Hi,
I am trying to convert a colleague from using SPSS to R, but am having
trouble generating a result that is similar enough to a bootstrapped cox
regression analysis that was run in SPSS. I tried unsuccessfully with
bootcens, but have had some success with the bootcov function in the rms
package, which at least generates confidence intervals similar to what is
observed in SPSS. However, the p-values associated with each predictor in
the model are not really close in many instances.
Here is the code I am using:
formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 + fac6
+ fac7 + fac8
fit=cph(formula, data=temp, x=T, y=T)
validate(fit, method="boot", B=9999, bw=F, type="residual",
sls=0.05,
aics=0,force=NULL, estimates=TRUE, pr=FALSE)
out=bootcov(fit, B=9999, pr=F, coef.reps=T, loglik=F)
for (i in 1:8) {
print(quantile(out$boot.Coef[,i], c(.025, .975)))
}
anova(out)
variable low CI high CI p-value
fac1 -8.919692 20.800878 .5917
fac2 -8.683579 3.091100 .6381
fac3 -1.848428 2.193492 .9312
fac4 -0.17575426 0.08333277 .8246
fac5 -3.1488578 0.5166171 .2946
fac6 -0.03621405 0.07241772 .5600
fac7 -0.62847922 0.08566296 .3433
fac8 -0.01553286 0.20909384 .5756
The results from SPSS I am trying to match (or come close to matching) are
the following:
variable low CI high CI p-value
fac1 -8.474 20.020 .456
fac2 -8.206 3.093 .524
fac3 -1.829 2.087 .900
fac4 -.173 .083 .749
fac5 -2.945 .450 .143
fac6 -.035 .070 .306
fac7 -.626 .092 .189
fac8 -.017 .203 .247
Sorry if this is a really basic question. I have searched for several
hours for an explanation, but cannot find anything that explains why the
p-values would be different despite similar confidence intervals.
Thanks in advance,
Eric
[[alternative HTML version deleted]]
R Users,
I just upgraded my version of R from R-2.15.0 to R-2.15.2 and installed the
latest versions of LyX and MikTex running Windows 7 Ultimate, 64-bit OS. Prior
to the upgrade, I was using Lyx with knitr to generate a document with no
problems. However, after the upgrade, and using the same LyX document, I'm
receiving the following error when I attempt to compile the document:
\end{verbatim}
The control sequence at the end of the top line
of your error message was never \def'ed. If you have
misspelled it (e.g., `\hobx'), type `I' and the correct
spelling (e.g., `I\hbox'). Otherwise just continue,
and I'll forget about whatever was undefined.
I have determined that the error is caused when printing the anova results from
the anova statement in my R source code, but can't seem to resolve the
issue. Here is an example code chunk that creates the error:
<<NonCP1, fig.width=6, fig.height=4, out.width='.8\\linewidth'
,par=FALSE>>#Read in data
y=c( 67, 73, 83, 89, 65, 91, 87, 86, 155, 127, 147, 212, 108, 100, 90, 153, 140,
142, 121, 150, 33, 8, 46, 54 )
temp=as.factor(c(rep(seq(360, 380, 10), each=4), rep(seq(380, 360, -10),
each=4)))
coat=as.factor(rep(seq(1, 4), 6))
replicate=as.factor(rep(seq(1, 6), each=4))
#Obtain Factorial/Incorrect Model
o=lm(y~temp*coat)
ano=anova(o)
ano
@
Removing the ano=anova(o) or ano lines in the code chunk allows the document to
compile with no problem. Does anyone else have this problem or did I do
something wrong when I migrated to the newer versions?
Thanks, in advance for any help!
Sincerely yours,
Mark J. Lamias
[[alternative HTML version deleted]]
Quite a few people have had this problem, but since I'm unable to reproduce it, I'm not exactly sure how to fix it either. A few references that might be helpful to you: http://stackoverflow.com/q/12448507/559676 https://github.com/yihui/knitr/issues/413 It is very likely to be a pure LaTeX problem. Letting MikTeX install the missing LaTeX packages on the fly might solve the problem. Regards, Yihui -- Yihui Xie <xieyihui at gmail.com> Phone: 515-294-2465 Web: http://yihui.name Department of Statistics, Iowa State University 2215 Snedecor Hall, Ames, IA On Thu, Nov 29, 2012 at 10:53 AM, Mark Lamias <mlamias at yahoo.com> wrote:> R Users, > > > I just upgraded my version of R from R-2.15.0 to R-2.15.2 and installed the latest versions of LyX and MikTex running Windows 7 Ultimate, 64-bit OS. Prior to the upgrade, I was using Lyx with knitr to generate a document with no problems. However, after the upgrade, and using the same LyX document, I'm receiving the following error when I attempt to compile the document: > > \end{verbatim} > The control sequence at the end of the top line > of your error message was never \def'ed. If you have > misspelled it (e.g., `\hobx'), type `I' and the correct > spelling (e.g., `I\hbox'). Otherwise just continue, > and I'll forget about whatever was undefined. > > I have determined that the error is caused when printing the anova results from the anova statement in my R source code, but can't seem to resolve the issue. Here is an example code chunk that creates the error: > > <<NonCP1, fig.width=6, fig.height=4, out.width='.8\\linewidth' ,par=FALSE>>> #Read in data > y=c( 67, 73, 83, 89, 65, 91, 87, 86, 155, 127, 147, 212, 108, 100, 90, 153, 140, 142, 121, 150, 33, 8, 46, 54 ) > temp=as.factor(c(rep(seq(360, 380, 10), each=4), rep(seq(380, 360, -10), each=4))) > coat=as.factor(rep(seq(1, 4), 6)) > replicate=as.factor(rep(seq(1, 6), each=4)) > #Obtain Factorial/Incorrect Model > o=lm(y~temp*coat) > ano=anova(o) > ano > @ > > > Removing the ano=anova(o) or ano lines in the code chunk allows the document to compile with no problem. Does anyone else have this problem or did I do something wrong when I migrated to the newer versions? > > Thanks, in advance for any help! > > Sincerely yours, > > Mark J. Lamias > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help at 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. >
