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Mumin dredge report se of coefficients2/21/2024 ![]() ![]() GsGlob_dredge <- MuMIn::pdredge(gsGlob, mycluster) #required packages must be also loaded there ![]() ![]() #data must exported to the cluster - see 'details' ĬlusterExport(mycluster,"allVars_node_dat") mycluster = makeCluster(5, type = "SOCK") # also need snow installed S(code, bs = 're') + s(station, bs = 're'),įamily=nb(), data=allVars_node_dat, na.action = "na.fail", discrete = TRUE)Īnd I'm using pdredge from MuMIn so I can increase the speed of the dredge. Salinity2 + SST.anomaly2 + s(SST.variability2) + wind2 + gsGlob <- bam(gs~ species + season + sex + TL2 + year + s(ri, bs="ad") + > # additive model - scaled predictors > vif( lm(y ~ cx1 + cx2, data)) cx1 cx2ġ.743817 1.743817 Multiple Linear Regression Assumptionsġ.743817 1.743817 > # multiplicative model - raw predictors > vif( lm(y ~ x1 * x2, data)) x1 x2 x1:x2ħ.259729 5.913254 16.949468 Multiple Linear Regression Assumptions > # multiplicative model - raw predictors > vif( lm(y ~ x1 * x2, data)) x1 x2 x1:x2ħ.259729 5.913254 16.I have been using the dredge function in the MuMIn package to conduct model averaging on my global GAM model (using bam from the mgcv package), with a priori selected explanatory variables and two random effects and a negative binomial distribution. Some prefer \(>3\) Multiple Linear Regression Assumptions \] Strong when \(R^2 \ge 0.8\) Multiple Linear Regression Variance inflation Multiple Linear Regression Variance inflation \] Centering data Multiple Linear Regression CenteringĪssumptions Multiple Linear Regression Assumptions ![]()
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