Re: [R] Insert elements into a vector in a defined positions
That's good . Your solution works for me.
Than you Rolf.
Rolf Turner-3 wrote:
On 26/11/2009, at 10:46 AM, Manuel Ramon wrote:
Dear R users,
I have a vector of length n and I want to insert some elements (in
my case
the NA string) into a defined positions. For example, my vector is
z1 and I
want to add NA's in positions 4, 6 y 7 so after that, my new
vector, z2,
should have a length of 10+3.
z1 - 1:10
id - c(4,6,7)
# And z2 should be:
z2 - c(1,2,3,NA,4,5,NA,NA,6,7,8,9,10)
Anyone knows how can I do that?
At first I thought append() might work ... but the after
argument to append seems to have to be of length 1.
How about:
z2 - numeric(length(z1)+length(id))
z2[id] - NA
z2[!is.na(z2)] - z1
A bit kludgy, but it appears to work.
cheers,
Rolf Turner
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[R] Insert elements into a vector in a defined positions
Dear R users, I have a vector of length n and I want to insert some elements (in my case the NA string) into a defined positions. For example, my vector is z1 and I want to add NA's in positions 4, 6 y 7 so after that, my new vector, z2, should have a length of 10+3. z1 - 1:10 id - c(4,6,7) # And z2 should be: z2 - c(1,2,3,NA,4,5,NA,NA,6,7,8,9,10) Anyone knows how can I do that? Thank you. - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://old.nabble.com/Insert-elements-into-a-vector-in-a-defined-positions-tp26520841p26520841.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How to deal with this random variable?
Thank you for your replay Bert. You are right, is complicated to get a good response when people do not know how the experiment was conducted, etc. The main problem, maybe, is that this experiment has a wrong design being complicated to get some good conclusion from it. I read this forum frequently and I found a lot of useful information on it. For that reason I decided to ask to the forum; maybe someone can help us. Thank you again for your response Bert. Bert Gunter wrote: This sounds way too complicated for this forum, which is designed to provide help to users on the use of the R language, not remote statistical consulting. While you may receive replies, I would argue that you would do better to find a local statistical expert with whom to work -- not least because they should probably have a deep understanding of how your experiment was conducted, data gathered, measurements made, etc. to be able to give you worthwhile advice. Long distance consulting based on incomplete understanding is very risky. Caveat emptor! Bert Gunter Genentech Nonclinical Biostatistics -Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Manuel Ramon Sent: Monday, July 27, 2009 9:54 AM To: r-help@r-project.org Subject: [R] How to deal with this random variable? Hello to everybody, I have a data frame with 100 measures of quality for 3 variables: A, B and C. These quality variables are measured in diferent times along the productive process. My data comes from 5 experiments (5 replicates with 20 measures for replicate). I also have a final measure (Z) but just one measure for each unit, that is, for the 20 units that are measured on each replica. My objetive is to study the relationships between the 3 quality parameters with the last measure, that is: lm(Z ~ A+B+C, data=mydata) I have found significant differences between replicas for each qualite parameters (A, B and C) and I would like to include the replica effect as a random effect: lme(Z ~ A+B+C, data=mydata, random=~1|replica) And here is my problem. I know that there are signifficant diferences between replicas but since the final measure, Z, is the same for each replica I do not know how to deal with. Can you help me? How could I take into account the variability due to the replica when I want to study the effects of variables A, B and C on the final result of a productive process? Thank you in advance. - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://www.nabble.com/How-to-deal-with-this-random-variable--tp24684341p2468 4341.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://www.nabble.com/How-to-deal-with-this-random-variable--tp24684341p24695050.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] How to deal with this random variable?
