BiostatR Blog

As shown in a previous plot, the two ways to use binomial data (grouped by independent variable or splited for each individual) produced two different estimations of R2 or Nagelkerke R2: https://biostatsr.blogspot.com/2021/06/nagelkerke-r2-for-binomial-data-is.html Let try if the problem occurs with AIC and AICc: Grouped data: n <- c(3, 4, 5, 2, 2) data_grouped <- data.frame(x=n, y=5-n) cof_grouped <- c(10, 7, 2, 3, 4) res_grouped <- glm(cbind(x,y) ~ cof_grouped, data=data_grouped, family=binomial()) cof_grouped2 <- c(9, 7, 2, 3, 4) res_grouped2 <- glm(cbind(x,y) ~ cof_grouped2, data=data_grouped, family=binomial()) library(AICcmodavg) # or library(MuMIn) AIC(res_grouped2)-AIC(res_grouped) AICc(res_grouped2)-AICc(res_grouped) Splited data: n <- c(3, 4, 5, 2, 2) data_splited <- data.frame(x=unlist(lapply(n,                                             ...

 Try to use these data to calculate Nagelkerke R2: library(fmsb) data_grouped <- data.frame(x=c(3, 4, 5), y=c(2, 1, 0)) cof_grouped <- c(10, 7, 2) res_grouped <- glm(cbind(x,y) ~ cof_grouped, data=data_grouped, family=binomial()) summary(res_grouped) or data_splited <- data.frame(x=c(1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1),                             y=1-c(1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1)) cof_splited <- rep(c(10, 7, 2), each=5) res_splited <- glm(cbind(x,y) ~ cof_splited, data=data_splited, family=binomial()) summary(res_splited) These data are exactly the same and indeed the fitted model is the same. But... > NagelkerkeR2(res_grouped)$R2 [1] 0.9538362 > NagelkerkeR2(res_splited)$R2 [1] 0.2879462 You will conclude for a very strong link and a weaker link in the second case. Note that you have the same problem with a common R2: > cor(x = data_grouped$x/(data_...

 Let try different packages to estimate Nagelkerke (1991) R^2. Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691-192 nagelkerke() function from package companion truncates the significance digits at 6. NagelkerkeR2() from package fmsb reports a different value as compare to other functions in the first example. I don't know why. I have contacted the author of the package and he does not know also. install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/HelpersMG.tar.gz", repos=NULL, type="source") library(HelpersMG) library(rcompanion) library(fmsb) res <- glm(cbind(ncases,ncontrols) ~ agegp+alcgp+tobgp, data=esoph, family=binomial()) NagelkerkeR2(res)$R2 # [1] 0.9759681 nagelkerke(res)$Pseudo.R.squared.for.model.vs.null["Nagelkerke (Cragg and Uhler)", "Pseudo.R.squared"] # [1] 0.965045 NagelkerkeScaledR2(x=esoph$ncases, size = esoph$ncases+esoph$ncontrols...

In terminal: sudo apt-get install libfribidi-dev sudo apt-get install libharfbuzz-dev Then in R install.packages("pkgdown")

# update indices sudo apt update -qq # install two helper packages we need sudo apt install --no-install-recommends software-properties-common dirmngr # import the signing key (by Michael Rutter) for these repo sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 # add the R 4.0 repo from CRAN -- adjust 'focal' to 'groovy' or 'bionic' as needed sudo add-apt-repository "deb https://cloud.r-project.org/bin/linux/ubuntu $(lsb_release -cs)-cran40/" Here we use lsb_release -cs to access which Ubuntu flavor you run: one of “hirsuite”, “groovy”, “focal”, “bionic”, … Then run sudo apt install --no-install-recommends r-base See:  https://cran.r-project.org/bin/linux/ubuntu/ pkgs <- as.data.frame(installed.packages(), stringsAsFactors = FALSE, row.names = FALSE) pkgs[, c("Package", "Version", "Built")] Will list the packages with their built version. Then you can do: update.packag...

In terminal. You must have MacBrew installed. If not, use: Install macbrew via: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" Then brew install proj brew install gdal brew install geos sudo R Then in R: library("devtools") install_github("r-spatial/sf", build_opts = c("--with-proj-include=/usr/local/include",                                                "--with-proj-lib=/usr/local/lib"), force=TRUE) And... > packageVersion("sf") [1] ‘0.9.9’

Informations are available here  https://bitbucket.org/nygcresearch/treemix/downloads/ In terminal, enter for  Ubuntu 20.04: wget https://bitbucket.org/nygcresearch/treemix/downloads/treemix-1.13.tar.gz tar -xvf treemix-1.13.tar.gz sudo apt-get install libgsl-dev sudo apt install libboost-dev sudo apt install libboost-all-dev cd treemix-1.13/ ./configure make sudo make install And it works: $ treemix TreeMix v. 1.13 $Revision: 231 $ Options: -h display this help -i [file name] input file -o [stem] output stem (will be [stem].treeout.gz, [stem].cov.gz, [stem].modelcov.gz) -k [int] number of SNPs per block for estimation of covariance matrix (1) -global Do a round of global rearrangements after adding all populations -tf [file name] Read the tree topology from a file, rather than estimating it -m [int] number of migration edges to add (0) -root [string] comma-delimited list of populations to set on one side of the root (for migration) -g [vertices file name] [edges file na...