[R] windows completion in 2.5?
Can anyone tell me if the new R 2.5 has autocompletion for the default windows shell? I know they integrated rcompletion but wasn't sure if it was just Unix. Cheers David __ 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] level sets
Hi, Can anyone suggest a good way to draw level curves of a function with R? I am thinking of something analogous to curve. If there is a package I should be looking at for this kind of mathematical stuff, I'd like to know too... cheers d __ 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] inplace assignment: solution
I worked this out over the weekend. I appreciate that using temporary
variables would be simpler but I think this makes for quite readable
code:
# in RProfile.site
inplace - function (f, arg=1)
eval.parent(call(-,substitute(f)[[arg+1]], f),2)
# examples in code
inplace(foo[bar,baz] *2)
# or
inplace(paste(foo[bar,baz], 1:10))
# or
inplace(sub(blah, bleh, foo[bar,baz]), 3)
cheers
Dave
On 16/06/06, David Hugh-Jones [EMAIL PROTECTED] wrote:
It's more a general point about having to write things out twice when
you do assignments. I could also have written:
data.frame[some.condition another.condition, big.list.of.columns] -
data.frame[some.condition another.condition, big.list.of.columns] * 2 + 55
or anything else. Equally, there could be any method of subsetting, or
any expression that can be an assignment target, on the left hand
side:
data.frame[[some.complex.expression.for.columnames]]
-data.frame[[some.complex.expression.for.columnames]] * 333 + foo *
56
rownames(matrix)[45:53] - paste(rownames(matrix)[45:53], blah)
David
On 16/06/06, Adaikalavan Ramasamy [EMAIL PROTECTED] wrote:
I do not fully understand your question but how about :
inplace - function( df, cond1, cond2, cols, suffix ){
w - which( cond1 cond2 )
df - df[ w, cols ]
paste(df, suffix)
return(df)
}
BTW, did you mean colnames(df) - paste(colnames(df), suffix) instead
of paste(df, suffix) ?
Regards, Adai
On Fri, 2006-06-16 at 10:23 +0100, David Hugh-Jones wrote:
I get tired of writing, e.g.
data.frame[some.condition another.condition, big.list.of.columns] -
paste(data.frame[some.condition another.condition,
big.list.of.columns], foobar)
I would a function like:
inplace(paste(data.frame[some.condition another.condition,
big.list.of.columns], foobar))
which would take the first argument of the inner function and assign
the function's result to it.
Has anyone done something like this? Are there simple alternative
solutions that I'm missing?
Cheers
David
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Re: [R] NLME: using the layout option with the plot command
hi greg If you are using windows, set up a plot window and click the Record option in the menu. Then run the command. Now you can scroll back through previous pages by hitting Page Up. Beware that if you save your workspace without clearing the history, you may have a lot of bloat from the graphs. David On 20/06/06, Greg Distiller [EMAIL PROTECTED] wrote: Hi This is the 2nd time I am posting this question as I never got a reply the 1st time round - apologies to anybody who might take offense at this but I dont know what else to do. I am struggling to split up the plots of the grouped objects in nlme in a usable way. The standard plot command generates plots for each group on a single page. When there are many groups however this does not look so good. I have discovered the layout option which allows one to split up these plots over a certain number of pages but the problem is it very quickly scrolls through all the pages only leaving the final page in the viewer. My question is how does one get to view all these pages? Or even better is there an option where the pages change only when prompted by the user? Thanks Greg __ 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-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] inplace assignment
I get tired of writing, e.g. data.frame[some.condition another.condition, big.list.of.columns] - paste(data.frame[some.condition another.condition, big.list.of.columns], foobar) I would a function like: inplace(paste(data.frame[some.condition another.condition, big.list.of.columns], foobar)) which would take the first argument of the inner function and assign the function's result to it. Has anyone done something like this? Are there simple alternative solutions that I'm missing? Cheers David __ 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] inplace assignment
It's more a general point about having to write things out twice when
you do assignments. I could also have written:
data.frame[some.condition another.condition, big.list.of.columns] -
data.frame[some.condition another.condition, big.list.of.columns] * 2 + 55
or anything else. Equally, there could be any method of subsetting, or
any expression that can be an assignment target, on the left hand
side:
data.frame[[some.complex.expression.for.columnames]]
-data.frame[[some.complex.expression.for.columnames]] * 333 + foo *
56
rownames(matrix)[45:53] - paste(rownames(matrix)[45:53], blah)
David
On 16/06/06, Adaikalavan Ramasamy [EMAIL PROTECTED] wrote:
I do not fully understand your question but how about :
inplace - function( df, cond1, cond2, cols, suffix ){
w - which( cond1 cond2 )
df - df[ w, cols ]
paste(df, suffix)
return(df)
}
BTW, did you mean colnames(df) - paste(colnames(df), suffix) instead
of paste(df, suffix) ?