Hello to everybody, I have a data frame with 100 measures of quality for 3 variables: A, B and C. These quality variables are measured in diferent times along the productive process. My data comes from 5 experiments (5 replicates with 20 measures for replicate). I also have a final measure (Z) but just one measure for each unit, that is, for the 20 units that are measured on each replica. My objetive is to study the relationships between the 3 quality parameters with the last measure, that is: lm(Z ~ A+B+C, data=mydata) I have found significant differences between replicas for each qualite parameters (A, B and C) and I would like to include the replica effect as a random effect: lme(Z ~ A+B+C, data=mydata, random=~1|replica) And here is my problem. I know that there are signifficant diferences between replicas but since the final measure, Z, is the same for each replica I do not know how to deal with. Can you help me? How could I take into account the variability due to the replica when I want to study the effects of variables A, B and C on the final result of a productive process? Thank you in advance. - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://www.nabble.com/How-to-deal-with-this-random-variable--tp24684341p24684341.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Principal components vs. raw variables
Hello to everyone, I am starting to work on classification procedures. I usualy do a principal component analysis (PCA) as a previous step in order to reduce variables and after I apply a cluster procedure. My question is if it will be better to use raw variables instead of use principal components obtained from these variables since the original variables keep all the variability. Now i am thinking to use a variable group analysis (VGA) and a correlation analysis together in order to identify which of my original variables could explain differences on my data better, and after apply a cluster analysis on selected variables. What do you think about it? What would be better: work with PCA or with raw variables. Thanks in advance. Manuel - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) mra...@jccm.es -- View this message in context: http://www.nabble.com/Principal-components-vs.-raw-variables-tp22824280p22824280.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Re siduals from a linear model
I'm working with a linear model with four factors as explicatory variables, being all of them significally (e.g. y ~ a + b + c + d). I thought that the residuals of a linear model keep the variance not explained by the model, so if I use my model with just three factors (y ~ a + b + c) and keep the residuals is expected that in a new model with the residuals as dependent variable and the four factor as independent (residuals ~ d) that factor (d) will be significally. Is that truth or not? - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) [EMAIL PROTECTED] -- View this message in context: http://www.nabble.com/Residuals-from-a-linear-model-tp20556033p20556033.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Anyone can help me with a model I have looking for?
Hello to everyone. I don't know if that forum is the rigth place to post my question but I would be greatful for your help. My problem is as follow: I have a performance trait as a dependent variable and measures of temperature in different days as a covariate. I assume that there is an accumulative effect of the temperature and heat load is faster than heat loss, that is, is necessary more time to drop the accumulative heat load. The question is that over a accumulative heat load threshold my performance trait stars to fall and i would like to estimate this threshold. How could i do that? Thanks again. - Manuel Ramón Fernández Group of Reproductive Biology (GBR) University of Castilla-La Mancha (Spain) [EMAIL PROTECTED] -- View this message in context: http://www.nabble.com/Anyone-can-help-me-with-a-model-I-have-looking-for--tp19509373p19509373.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] how to calculate the mode of a continuous variable
Is there any R funtion that allow the estimation of mode in a continuous variable? Thank you -- View this message in context: http://www.nabble.com/how-to-calculate-the-mode-of-a-continuous-variable-tp19214243p19214243.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] how to calculate the mode of a continuous variable
Thanks Peter, it's a good solution.
Finding on RSiteSearch I found a similar solution and I wrote a function to
obtain the mode. That function is as follows.
mode - function(data) {
# Function for mode estimation of a continuous variable
# Kernel density estimation by Ted Harding Douglas Bates (found on
RSiteSearch)
x-data
lim.inf=min(x)-1; lim.sup=max(x)+1
hist(x,freq=FALSE,breaks=seq(lim.inf,lim.sup,0.2))
s-density(x,from=lim.inf,to=lim.sup,bw=0.2)
n-length(s$y)
v1-s$y[1:(n-2)];
v2-s$y[2:(n-1)];
v3-s$y[3:n]
ix-1+which((v1v2)(v2v3))
lines(s$x,s$y,col=red)
points(s$x[ix],s$y[ix],col=blue)
md - s$x[which(s$y==max(s$y))]
md
}
Thanks for your help,
Manuel Ramon
Peter Dalgaard wrote:
Henrique Dallazuanna wrote:
Try:
as.numeric(names(which.max(table(x
On Fri, Aug 29, 2008 at 3:13 AM, Manuel Ramon [EMAIL PROTECTED] wrote:
You missed the word continuous there...
