[R] making a plot
Dear all,
I am struggling to make a plot for my survival analysis
class.
This is my script
labels-c('1','2','3','4','5','6')
ano-c(2001,2002,2003,2004,2006,2008)
ranges-c(6,3,4,5,4,2)
dotchart(ano, labels=labels, xlab='ano',
ylab='Pacientes',pch=20,xlim=c(min(ano), max(ano+ranges)))
segments(ano,1:6,ano+ranges,1:6,pch=25,lty=1,lend=4)
I need to put an asterix (failure) by the end of the three first lines and
a small circle (censoring)
by the end of the last three.
Someone can help me?
Thanks,
Enrico.
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Re: [R] making a plot
Thanks David and Adrés,
it worked fine.
Enrico.
2014-10-20 15:37 GMT-02:00 David Winsemius dwinsem...@comcast.net:
On Oct 20, 2014, at 8:24 AM, Andrés Aragón wrote:
Enrico,
This may help you:
text(locator(1), *, cex=1.5,adj=0.5
and
text(locator(1), º, cex=1.5,adj=0.5
Why not just use the values of x2 and y2 that were given to segments:
text( (ano+ranges)[1:3], 1:3, *, cex=1.5,adj=0.5)
text( (ano+ranges)[4:6], 4:6 , º, cex=1.5,adj=0.5)
Draw your plot, then write the code, locate the cursor on your plot, put
the symbols where you want itl and click.
Regards,
Andrés
PS ?locator
2014-10-20 9:46 GMT-05:00 Enrico Colosimo enrico...@gmail.com:
Dear all,
I am struggling to make a plot for my survival analysis
class.
This is my script
labels-c('1','2','3','4','5','6')
ano-c(2001,2002,2003,2004,2006,2008)
ranges-c(6,3,4,5,4,2)
dotchart(ano, labels=labels, xlab='ano',
ylab='Pacientes',pch=20,xlim=c(min(ano), max(ano+ranges)))
segments(ano,1:6,ano+ranges,1:6,pch=25,lty=1,lend=4)
I need to put an asterix (failure) by the end of the three first lines
and
a small circle (censoring)
by the end of the last three.
Someone can help me?
Thanks,
Enrico.
[[alternative HTML version deleted]]
--
David Winsemius
Alameda, CA, USA
[[alternative HTML version deleted]]
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R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Kaplan Meier analysis: 95% CI wider in R than in SAS
What are the significant problems of the log-log transformations? Any papers published about it? Enrico. 2012/4/14 Frank Harrell f.harr...@vanderbilt.edu I used log-log in my book too until Terry Therneau alerted me to the significant problems this creates. In the 2nd edition it will use log S(t). Frank Paul Miller wrote Hello Drs. Colosimo and Harrell, Thank you for your replies to my question. From Dr. Colosimo, I was able to determine that the SAS results can be replicated by adding the option conf.type=log-log to my code as in : survobj - survfit(survfrm, conf.type=log-log, data=Survival) Originally, it looked like the SAS results could be replicated using conf.type=plain. Applying this option to my actual data revealed that this was not the case, however. From Dr. Harrell, I learned that using conf.type=log-log may not be such a good idea. Interestingly though, I've seen at least one instance where experts in the R community use this option in their book. The book is about 10 years old. So maybe opinion about the use of this option has shifted since then. Thanks, Paul [[alternative HTML version deleted]] __ R-help@ 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. - Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Kaplan-Meier-analysis-95-CI-wider-in-R-than-in-SAS-tp4554559p4557695.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Kaplan Meier analysis: 95% CI wider in R than in SAS
But it is very very unlikely to have a time with survival probability of 0. in a real data set. It would be necessary huge data set. A Monte Carlo simulation could put a little more light in this issue? 2012/4/16 Frank Harrell f.harr...@vanderbilt.edu Just generate some data where the estimated survival probability is 0. at a certain time. The log-log transformation blows up. Frank Enrico Colosimo wrote What are the significant problems of the log-log transformations? Any papers published about it? Enrico. 2012/4/14 Frank Harrell lt;f.harrell@gt; I used log-log in my book too until Terry Therneau alerted me to the significant problems this creates. In the 2nd edition it will use log S(t). Frank Paul Miller wrote Hello Drs. Colosimo and Harrell, Thank you for your replies to my question. From Dr. Colosimo, I was able to determine that the SAS results can be replicated by adding the option conf.type=log-log to my code as in : survobj - survfit(survfrm, conf.type=log-log, data=Survival) Originally, it looked like the SAS results could be replicated using conf.type=plain. Applying this option to my actual data revealed that this was not the case, however. From Dr. Harrell, I learned that using conf.type=log-log may not be such a good idea. Interestingly though, I've seen at least one instance where experts in the R community use this option in their book. The book is about 10 years old. So maybe opinion about the use of this option has shifted since then. Thanks, Paul [[alternative HTML version deleted]] __ R-help@ 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. - Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Kaplan-Meier-analysis-95-CI-wider-in-R-than-in-SAS-tp4554559p4557695.html Sent from the R help mailing list archive at Nabble.com. __ R-help@ 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. [[alternative HTML version deleted]] __ R-help@ 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. - Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Kaplan-Meier-analysis-95-CI-wider-in-R-than-in-SAS-tp4554559p4561432.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] lme command
Hi, I am doing a longitudinal data set fit using lme. I used two forms of the lme command and I am getting two different outputs. FIRST out-lme(Altura~Idade+Idade2+sexo+status+Idade:sexo+Idade:status+Idade2:sexo+Idade2:status, random=(list(ident=~Idade+Idade2))) SECOND out-lme(Altura~Idade+Idade2+sexo+status+Idade:sexo+Idade:status+Idade2:sexo+Idade2:status, random= ~Idade+Idade2|ident,data=dados) I got weird results from the first one and could not understand the reason of it. All the results are exactly the same but the intercetp, and the two main terms sexo (gender) and status (treatment). That differences made a lot of difference in the final results. Anybody can tell me what is the differences between them? Thanks. Enrico. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] glm output for binomial family
Hello, I am having some trouble running a very simple example. I am running a logistic regression entering the SAME data set in two different forms and getting different values for the deviance residual. Just look with this naive data set: # 1- Entering as a Bernoulli data set y-c(1,0,1,1,0) x-c(2,2,5,5,8) ajust1-glm(y~x,family=binomial(link=logit)) ajust1 # Coefficients: (Intercept) x 1.3107 -0.2017 Degrees of Freedom: 4 Total (i.e. Null); 3 Residual Null Deviance: 6.73 Residual Deviance: 6.491 AIC: 10.49 # # 2- Entering as Binomial data set # ysim-c(1,2,0) ynao-c(1,0,1) x-c(2,5,8) dados-cbind(ysim,ynao,x) dados-as.data.frame(dados) attach(dados) ajust2-glm(as.matrix(dados[,c(1,2)])~x,family=binomial, data=dados) summary(ajust2) # Coefficients: (Intercept) x 1.3107 -0.2017 Degrees of Freedom: 2 Total (i.e. Null); 1 Residual Null Deviance: 3.958 Residual Deviance: 3.718 AIC: 9.104 = It seems that there is problem with the first fitting!!! Best Enrico Colosimo Dept Statistics, UFMG Brazil __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
