[R] confidence intervals of proportions from complex surveys
This is partly an R and partly a general statistics question. I'm trying to get confidence intervals of proportions (sometimes for subgroups) estimated from complex survey data. Because a function like prop.test() does not exist for the survey package I tried the following: 1) Define a survey object (PSU of clustered sample, population weights); 2) Use svyglm() of the package survey to estimate a binary logistic regression (family='binomial'): For the confidence interval of a single proportion regress the binary dependent variable on a constant (1), for confidence intervals of that variable for subgroups regress this variable on the groups (factor) variable; 3) Use predict() to obtain estimated logits and the respective standard errors (mod.dat specifiying either the constant or the subgroups): pred=predict(model,mod.dat,type='link',se.fit=T) and apply the following to obtain the proportion with its confidence intervals (for example, for conf.level=.95): lo.e = pred[1:length(pred)]-qnorm((1+conf.level)/2)*SE(pred) hi.e = pred[1:length(pred)]+qnorm((1+conf.level)/2)*SE(pred) prop = 1/(1+exp(-pred[1:length(pred)])) lo = 1/(1+exp(-lo.e)) hi = 1/(1+exp(-hi.e)) I think that in that way I get CI's based on asymptotic normality - either for a single proportion or split up into subgroups. Question: Is this a correct or a defensible procedure? Or should I use a different approach? Note that this approach should also allow to estimate CI's for proportions of subgroups taking into account the complex survey design. TIA, Dirk R version 2.5.1 Patched (2007-08-10 r42469) i386-pc-mingw32 __ R-help@stat.math.ethz.ch 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] Confidence intervals for ccf()
Hello, This is not a purely R-question, but perhaps someone can help me anyway. I am trying to estimate the correlation between two time series (which are both basically different types of measurements of the same phenomena), using both cor.test() (with pearson as method) and ccf(). Now, cor.test gives a confidence interval for the pearson correlation, while ccf does not. I've tried to use bootstrap methods to get confidence interval for the ccf function, but no luck. It is a bit tricky, since the time series are non-stationary, and so I'm not sure how to go about to generate the bootstrap-sample. Does anyone have any ideas on how to do this, i.e get a confidence interval for the ccf at different time lags? Many thanks in advance, Gustaf -- Gustaf Rydevik, M.Sci. tel: +46(0)703 051 451 address:Essingetorget 40,112 66 Stockholm, SE skype:gustaf_rydevik __ R-help@stat.math.ethz.ch 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] confidence intervals for multinomial
Hi All, I want to test an H0 hypothesis about the proportions of observed counts in k classes. I know that I can do this with the chisq.test. However, besides of the overall acceptance or rejection of the H0, I would like to know which of the k classes cause(s) rejection and I would like to know the observation-based confidence envelopes for the proportions for the k classes. My quick-and-dirty approach thus far is to do an initial chisq.test on the original k classes and then to lump data into two classes (=one of the original classes and all other original classes lumped into one new class) and do a binom.test. I interpret the result of the binom.test as indicating whether the current class might be the reason for the rejection of the overall H0. Additionally, it gives me a confidence envelope for this class. This approach seems fairly straightforward, but I just do not feel totally comfortable with it. I would feel so much better if there was something like a multinom.test, but to my knowledge there is none. Do you have any suggestions what I could rather do? For instance, I might follow a Monte Carlo-like approach: I simulate proportions for the k classes based on the proportions of observed counts with rmultinom. After exclusion of the most extreme values I construct my confidence envelope based on the remaining simulated proportions. Based on whether the hypothesized proportions fall into the observation-based confidence envelopes, I accept or reject. Do you think that either of these approaches is better or would you suggest doing something totally different? All comments and suggestions are highly appreciated. Kind regards, Michael PS: I guess my request parallels that of Matthias Schmidt from Apr 5, 2004, that was answered by Brian Ripley ... Michael Drescher Ontario Forest Research Institute Ontario Ministry of Natural Resources 1235 Queen St East Sault Ste Marie, ON, P6A 2E3 Tel: (705) 946-7406 Fax: (705) 946-2030 __ R-help@stat.math.ethz.ch 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] confidence intervals on multiple comparisons
Enrico, prop.test is for testing proportions two at a time. If you want to test for differences between 4 proportions simultaneously (rather than two at a time), try a logistic regression model (from which you can get confidence intervals for each of your groups). Cody Hamilton, PhD Staff Biostatistician Edwards Lifesciences Salvatore Enrico Indiogine [EMAIL PROTECTED] To .com R-help@stat.math.ethz.ch Sent by: cc [EMAIL PROTECTED] at.math.ethz.ch Subject [R] confidence intervals on multiple comparisons 05/13/2007 10:51 AM Greetings! I am using prop.test to compare 4 proportions to find out whether they are equal. According to the help function you can not have confidence intervals if you compare more than 2 proportions. I need to find an effect size or confidence interval for these proportions. Any suggestions? Enrico -- Enrico Indiogine Mathematics Education Texas AM University [EMAIL PROTECTED] __ R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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] confidence intervals on multiple comparisons
-Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED] Sent: Tuesday, May 15, 2007 12:52 PM To: Salvatore Enrico Indiogine Cc: R-help@stat.math.ethz.ch; [EMAIL PROTECTED] Subject: Re: [R] confidence intervals on multiple comparisons Enrico, prop.test is for testing proportions two at a time. If you want to test for differences between 4 proportions simultaneously (rather than two at a time), try a logistic regression model (from which you can get confidence intervals for each of your groups). Cody Hamilton, PhD Staff Biostatistician Edwards Lifesciences Yes, but beware: in the default contr.treatment coding for contrasts, you get estimates and confidence intervals for the first group and for the **differences** between the first group and the others. As you said, it's easy to get what you want from this, but you must pay attention to the details here. Bert Gunter Genentech Nonclinical statistics Salvatore Enrico Indiogine [EMAIL PROTECTED] To .com R-help@stat.math.ethz.ch Sent by: cc [EMAIL PROTECTED] at.math.ethz.ch Subject [R] confidence intervals on multiple comparisons 05/13/2007 10:51 AM Greetings! I am using prop.test to compare 4 proportions to find out whether they are equal. According to the help function you can not have confidence intervals if you compare more than 2 proportions. I need to find an effect size or confidence interval for these proportions. Any suggestions? Enrico -- Enrico Indiogine Mathematics Education Texas AM University [EMAIL PROTECTED] __ R-help@stat.math.ethz.ch 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@stat.math.ethz.ch 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@stat.math.ethz.ch 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] Confidence-Intervals.... help...
Hi... I have to use R to find out the 90% confidence-interval for the sensitivity and specificity of the following diagnostic test: A particular diagnostic test for multiple sclerosis was conducted on 20 MS patients and 20 healthy subjects, 6 MS patients were classified as healthy and 8 healthy subjects were classified as suffering from the MS. Furthermore, I need to find the number of MS patients required for a sensitivity of 1%... Is there a simple R-command which can do that for me? I am completely new to R... Help please! Jochen -- View this message in context: http://www.nabble.com/Confidence-Intervals-help...-tf3544217.html#a9894014 Sent from the R help mailing list archive at Nabble.com. __ R-help@stat.math.ethz.ch 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] Confidence-Intervals.... help...
