r - Which link function and error structure for standardized response variable consisting of proportion & count data in a mixed effect model -

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This is my first question so apologize if my question is not asked in accordance with the appropriate guidelines, but the extent to which I will be expanded possible.

I want to check the interval difference between fitness between different transmitted lines using Drosophila. I have tested fitness for 10 people of sexes from each line (n = 33 isolin). The woman had fitness frenzy because the number of children in the form of a single female-produced and male fitness as the proportion of children transmitted by the focal male in competition with competing men (competing ability) As both measures are on different scales (i.e. calculation and ratio), IZ has changed the data before analysis.

A sample of my data looks like this (I have some NAS due to death)

line sex fitness 3a male -0.086616292 3a male 1.077458823 3a male -1.011856768 3a Men 1.077458823 3a Men -0.126015757 3a Men 1.077458823 3a Men -1.429779886 3a Male-NA.1350 Male Male -0.150576463 4a Men 0.498865275 4a Male - 0.651671321 4a Male 0.472263273 4a Male -0.426884402 4a Male 0.165735656 4a Male -1.429779886 4a Male -1.429779886 4a Male -1.429779886

I am interested in line main effects and sex * line contact . I have treated the gender as a fixed effect and line and random effect as the line * sex interaction

This is the first such model fitting, so I suspect that I have some errors, below See.

My questions are as follows

(1) Do I need to specify a specific link function and error structure other than the default Gaussian and identity? Since my reaction is a mixture of variable counting and mix of proportion that has been converted to Z at the same scale and in the same column (i.e. fitness), it is not posan or binary after its replacement (i.e. standardization) my fitness Measures of my response variable is not normal, so does the account have some error structure for it?

(2) About my model test procedure, I can not find the account to test the conversation between a fixed and random effect (sex * line) for the direct answer and in the books or books That is where I am interested I am not completely confident with my under-process if someone can provide me some suggestions about it. I really appreciate it.

I have included the output for this, I was testing for the importance of each model under the code 

  Model 1 < -Lmer (fitness ~ sex + (1 | line) + (1 | Gender: line), data = BL1InxNoOutLiars, REML = FALSE) #FULL Model Summary (Model 1) Fitted by Linear Mixed Model REML ['Lemerom'] Formula: Fitness ~ Sex + (1 | Line) + (1 | Gender: Line) Data: Criteria on BL1InxNoOutLiarsR Criterion: 1682.278 Random Effects: Group Name Variation STD.Dev Gender: Line (Blocking) 0.03565 0.1888 Line (Interception) 0.08432 0.2904 Residual 0.78479 0.8859 Number of numbers: 626, group: gender: line, 66; Line, 33 Fixed effects: STD estimation error T value (interception) -0.05413 0.07800 -0.694 sexmale 0.05028 0.084 9 1 0.592 Correlation of fixed effect: (intr) sexmale -0.533 model & lt; -lmer (fitness || (1 | line) + (1 | sex: line), data = BL1InxNoOutLiars, REML = false) ANOVA (MODEL1, MODEL2) sex, #test value of P = 0.5365 Output MODEL2: Fitness ~ (1 | Line) MODEL1: Fitness ~ Sex + (1 | line) + (1 | Sex: Line) Df AIC bic logLik Deviation Chisq Chi Domo PR (> Chisq) MODEL2 4 1683.9 1701.7 -837.96 1675.9 MODEL1 5 1685,5 1707,8 -837.78 1675,5 0,3644 1,5461 model3 & lt; -lmer (fitness ~ (1 | sex: line), data = BL1InxNoOutLiars, REML = false) model4 & lt; This non-important ANOVA (model4, model3) #test is important as a line-of -update (model1.1, ~. Sex, REML = TRUE) #remove sex, # of the line is significant, P = 0.012 Output model: Model 3: Fitness ~ (1 | Gender: Line) Model 4: Fitness ~ (1 | Line) + (1 | Gender: Line) DF AIC BIC Loglink Divinity Chisak Chi DFR (> Chisq) model3 3 1688.0 1701.3 -841.00 1682.0 model4 4 1683,9 1701,7 -837,96 1675,9 6,074 1, 101372 * model5 & LT; ANNOVA (model4, model5) ### Line X is sex interaction: NS-P = 0.1558 output model 5: fitness ~ (1 | line) model4: -lmer (fitness ~ (1 | line), data = BL1InxNoOutLiars, REML = false) Fitness ~ (1 | line) + (1 | sex: line) Df AIC bic logLik deviation Chisq Chi Domo PR (> Chisq) model5 3 1683.9 1697.2 -838.97 1677.9 model4 4 1683,9 1701,7 -837,96 1675 , 9 00011,11558

If I have not been clarified or asked my questions in the wrong questions, apology is also appreciated.

Thanks in advance


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