Regards, Adai
On Fri, 2006-06-16 at 10:23 +0100, David Hugh-Jones wrote:
I get tired of writing, e.g.
data.frame[some.condition another.condition, big.list.of.columns] -
paste(data.frame[some.condition another.condition,
big.list.of.columns], foobar)
I would a function like:
inplace(paste(data.frame[some.condition another.condition,
big.list.of.columns], foobar))
which would take the first argument of the inner function and assign
the function's result to it.
Has anyone done something like this? Are there simple alternative
solutions that I'm missing?
Cheers
David
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Re: [R] Name of a column
1. You don't need to say truc==T. truc is already a logical vector. 2. colnames are just another vector, so do colnames(truc)[truc] Dave On 15/06/06, David Hajage [EMAIL PROTECTED] wrote: Hello, My problem is quite simply, but I didn't find any solution... I have a vector : truc longueur30 longueur40 longueur50 longueur60 longueur70 longueur80 longueur90 34 FALSE FALSE FALSE FALSE TRUE FALSE FALSE I would like to have the name of the column where there is TRUE. colnames(truc) [1] longueur30 longueur40 longueur50 longueur60 longueur70 [6] longueur80 longueur90 truc[truc == T] [1] TRUE colnames(truc[truc == T]) NULL How can I do it ? Thank you for your help. -- David [[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 __ 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] help with combination problem
hi Srinivas
I'm not sure I understand: your example result has 10-9 for mu1 but 1
for mu2, which is not equal to 11-9. I will assume you just got the
numbers wrong. (Also J100 should be J1, no?)
I am also wondering why you want what you say. It doesn't seem very
sensible to mix the results from two data frames like this.
The simplest thing is probably to do the two data frames separately,
then intermix the rows. You can do this with for loops:
for (col1 in paste(T, 1:14, sep=)) {
for (col2 in paste(N, 1:16, sep=)) result[[paste(col1, -,
col2, sep=)]] = df1[[col1]] - df1[[col2]]
}
Now repeat for data frame 2 with another data frame, e.g. result2
Then intermix the rows from result and result2. I'll leave you to
figure that bit out.
David
On 07/06/06, Srinivas Iyyer [EMAIL PROTECTED] wrote:
hello:
I have 3 data.frame objects.
First df object:
Of dim (149,31). Columns 2:31 are marked as T1..T14
and N1..N16.
Name T1T2N1 T3 N2 N3 N4 T4
mu1 1010910 9 9 8 10
mu2 1111911 9 9 9 11
...
muN 1212911 9 9 8 12
Second df object:
of Dim (5,31). Columns 2:31 are maked as T1...T14
and N1..N16.
Name T1T2N1 T3 N2 N3 N4 T4
J1 2 3 20 222 21 29 3
J2 4 1 20 320 21 22 4
J3 3 1 33 131 31 33 3
...
JX 3 2 20 221 22 24 2
The column samples are identical in both first and
second data frames.
Third df object:
of Dim (200,2).
