Dear all, First a bit of background: I currently work on an anlysis of phenotypic plasticity of avian phenology in response to temperature. Using a random regression model I found that individual reaction norms (defined by slope and intercept) vary among individuals, i.e. some individuals change their phenology more strongly in response to temperatures than others and also that some individuals have a consistently earlier phenology than others. I now want to test whether there is selection on reaction norm slopes, i.e. whether individuals with steeper/shallower slope have a higher/lower fitness. This means I have to fit a bivariate random regression model but only one trait (phenology) should be regressed against temperature. For the random effects part this should give me a 3x3 covariance matrix with variation in slopes, intercepts, fitness plus all the covariances and then the covariance between slope and fitness indicates selection on reaction norm slopes. I figured how to regress only phenology and not fitness against temperature for the fixed effects part but am still struggling with the syntax for the random effects part. The univariate random regression model (omitting obvious syntax parts) is: phenology~age + temp, random=~us(1+temp):individual For the multivariate model I came up with: cbind(phenology,fitness)~trait:age + at.level(trait,1):temp, random=~us(at.level(trait,1):(1+mt2):at.level(trait,2):1):individual, rcov=~us(trait):units but curiously this fits only a single variance for individual and not the desired 3x3 matrix... I hope I managed to explain my problem clearly enough (maybe there was too much non-technical detail...). Any ideas to fit the desired model are highly welcome! Best, Phillip
Bivariate random regression model in MCMCglmm to estimate selection on reaction norm slopes
2 messages · Phillip Gienapp, Jarrod Hadfield
9 days later
Hi Phillip, The correct syntax (assuming fitness is an annual measure so you have repeat records?) is: us(trait+at.level(trait,1):mt2):individual Cheers, Jarrod Quoting Phillip Gienapp <phillip.gienapp at helsinki.fi> on Wed, 18 Mar 2015 11:25:07 +0100:
Dear all, First a bit of background: I currently work on an anlysis of phenotypic plasticity of avian phenology in response to temperature. Using a random regression model I found that individual reaction norms (defined by slope and intercept) vary among individuals, i.e. some individuals change their phenology more strongly in response to temperatures than others and also that some individuals have a consistently earlier phenology than others. I now want to test whether there is selection on reaction norm slopes, i.e. whether individuals with steeper/shallower slope have a higher/lower fitness. This means I have to fit a bivariate random regression model but only one trait (phenology) should be regressed against temperature. For the random effects part this should give me a 3x3 covariance matrix with variation in slopes, intercepts, fitness plus all the covariances and then the covariance between slope and fitness indicates selection on reaction norm slopes. I figured how to regress only phenology and not fitness against temperature for the fixed effects part but am still struggling with the syntax for the random effects part. The univariate random regression model (omitting obvious syntax parts) is: phenology~age + temp, random=~us(1+temp):individual For the multivariate model I came up with: cbind(phenology,fitness)~trait:age + at.level(trait,1):temp, random=~us(at.level(trait,1):(1+mt2):at.level(trait,2):1):individual, rcov=~us(trait):units but curiously this fits only a single variance for individual and not the desired 3x3 matrix... I hope I managed to explain my problem clearly enough (maybe there was too much non-technical detail...). Any ideas to fit the desired model are highly welcome! Best, Phillip
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