Hm... sounds like a homework problem to me... Maybe start by figuring out how to do it without R - what's the approach, and how would you calculate it? Then search R help for the possible key words you came up with. Sarah On 4/8/07, Jochen.F [EMAIL PROTECTED] wrote: Hi... I have to use R to find out the 90% confidence-interval for the sensitivity and specificity of the following diagnostic test: A particular diagnostic test for multiple sclerosis was conducted on 20 MS patients and 20 healthy subjects, 6 MS patients were classified as healthy and 8 healthy subjects were classified as suffering from the MS. Furthermore, I need to find the number of MS patients required for a sensitivity of 1%... Is there a simple R-command which can do that for me? I am completely new to R... Help please! Jochen -- -- Sarah Goslee http://www.functionaldiversity.org __ R-help@stat.math.ethz.ch 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] confidence intervals
Hi, I'm having trouble with the confidence interval of the nls function. I did my home work, and searched acros the support list until I came up with following solution of Peter Dalgaard: example(predict.nls) se.fit - sqrt(apply(attr(predict(fm,list(Time = tt)),gradient),1, function(x) sum(vcov(fm)*outer(x,x matplot(tt, predict(fm,list(Time = tt))+ outer(se.fit,qnorm(c(.5, .025,.975))),type=l) points(demand ~ Time, data = BOD) One slight issue is that it doesn't work if newdata is omitted, but then you can easily get the gradient from fm$m$gradient() I tried this with my own data: Data - data.frame(Temp=rep(c(25,40),each=3), Mnd = c(1:3),Degr = c(0.057,0.077,0.108,0.148,0.198,0.223)) model - nls(Degr~exp((A/(Temp)+log(Mnd))*B),Data,start=list(A=-10,B=1)) Months - c(1,2,3,6,9,12,24,48) se.fit - sqrt(apply(attr(predict(model,list(Temp = 25,Mnd=Months)),gradient),1, function(x) sum(vcov(fm)*outer(x,x But unfortunately I get an error ( Error in apply(attr(predict(model, list(Temp = 25, Mnd = Months)), gradient), : dim(X) must have a positive length) When I try using the gradient of the model instead of using the new data then there is no problem: se.fit - sqrt(apply(model$m$gradient(),1, function(x) sum(vcov(model)*outer(x,x matplot(Data$Mnd, predict(model,list(Temp = Data$Temp,Mnd=Data$Mnd))+outer(se.fit,qnorm(c(.5, 025,.975))),type=l) But how about calculating confidence intervals of new data? How do I get an gradient for these values? I'm using Windows XP, R 2.4.1. Thanks Bart [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch 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] Confidence intervals of quantiles
Many thanks for all the contributions to this problem.
As inferred by Ted Harding, I was after a distribution-free CIs as a lot
of
the data I use is not normally distributed.
The method provided by Ted for calculating exact CIs gave good results with
the limits almost symmetric about the quantile. Even for my smaller data
set with only 115 samples and a very skewed distribution the results clearly
showed the increase in range and asymmetry of the confidence limits.
The bootstrap method provided by Dimitris also works reasonably well but
is
slower and the ranges for the CIs are sometimes very asymmetric and in one
case did not actually encompass the quantile. The method would not work at
all with the skewed distribution of 115 samples until I reduced the quantile
range from 0.1 and 0.9 to 0.2 and 0.8 However, as Dimitris warned, you have
to be careful with this method for extreme quantiles and small samples.
I was also sent a method by Søren Merser, but this was incomplete. This
method was written by Scott Chasalow and the full code can be found at
http://www.dpw.wau.nl/pv/pub/chasalow/S/win/ci.quantile/
The code was written for S-Plus but it worked ok for me in R. This method
actually gives several ranges about the quantile, each with about the same
level of confidence and the level of confidence is also in the output. As
with Ted Harding's method, these may not exactly match the desired
confidence level. There is an option to select the shortest range but it
would be easy enough to add code to give the most symmetric range.
As a chemist I am not able to comment on the statistical pros and cons of
the methods but they are certainly very helpful for my purposes.
Many thanks
Mike White
- Original Message -
From: Dimitris Rizopoulos [EMAIL PROTECTED]
To: Mike White [EMAIL PROTECTED]
Cc: R-help@stat.math.ethz.ch
Sent: Monday, February 05, 2007 2:43 PM
Subject: Re: [R] Confidence intervals of quantiles
you could use the Bootstrap method, using package 'boot', e.g.,
library(boot)
f.quantile - function(x, ind, ...){
quantile(x[ind], ...)
}
###
x - rgamma(750, 2)
quant.boot - boot(x, f.quantile, R = 1000, probs = c(0.025, 0.25,
0.5, 0.75, 0.975))
lapply(1:5, function(i) boot.ci(quant.boot, c(0.90, 0.95), type =
c(perc, bca), index = i))
y - rgamma(150, 2)
quant.boot - boot(y, f.quantile, R = 1000, probs = c(0.025, 0.25,
0.5, 0.75, 0.975))
lapply(1:5, function(i) boot.ci(quant.boot, c(0.90, 0.95), type =
c(perc, bca), index = i))
However, you should be a little bit careful with Bootstrap if you wish
to obtain CIs for extreme quantiles in small samples.
I hope it helps.
Best,
Dimitris
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
- Original Message -
From: Mike White [EMAIL PROTECTED]
To: R-help@stat.math.ethz.ch
Sent: Monday, February 05, 2007 2:47 PM
Subject: [R] Confidence intervals of quantiles
Can anyone please tell me if there is a function to calculate
confidence
intervals for the results of the quantile function.
Some of my data is normally distributed but some is also a squewed
distribution or a capped normal distribution. Some of the data sets
contain
about 700 values whereas others are smaller with about 100-150
values, so I
would like to see how the confidence intervals change for the
different
distributions and different data sizes.
Thanks
Mike White
__
R-help@stat.math.ethz.ch 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.
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
___
Tiscali Broadband only 9.99 a month for your first 3 months!
http://www.tiscali.co.uk/products/broadband/
__
R-help@stat.math.ethz.ch 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] Confidence intervals of quantiles
Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change for the different distributions and different data sizes. Thanks Mike White __ R-help@stat.math.ethz.ch 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] Confidence intervals of quantiles
you could use the Bootstrap method, using package 'boot', e.g.,
library(boot)
f.quantile - function(x, ind, ...){
quantile(x[ind], ...)
}
###
x - rgamma(750, 2)
quant.boot - boot(x, f.quantile, R = 1000, probs = c(0.025, 0.25,
0.5, 0.75, 0.975))
lapply(1:5, function(i) boot.ci(quant.boot, c(0.90, 0.95), type =
c(perc, bca), index = i))
y - rgamma(150, 2)
quant.boot - boot(y, f.quantile, R = 1000, probs = c(0.025, 0.25,
0.5, 0.75, 0.975))
lapply(1:5, function(i) boot.ci(quant.boot, c(0.90, 0.95), type =
c(perc, bca), index = i))
However, you should be a little bit careful with Bootstrap if you wish
to obtain CIs for extreme quantiles in small samples.
I hope it helps.
Best,
Dimitris
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/(0)16/336899
Fax: +32/(0)16/337015
Web: http://med.kuleuven.be/biostat/
http://www.student.kuleuven.be/~m0390867/dimitris.htm
- Original Message -
From: Mike White [EMAIL PROTECTED]
To: R-help@stat.math.ethz.ch
Sent: Monday, February 05, 2007 2:47 PM
Subject: [R] Confidence intervals of quantiles
Can anyone please tell me if there is a function to calculate
confidence
intervals for the results of the quantile function.
Some of my data is normally distributed but some is also a squewed
distribution or a capped normal distribution. Some of the data sets
contain
about 700 values whereas others are smaller with about 100-150
values, so I
would like to see how the confidence intervals change for the
different
distributions and different data sizes.
Thanks
Mike White
__
R-help@stat.math.ethz.ch 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.
Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm
__
R-help@stat.math.ethz.ch 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] Confidence intervals of quantiles
On 05-Feb-07 Mike White wrote:
Can anyone please tell me if there is a function to calculate
confidence intervals for the results of the quantile function.
Some of my data is normally distributed but some is also a
squewed distribution or a capped normal distribution. Some of
the data sets contain about 700 values whereas others are smaller
with about 100-150 values, so I would like to see how the confidence
intervals change for the different distributions and different data
sizes.
As well as the bootstrap suggestion (which may do what you want)
I'd like to suggest calcualting exact CIs for the distribution
quantiles. I don't know of an R function which does this, though
the principle is straightforward enough (I once implemented it
for octave/matlab).
