mean corr is equal to sample corr

at least for independent pairs. For prove see here.

winbugs notes

1. in winbugs script, the file path cannot be longer than 108 characters; otherwise, there will be an incompatible copy error message
2. from R2winbugs, even seed is set to NULL (the default), chains from winbugs are identical given identical initial values and data sets. Guess winbugs figure out a fixed seed based upon these input.
3.  a good reference for specify covariance matrix prior for multivariate normal. However, there are comments that promote scaled inverse Wishart instead of inverse Wishart as the prior for precision. An illustration against inverse Wishart can be found here. 
4. scaled inversed wishart recommended by Gelman. Need set df = K+ 1 to make individual correlation coefficient uniform on [-1,1], where K is the dimension of the Cov matrix (although df=K is the minimum allowed df). A winbugs implementation can be found here: it actually scales the y variable, which indirectly scale the covariance matrix.
5. From winbugs manual: "Note that WinBUGS simulates each node in turn: this can make convergence very slow and the program very inefficient for models with strongly related parameters, such as hidden-Markov and other time series structures." Thus, strongly related parameters should be put into one multivariate node.
6. For parameters for repeated measures, reference coding of effect (treating a visit as a reference, and other visits as deviation from the reference) seems to perform worse than cell mean effect coding (just a separate parameter for each visit). This is probably because the later gives rise to a orthogonal design matrix. 
7. to convert a object (eg. wb) returned from function 'bugs' in R2WinBUGS (codaPkg=FALSE ), we can use coda1 <- as.mcmc.list(wb). See the documentation of 'codaPkg' for more details.
8. openbugs is not necessary better than winbugs in perfomance and scalibility. 
1. openbugs has no automatic graphic output, which is good when we are not worried about convergence and when we run it through many cycles (eg. in simulations)
2. openbugs runs in unix.
9. Missing values are treated as a stochastic node and effectively 'imputed' by winbugs. However, such imputed data will not inflate precision (since they are not treated as data, but parameters by winbugs). It is possible to take this imputation procedure out: run multiple imputation and feed full data into winbugs to mimic the internal imputation procedure implemented by winbugs.We will have additional data from imputation (hence increase precision), but the variation among different imputed datasets, which will be fed one by one and posterior distributions merged, may compensate for the increased precision.
10. A good resource for DIC

mmrm model example

proc mixed data=dsin;
class treat;
model change = baseline treat genotype treat*genotype;
estimate 'additive effect' genotype 1 /e;
run;

proc mixed data=dsin;

class visit;

model change = baseline visit genotype visit*genotype;

estimate 'additive effect at the last visit' genotype 1 genotype*visit 0 0 0 0 1/e;

run;

Hello, Bugs world!

Here is my first Bayesian code modified from the sample code for the Chapter 3 of Bayesian Adaptive Methods for Clinical Trials. The R code depends on BRugs that interfaces with Openbugs (manual is here).

IBS alternative

The publicly available IBS (Identity by State) macro in SAS is quite slow, and has a bug that IBS is not adjusted for call rate. I made an alternative implementation which is about 1/2 or 1/3 faster. However, SAS apparently handles the standard correlation calculations much much faster, and resulting Pearson correlation, Kendall correlation and Spearman correlation may give similar measurements to IBS (code is here, data include about 1000 markers clustered in candidate genes in 400 subjects, primary Caucasians). Therefor I guess in certain Kernel application, we can use usual correlation to replace IBS.

64bit perl debugger

It turns out Active Perl does not support 64bit debugger in 64 bit windows under PDK. From here:

The PDK's debugger was written for 32-bit Windows only. It's not available on ANY other platform, hence the 64-bit Windows PDK does not include a debugger. This has been documented in the release notes since the first 64-bit Windows PDK appeared (PDK 8.0).

There are no plans to update the PDK debugger, since a viable (and better) alternative exists in Komodo IDE. If you want a debugger for 64-bit, the multi-platform, multi-language Komodo IDE debugger is our suggested choice.

But still we can install 32 bit Active Perl for windows and use an older version of PDK (version 6 works).

sample code - MMRM

the following code implements the analyses suggested at the lower right corner of page 10 (i.e., page 312) of this paper for genetic predictors.

pairs() and more

Getting genetics done has a piece of code to make a more informative pairwise scatter plot than pairs()

# panel.smooth function is built in.
# panel.cor puts correlation in upper panels, size proportional to correlation
panel.cor <- function(x, y, digits=2, prefix="", cex.cor, ...)
{
    usr <- par("usr"); on.exit(par(usr))
    par(usr = c(0, 1, 0, 1))
    r <- abs(cor(x, y))
    txt <- format(c(r, 0.123456789), digits=digits)[1]
    txt <- paste(prefix, txt, sep="")
    if(missing(cex.cor)) cex.cor <- 0.8/strwidth(txt)
    text(0.5, 0.5, txt, cex = cex.cor * r)
}
 
# Plot #2: same as above, but add loess smoother in lower and correlation in upper
pairs(~Sepal.Length+Sepal.Width+Petal.Length+Petal.Width, 

data=iris,lower.panel=panel.smooth, upper.panel=panel.cor, 
      pch=20, main="Iris Scatterplot Matrix")

From the comments, we see alternatives are pairs.panels() from package psych and chart.Correlation() from package PerformanceAnalytics.

cgm to ps

http://technologytales.com/tag/ralcgm/
RALCGM is a handy command line tool that can covert from CGM to Postscript on its own without any need for ImageMagick at all.