V1 V2
mu1:J1 -11
mu1:J100 -10.4
mu2:J31 11.3
mu2:J2 10.4
. .
muN:JX 34.5
I want to create a combination of Ts and Ns. Where I
want to subtract value of T-N in all combinations(225
combinations). Such as
T1-N1,T1-N2,T1-N3,T1-N4,T1-N5...T14-N16
The rows should be the row pairs from 3rd dataframe.
The final resultant matrix should look like the
following:
T1-N1 T1-N2 T1-N3 T1-N4 T1-N5...T14-N16
mu1 1(10-9) 1(10-9) 12 11
J100 -18(2-20) -20 -19 -27-20 -29
mu2 1 32 2 11
J2 -19 -21 -39-31-31 -28
I am a beginner level in R. I apologise for asking
such a big question and subsequent help. I am unable
to go forward as I have no idea as to how to do this.
Could any one please help me.
Thanks
sri
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[R] interpolating a lot of data at once
I have a big dataset containing a lot of values for 1970, 1980 and 1990. I want to interpolate values for the years in between, and also if possible to extrapolate to 1968 and 1969. The method doesn't have to be clever but I am looking for a function that will do all the data at once. (Doing foreach, or apply, is just too slow!) Is there something that will take list(df$val.1970, df$val.1980, df$val.1990) as inputs and output an interpolated matrix? Cheers David __ 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] Vector elements and ratios
outer(c(20,40,20,60), c(5,4,2), /) [,1] [,2] [,3] [1,]45 10 [2,]8 10 20 [3,]45 10 [4,] 12 15 30 cheers D On 26/05/06, Andrej Kastrin [EMAIL PROTECTED] wrote: Dear useRs, I have two different length vectors: one column (1...m) and one row vector (1...n): 20 40 20 60 5 4 2 Now I have to calculate ratios between column vector elements and each row vector elements: 4 5 10 8 10 20 4 5 20 15 12 30 Thank's in advance for any suggestions, Andrej __ 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-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] GA-packages
Have you looked at package gafit on CRAN? Cheers David On 01/05/06, j.joshua thomas [EMAIL PROTECTED] wrote: Hi, I'm looking for help on how to use *R for making use of Genetic Algorithm*to make optimal solution on the examination timetabling dataset? another point to analysis the dataset, in a manner similar to the method applied in InfoViz. Say, what is the optimal step, if: Step 1 - What is the name of the package that i can install from CRAN for me to identify GA (Please consider) - I am using RGui 1.8.0 Step 2 - For the second doubt, i used the multiv, mva (EDA- packages) - I use RGui 1.8.0 because i couldn't find multiv, mva Or i'm not sure of getting it from RGui 2.3.0 (Please Consider) I have tried to read up on the 'R-search for Step-1 but i couldn't find the appropriate package. Can anyone point me in the right direction? Best regards, JJ [[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 __ 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] Writing a function to fit ALSOS models. problem with normalization?
Dear all,
Below is my attempt at a function to fit Alternate Least Squares
Optimal Scaling models, as described in Young (1981) Quantitative
Analysis of Qualitative Data and Jacoby (1999) Levels of Measurement
and Political Research: An Optimistic View.
I would welcome any comments on coding style, tips tricks etc.
I also have a specific problem: the output tends to give very small
coefficients, and very large fitted values for specific factor levels.
Am I doing the normalization right?
