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 wi
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 th
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
array_values_10<-arra
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
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 survey
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 th
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)), "fac
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]
eval(parse(t
tation, TX 77840-4352
-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 data
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
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 ]
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 is,
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 to
> -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, 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 th
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 mi
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 nee
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( head(M
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
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 f
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 e
On Sun, Nov 27, 2011 at 8:10 PM, Kevin Burton 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 same day of the week re
iginal Message-
From: Gabor Grothendieck [mailto:ggrothendi...@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
wrote:
> I admit it isnt reality but I was hoping through
On Sun, Nov 27, 2011 at 4:08 PM, Kevin Burton 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 way to
> recognize thi
..@gmail.com]
> Sent: 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 tim
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
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
On Tue, Nov 22, 2011 at 6:50 PM, Kevin Burton 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
> have to sequence
amiliar with the conversion because zoo doesn't convert
> to a regular 'ts' very well. But zoo remembers the frequency setting
> whereas xts just ignores it.
>
> It seems like there is still considerable work to solve the original
> problem. If I create a time ser
"2011-12-31"), by="day"), frequency=52)
> to.weekly(ds, sum)
Error in if (drop.time) x <- .drop.time(x) :
argument is not interpretable as logical
In addition: Warning message:
In to.period(x, "weeks", name = name, ...) :
missing values removed from data
---
e first column should be consistent.
Notice that 10-09 and 10-10 are properly considered different weeks because
the 9th is a Sunday and the 10th is a Monday (the beginning of the week).
-Original Message-
From: R. Michael Weylandt
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, Novembe
--
From: R. Michael Weylandt
[mailto:michael.weyla...@gmail.com]
Sent: Tuesday, November 22, 2011 3:10 PM
To: Kevin Burton
Cc:
Subject: Re: [R] Missing data?
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
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" wrote:
> I was wondering what the best approach is for missing data in a time series.
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 w
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
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 differ
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
set.see
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 mi
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
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 = math_
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
> DATO
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
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
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.
>>
>>
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
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 randomForest
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
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
https://stat.ethz.ch/mailman/listinf
Excellent. Thanks!
===
David Kaplan, Ph.D.
Professor
Department of Educational Psychology
University of Wisconsin - Madison
Educational Sciences, Room 1061
1025 W. Johnson Street
Madison, WI 53706
email: [EMAIL PROTECTED]
Web:
The mice package might also be useful, especially the md.pattern function:
http://finzi.psych.upenn.edu/R/library/mice/html/md.pattern.html
James
--
James Reilly
Department of Statistics, University of Auckland
Private Bag 92019, Auckland, New Zealand
On 12/9/07 1:33 PM, Bill Pikounis wrote:
> D
David,
Frank Harrell's pair of packages Hmisc and Design has some functions
for tabulating, visualizing, and accounting for missing data. I
recommend them as one avenue to investigate. Frank's companion book
"Regression Modeling Strategies" covers their use in-depth.
Hope that helps,
Bill
__
gt;>
>> -Original Message-
>> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
>> project.org] On
>> Behalf Of David Kaplan
>> Sent: Tuesday, September 11, 2007 2:36 PM
>> To: [EMAIL PROTECTED]
>> Subject: [R] Missing data
>>
>>
PROTECTED] On
> Behalf Of David Kaplan
> Sent: Tuesday, September 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 perha
avid Kaplan
Sent: Tuesday, September 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
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 there?
Thanks in advance,
David
--
===
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