Eric, the output you showed for anova(out) is not correct. anova.rms does not produce such output. Please give us the correct script that obtained those results and let us know if you are overriding the anova command somehow. To your point, make sure that SPSS does not use the bootstrap to obtain a new point estimate of beta but rather uses the original Cox beta coefficients in the test. Frank Eric Claus wrote> Hi, > I am trying to convert a colleague from using SPSS to R, but am having > trouble generating a result that is similar enough to a bootstrapped cox > regression analysis that was run in SPSS. I tried unsuccessfully with > bootcens, but have had some success with the bootcov function in the rms > package, which at least generates confidence intervals similar to what is > observed in SPSS. However, the p-values associated with each predictor in > the model are not really close in many instances. > > Here is the code I am using: > > formula=Surv(months, recidivate) ~ fac1 + fac2 + fac3 + fac4 + fac5 + fac6 > + fac7 + fac8 > fit=cph(formula, data=temp, x=T, y=T) > validate(fit, method="boot", B=9999, bw=F, type="residual", sls=0.05, > aics=0,force=NULL, estimates=TRUE, pr=FALSE) > out=bootcov(fit, B=9999, pr=F, coef.reps=T, loglik=F) > for (i in 1:8) { > print(quantile(out$boot.Coef[,i], c(.025, .975))) > } > anova(out) > > variable low CI high CI p-value > fac1 -8.919692 20.800878 .5917 > fac2 -8.683579 3.091100 .6381 > fac3 -1.848428 2.193492 .9312 > fac4 -0.17575426 0.08333277 .8246 > fac5 -3.1488578 0.5166171 .2946 > fac6 -0.03621405 0.07241772 .5600 > fac7 -0.62847922 0.08566296 .3433 > fac8 -0.01553286 0.20909384 .5756 > > The results from SPSS I am trying to match (or come close to matching) are > the following: > variable low CI high CI p-value > fac1 -8.474 20.020 .456 > fac2 -8.206 3.093 .524 > fac3 -1.829 2.087 .900 > fac4 -.173 .083 .749 > fac5 -2.945 .450 .143 > fac6 -.035 .070 .306 > fac7 -.626 .092 .189 > fac8 -.017 .203 .247 > > Sorry if this is a really basic question. I have searched for several > hours for an explanation, but cannot find anything that explains why the > p-values would be different despite similar confidence intervals. > > Thanks in advance, > Eric > > [[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/bootstrapped-cox-regression-rms-package-tp4651306p4651344.html Sent from the R help mailing list archive at Nabble.com.
Hi Frank, Below is the actual output from the anova(out) command. I had copied in the p-values and from the previous output from anova(out) and the confidence intervals from print(quantile(out$boot.Coef[,i], c(.025, .975))) to illustrate that the confidence intervals were similar to SPSS while the p-values were not. Actual output from anova.rms(out): Wald Statistics Response: Surv(months, recidivate) Factor Chi-Square d.f. P fac1 0.27 1 0.6055 fac2 0.20 1 0.6514 fac3 0.01 1 0.9338 fac4 0.05 1 0.8311 fac5 1.06 1 0.3036 fac6 0.33 1 0.5647 fac7 0.81 1 0.3670 fac8 0.30 1 0.5832 TOTAL 1.48 8 0.9930 Regarding your second question, it looks like SPSS is using the original estimate of Cox beta coefficients in the test (i.e. a new point estimate is not generated for the statistical test) Thanks again, Eric -- View this message in context: http://r.789695.n4.nabble.com/bootstrapped-cox-regression-rms-package-tp4651306p4651363.html Sent from the R help mailing list archive at Nabble.com.
Thanks Eric. It would be good to show your entire script next time as stated in the posting guidance. Regarding matching with SPSS please describe the bootstrapping algorithm used there. In rms I do the unconditional bootstrap, i.e., I sample with replacement from the rows of the raw data. And also make sure that SPSS ran a large number of bootstrap replications. Frank Eric Claus wrote> Hi Frank, > Below is the actual output from the anova(out) command. I had copied in > the p-values and from the previous output from anova(out) and the > confidence intervals from print(quantile(out$boot.Coef[,i], c(.025, > .975))) to illustrate that the confidence intervals were similar to SPSS > while the p-values were not. > > Actual output from anova.rms(out): > > Wald Statistics Response: Surv(months, recidivate) > > Factor Chi-Square d.f. P > fac1 0.27 1 0.6055 > fac2 0.20 1 0.6514 > fac3 0.01 1 0.9338 > fac4 0.05 1 0.8311 > fac5 1.06 1 0.3036 > fac6 0.33 1 0.5647 > fac7 0.81 1 0.3670 > fac8 0.30 1 0.5832 > TOTAL 1.48 8 0.9930 > > Regarding your second question, it looks like SPSS is using the original > estimate of Cox beta coefficients in the test (i.e. a new point estimate > is not generated for the statistical test) > > Thanks again, > Eric----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/bootstrapped-cox-regression-rms-package-tp4651306p4651438.html Sent from the R help mailing list archive at Nabble.com.
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