Cheers
David
library(car) # for recode
alsos.fit = function (y, x, tol = 1e-7) {
# y is a vector or factor or ordered factor
# x is a data frame of vectors/factors/ordereds
# we treat the y's as the first column of the matrix
x = cbind(y, x)
x = x[complete.cases(x),]
ox = x
x.facs = sapply(x, is.factor)
x.ords = sapply(x, is.ordered)
# start with our numeric values whatever they are
x = sapply(x, as.numeric)
old.SSE = Inf
while(T) {
# least squares regression with an intercept
ols = lm.fit(cbind(rep(1,nrow(x)), x[,-1]) , x[,1])
b = ols$coefficients
SSE = drop(ols$residuals %*% ols$residuals)
if (old.SSE-SSEtol) {
factor.scores=list()
for (i in (1:ncol(x))[x.facs]) {
nm = colnames(x)[i]
factor.scores[[nm]] = tapply(x[,i], ox[,i],
function (foo) {return(foo[1])})
names(factor.scores[[nm]]) = levels(ox[,i])
}
return(list(
factor.scores=factor.scores,
scaled.y=x[,1], scaled.x=x[,-1],
coefficients=b, SSE=SSE,
))
}
old.SSE=SSE
mx = nx = x
mx[] = 0
nx[] = 0
for (i in (1:ncol(x))[x.facs]) {
# optimal scaling
if (i==1) nx[,i] = ols$fitted.values
else nx[,i] = (x[,1] - cbind(rep(1,nrow(x)),
x[,c(-1,-i)]) %*% b[-i])/b[i]
# create within-category means
tmpfac = factor(ox[,i], labels=1:nlevels(ox[,i]))
catmeans = tapply(nx[,i], tmpfac, mean)
# ensure ordinal values are correctly ordered
if (x.ords[i]) {
tmp = kruskal.ordering(nx[,i], tmpfac)
tmpfac = tmp$tmpfac
catmeans = tmp$catmeans
}
# set values to within-category means
mx[,i] = catmeans[tmpfac]
# normalize according to Young (1981)
mx[,i] = mx[,i] * (nx[,i] %*% nx[,i]) / (mx[,i] %*%
mx[,i])
}
x[,x.facs] = mx[,x.facs]
}
}
# as described in Kruskal (1964)
kruskal.ordering = function(numeric.data, tmpfac) {
j = 1
upact = T
while(T) {
catmeans = tapply(numeric.data, tmpfac, mean) # vector w as many
items as tmpfac cats
# have we finished?
if (jnlevels(tmpfac)) return
(list(catmeans=catmeans,tmpfac=tmpfac))
if ((j==nlevels(tmpfac) || catmeans[j] = catmeans[j+1])
(j==1 || catmeans[j] = catmeans[j-1])) {
j=j+1
upact=T
next
}
if (upact) {
if (j nlevels(tmpfac) catmeans[j] catmeans[j+1])
{
tmpfac = recode(tmpfac, paste(j, :, j+1,=',
j+1, ', sep=))
levels(tmpfac) = 1:nlevels(tmpfac)
}
upact=F
}
else {
if (j 1 catmeans[j] catmeans[j-1]) {
tmpfac = recode(tmpfac, paste(j-1, :, j,
=', j, ', sep=))
levels(tmpfac) = 1:nlevels(tmpfac)
j=j-1
}
upact=T
}
}
}
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Re: [R] heckit with a probit
Thank you both for your help. I've tried VGAM and it seems useful. Cheers David On 27/02/06, Robert Duval [EMAIL PROTECTED] wrote: I don't know if I understand your problem very well but the first reference that comes to mind is James Heckman, Dummy Endogenous Variables in a Simultaneous Equation System, Econometrica, (July 1978). also you might find a good survey of the literature on Francis Vella Estimating models with sample selection bias: A survey, Journal of Human Resources, 1998, Vol 33 pp 127-169. I don't know how many of the methods here proposed are already implemented in R, but in principle many of them are Likelihood models that you could program. best robert On 2/27/06, David Hugh-Jones [EMAIL PROTECTED] wrote: Hi I have data for voting behaviour on two (related) binary votes. I want to examine the second vote, running separate regressions for groups who voted different ways on the first vote. As the votes are not independent, I guess that there is an issue with selection bias. So, I think I would like to fit a heckit style model but with a binary dependent variable - so, in effect, two successive probits. Is there a way to do it in R? (Alternatively: am I thinking about this the right way?) Cheers David __ 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-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-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] Help - lm, glm, aov results inconsistent with other stati stical package