I'm assuming you're after a truly distribution-free CI (as your
query suggests). In that case, the sample quantiles provide the
CI limits for the distribution quantiles, with the proviso that
you will not in general get an exact match for the confidence
level P that you desire for your confidence interval, so you
need to use P as a lower bound for the confidence level.
I'll illustrate with an equi-tailed 95% CI.
Notation:
x[r] (r = 1:n) is the r-th order statistic of a sample of n
values of a random variable X, drawn from a continuous distribution.
X[r] is the corresponding random variable.
X(p) is the p-th quantile of the distribution in question,
i.e. the value of X such that P(X = X[p]) = p. 0 p 1.
Objective: You want a 95% CI (X(p)L,X(p).R) for the quantile X(p)
for given p.
Principle:
P( X[r] = X(p) ) = pbinom(r, n, p) [*]
(i.e. the probability that you get at least r of the x's
below X(p)).
Now, for the upper limit X(p).R of the CI, you want the
smallest value of X(p) such that the probability [*] is not
less than 0.925 (i.e. 1 - (1-P)/2 = 1 - (1-0.95)/2 ).
This is achieved by X(p).R = x[s] where
s = min(which(pbinom((0:n),n,p) = 0.025))
and, similalry, for the lower limit, X(p).L = x[r] where
r = max(which(pbinom((0:n),n,p) = 0.025))
(Note that the which counts the binomial case r=0 as number 1)
If I've got my head around the above right, the following
function does the above job, and caould be fairly easily
modified for asymmetric confidence intervals (e.g. a 95%
confidence interval with 96% for inclusion below the upper
limit and 99% for inclusion above the lower limit).
q.CI-function(x,p,P){
# x is the sample, p (0p1) the quantile, P the confidence level
x-sort(x)
n-length(x)
s - min(which(pbinom((0:n),n,p) = 1-(1-P)/2))
r - max(which(pbinom((0:n),n,p) = (1-P)/2))
c(x[r],x[s])
# x[r] is the lower limit, x[s] the upper limit, of the CI
}
Note that it gives a confidence level P at least equal to P,
not in general exactly equal to P.
I've tested it as follows (1 simulations with samples
of size 101 from a Normal distribution -- why not, after
all? No loss of generality!):
p-0.5; Xq-qnorm(p); P-0.95
N-1; incls-numeric(N)
for(i in (1:N)){
x-rnorm(101)
CI-q.CI(x,p,P)
if((CI[1]=Xq)(CI[2]=Xq)){ incls[i]-1 }
}; sum(incls)/N
# [1] 0.9564
# [1] 0.9548
p-0.75; Xq-qnorm(p); P-0.95
[etc. ... ]
# [1] 0.9631
# [1] 0.9596
p-0.90; Xq-qnorm(p); P-0.95
[etc. ... ]
# [1] 0.9540
# [1] 0.9533
p-0.95; Xq-qnorm(p); P-0.95
[etc. ... ]
# [1] 0.9840
# [1] 0.9826
p-0.99; Xq-qnorm(p); P-0.95
[etc. ... ]
Error in if ((CI[1] = Xq) (CI[2] = Xq)) { :
missing value where TRUE/FALSE needed
showing that for extreme values of p relative to the
sample size, trouble occurs (as is to be expected)!
The solution is to test for NA in CI[1] and/or CI[2],
so I modified my test routine as follows, to give notional
lower confidence limit -Inf if CI[1] is NA, and/or upper
confidence limit +Inf if CI[2] is NA (of course this
could be done in the function q.CI() itself, but perhaps
it offers more flexibility for the user to do something
else with it, if it is left as NA).
p-0.99; Xq-qnorm(p); P-0.95
N-1; incls-numeric(N)
for(i in (1:N)){
x-rnorm(101)
CI-q.CI(x,p,P)
if(is.na(CI[1])){ CI[1]-(-Inf) }
if(is.na(CI[2])){ CI[2]-(+Inf) }
if((CI[1]=Xq)(CI[2]=Xq)){ incls[i]-1 }
}; sum(incls)/N
# [1] 0.9841
# [1] 0.9821
Comments welcome!!!
Ted.
E-Mail: (Ted Harding) [EMAIL PROTECTED]
Fax-to-email: +44 (0)870 094 0861
Date: 05-Feb-07 Time: 23:40:23
-- XFMail --
__
R-help@stat.math.ethz.ch 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] Confidence intervals for relative risk
Wolfgang,
It is common to handle relative risk problems using Poisson regression.
In your example you have 8 events out of 508 tries, and 0/500 in the second
data set.
tdata - data.frame(y=c(8,0), n=c(508,500), group=1:0)
fit - glm(y ~ group + offset(log(n)), data=tdata, family=poisson)
Because of the zero, the standard beta/se(beta) confidence intervals don't
work. In fact, the actual MLE for the ratio is infinity, which the above
glm decides is exp(33.85) = 5* 10^14 --- which is close enough to infinity
for me. What you want is the lower limit of the interval, which you can
find with a profile likelihood. That is, look at the curve of deviance vs
beta, draw a horizontal line 3.84 units down from the top (chisq on 1 df),
and where it itersects the curve is your confidence limit.
For the above, since it is 2 groups with 2 coefficients, the deviance of
the full model happens to be 0. Your approximation gave a lower limit of
.97 which is about exp(0), so I'll guess a solution between -2 and 2.
xx - seq(-2, 2, length=21)
for (i in 1:21) {
fit - glm(y ~ offset(group*xx[i] + log(n)), poisson, tdata)
print(c(xx[i], fit$deviance))
}
[1] -2.0 33.80736
[1] -1.8 31.02994
[1] -1.6 28.33138
[1] -1.4 25.72327
[1] -1.2 23.21769
[1] -1.0 20.82691
[1] -0.8 18.56281
[1] -0.6 16.43629
[1] -0.4 14.45668
[1] -0.2 12.63108
[1] 0.0 10.96387
[1] 0.2 9.45639
[1] 0.40 8.106805
[1] 0.60 6.910274
[1] 0.80 5.859303
[1] 1.00 4.944278
[1] 1.20 4.154088
[1] 1.40 3.476757
[1] 1.6 2.90003
[1] 1.8 2.41186
[1] 2.00 2.000785
So the exact lower limit is somewhere between exp(1.4) and exp(1.2), which
is around 3.6. One can easily refine this by tucking the fit into an
iterative search, or just resetting xx to a new range.
The offset(log(n)) part of the model, BTW, is a well known trick in poisson
models. It has to do with the fact that the likelihood is in terms of the
number of events, but we want coefficients in terms of rates, and E(y) = rate*n.
Adding an offset of xx[i] * group fits a model with the group effect fixed at
xx, but allowing the intercept to vary.
For more conceptual depth, you could look up 'rate regression' or
'standardized mortality ratio' in the Encyclopedia of Biostatistics --- it is
in the latter computation that this approach is quite common. The idea of
adding a fraction is found in Anscombe(1949), Transformations of Poisson,
binomial and negative-binomial data. Biometrika, vol 35, p246-254, but for
each single estimate not for the ratio.
