If the non-integer values are between zero and one, there should be no problem. By the way, the warning message You've mentioned is no error: it simply notifies You, that the binomial response variable was continuous (having non-integer, i.e. I think that You might use simply a discrete binomial GLMM rather than continuous, which is just slightly different from the model You described. So now I am wondering if the first test is ok to use after all? This in it's turn results in these very low p-values. So after discussing this with my supervisors I found out that via using the cbind or weights command my sample size is increased by a factor 100 in this case. 70 for 70% grass cover), I created VS_G_inv = 100-VS_G so in case of VS_G = 70, then VS_G_inv = 30. Note: VS_G is expressed as a integer (i.e. #R Occupancy -2.574 0.777 -3.312 0.000925 ***ΔΆ) After looking up this error I found out I had to change my test since I am working with proportional data, so I applied 2 tests with the cbind command and the weights command: Note: VS_G is expressed as a decimal (i.e. I will use one of my variables as an example here: VS_G = vegetation structure grass = the percentage of grass cover within a hostplant site. I have performed 3 different kind of glmer: In the GLMM I want to use my environmental variables as the response variable and use Occupancy (0 = unoccupied = no egg found 1 = occupied) as the independent variable to check for differences within each environmental variable between the two levels of occupancy. The test I chose to use is a GLMM ( glmer from the package lme4) since I want to account for several random effects such as the hostplant species ( HP_spp), the date on which I measured these variables ( VS_Date) and the pair number ( Pair_nr) of each occupied and unoccupied hostplant pair. For each occupied hostplant I selected a paired unoccupied hostplant. Each site equals a circle with a radius of 25 cm with the hostplant in the center. I measured several environmental variables as the percentage of the hostplant site area, more specifically with an example: the percentage of dwarf-shrub cover per host-plant site, or the percentage of wild-boar digging per hostplant site. I studied the micro-habitat characteristics for the ovipostion of the Pyrgus Malvae butterfly. This is for my master-dissertation and up untill 1,5 months ago I never worked with R. After looking through several questions on stackexchange, and even applying many of the suggested methods I have come to a point where I would like to have an expert's advice if the methods applied on my dataset are correct.