Perhaps it's worth mentioning that you can view and edit the contrasts for a particular factor by doing e.g. contrasts(A) contrasts(A) = contr.treatment(levels(A), base=2) # make the second category the base cheers David On 01/03/06, Christoph Buser [EMAIL PROTECTED] wrote: Dear Ben Berwin is correct in his answer about different parameterizations. After changing the contrasts in R from treatment to sum options(contrasts = c(contr.sum, contr.poly )) test.lm-lm(y~A*x) summary(test.lm) I got similar results to JMP. Be careful in doing a correct interpretation of your coefficients, especially when you have an interaction in your model. Regards, Christoph Buser -- Christoph Buser [EMAIL PROTECTED] Seminar fuer Statistik, LEO C13 ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND phone: x-41-44-632-4673 fax: 632-1228 http://stat.ethz.ch/~buser/ -- Ben Ridenhour writes: Okay, I took the data to SAS and it gives me the same answer that R does. I don't why JMP is giving me an incorrect answer, but it seems to be. (Either that or I have made the same mistake in SAS and R.) Any ideas what JMP might be doing? Ben --- Benjamin Ridenhour School of Biological Sciences Washigton State University P.O. Box 644236 Pullman, WA 99164-4236 Phone (509)335-7218 Nothing in biology makes sense except in the light of evolution. -T. Dobzhansky - Original Message To: Liaw, Andy [EMAIL PROTECTED]; r-help@stat.math.ethz.ch Sent: Tuesday, February 28, 2006 10:50:18 PM Subject: Re: [R] Help - lm, glm, aov results inconsistent with other stati stical package Alright, I'll try to give some sample code. # create A with 2 levels - 2 and 4 A-c(rep(2,times=30),rep(4,times=42)) # make A a factor A-as.factor(A) #generate 72 random x points x-rnorm(72) #create different slopes intercepts for A=2 and A=4 #add a random error term y-c(x[(1:30)]+1,2*x[(31:72)]-2)+rnorm(72,mean=0,sd=2) #use model y~A*x for lm (or glm) test.lm-lm(y~A*x) #check the output summary(test.lm) This essentially creates something like my data set and uses the same model. In response to (1), I was just using 0/1 because these are codings for the 2 levels, correct? (i.e., when A=2 the dummy variable=0, when A=4 the dummy variable=1?). In response to (2), yes, I do want different slopes for the two categories (that is what I am interested in testing). If I export the data created above to JMP and run what I believe to be the same model, I get a different answer for my equations :( --- Benjamin Ridenhour School of Biological Sciences Washigton State University P.O. Box 644236 Pullman, WA 99164-4236 Phone (509)335-7218 Nothing in biology makes sense except in the light of evolution. -T. Dobzhansky - Original Message From: Liaw, Andy [EMAIL PROTECTED] Sent: Tuesday, February 28, 2006 5:14:57 PM Subject: RE: [R] Help - lm, glm, aov results inconsistent with other stati stical package 1. You have levels(A) as 2 and 4, yet you showed equations for A=0 and A=1? 2. y = A + X + A*X means you're allowing the different groups of A to have different slopes. Probably not what you intended. 3. It's probably best to provide a small sample of the data (and R code) so we know how you got what you got. Andy From: Ben Ridenhour Hello, I 'm sure there must a be a simple explanation for what I'm doing wrong but I am stumped. I am a novice R user and this has shaken my confidence in what I'm doing! I am trying to run a simple ANCOVA using the model y~A*x, where y and x are continuous and A has two levels. Everything seems fine until I compare the output with what I get from another statistical package (JMP). JMP has the model y=A+x+A*x (this should be the same as what I specified to R, correct?). In comparison I get the line equations y = 7072.09-1024.94 x (for A=0) and y = 7875.58-970.088 x (for A=1) from JMP. And from R I get y = 6276.7-1259.8 x (for A=0) and y = 7867.5-1150.1 x (for A=1). Obviously, these aren't even close to the same answer. I've tried this using glm(), lm(), and aov() and as expected they all give the same answer. If I do levels(A) [1] 2 4 which are the two levels of A. Why can't I get the same answer from JMP as in R? This is very disturbing to me! Thanks, Ben --- Benjamin Ridenhour School of