Terry Therneau
Mayo Clinic
__
R-help@stat.math.ethz.ch 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] Confidence intervals for Brier score
I am using the ipred library to calculate the censored Brier score for a Cox proportional hazards model. I would like to know if anyone has developed a method of calculating confidence intervals for the various forms of the Brier score that are used in the analysis of survival/censored data. If so, are these implemented in S? Thank you Brant Inman [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch 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] Confidence intervals on Lowess (eg SiZer from J.S.Marron)
Hi, I have some very noisy, relatively sparse data; a biological response of roughly ~8 subjects at ~8 times points). I've been following the data trend using a lowess line, over-plotted with several values of bandwidth, 'f - seq(0.3, 0.9, by=0.1)'. At this point, we have no models for these data. I wonder if there is any way under R to assign some sort of confidence interval to the lowess line. For example, I have seen a method from the lab of J.S.Marron, which his group has implemented in the Matlab program, SiZer. http://www.stat.unc.edu/faculty/marron/DataAnalyses/SiZer_Intro.html and, the more interesting: http://www.stat.unc.edu/faculty/marron/DataAnalyses/SiZer/SiZer_Basics.html An implementation under R of SiZer would obviously answer this question; does it exist? Suggestions of alternative approaches would also be welcome. My searchs of the R-maillist archive for 'sizer', 'Marron' and 'scale-space' didn't return anything that I recognized as useful here, but I am new to the idea of confidence in a lowess line so I may not be using the appropriate vocabulary. Thank-you, Mark -- Mark Dalphin Dept Comp Biol, M/S AW2/D3262 Amgen, Inc. 1201 Amgen Court W Seattle, WA 98119 Phone: +1-206-265-7951 __ R-help@stat.math.ethz.ch 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] Confidence Intervals for Mixed Effects
Are you familiar with intervals and lme in the nlme package? These are documented in Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). I'm not familiar with the algorithm, but if it's different from Satterthwaite's method, I suspect that Prof. Bates had a good reason for choosing something different. Since R is open source, you could read the code and modify it to use Sattherthwaite and compare the two side by side. The nlme package includes a simulate.lme function, which you could use to compare the different methods. hope this helps. spencer graves Michel Friesenhahn wrote: I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA [EMAIL PROTECTED] www.pdf.com http://www.pdf.com Tel: 408-938-4420 Fax: 408-280-7915 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence Intervals for Mixed Effects
I'm fairly new to R and am wondering if anybody knows of R code to calculate confidence intervals for parameters (fixed effects and variance components) from mixed effects models based on Sattherthwaite's method? I'm also interested in Satterthwaite-based confidence intervals for linear combinations (mostly sums) of various variance components. [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence Intervals for Arbitrary Functions
Before you spend a lot of time worrying about the distributions of ratios of normal variates, bootstrapping, etc., I suggest you make normal probability plots of your data AND OF THEIR LOGARITHMS, e.g., using qqnorm(y, datax=TRUE) qqnorm(y, datax=TRUE, log=x) With many electrical measurements from integrated circuits, these two plots will both likely be equally close to normality. That's not true for leakage currents, for which I often see highly skewed images, unless I take logarithms, as: qqnorm(exp(rnorm(99)), datax=TRUE) qqnorm(exp(rnorm(99)), datax=TRUE, log=x) I routinely see distributions of this nature in estimates of Poisson defect rates in wafer fab as well as in leakage currents, distributions of income, etc. This is important, because the distribution of a ratio of normally distributed random variables has known pathologies, while the distribtuion of a ratio of lognormal variates is lognormal. (With a ratio of normals, if the denominator has mean zero, the ratio follows the Cauchy disribution, also known as Student's t with one degree of freedom. This distribution has infinite variance, and the mean is not even defined, being Inf-Inf.) In sum, you will likely get better answers in less time if you can find it politically acceptable in your professional efforts to work in decibels or logaritms, where ratios become differences. spencer graves Gabor Grothendieck wrote: On 7/16/05, Jeff Newmiller [EMAIL PROTECTED] wrote: I have a rather basic background in statistics, and am looking for assistance in solving what I expect is a common type of problem. I have measurements of physical processes, and mathematical models of those processes that I want to feed the measurements into. A simple case is using measurements of electric power entering and leaving a power conversion device, sampled at regular intervals, and summed to estimate energy in and out, and dividing the energy out by the energy in to get an estimate of efficiency. I know that power efficiency varies with power level, but for this calculation I am interested in the quantifying the overall efficiency rather than the instantaneous efficiency. If the energy quantities are treated as a normally-distributed random variable (per measurement uncertainty), is there a package that simplifies the determination of the probability distribution function for the quotient of these values? Or, in the general sense, if I have a function that computes a measure of interest, are such tools general enough to handle this? (The goal being to determine a confidence interval for the computed quantity.) As an attempt to understand the issues, I have used SQL to generate discrete sampled normal distributions, and then computed new abscissa values using a function such as division and computing the joint probability as the ordinate, and then re-partitioned the result into new bins using GROUP BY. This is general enough to handle non-normal distributions as well, though I don't know how to quantify the numerical stability/accuracy of this computational procedure. However, this is pretty tedious... it seems like R ought to have some straightforward solution to this problem, but I don't seem to know what search terms to use. There is some discussion about the ratio of normals at: http://www.pitt.edu/~wpilib/statfaq.html but you may just want to use bootstrapping: library(boot) library(simpleboot) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA [EMAIL PROTECTED] www.pdf.com http://www.pdf.com Tel: 408-938-4420 Fax: 408-280-7915 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence Intervals for Arbitrary Functions
I have a rather basic background in statistics, and am looking for assistance in solving what I expect is a common type of problem. I have measurements of physical processes, and mathematical models of those processes that I want to feed the measurements into. A simple case is using measurements of electric power entering and leaving a power conversion device, sampled at regular intervals, and summed to estimate energy in and out, and dividing the energy out by the energy in to get an estimate of efficiency. I know that power efficiency varies with power level, but for this calculation I am interested in the quantifying the overall efficiency rather than the instantaneous efficiency. If the energy quantities are treated as a normally-distributed random variable (per measurement uncertainty), is there a package that simplifies the determination of the probability distribution function for the quotient of these values? Or, in the general sense, if I have a function that computes a measure of interest, are such tools general enough to handle this? (The goal being to determine a confidence interval for the computed quantity.) As an attempt to understand the issues, I have used SQL to generate discrete sampled normal distributions, and then computed new abscissa values using a function such as division and computing the joint probability as the ordinate, and then re-partitioned the result into new bins using GROUP BY. This is general enough to handle non-normal distributions as well, though I don't know how to quantify the numerical stability/accuracy of this computational procedure. However, this is pretty tedious... it seems like R ought to have some straightforward solution to this problem, but I don't seem to know what search terms to use. --- Jeff NewmillerThe . . Go Live... DCN:[EMAIL PROTECTED]Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence Intervals for Arbitrary Functions
