[R] 'fractal' package
https://rdrr.io/rforge/fractal/ https://cran.r-project.org/web/packages/fractal/fractal.pdf Hi, I am trying to learn about nonlinear time series, and fractal time series analysis in particular. I am interested in becoming proficient with the 'fractal' package. I have two specific questions: 1. I have a basic understanding of ARMA and ARIMA models. Can someone recommend a good introductory text on nonlinear time series that emphasizes practical application? I am definitely not looking for a theoretical text on stochastic process. 2. Can someone point me in the direction of case studies where the 'fractal' package has been used to analyze the stock market? My main interests are in the DOW Transportation Index and the S&P500, but in the absence of these kinds of case studies I will happily study any practical case studies that utilize the 'fractal' package. Many Thanks in Advance, David Paul [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] propensity scores & imputation
Hi Mr. Gunter, Will do. Thanks, I've not visited stats.stackexchange before. Kind Regards, David -Original Message- From: Bert Gunter [mailto:bgunter.4...@gmail.com] Sent: Thursday, March 16, 2017 7:51 PM To: david.p...@statmetrics.biz Cc: R-help Subject: Re: [R] propensity scores & imputation Way out of bounds for this list (see the posting guide). Try posting on stats.stackexchange.com instead. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Mar 16, 2017 at 10:42 AM, David Paul wrote: > Hi, > > > > Many thanks in advance for whatever advice / input I may receive. > > > > I have a propensity score matching / data imputation question. The > purpose of the propensity > > score modeling is to put subjects from two different clinical trials > on a similar footing so that a key > > clinical measurement from one study can be attributed / imputed to the > other study. The goal is > > NOT to directly compare the two studies, so this is a very atypical > kind of propensity score usage. > > > > I am using lrm( ) to obtain estimated propensity scores, and my > question to this List is rather more > > philosophical than R-syntax. > > > > > > Here is the data setup: > > > >a.frame > b.frame > >--- > > >1. Represents data from clinical trial A1. > Represents data from clinical trial B > > 2. Two arms, 'ACTIVE' and 'PLACEBO' 2. Two > arms, 'ACTIVE' and 'PLACEBO' > >3. The active drug is the same as with Study B 3. The active > drug is the same as with Study A > >4. The trial design is very similar to Study B4. The > trial design is very similar to Study A > >5. One measurement is a clinical continuous 5. Does NOT > have the clinical continuous measure > > measure obtained via laboratory assay that > is available in Study A > >6. Number of randomized subjects = 500 6. Number of > randomized subjects = 5,000 > >7. A subset of the baseline covariates (call it 7. A > subset of the baseline covariates (call it > > a.subset.frame) has 100% commonality > b.subset.frame) has 100% commonality > > with b.subset.frame > with a.subset.frame > > > 8. Primary endpoint is time-to-event > > > > > > Here is the analysis setup: > > > > I have separately split a.frame and b.frame into 'ACTIVE' and 'PLACEBO' > subjects. > > > > For the 'PLACEBO' subjects I have entered the a.subset.frame = > b.subset.frame baseline > > covariates into lrm( ). The outcome variable is a factor variable > representing Study A = 'Y', > > so the estimated propensity scores are the estimated probabilities > that a 'PLACEBO' subject is > > from Study A. I then, finally, used the %GREEDY algorithm (posted on > Mayo Clinic website) > > in SAS to match 1-to-many where the Study A subjects are thought of as > 'case' subjects and > > the Study B subjects are thought of as 'control' subjects. [I know the > matching can be done > > in R, I'm working on that now.] The average number of Study B > subjects matched to a > > single Study A subject is approximately 5. > > > > I have done a similar analysis for the 'ACTIVE' subjects. > > > > > > > > Here is my question: > > > > At the end, I will combine the Study B matched 'PLACEBO' and 'ACTIVE' > subjects and > > perform a Cox PH regression to compare 'PLACEBO' and 'ACTIVE' - there > will be no Study A > > subjects in this analysis. I want to incorporate the clinical > continuous measurement "borrowed" > > from Study A as a covariate. When doing this, how should I best take > into account the > > 1-to-many matching? Do I need to weight the Study B subjects, or can > I simply enter the > > matched Study B subjects into a Cox PH regression and ignore the > 1-to-many issue? > > > > > > Kind Regards, > > > > David > > > > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. PGP.sig Description: PGP signature __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] propensity scores & imputation