[R] heckit with a probit
Hi I have data for voting behaviour on two (related) binary votes. I want to examine the second vote, running separate regressions for groups who voted different ways on the first vote. As the votes are not independent, I guess that there is an issue with selection bias. So, I think I would like to fit a heckit style model but with a binary dependent variable - so, in effect, two successive probits. Is there a way to do it in R? (Alternatively: am I thinking about this the right way?) Cheers David __ 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] Re: nlme: Deficient rank in gls_loglik when creating corAR1()
I tried that but it didn't make any difference to the output. David On 04/05/05, Douglas Bates [EMAIL PROTECTED] wrote: David Hugh-Jones wrote: Is this a bug? Should I attach a test case? Try setting verbose=TRUE in the call to gls first and see if that gives you any insight into what is happening. D On 03/05/05, David Hugh-Jones [EMAIL PROTECTED] wrote: I have a bunch of data which is structured by year and US state, so I have created a nlme groupedData object for it: formula(gd2) DEPVAR ~ YEAR | ABREV Now I am trying to run a gls regression on it. I want the error correlation structure to be AR1 with a different rho for each state, so I do mdyn.1.1 = gls(model = DEPVAR ~ BLAH + BLAH, data=gd2, corr=corAR1(form= ~ YEAR | ABREV),na.action=na.omit) YEAR and ABREV are always present; DEPVAR is absent for one state. I get the following error message: Error in logLik.glsStruct(glsSt, glsPars) : Deficient rank in gls_loglik Can anyone enlighten me? The error message goes away if I just do corAR1(form = ~1), but this is not meaningful for my data. Cheers David __ 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-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] Combining numeric vs numeric numeric vs factor graphs into one ps/pdf file
Hi David
You probably want to write your own panel function and pass it into
xyplot(). Something like
mypanel - function (x,y, groups, subscripts) {
if (status[subscripts] == pass) {
panel.xyplot(H,CD)
}
else {
panel.xyplot(site, CD)
}
}
Check out the groups and subscripts arguments to xyplot.
cheers
Dave
On 03/05/05, Arenas, David R. CIV NAVAIR DEPT [EMAIL PROTECTED] wrote:
Dear R community,
My previous email was incomplete because I used html format. Here it is
again and sorry for any inconvenience:
xyplot (lattice) has been great in displaying tons of data for my research.
I have used the following two xyplot commands (with example dataframe) to
create two separate postscript/pdf files with respect to the variable acft
and subset status:
test.df - data.frame(acft=factor(c(A,B,C,D)),
status=factor(c(fail,pass,fail,pass)),
site=factor(c(E1,E1,E2,E2)),
CD=as.numeric(c(1,1,3,3)),
H=as.numeric(c(80,NA,60,NA)))
xyplot(H ~ CD | acft,
data=test.df,
subset=status==fail,
layout=c(1,1) )
xyplot(site ~ CD | acft,
data=test.df,
subset=status==pass,
layout=c(1,1) )
I would like to combine all graphs into one file in alphabetical order of
variable acft. The graphs would be one per page where in fact I use
layout=c(1,1) for the nice and easily seen strip labels for acft. The
problem I am having is combining x-y plots that are numeric vs numeric
numeric vs factor. I have search the R-help archives and R-project
references for an example to no avail. I am thinking I may have to use
something (lattice or not) like ...
if any(test.df$Status==fail)
plot(H ~ CD)
else
plot(site ~ CD)
with for in the beginning to loop through all data with respect to acft. I
need a hint on how to further this along. I am using R.2.1.0 via Windows XP.