On 7/16/05, Jeff Newmiller [EMAIL PROTECTED] wrote: I have a rather basic background in statistics, and am looking for assistance in solving what I expect is a common type of problem. I have measurements of physical processes, and mathematical models of those processes that I want to feed the measurements into. A simple case is using measurements of electric power entering and leaving a power conversion device, sampled at regular intervals, and summed to estimate energy in and out, and dividing the energy out by the energy in to get an estimate of efficiency. I know that power efficiency varies with power level, but for this calculation I am interested in the quantifying the overall efficiency rather than the instantaneous efficiency. If the energy quantities are treated as a normally-distributed random variable (per measurement uncertainty), is there a package that simplifies the determination of the probability distribution function for the quotient of these values? Or, in the general sense, if I have a function that computes a measure of interest, are such tools general enough to handle this? (The goal being to determine a confidence interval for the computed quantity.) As an attempt to understand the issues, I have used SQL to generate discrete sampled normal distributions, and then computed new abscissa values using a function such as division and computing the joint probability as the ordinate, and then re-partitioned the result into new bins using GROUP BY. This is general enough to handle non-normal distributions as well, though I don't know how to quantify the numerical stability/accuracy of this computational procedure. However, this is pretty tedious... it seems like R ought to have some straightforward solution to this problem, but I don't seem to know what search terms to use. There is some discussion about the ratio of normals at: http://www.pitt.edu/~wpilib/statfaq.html but you may just want to use bootstrapping: library(boot) library(simpleboot) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence intervals for rates (dependent events)
I need advice or opinions for the following problem: In a sample a part of the respondents has experienced victimizing events. Only a part of these events have been reported to the police. The rate of events reported to the police is number of experienced events / number of reported events. Because the events cannot be assumed to be independent (some victims have a high rate of vicitimization because they are more prone to become a victim) the construction of a confidence interval for the rate of events reported becomes difficult (to me). Question 1: If nevertheless a use a binomial test to construct a confidence interval (say: 0 of 13 events reported, binom.test(0,13,0/13) CI = 0 to 24.7 %), is it correct that the width of this interval is a lower bound and thus a conservative estimate? Question 2: (a) My intuition tells me that multilevel modeling could be a solution to obtain a correct confidence interval by treating the events and the events reported as the first level and the victims as the second. Is this correct and how should I specify this model? (b) Alternatively, could I treat the events (and events reported?) as coming from a negative binomial distribution and use this for constructing a confidence interval? How can this be done technically, for example by using nb.glm? Thanks in advance, Dirk * Dr. Dirk Enzmann Institute of Criminal Sciences Dept. of Criminology Schlueterstr. 28 D-20146 Hamburg Germany phone: +49-040-42838.7498 (office) +49-040-42838.4591 (Billon) fax: +49-040-42838.2344 email: [EMAIL PROTECTED] www: http://www2.jura.uni-hamburg.de/instkrim/kriminologie/Mitarbeiter/Enzmann/Enzmann.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Confidence Intervals from Bootstrap Replications
Thank you for the response. The suggestions are helpful. I would look into the capabilities of the boot function. The solution in 8.3 is the last resort that I was trying to avoid. Regards, Ashraf -Original Message- From: Berton Gunter [mailto:[EMAIL PROTECTED] Sent: Friday, December 17, 2004 5:54 PM To: 'Mohammad A. Chaudhary'; [EMAIL PROTECTED] Subject: RE: [R] Confidence Intervals from Bootstrap Replications See , e.g. section 8.3 The two-sample problem of Efron and Tibshirani's AN INTRODUCTION TO THE BOOTSTRAP. It makes it clear there why one just bootstrap samples independently from the two separate samples. The strata argument of boot() in the boot package allows one to do such independent sampling and use the capabilities of that function. -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA The business of the statistician is to catalyze the scientific learning process. - George E. P. Box -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Mohammad A. Chaudhary Sent: Friday, December 17, 2004 2:39 PM To: [EMAIL PROTECTED] Subject: [R] Confidence Intervals from Bootstrap Replications Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a bootstrap function (if available) that takes the B bootstrap replications and other descriptive statistics and can get me the confidence intervals. Please write me if I have not been clear in explaining my problem. Regards, Ashraf ___ M. Ashraf Chaudhary, Ph.D. Associate Scientist/Biostatistician Department of International Health Disease Prevention and Control Program Johns Hopkins University Bloomberg School of Public Health 615 North Wolfe Street, Room W5506 Baltimore MD 21205 [EMAIL PROTECTED] Phone: (410) 502-0741/Fax: (410) 502-6733 http://faculty.jhsph.edu/?F=MohammadL=Chaudhary [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence Intervals from Bootstrap Replications
Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a bootstrap function (if available) that takes the B bootstrap replications and other descriptive statistics and can get me the confidence intervals. Please write me if I have not been clear in explaining my problem. Regards, Ashraf ___ M. Ashraf Chaudhary, Ph.D. Associate Scientist/Biostatistician Department of International Health Disease Prevention and Control Program Johns Hopkins University Bloomberg School of Public Health 615 North Wolfe Street, Room W5506 Baltimore MD 21205 [EMAIL PROTECTED] Phone: (410) 502-0741/Fax: (410) 502-6733 http://faculty.jhsph.edu/?F=MohammadL=Chaudhary [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Confidence Intervals from Bootstrap Replications
See , e.g. section 8.3 The two-sample problem of Efron and Tibshirani's AN INTRODUCTION TO THE BOOTSTRAP. It makes it clear there why one just bootstrap samples independently from the two separate samples. The strata argument of boot() in the boot package allows one to do such independent sampling and use the capabilities of that function. -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA The business of the statistician is to catalyze the scientific learning process. - George E. P. Box -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Mohammad A. Chaudhary Sent: Friday, December 17, 2004 2:39 PM To: [EMAIL PROTECTED] Subject: [R] Confidence Intervals from Bootstrap Replications Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a bootstrap function (if available) that takes the B bootstrap replications and other descriptive statistics and can get me the confidence intervals. Please write me if I have not been clear in explaining my problem. Regards, Ashraf ___ M. Ashraf Chaudhary, Ph.D. Associate Scientist/Biostatistician Department of International Health Disease Prevention and Control Program Johns Hopkins University Bloomberg School of Public Health 615 North Wolfe Street, Room W5506 Baltimore MD 21205 [EMAIL PROTECTED] Phone: (410) 502-0741/Fax: (410) 502-6733 http://faculty.jhsph.edu/?F=MohammadL=Chaudhary [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] confidence intervals
Robert,
I have this quick hack to obtain approximate Shewhart prediction intervals
for variance component models fit with lme (to nitpick slightly,
confidence intervals have the interpretation of containing parameters,
while prediction and tolerance intervals have the interpretation of
containing future observations or statistics).
Back of the envelope documentation: the only argument that probably needs
explaining is the reps argument to shewhart(). If your model is, e.g.
fixed = Y ~ 1, random = ~ 1 | Batch
then specify reps = c(1, 1) if you want to predict a single future
observation from a single future batch, reps = c(1, 2) if you want to
predict the mean of two future observations from a single future batch, reps
= c(2, 2) if you want to predict the mean of 4 observations spread evenly
over 2 future batches, ...
Leave mult.check = 1, unless you want to do a Bonferroni correction.
HTH,
Jim Rogers
valStats2 -
function (x, fixed, random, ...)
{
mod - lme(fixed = fixed, data = x, random = random, ...)
mn - fixef(mod)
vc - VarCorr(mod)
err - Expecting only random intercept terms and a single fixed
intercept.\n
if (length(mn) 1 || ncol(vc) 2)
stop(err)
rn - rownames(vc)
skip - regexpr(=, rn) 0
if (!any(skip))
vnms - attr(vc, title)
else vnms - grep(=, rn, value = TRUE)
vc - vc[!skip, ]
vnms - trim(sub(=.*, , vnms))
vnms - c(vnms, Residual)
vnms - paste(V, vnms, sep = .)
vars - as.numeric(vc[, Variance])
stats - c(mn, vars)
names(stats) - c(Intercept, vnms)
stats
}
shewhart -
function (x, meancol = Intercept, varcols = grep(^V\\., names(x), value
= TRUE), reps = c(1, 1), alpha = 0.02, mult.check = 1)
{
mn - x[[meancol]]
vr - as.matrix(x[varcols])
totvar - vr %*% (1/reps)
totsd - sqrt(totvar)
LL.mean - mn + qnorm(alpha/2/mult.check) * totsd
UL.mean - mn + qnorm(1 - alpha/2/mult.check) * totsd
out - data.frame(V.Total = totvar, LL.mean = LL.mean, UL.mean =
UL.mean)
out
}
### Example, where x is your data.frame:
foo - valStats2(x, fixed = Y ~ 1, random = ~ 1|Batch)
foo - as.data.frame(t(as.matrix(foo)))
data.frame(foo, shewhart(foo))
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Spencer Graves
Sent: Friday, September 03, 2004 11:58 AM
To: [EMAIL PROTECTED]
Cc: Robert Waters; [EMAIL PROTECTED]
Subject: Re: [R] confidence intervals
Hi, Robert:
While it may be difficult to program this in general
(as suggested
by it's position on Doug's To Do list), all the pieces should be
available to support a special script for your specific application.
What fixed and random model(s) interest you most?
hope this helps. spencer graves
Douglas Bates wrote:
Robert Waters wrote:
Dear R users;
Im working with lme and Id like to have an idea of how
can I get CI for the predictions made with the model.
