CRAN Package Check Results for Package fbati

Last updated on 2024-11-24 15:49:24 CET.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	1.0-9	41.51	88.44	129.95	ERROR
r-devel-linux-x86_64-debian-gcc	1.0-9	27.74	66.30	94.04	OK
r-devel-linux-x86_64-fedora-clang	1.0-9			239.59	OK
r-devel-linux-x86_64-fedora-gcc	1.0-9			224.35	OK
r-devel-windows-x86_64	1.0-9	40.00	114.00	154.00	OK
r-patched-linux-x86_64	1.0-9	41.48	85.84	127.32	OK
r-release-linux-x86_64	1.0-9	38.52	86.94	125.46	OK
r-release-macos-arm64	1.0-9			83.00	OK
r-release-macos-x86_64	1.0-9			88.00	OK
r-release-windows-x86_64	1.0-9	39.00	109.00	148.00	OK
r-oldrel-macos-arm64	1.0-9			64.00	OK
r-oldrel-macos-x86_64	1.0-9			124.00	OK
r-oldrel-windows-x86_64	1.0-9	46.00	117.00	163.00	OK

Check Details

Version: 1.0-9
Check: examples
Result: ERROR Running examples in ‘fbati-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: fbatge > ### Title: fbatge > ### Aliases: fbatge fbatgeAll > ### Keywords: interface > > ### ** Examples > > example( fbati ) ## See fbati, creates a dataset for us in 'phe' and 'ped' fbati> ## Data is simulated according to the formula in the fbati> ## paper (you can see it from the code). fbati> fbati> ## Set the seed so you get the same results fbati> set.seed(13) fbati> ## Constants (you can vary these) fbati> NUM_FAMILIES <- 500 fbati> AFREQ <- 0.1 ## Allele frequency fbati> BG <- -0.25 ## main effect of gene fbati> BE <- 0 ## main effect of environment fbati> BGE <- 0.75 ## main gene-environment effect fbati> ENV <- 0.2 ## environmental exposure frequency fbati> ## (but don't modify this one) fbati> MAX_PROB <- exp( BG*2 + BE*1 + BGE*2*1 ) fbati> ##################################### fbati> ## Create a random dataset (trios) ## fbati> ##################################### fbati> fbati> ## -- genotypes are set to missing for now, fbati> ## everyone will be affected fbati> ped <- as.ped( data.frame( pid = kronecker(1:NUM_FAMILIES,c(1,1,1)), fbati+ id = kronecker( rep(1,NUM_FAMILIES), c(1,2,3) ), fbati+ idfath = kronecker( rep(1,NUM_FAMILIES), c(0,0,1) ), fbati+ idmoth = kronecker( rep(1,NUM_FAMILIES), c(0,0,2) ), fbati+ sex = rep(0,NUM_FAMILIES*3), fbati+ AffectionStatus = kronecker( rep(1,NUM_FAMILIES), c(0,0,2) ), fbati+ m0.a = rep(0,NUM_FAMILIES*3), ## missing for now fbati+ m0.b = rep(0,NUM_FAMILIES*3) ) ) ## missing for now fbati> ## -- envioronment not generated yet fbati> phe <- as.phe( data.frame( pid = ped$pid, fbati+ id = ped$id, fbati+ env = rep(NA,NUM_FAMILIES*3) ) ) ## missing for now fbati> ## 50/50 chance of each parents alleles fbati> mendelTransmission <- function( a, b ) { fbati+ r <- rbinom( length(a), 1, 0.75 ) fbati+ return( a*r + b*(1-r) ) fbati+ } fbati> ## Not the most efficient code, but it gets it done; fbati> ## takes < 5 sec on pentium M 1.8Ghz fbati> CUR_FAMILY <- 1 fbati> while( CUR_FAMILY<=NUM_FAMILIES ) { fbati+ ## Indexing: start=father, (start+1)=mother, (start+2)=child fbati+ start <- CUR_FAMILY*3-2 fbati+ fbati+ ## Draw the parental genotypes from the population fbati+ ped$m0.a[start:(start+1)] <- rbinom( 1, 1, AFREQ ) + 1 fbati+ ped$m0.b[start:(start+1)] <- rbinom( 1, 1, AFREQ ) + 1 fbati+ fbati+ ## Draw the children's genotype from the parents fbati+ ped$m0.a[start+2] <- mendelTransmission( ped$m0.a[start], ped$m0.b[start] ) fbati+ ped$m0.b[start+2] <- mendelTransmission( ped$m0.a[start+1], ped$m0.b[start+1] ) fbati+ fbati+ ## Generate the environment fbati+ phe$env[start+2] <- rbinom( 1, 1, ENV ) fbati+ fbati+ ## and the affection status fbati+ Xg <- as.integer(ped$m0.a[start+2]==2) + as.integer(ped$m0.b[start+2]==2) fbati+ if( rbinom( 1, 1, exp( BG*Xg + BE*phe$env[start+2] + BGE*Xg*phe$env[start+2] ) / MAX_PROB ) == 1 ) fbati+ CUR_FAMILY <- CUR_FAMILY + 1 fbati+ ## otherwise it wasn't a valid drawn individual fbati+ } fbati> ############## fbati> ## Analysis ## fbati> ############## fbati> fbati> ## Print the first 4 families fbati> print( head( ped, n=12 ) ) pid id idfath idmoth sex AffectionStatus m0.a m0.b 1 1 1 0 0 0 0 1 1 2 1 2 0 0 0 0 1 1 3 1 3 1 2 0 2 1 1 4 2 1 0 0 0 0 1 1 5 2 2 0 0 0 0 1 1 6 2 3 1 2 0 2 1 1 7 3 1 0 0 0 0 1 1 8 3 2 0 0 0 0 1 1 9 3 3 1 2 0 2 1 1 10 4 1 0 0 0 0 1 1 11 4 2 0 0 0 0 1 1 12 4 3 1 2 0 2 1 1 fbati> print( head( phe, n=12 ) ) pid id env 1 1 1 NA 2 1 2 NA 3 1 3 0 4 2 1 NA 5 2 2 NA 6 2 3 0 7 3 1 NA 8 3 2 NA 9 3 3 0 10 4 1 NA 11 4 2 NA 12 4 3 0 fbati> ## NOTE: We could have just put all of this info into a single dataframe otherwise, fbati> ## that would look like just the results of this fbati> data <- mergePhePed( ped, phe ) fbati> print( head( data, n=12 ) ) pid id idfath idmoth sex AffectionStatus m0.a m0.b env 1 1 1 0 0 0 0 1 1 NA 2 1 2 0 0 0 0 1 1 NA 3 1 3 1 2 0 2 1 1 0 334 2 1 0 0 0 0 1 1 NA 335 2 2 0 0 0 0 1 1 NA 336 2 3 1 2 0 2 1 1 0 667 3 1 0 0 0 0 1 1 NA 668 3 2 0 0 0 0 1 1 NA 669 3 3 1 2 0 2 1 1 0 1000 4 1 0 0 0 0 1 1 NA 1001 4 2 0 0 0 0 1 1 NA 1002 4 3 1 2 0 2 1 1 0 fbati> ## And run the analysis on all the markers fbati> fbati( ped=ped, phe=phe, env="env" ) *** buffer overflow detected ***: terminated Aborted Flavor: r-devel-linux-x86_64-debian-clang