BiostatR Blog

Install the last version of fftw to have full possibilities of imager package: Go directly at the end of that post to have the solution ! Check the last version of  fftw here http://www.fftw.org wget http://www.fftw.org/fftw-3.3.10.tar.gz tar -xf fftw-3.3.10.tar.gz cd fftw-3.3.10 ./configure make sudo make install cd .. rm fftw-3.3.10.tar.gz rm -r fftw-3.3.10 then in R install.packages("imager") It fails with imager 1.0.1 with the following error : g++ -std=gnu++11 -shared -L/usr/lib/R/lib -Wl,-Bsymbolic-functions -flto=auto -ffat-lto-objects -flto=auto -Wl,-z,relro -o imager.so RcppExports.o colourspace.o coordinates.o display.o drawing.o filtering.o hough.o imgraphs.o interact.o interpolation.o morphology.o reductions.o transformations.o utils.o wrappers.o -fopenmp -lX11 -Dcimg_use_fftw3 -L/usr/local/lib -lfftw3 -ltiff -L/usr/lib/R/lib -lR /usr/bin/ld: /usr/local/lib/libfftw3.a(assert.o): warning: relocation against `stdout@@GLIBC_2.2.5' in read-only section `.text'

You cannot compare the AIC or BIC when fitting to two different data sets i.e. 𝑌 and 𝑍. You only can compare two models based on AIC or BIC just when fitting to the same data set. Have a look at Model Selection and Multi-model Inference: A Practical Information-theoretic Approach (Burnham and Anderson, 2004). They mentioned my answer on page 81 (section 2.11.3 Transformations of the Response Variable): Investigators should be sure that all hypotheses are modeled using the same response variable (e.g., if the whole set of models were based on log(y), no problem would be created; it is the mixing of response variables that is incorrect). Akaike (1978, pg. 224) describes how the AIC can be adjusted in the presence of a transformed outcome variable to enable model comparison. He states: “the effect of transforming the variable is represented simply by the multiplication of the likelihood by the corresponding Jacobian to the AIC ... for the case of log{𝑦(𝑛)+1}, it is −2 ⋅∑log{𝑦(𝑛)+1},