Im not a stats guy but, if Im not wrong, the CIs
should be different if Im predicting a new data point
or a new group. Ive been searching through the web and
in help-lists with no luck. I know this topic had been
asked before but without replies. Can anyone give an
idea of where can I found information about this or
how can I get it from R?
Thanks for any hint
That's not currently implemented in lme. It's on the To
Do list but
it is not very close to the top.
LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}
__
[EMAIL PROTECTED] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] confidence intervals
You should be able to make small modifications to the ci.lme function
provided in the gregmisc/gmodels package.
-Greg
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] Behalf Of Spencer Graves
Sent: Friday, September 03, 2004 11:58 AM
To: [EMAIL PROTECTED]
Cc: Robert Waters; [EMAIL PROTECTED]
Subject: Re: [R] confidence intervals
Hi, Robert:
While it may be difficult to program this in general
(as suggested
by it's position on Doug's To Do list), all the pieces should be
available to support a special script for your specific application.
What fixed and random model(s) interest you most?
hope this helps. spencer graves
Douglas Bates wrote:
Robert Waters wrote:
Dear R users;
Im working with lme and Id like to have an idea of how
can I get CI for the predictions made with the model.
Im not a stats guy but, if Im not wrong, the CIs
should be different if Im predicting a new data point
or a new group. Ive been searching through the web and
in help-lists with no luck. I know this topic had been
asked before but without replies. Can anyone give an
idea of where can I found information about this or
how can I get it from R?
Thanks for any hint
That's not currently implemented in lme. It's on the To
Do list but
it is not very close to the top.
__
[EMAIL PROTECTED] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
--
Spencer Graves, PhD, Senior Development Engineer
O: (408)938-4420; mobile: (408)655-4567
__
[EMAIL PROTECTED] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
LEGAL NOTICE\ Unless expressly stated otherwise, this messag...{{dropped}}
__
[EMAIL PROTECTED] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence intervals
Robert Waters wrote: Dear R users; Im working with lme and Id like to have an idea of how can I get CI for the predictions made with the model. Im not a stats guy but, if Im not wrong, the CIs should be different if Im predicting a new data point or a new group. Ive been searching through the web and in help-lists with no luck. I know this topic had been asked before but without replies. Can anyone give an idea of where can I found information about this or how can I get it from R? Thanks for any hint That's not currently implemented in lme. It's on the To Do list but it is not very close to the top. __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] confidence intervals
Dear R users; Im working with lme and Id like to have an idea of how can I get CI for the predictions made with the model. Im not a stats guy but, if Im not wrong, the CIs should be different if Im predicting a new data point or a new group. Ive been searching through the web and in help-lists with no luck. I know this topic had been asked before but without replies. Can anyone give an idea of where can I found information about this or how can I get it from R? Thanks for any hint RW __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] confidence intervals for linear combinations when using lme
Hi I really hope someone can help me. I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with contrast or estimate or lsmeans in SAS). I have now found out that the vcov-function in the lme4-package could be of great help for me, but I do not know if I can use lme4 when working with S-PLUS 6.1, or is it only for R. When I write library(MASS) I find lme3, where there is a vcov-function, but if I understand it correctly it works with lm and not with lme... If I can use lme4 with s-plus 6.1, what shall I do in order to install it on my computer? I am grateful for all help I can get? best regards Anna Persson __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence intervals for linear combinations when using lme
On Friday 23 July 2004 05:36, [EMAIL PROTECTED] wrote: Hi I really hope someone can help me. I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with contrast or estimate or lsmeans in SAS). I have now found out that the vcov-function in the lme4-package could be of great help for me, but I do not know if I can use lme4 when working with S-PLUS 6.1, or is it only for R. As far as I know, there are no plans (by the developers) to port lme4 to work with S-PLUS. Deepayan __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence intervals for linear combinations when using lme
[EMAIL PROTECTED] wrote: I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with contrast or estimate or lsmeans in SAS). I have now found out that the vcov-function in the lme4-package could be of great help for me, but I do not know if I can use lme4 when working with S-PLUS 6.1, or is it only for R. The lme4 package is only available for R at this time. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals for predicted values in nls
Cristina Silva wrote:
Dear all
I have tried to estimate the confidence intervals for predicted values of a
nonlinear model fitted with nls. The function predict gives the predicted
values and the lower and upper limits of the prediction, when the class of
the object is lm or glm. When the object is derived from nls, the function
predict (or predict.nls) gives only the predicted values. The se.fit and
interval aguments are just ignored.
Could anybody tell me how to estimate the confidence intervals for the
predicted values (not the model parameters), using an object of class nls?
Regards,
Cristina
--
Cristina Silva
IPIMAR - Departamento de Recursos Marinhos
Av. de Brasília, 1449-006 Lisboa
Portugal
Tel.: 351 21 3027096
Fax: 351 21 3015948
[EMAIL PROTECTED]
__
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
maybe this example helps:
==cut here===
#define a model formula (a and b are the parameters, f is x):
frml - k1 ~ f*(1-a*exp(-b/f))
#simulate some data:
a0 - .6
b0 - 1.2
f - seq(0.01,4,length=20)
k1true- f*(1-a0*exp(-b0/f))
#include some noise
amp - .1
k1 - rnorm(k1true,k1true,amp*k1true)
#fit:
fifu - deriv(frml,c(a,b),function(a,b,x){})
rr-nls(k1~fifu(a,b,f),start=list(a=.5,b=1))
#the derivatives and variance/covariance matrix:
#(derivs could be extracted from fifu, too)
dk1.da - D(frml[[3]],'a')
dk1.db - D(frml[[3]],'b')
covar - vcov(rr)
#gaussian error propagation:
a - coef(rr)['a']
b - coef(rr)['b']
vark1 -
eval(dk1.da)^2*covar[1,1]+
eval(dk1.db)^2*covar[2,2]+
2*eval(dk1.da)*eval(dk1.db)*covar[1,2]
errk1 - sqrt(vark1)
lower.bound - fitted(rr)-2*errk1 #two sigma !
upper.bound - fitted(rr)+2*errk1 #dito
plot(f,k1,pch=1)
ff - outer(c(1,1),f)
kk - outer(c(1,1),k1)*c(1-amp,1+amp)
matlines(ff,kk,lty=3,col=1)
matlines(f,cbind(k1true,fitted(rr),lower.bound,upper.bound),col=c(1,2,3,3),lty=c(1,1,2,2))
xylim - par('usr')
xpos - .1*(xylim[2]-xylim[1])+xylim[1]
ypos - xylim[4] - .1*(xylim[4]-xylim[3])
legend(xpos,ypos,
c(
'data',
'true',
'fit',
'confidence'
),
pch=c(1,-1,-1,-1),
lty=c(0,1,1,2),
col=c(1,1,2,3)
)
==cut here===
if you put this in a file and source it a few times from within R you'll
get an impression how often the fit deviates from the 'true' curve more
than expected from
the shown confidence limits.
I believe this approach is 'nearly' valid as long as gaussian error
probagation is valid (i.e. only to first order in covar and therefore
for not too large std. errors, am I right?).
to my simple physicist's mind this should suffice to get 'reasonable'
(probably, in strict sense, not completely correct?) confidence
intervals for the fit/the prediction.