Thank you for any help,
D. Arenas
[[alternative HTML version deleted]]
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[R] Re: nlme: Deficient rank in gls_loglik when creating corAR1()
Is this a bug? Should I attach a test case? D On 03/05/05, David Hugh-Jones [EMAIL PROTECTED] wrote: I have a bunch of data which is structured by year and US state, so I have created a nlme groupedData object for it: formula(gd2) DEPVAR ~ YEAR | ABREV Now I am trying to run a gls regression on it. I want the error correlation structure to be AR1 with a different rho for each state, so I do mdyn.1.1 = gls(model = DEPVAR ~ BLAH + BLAH, data=gd2, corr=corAR1(form= ~ YEAR | ABREV),na.action=na.omit) YEAR and ABREV are always present; DEPVAR is absent for one state. I get the following error message: Error in logLik.glsStruct(glsSt, glsPars) : Deficient rank in gls_loglik Can anyone enlighten me? The error message goes away if I just do corAR1(form = ~1), but this is not meaningful for my data. Cheers David __ 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] correcting for autocorrelation in models with panel data?
Another question - is there a way to use autocorrelation with OLS, rather than GLS? I am really blindly following Beck and Katz (1995) here, and they recommend OLS rather than Feasible Generalized Least Squares for panel data where the number of individuals is larger than the number of time units, which is my case. Cheers David On Thu, 10 Feb 2005 12:36:32 -0500, Doran, Harold [EMAIL PROTECTED] wrote: In the nlme package you can find the gls() function to account for autocorrelation over time using corAR1. Syntax might look something like this: fm1 - gls(response ~ IV, long, correlation=corAR1(form=~1|ID), method='ML') You can also use weights() for heteroscedasticity. -Harold -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Hugh-Jones Sent: Thursday, February 10, 2005 12:15 PM To: r-help@stat.math.ethz.ch Subject: [R] correcting for autocorrelation in models with panel data? Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a package for panel data that I have missed? I appreciate that this is probably just as much about my ignorance of econometrics as about R itself! Cheers David __ 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-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] correcting for autocorrelation in models with panel data?
Specifically the Beck and Katz article points out that Feasible GLS,
which involves a special method for correcting standard errors for
panel data, doesn't work when time period is less than number of
individuals. They suggest using OLS, then correcting the standard
errors. But their method does not correct for autocorrelation over
time - only for heteroskedasticity and correlation between different
units at time t.
Cheers
David
On Fri, 11 Feb 2005 06:12:30 -0500, Doran, Harold [EMAIL PROTECTED] wrote:
No, by definition the off-diagonal elements in the covariance matrix for an
OLS are 0. Thus, OLS is a special case of a GLS. You can see this if you
write out the formulae for an OLS solution and GLS solution.
The typical solution for the standard errors in an OLS are (X'X)^{-1}*s^2.
This is the same as (X'V^{-1}X)^{-1} when V= s^2*I, I being the identity
matrix, which is also the gls solution. But in the gls solution the off
diagonal elements of V are covariances, not 0. In the case of autocorrelation
(AR1) the off-diagonal elements decay exponentially over time.
I'm not familiar with Beck and Katz or why they would recommend that OLS be
used when the number of time units is smaller than the number of individuals
in the data. But to me, this seems rather silly, isn't this often the case?
HTH
Harold
-Original Message-
From: David Hugh-Jones [mailto:[EMAIL PROTECTED]
Sent: Fri 2/11/2005 5:23 AM
To: Doran, Harold
Cc: [EMAIL PROTECTED]
Subject: Re: [R] correcting for autocorrelation in models with panel
data?
Another question - is there a way to use autocorrelation with OLS,
rather than GLS?
I am really blindly following Beck and Katz (1995) here, and they
recommend OLS rather than Feasible Generalized Least Squares for
panel data where the number of individuals is larger than the number
of time units, which is my case.
Cheers
David
On Thu, 10 Feb 2005 12:36:32 -0500, Doran, Harold [EMAIL
PROTECTED] wrote:
In the nlme package you can find the gls() function to account for
autocorrelation over time using corAR1. Syntax might look
something like
this:
fm1 - gls(response ~ IV, long, correlation=corAR1(form=~1|ID),
method='ML')
You can also use weights() for heteroscedasticity.
-Harold
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of David Hugh-Jones
Sent: Thursday, February 10, 2005 12:15 PM
To: r-help@stat.math.ethz.ch
Subject: [R] correcting for autocorrelation in models with panel
data?
