@r-project.org mailing list
11, 2007 2:36 PM
To: [EMAIL PROTECTED]
Subject: [R] Missing data
Hi all,
I'm looking for a contributed package that can provide a detailed
account of missing data patterns and perhaps also provide imputation
procedures, such as mean imputation or hot deck imputation and the like
To: [EMAIL PROTECTED]
Subject: [R] Missing data
Hi all,
I'm looking for a contributed package that can provide a detailed
account of missing data patterns and perhaps also provide imputation
procedures, such as mean imputation or hot deck imputation and the like.
Is there anything out
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