If somebody objects, please let me know!
joerg
__
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals for predicted values in nls
Parece que não tiveste grandes ajudas. Eu se fosse a ti fazia isso com um bootstrap bem planeado Alberto On Thursday 03 June 2004 21:22, Prof Brian Ripley wrote: On Thu, 3 Jun 2004, Cristina Silva wrote: I have tried to estimate the confidence intervals for predicted values of a nonlinear model fitted with nls. The function predict gives the predicted values and the lower and upper limits of the prediction, when the class of the object is lm or glm. When the object is derived from nls, the function predict (or predict.nls) gives only the predicted values. The se.fit and interval aguments are just ignored. Thre are no such arguments either to the generic function nls() nor its nls method. Please do read the documentation! Could anybody tell me how to estimate the confidence intervals for the predicted values (not the model parameters), using an object of class nls? First you need to understand how to do this in theory: thereafter it is a programming task. Hint: to find a confidence region for the parameters is not at all easy, as the examples in MASS (the book) and elsewhere show, and there is no guarantee that the confidence region for the prediction will be a single interval. -- Alberto G. Murta Institute for Agriculture and Fisheries Research (INIAP-IPIMAR) Av. Brasilia, 1449-006 Lisboa, Portugal | Phone: +351 213027062 Fax:+351 213015948 | http://ipimar-iniap.ipimar.pt/pelagicos/ __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals for predicted values in nls
Ops! I apologise for posting my last message to the R list by mistake. -- Alberto G. Murta Institute for Agriculture and Fisheries Research (INIAP-IPIMAR) Av. Brasilia, 1449-006 Lisboa, Portugal | Phone: +351 213027062 Fax:+351 213015948 | http://ipimar-iniap.ipimar.pt/pelagicos/ __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence intervals for predicted values in nls
Dear all I have tried to estimate the confidence intervals for predicted values of a nonlinear model fitted with nls. The function predict gives the predicted values and the lower and upper limits of the prediction, when the class of the object is lm or glm. When the object is derived from nls, the function predict (or predict.nls) gives only the predicted values. The se.fit and interval aguments are just ignored. Could anybody tell me how to estimate the confidence intervals for the predicted values (not the model parameters), using an object of class nls? Regards, Cristina -- Cristina Silva IPIMAR - Departamento de Recursos Marinhos Av. de Brasília, 1449-006 Lisboa Portugal Tel.: 351 21 3027096 Fax: 351 21 3015948 [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals for predicted values in nls
On Thu, 3 Jun 2004, Cristina Silva wrote: I have tried to estimate the confidence intervals for predicted values of a nonlinear model fitted with nls. The function predict gives the predicted values and the lower and upper limits of the prediction, when the class of the object is lm or glm. When the object is derived from nls, the function predict (or predict.nls) gives only the predicted values. The se.fit and interval aguments are just ignored. Thre are no such arguments either to the generic function nls() nor its nls method. Please do read the documentation! Could anybody tell me how to estimate the confidence intervals for the predicted values (not the model parameters), using an object of class nls? First you need to understand how to do this in theory: thereafter it is a programming task. Hint: to find a confidence region for the parameters is not at all easy, as the examples in MASS (the book) and elsewhere show, and there is no guarantee that the confidence region for the prediction will be a single interval. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals pointwise and family
?predict.lm in R 1.9.0 provides examples. hope this helps. spencer graves Linda portman wrote: How can I add confidence intervals (pointwise and family) around the curves? The curve is made by plot(x,y). Thanks! - [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence Intervals for slopes
Hi, Thanks for the pointer - the 'lm(y~x:z)' model does give the slopes directly and hence confint gives the confidence intervals. The thing that puzzles me is that my dummy data explicitly sets the three levels of the factor to have different variances and yet the standard error is the same for all three parameter estimates in the summary.lm output - is this a common standard error of the 'x:z' term in the model? If you fit a separate regression to subsets of the data for each level in 'z' then the standard errors of the slope reflect these differences in variance. What I was trying to get was confidence limits from within a single model that also reflect the difference in certainty about the three slopes. I realize that this is a failing of my understanding and more a stats question than an R question - if anyone can give me any advice or pointers, that would be great. Thanks, David On 29 Mar 2004, at 20:04, BXC (Bendix Carstensen) wrote: You may want: lm( y ~ x:z ) This is the same model you fitted, but prametrized differently. But please check that what you REALLY want is not lm( y ~ z + x:z ) This is the model with different intercepts as well. Bendix Carstensen -- Bendix Carstensen Senior Statistician Steno Diabetes Center Niels Steensens Vej 2 DK-2820 Gentofte Denmark tel: +45 44 43 87 38 mob: +45 30 75 87 38 fax: +45 44 43 07 06 [EMAIL PROTECTED] www.biostat.ku.dk/~bxc -- -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Orme Sent: Monday, March 29, 2004 4:44 PM To: [EMAIL PROTECTED] Subject: [R] Confidence Intervals for slopes Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: # # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x - rep(1:10, length=30) y - 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10, sd=0.6),rnorm(10, sd=1.1)) z - gl(3,10) plot(y~x, pch=unclass(z)) # model as three slopes with common intercept options(contrasts=c(contr.treatment,contr.poly)) model - lm(y~x+x:z) # coefficient table in summary gives the intercept, first slope and the difference in slopes summary(model) # confint gives the confidence interval for the intercept and first slope, # and the CIs for the _differences_ confint(model) # What I'd like to report are the actual CI's for the slopes for the second and third treatments, in the same way that confint returns the parameter estimates for the first treatment. Can anyone point me in the right direction? Thanks, David __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo /r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence Intervals for slopes
Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: # # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x - rep(1:10, length=30) y - 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10, sd=0.6),rnorm(10, sd=1.1)) z - gl(3,10) plot(y~x, pch=unclass(z)) # model as three slopes with common intercept options(contrasts=c(contr.treatment,contr.poly)) model - lm(y~x+x:z) # coefficient table in summary gives the intercept, first slope and the difference in slopes summary(model) # confint gives the confidence interval for the intercept and first slope, # and the CIs for the _differences_ confint(model) # What I'd like to report are the actual CI's for the slopes for the second and third treatments, in the same way that confint returns the parameter estimates for the first treatment. Can anyone point me in the right direction? Thanks, David __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence Intervals for slopes
David, try the estimable() function in the gregmisc package. Andrew On Monday 29 March 2004 06:44, David Orme wrote: Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: # # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x - rep(1:10, length=30) y - 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10, sd=0.6),rnorm(10, sd=1.1)) z - gl(3,10) plot(y~x, pch=unclass(z)) # model as three slopes with common intercept options(contrasts=c(contr.treatment,contr.poly)) model - lm(y~x+x:z) # coefficient table in summary gives the intercept, first slope and the difference in slopes summary(model) # confint gives the confidence interval for the intercept and first slope, # and the CIs for the _differences_ confint(model) # What I'd like to report are the actual CI's for the slopes for the second and third treatments, in the same way that confint returns the parameter estimates for the first treatment. Can anyone point me in the right direction? Thanks, David __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Andrew Robinson Ph: 208 885 7115 Department of Forest Resources Fa: 208 885 6226 University of Idaho E : [EMAIL PROTECTED] PO Box 441133W : http://www.uidaho.edu/~andrewr Moscow ID 83843 Or: http://www.biometrics.uidaho.edu No statement above necessarily represents my employer's opinion. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] Confidence Intervals for slopes
You may want: lm( y ~ x:z ) This is the same model you fitted, but prametrized differently. But please check that what you REALLY want is not lm( y ~ z + x:z ) This is the model with different intercepts as well. Bendix Carstensen -- Bendix Carstensen Senior Statistician Steno Diabetes Center Niels Steensens Vej 2 DK-2820 Gentofte Denmark tel: +45 44 43 87 38 mob: +45 30 75 87 38 fax: +45 44 43 07 06 [EMAIL PROTECTED] www.biostat.ku.dk/~bxc -- -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Orme Sent: Monday, March 29, 2004 4:44 PM To: [EMAIL PROTECTED] Subject: [R] Confidence Intervals for slopes Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: # # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x - rep(1:10, length=30) y - 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10, sd=0.6),rnorm(10, sd=1.1)) z - gl(3,10) plot(y~x, pch=unclass(z)) # model as three slopes with common intercept options(contrasts=c(contr.treatment,contr.poly)) model - lm(y~x+x:z) # coefficient table in summary gives the intercept, first slope and the difference in slopes summary(model) # confint gives the confidence interval for the intercept and first slope, # and the CIs for the _differences_ confint(model) # What I'd like to report are the actual CI's for the slopes for the second and third treatments, in the same way that confint returns the parameter estimates for the first treatment. Can anyone point me in the right direction? Thanks, David __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo /r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence intervals for logistic regression
