Modelling, ecology, biogeography, evolution, reproducible

My directory of R packages for data analysis and visualization,Р”В«ayesian statistics, mapping, GIS, climate, Species Distribution.

Neiborhood matrix when spatial option is TRUE. Variable for latitude when spatial option is TRUE. Variable for longitude when spatial option is TRUE. Speicification of correlation structures. The default (NULL) is set to constant variance for the random effects in LinPredRandVariance. Xxx1 and xxx2 as fixed covariates for the linear predictor of random-effect variances for id11 and id12, respectively. Is specified, LinPredRandVariance2=c(~xxx1,~xxx2) specifies Predictor of the variance of random effects, which is specified in the option LinPredRandVariance.Р”Р‡or example, when LinPredRandVariance=c(lambda~xx1+(1|id11),lambda~xx1+(1|id12)) This option specifies the fixed terms for the linear The choice is either 'log' (default) or 'inverse'. Predictor of the variance of random effects, which are specified in the option This option specifies the link function for the linear The choice is 'gaussian' (default), 'gamma', or 'inverse-gamma'. The option RandDistRandVariance specifies theР”Вistributions for the random terms in the LinPredRandVariance. The default (NULL) is set to intercept only model for the corresponding linear predictors. Specifies xx1 and xx2 as fixed covariates and id11 and id12 as random terms in the lienar predictors for the variances of the random terms id1 and id2, respectively.Р”Р‡or Model='dispersion', the random term is not allowed in the linear predictor of the random-effect variance. When y~x1+x2+(1|id1)+(1|id2) is specified in the option LinPred, The option LinPredRandVariance specifies theР”Р‡ixed and random terms for the linear predictor of the random-effect The user can set different link functions, e.g., LinkRandVariance=c('log','inverse') for each random term. When more than two random terms are specified in the option LinPred, Predictor of the random-effect variances.

Specifies the link function for the linear V2=L2 r2, specified in the option LinPred. Z1 and Z2 are the model matrices for the random effects v1=L1 r1 and Linear predictor for mu takes X beta + Z1 L1 r1 + Z2 L2 r2, where ForР”В®xample, when specified as Model='mean' and Lmatrix=L1+L2, the

Option allows correlation structures to be defined for random effects. Post-multiplier for the model matrix of the corresponding random effects. The option LMatrix sets a matrix that is used as a Vector of length equal to that of the appropriate data. This should be the default (NULL) or a numeric Specify a known component to be included in the linear predictor specified by LinPredР”Вuring fitting. The random term 'id1' and gamma distribution for the random term 'id2', respectively. In the option LinPred, the different distributions for each random term can be specified, e.g.,Р”В¬('gaussian', 'gamma'), which assumes normal distribution for When more than one random terms are specified, e.g., y~x1+x2+(1|id1)+(1|id2) 'beta', 'gamma', or 'inverse-gamma' when Model='mean'.Р”Р‡or Model='dispersion', the choice is 'gaussian' (default), 'gamma', or 'inverse-gamma'. It is set as a vector of distribution names from 'gaussain' (default), To intercept only the model for the corresponding linear predictors.Р”Вistributions of the random terms represented in the option LinPred. This optionР”В¬an specify the model without random effects, e.g., LinPred=phi~x1+x2. Specifies y as the main response, x1 and x2 as fixed covariates and id1 and id2 as random terms.Р”Р‡or Model='dispersion', the main response should be phi, e.g. Random terms for the linear predictor for mu when specified as Model='mean'

The option LinPred specifies the fixed and The default, specified as NULL link, is 'identity' for Model='mean' and 'logit', 'probit', 'cloglog', 'log', or 'inverse'.Р”Р‡or Model='dispersion', the choice is either 'log' or 'inverse'. For Model='mean', Link can be 'identity', The link function for the linear predictor is This option specifies a GLM, HGLM or DHGLM model for mu when Model='mean' (default),Р”Р„nd a GLM or HGLM for phi when Model='dispersion'. DHGLMMODELING ( Model = 'mean', Link = NULL, LinPred = 'constant', RandDist = NULL, Offset = NULL, LMatrix = NULL, LinkRandVariance = NULL, LinPredRandVariance = NULL, RandDistRandVariance = 'gaussian', LinkRandVariance2 = NULL, LinPredRandVariance2 = NULL, corr = NULL, spatial = NULL, longitude = NULL, latitude = NULL, spline = NULL, Neighbor = NULL )

Dhglm asreml-r