x - rnorm(10)
table(x)
x
-1.64244637710945 -0.836534097622312 -0.810292826933485
-0.721008996586432
1 1 1
1
-0.679702422788255 -0.667735659553467 -0.263432175981501
0.0795699932826675
1 1 1
1
0.387151850978792 0.761964511475389
1 1
as.numeric(names(which.max(table(x
[1] -1.642446
Instead, how about
dd - density(x)
which.max(dd$y)
[1] 227
dd$x[which.max(dd$y)]
[1] -0.6938049
plot(dd)
rug(x)
abline(v=dd$x[which.max(dd$y)])
Is there any R funtion that allow the estimation of mode in a continuous
variable?
Thank you
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Re: [R] Is there a model like that in R?
Thanks for your response Gustav, The dots in the models mean that there are other effects in the models. In relation to the model, it would be like this: Q(i) = Q(i-1) + b*max(s(i) - s(0), 0) + c*min(s(i) - s(0), 0) If( s(i) s(0)) then max(s(i) - s(0), 0) = s(i)-s(0) min(s(i) - s(0), 0) = 0 There is an accumulative positive effect and Q(1) is greater than Q(0) If( s(1) s(0)) then max(s(i) - s(0), 0) = 0 min(s(i) - s(0), 0) = s(i)-s(0) There is an accumulative negative effect and Q(1) is lower than Q(0) For i=1: Q(1) = Q(0) + b*max(s(1) - s(0), 0) + c*min(s(1) - s(0), 0) If( s(1) s(0)) then max(s(1) - s(0), 0) = s(1)-s(0) min(s(1) - s(0), 0) = 0 If( s(1) s(0)) then max(s(1) - s(0), 0) = 0 min(s(1) - s(0), 0) = s(1)-s(0) In the model the value of Q depends on the value of s. If that value is above a threshold, Q increases and if the value of s is below the threshold Q decreases. Then, when Q is above a threshold (different that the threshold of s) it has an effect. In my particular case, s is temperature and Q would be the amount of temperature (or something similar) that is accumulated. -Original Message- From: Gustaf Rydevik [mailto:[EMAIL PROTECTED] Sent: Friday, September 28, 2007 11:13 AM To: Manuel Ramon Cc: r-help@r-project.org Subject: Re: [R] Is there a model like that in R? On 9/28/07, Manuel Ramon [EMAIL PROTECTED] wrote: Hi to everyone, I am starting to work with a model that is not familiar to me. The model would be like that: y = . - a*max(Q(i) - Q(0), 0) + . where Q(i) is the accumulated effect of a variable at time i and Q(0) a threshold above it there is effect on y. The value of Q(i) could be estimated as: Q(i+1) = Q(i) + b*max(s(i) - s(0), 0) + c*min(s(i) - s(0), 0) + . Where s would be the effect that produces the accumulate effect of Q, s(i) would be this effect at time i and s(0) another threshold above it the accumulative effect is produced. The coefficient b would be the rate of accumulation and c the rate of decay. What kind of model is it? Is it somewhat similar to time series? I appreciate your help. Manuel Ramon I'm not sure what you're describing, but something is wrong for your definition of Q(i). For i=1: Q(1) = Q(0) + b*max(s(0) - s(0), 0) + c*min(s(0) - s(0), 0) + .=Q(0)+b*0+c*0=Q(0). Thus, Q(n)=Q(n-1)=...=Q(0) In addition, what is intended by the dots and the final plus in y = . - a*max(Q(i) - Q(0), 0) + .? I think you have to describe things a bit more careful, if we are to understand what's happening. Best, Gustaf -- Gustaf Rydevik, M.Sci. tel: +46(0)703 051 451 address:Essingetorget 40,112 66 Stockholm, SE skype:gustaf_rydevik Manuel [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