Hi, I found myself trying to figure out the type of confidence interval used for the coefficients of the logistic regression fit by using glm(family=binomial)... I suspect it is Wald confidence interval but am not sure...Does anybody know? Also, if so, how can I ask for likelihood ratio and/or score-based confidence intervals? Yours, Michael ~ Michael Levine Assistant Professor Department of Statistics School of Arts and Sciences Purdue University Office: MATH 438 150 N. University Street West Lafayette, IN 47907 phone (765)496-7571 e-mail: [EMAIL PROTECTED] [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Confidence intervals for logistic regression
On Thu, 19 Feb 2004, Michael Levine wrote: I found myself trying to figure out the type of confidence interval used for the coefficients of the logistic regression fit by using glm(family=binomial)... AFAIK, no confidence interval is given by that call: it does not even calculate standard errors for the coefs (the summary method does that). I could guess what you meant, but the answer depends on the guess I suspect it is Wald confidence interval but am not sure...Does anybody know? Also, if so, how can I ask for likelihood ratio and/or score-based confidence intervals? You can use confint() (whose glm method is in package MASS, which must be attached) to give you profile-likelihood confidence intervals. There would be no point in trying to base confidence intervals on score tests, as you would have to re-fit the model at all possible alternative values and so the profile likelihood would be available. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] confidence-intervals in barchart
Try 'parplot2' -- 'gregmisc' package. Marwan --- Marwan Khawaja http://staff.aub.edu.lb/~mk36/ --- -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of [EMAIL PROTECTED] Sent: Tuesday, February 10, 2004 4:01 AM To: [EMAIL PROTECTED] Subject: [R] confidence-intervals in barchart Hi R users, 1) How does one show confidence-intervals in a barchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a barchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a barchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence-intervals in barchart
hello, you can find a very detailed example of barplot with CI in the volume 3/2 of R-news (october 2003). hope this help. dlc Hi R users, 1) How does one show confidence-intervals in a barchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a barchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a barchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence-intervals in barchart
[EMAIL PROTECTED] wrote: Hi R users, 1) How does one show confidence-intervals in a barchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a barchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a barchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html See the R Help Desk by Marc Schwartz in R News 3/2, 2003, pp. 2-6, http://cran.r-project.org/doc/Rnews/Rnews_2003-2.pdf Uwe Ligges __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] confidence-intervals in dotchart
My earlier posting should have said dotchart, not barchart. 1) How does one show confidence-intervals in a dotchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a dotchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a dotchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence-intervals in dotchart
On Tue, 10 Feb 2004 04:30:46 EST [EMAIL PROTECTED] wrote: My earlier posting should have said dotchart, not barchart. 1) How does one show confidence-intervals in a dotchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a dotchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a dotchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. You might look at the example on p.149 http://cran.r-project.org/doc/contrib/Harrell-statcomp-notes.pdf and other examples in that section. These use the xYplot and Dotplot functions in the Hmisc package, which extend lattice graphics to easily handle error bars and bands. --- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] confidence-intervals in dotchart
In a message dated 2/10/04 3:33:07 AM Pacific Standard Time, [EMAIL PROTECTED] writes: On Tue, 10 Feb 2004 04:30:46 EST [EMAIL PROTECTED] wrote: My earlier posting should have said dotchart, not barchart. 1) How does one show confidence-intervals in a dotchart and use rownames for labels on the y-axes? I have looked at plotCI in gregmisc package . But it does not seem to produce something like a dotchart. The statistic, error, upper-bound, and lower-bound are in a dataframe. 2) How to show CI in a dotchart either using the statistic and, either (a) errors or (b) upper and lower bounds from a dataframe? An example will be very helpful. You might look at the example on p.149 http://cran.r-project.org/doc/contrib/Harrell-statcomp-notes.pdf and other examples in that section. These use the xYplot and Dotplot functions in the Hmisc package, which extend lattice graphics to easily handle error bars and bands. This is wonderful! Thanks. How do I change the default colors for the session and the colors for each Dotplot() individually? I can't do that from within the Dotplot() function. [[alternative HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] confidence intervals
Hi Yun Fan! Have you looked at predict.lm? It can give you confidence and prediction intervals. Hope this helps! Sincerely, Erin Hodgess mailto: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Confidence intervals in ANOVA
Hallo! I have the a model with 3 time points, 2 treatments and N subjects. I can calculate an ANOVA but I can not calculate the CI of the interaction term (time and treatment), which I need for a closer look at the effect of the treatment to the 3 time points. I do NOT want to use lme because I can not manage it to reproduce text book examples (see my posting [R] lme: reproducing example Karl Knoblick (Tue 02 Dec 2003 - 21:34:54 EST)). Here some sample data: # Data # 35 subjects ID-factor(rep(1:35,each=3)) TREAT-factor(c(rep(A, 60), rep(B, 45))) TIME-factor(rep(1:3, 35)) Y-numeric(length=105) set.seed(1234) Y-rnorm(105) # want to see an effect: Y[TREAT==A TIME==2]-Y[TREAT==A TIME==2] - 1 DF-data.frame(Y, ID, TREAT, TIME) # 2 possible designs: # Design 1 with random term DF.aov1-aov(Y ~ TIME*TREAT + Error(TREAT:ID), data=DF) summary(DF.aov1) # Design 2 without random term DF.aov2-aov(Y ~ TIME*TREAT, data=DF) summary(DF.aov2) I am also not sure about the design - I think design 1 is more appropriate. What I have tried is to calculate the CI of the coefficients: confint(DF.aov1[[2]]) confint(DF.aov1[[3]]) (or: confint(DF.aov2) ) But how can I get the CI for a concrete difference for example between the treatments at time point 2? I really hope, sombody can help! Karl __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
[R] Confidence intervals plot
Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points would be the confidence interval limits. I do not know if I am clear enough. Anyway, thank you in advance. Ramon. [[alternate HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Confidence intervals plot
On Sat, 14 Jun 2003 20:44:09 +0200, you wrote: Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points would be the confidence interval limits. I do not know if I am clear enough. Anyway, thank you in advance. Suppose the upper limits are in U, the lower limits in L, and the x values in X. Then # set up the axes etc. plot(X, U, ylim=range(c(L,U)), type='n') segments(X, L, X, U) will do what you describe. You should also look at arrows(), in case you want points or crossbars on the ends of the segments. Duncan Murdoch __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
RE: [R] Confidence intervals plot
-Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ramon Martínez Coscollà Sent: Saturday, June 14, 2003 1:44 PM To: [EMAIL PROTECTED] Subject: [R] Confidence intervals plot Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points would be the confidence interval limits. I do not know if I am clear enough. Anyway, thank you in advance. Ramon. You have several options depending upon whether you simply want vertical CI lines above and below xy data points or if you might want a barplot with CI's. You could draw the xy data points using plot() and then draw the CI's yourself using either segments() or arrows() in the base package or see plotCI(), plotmeans() and barplot2() in the 'gregmisc' package on CRAN. plotmeans() is a wrapper function that can call plotCI(). HTH, Marc Schwartz __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
Re: [R] Confidence intervals plot
On 14 Jun 2003 at 20:44, Ramon Martínez Coscollà wrote: Hola! First set up a plot with plot( c(x.lower, x.upper) , c(y.lower, y.upper), type=n ) and then add each line using segments Kjetil Halvorsen Hi all!! I am trying to plot several confidence intervals in a unique plot. That is, for each x, I have a confidence interval for a parameter related to x and I would like to plot them in the same plot, in order to compare them. The plot would look like some parallel vertical lines, each one corresponding to a x value. Their extrem points would be the confidence interval limits. I do not know if I am clear enough. Anyway, thank you in advance. Ramon. [[alternate HTML version deleted]] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