Hi
I have some panel data for the 50 US states over about 25 years,
and I
would like to test a simple model via OLS, using this data. I
know how
to run OLS in R, and I think I can see how to create Panel
Corrected
Standard Errors using
http://jackman.stanford.edu/classes/350C/pcse.r
What I can't figure out is how to correct for autocorrelation
over time.
I have found a lot of R stuff on time series models but they all
seem
focused on predicting a single variable from its previous values.
Can anyone explain to me how to detect and get round
autocorrelation?
Is there a package for panel data that I have missed?
I appreciate that this is probably just as much about my
ignorance of
econometrics as about R itself!
Cheers
David
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[R] correcting for autocorrelation in models with panel data?
Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a package for panel data that I have missed? I appreciate that this is probably just as much about my ignorance of econometrics as about R itself! Cheers David __ 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] correcting for autocorrelation in models with panel data?
That's very helpful - I was on the point of giving up and going with Stata! I will look into that in more detail. I assume that afterwards it would be ok to apply the Beck and Katz procedure to get panel corrected standard errors. Cheers David On Thu, 10 Feb 2005 12:36:32 -0500, Doran, Harold [EMAIL PROTECTED] wrote: In the nlme package you can find the gls() function to account for autocorrelation over time using corAR1. Syntax might look something like this: fm1 - gls(response ~ IV, long, correlation=corAR1(form=~1|ID), method='ML') You can also use weights() for heteroscedasticity. -Harold -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Hugh-Jones Sent: Thursday, February 10, 2005 12:15 PM To: r-help@stat.math.ethz.ch Subject: [R] correcting for autocorrelation in models with panel data? Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a package for panel data that I have missed? I appreciate that this is probably just as much about my ignorance of econometrics as about R itself! Cheers David __ 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-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] correcting for autocorrelation in models with panel data?
Assuming I have years in YEAR and state ids in ID, I guess the correlation ought to be corAR1(form = ~ YEAR | ID) ? Thanks a lot, David On Thu, 10 Feb 2005 12:36:32 -0500, Doran, Harold [EMAIL PROTECTED] wrote: In the nlme package you can find the gls() function to account for autocorrelation over time using corAR1. Syntax might look something like this: fm1 - gls(response ~ IV, long, correlation=corAR1(form=~1|ID), method='ML') You can also use weights() for heteroscedasticity. -Harold -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of David Hugh-Jones Sent: Thursday, February 10, 2005 12:15 PM To: r-help@stat.math.ethz.ch Subject: [R] correcting for autocorrelation in models with panel data? Hi I have some panel data for the 50 US states over about 25 years, and I would like to test a simple model via OLS, using this data. I know how to run OLS in R, and I think I can see how to create Panel Corrected Standard Errors using http://jackman.stanford.edu/classes/350C/pcse.r What I can't figure out is how to correct for autocorrelation over time. I have found a lot of R stuff on time series models but they all seem focused on predicting a single variable from its previous values. Can anyone explain to me how to detect and get round autocorrelation? Is there a package for panel data that I have missed? I appreciate that this is probably just as much about my ignorance of econometrics as about R itself! Cheers David __ 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-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] expand.model.frame gives object not found
Hello, I am a (relatively) experienced programmer, but new to R. I have a problem using R 1.9.1. I have fit some data using glm(), from within a function: formula = as.formula(paste(depvarname, ~, rhs), env=globalenv()) return (glm(formula, family=binomial(link=logit))) I have now come back to the formula and want to add some more variables to it. So I do: expand.model.frame(formulaname, ~ new_variable) but I get the response Error in eval(expr, envir, enclos) : Object foreignaid.dummy not found where foreignaid.dummy is my dependent variable. However, foreignaid.dummy is clearly visible in the global environment: ls(pat=foreignaid.dummy, envir=globalenv()) [1] foreignaid.dummy ... So why is my dependent variable lost? I have read the earlier comments on the same topic, but they seem to indicate that a previous bug was fixed. Am I missing the point about scoping? Any help much appreciated. David Hugh-Jones Essex University Govt Dept __ [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