NGS reading list (Oct 2011)

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2581791/
http://cs.calstatela.edu/wiki/images/5/5f/Sequence_census_methods_for_functional_genomics.pdf
http://raindancetech.com/documents/Next-Generation-Sequencing_Nature-Genetics_Jan-2010.pdf

23 Sure-Fire Ways To Improve Your Presentations

from here
BEFORE YOU START

test linear hypothesis in proc phreg

The idea of LSmean becomes so integrated part of statistical reporting that we feel so clumsy in proc phreg from sas 9.1.3 or earlier, where only numerical predictors are allowed. In the following test, a few examples of TEST statement for some common linear hypotheses are presented. The results have been comfirmed using proc phreg in sas 9.2, where a CLASS statement is available. Assuming a genotype effect with 3 levels (AA, AB and BB) and a treatment effect with 2 levels (0, 1) (and it seems p-value for the genetic effect across treatment arm will be different if the 2 treatment levels are codes as 1 and 2), a Cox proportional hazards model of survival time as a function of genotype, treatment and genotype-by-treatment interaction is fitted.

Comparison of general and generalized linear models

wiki

conditional exact logistic regression

Conditional Logistic Regression:

Taking the stratification into account by "conditioning out" (and not estimating) the stratum-specific intercepts gives consistent and asymptotically normal MLEs for the slope coefficients. If your nuisance parameters are not just stratum-specific intercepts, you can perform an exact conditional logistic regression.

The likelihood function to be maximized feels really like that in proc phreg with a discrete option. Each item (for a stratum) in the likelihood function is the conditional probability of observing subject 1 to h who actually had an event conditioning on h event happened (for any h subjects) in the stratum. The concept of sufficient statistic is not explicitly. Conditional logistic regression is called with the strata statement. More examples are from here and here.

Exact Conditional Logistic Regression
Exact conditional inference is based on generating the conditional distribution for the sufficient statistics of the parameters of interest. This distribution is called the permutation or exact conditional distribution. One example from sas suggests that we move from conditional regression to exact conditional regression when "you believe the data set is too small or too sparse for the usual asymptotics to hold.":

• by "sparse", they probably refer to the problem of complete separation of quasi-complete separation, and the exact method is one way to handle this, especially when the firth option is only available in sas 9.1.3;
• by "usual asymptotics", they probably refer to the issue of  large-sample asymptotic normality when we do not have a large sample size compared to the number of parameters to estimate.

%plotit macro

a good document with graphic examples are here.
this one is more technical.

use %scan function to cycle through a list of items

There are many examples on line.  But I have difficulty to break from the loop following them in one SAS system.

model selection for mmrm

it seems glmselect cannot handle this. See a proposed procedure here.

Geometric Statistics, geometric CV, intra-subject variation

found from onbiostatistics and here. Additional discussion can be found here.
also for a log normal variable x, there are

y=log(x)~N(mu, sigma**2)
E(x)=exp(mu+sigma**2/2) and
sd(x)=exp(mu+sigma**2/2)*sqrt(exp(sigma**2)-1)

cv(x)=sqrt(exp(sigma**2)-1)

left join A and B may still lose obs in A

Imaging data set A has the demog information, and data set B has multiple outcome variable and covariate information. B has less subjects than A. To create data set C which is merged from A and B and contains all subjects in A (even the subject is not in B), the following sql statement would be fine

proc sql,
create table c (drop=subjid) as;
select a.*, b.* from a left join b (rename=(pat_id=subjid))
on a.pat_id=b.subjid;
quit;

However, since B has multiple outcome variables, there can be an intent to filter out one outcome variable like the following

proc sql,
create table c (drop=subjid) as;
select a.*, b.* from a left join b (rename=(pat_id=subjid))
on a.pat_id=b.subjid
where b.outcomeName='outcome A';
quit;

This is wrong, because the field of outcomeName will be missing (and filtered out) for subjects in A but not in B. The solution can be either to filter in the separate data step, or the following sql statement

proc sql,
create table c (drop=subjid) as;
select a.*, b.* from a left join b (rename=(pat_id=subjid) where=(outcomeName='outcome A'))
on a.pat_id=b.subjid;

quit;

create a design matrix

Usage Note 23217: If I know the model, how can I create the corresponding design matrix (dummy, indicator, or design variables) in a data set?