Hi, Many thanks in advance for whatever advice / input I may receive. I have a propensity score matching / data imputation question. The purpose of the propensity score modeling is to put subjects from two different clinical trials on a similar footing so that a key clinical measurement from one study can be attributed / imputed to the other study. The goal is NOT to directly compare the two studies, so this is a very atypical kind of propensity score usage. I am using lrm( ) to obtain estimated propensity scores, and my question to this List is rather more philosophical than R-syntax. Here is the data setup: a.frame b.frame --- 1. Represents data from clinical trial A1. Represents data from clinical trial B 2. Two arms, 'ACTIVE' and 'PLACEBO' 2. Two arms, 'ACTIVE' and 'PLACEBO' 3. The active drug is the same as with Study B 3. The active drug is the same as with Study A 4. The trial design is very similar to Study B4. The trial design is very similar to Study A 5. One measurement is a clinical continuous 5. Does NOT have the clinical continuous measure measure obtained via laboratory assay that is available in Study A 6. Number of randomized subjects = 500 6. Number of randomized subjects = 5,000 7. A subset of the baseline covariates (call it 7. A subset of the baseline covariates (call it a.subset.frame) has 100% commonality b.subset.frame) has 100% commonality with b.subset.frame with a.subset.frame 8. Primary endpoint is time-to-event Here is the analysis setup: I have separately split a.frame and b.frame into 'ACTIVE' and 'PLACEBO' subjects. For the 'PLACEBO' subjects I have entered the a.subset.frame = b.subset.frame baseline covariates into lrm( ). The outcome variable is a factor variable representing Study A = 'Y', so the estimated propensity scores are the estimated probabilities that a 'PLACEBO' subject is from Study A. I then, finally, used the %GREEDY algorithm (posted on Mayo Clinic website) in SAS to match 1-to-many where the Study A subjects are thought of as 'case' subjects and the Study B subjects are thought of as 'control' subjects. [I know the matching can be done in R, I'm working on that now.] The average number of Study B subjects matched to a single Study A subject is approximately 5. I have done a similar analysis for the 'ACTIVE' subjects. Here is my question: At the end, I will combine the Study B matched 'PLACEBO' and 'ACTIVE' subjects and perform a Cox PH regression to compare 'PLACEBO' and 'ACTIVE' - there will be no Study A subjects in this analysis. I want to incorporate the clinical continuous measurement "borrowed" from Study A as a covariate. When doing this, how should I best take into account the 1-to-many matching? Do I need to weight the Study B subjects, or can I simply enter the matched Study B subjects into a Cox PH regression and ignore the 1-to-many issue? Kind Regards, David PGP.sig Description: PGP signature __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] plotting varclus( ) results
Hello, I am running R on a Win7 operating system. Many thanks in advance for any help. I am using >library(Hmisc) >library(rms) >varclus.out <- varclus(~.,data=temp.df, sim = 'hoeffding') >plot(varclus.out) to generate a plot suitable for feature space dimension reduction. Is it possible to modify the plot so variable names are color-coded? For example, half of the variable names in red and half in blue. Kind Regards, David Paul PGP.sig Description: PGP signature __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] obtaining prediction intervals from lrm() in rms library
Hi,
Apologies if this is a silly question -- I am just now learning how to use
some of the basic functions in
the rms library.
I have been using
foo.dist <- datadist(foo.frame)
options(datadist='foo.dist')
lrm.model <- lrm(binary.outcome ~ rcs(contin.var,5)+categ.var, data =
foo.frame, x=TRUE)
lrm.predict <- predict(lrm.model, type = "fitted")
to obtain the predicted probabilities from a logistic regression model, but
now I need the associated 95%
prediction intervals associated with these predicted probabilities. I've
read the examples in the ?lrm
help page, and from the information about "predict" from
http://cran.r-project.org/web/packages/rms/rms.pdf
I have tried
predict(lrm.model, conf.int = 0.95, conf.type = c("individual"))
but I get the error message
Error in predictrms(object, ..., type = type, se.fit = se.fit) :
conf.type="individual" requires that fit be from ols
>From the same PDF, I have read the "predict.lrm" pages and was not able to
figure out how to get
prediction intervals.
Many thanks in advance for some help,
David
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[R] transcan() imputation of categorical (factor) variables
Hi, and thanks in advance.
I have used the following to try to obtain singly-imputed values for missing
data comprising no more than 15%
of any variable in the data:
> library(Hmisc)
> some.df = read.csv("N:/.../some.csv", header = TRUE, stringsAsFactors = TRUE)
> some.trans <- transcan(~ contin.var1 + contin.var2 + categ.var1 + categ.var2,
> categorical =c("categ.var1","categ.var2"),
> transformed = TRUE,
> imputed = TRUE,
> impcat = "score",
> data = some.df,
> iter.max = 100,
> shrink = TRUE,
> method = "canonical"
> )
> attach(some.df,pos=1)
> some.df.imputed <- impute(some.trans)
It seems to run, but the object "some.df.imputed" isn't a dataframe, and R
issues the message
> Imputed missing values with the following frequencies
> and stored them in variables with their original names:
>
> contin.var1 contin.var2
> 139
which seems to imply that the categorical variables were not imputed. What I
want, simply put, is a dataframe
with imputed values for both the continuous and categorical variables. I'm
sure I'm doing something silly.
Any help would be greatly appreciated.
Thanks,
David
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