summary.glmFAB.Rdsummary method for class glmFAB
# S3 method for glmFAB summary( object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ... )
| object | an object of class |
|---|---|
| dispersion | see |
| correlation | see |
| symbolic.cor | see |
| ... | see |
A list of summary statistics of the fitted generalized linear model
A mod of summary.glm that shows FAB p-values in table
# n observations, p FAB variables, q=2 control variables n<-100 ; p<-25 # X is design matrix for params of interest # beta is vector of true parameter values # v a variable in the linking model - used to share info across betas v<-rnorm(p) ; beta<-(2 - 2*v + rnorm(p))/3 ; X<-matrix(rnorm(n*p),n,p)/8 # control coefficients and variables alpha1<-.5 ; alpha2<- -.5 w1<-rnorm(n)/8 w2<-rnorm(n)/8 # simulate data lp<-1 + alpha1*w1 + alpha2*w2 + X%*%beta y<-rpois(n,exp(lp)) # fit model fit<-glmFAB(y~w1+w2,X,~v,family=poisson)#> #> Fitting sampling model: - Fitting sampling model: # #> Fitting linking models: #------------------------ Fitting linking models: ##----------------------- Fitting linking models: ###---------------------- Fitting linking models: ####--------------------- Fitting linking models: #####-------------------- Fitting linking models: ######------------------- Fitting linking models: #######------------------ Fitting linking models: ########----------------- Fitting linking models: #########---------------- Fitting linking models: ##########--------------- Fitting linking models: ###########-------------- Fitting linking models: ############------------- Fitting linking models: #############------------ Fitting linking models: ##############----------- Fitting linking models: ###############---------- Fitting linking models: ################--------- Fitting linking models: #################-------- Fitting linking models: ##################------- Fitting linking models: ###################------ Fitting linking models: ####################----- Fitting linking models: #####################---- Fitting linking models: ######################--- Fitting linking models: #######################-- Fitting linking models: ########################- Fitting linking models: #########################fit$FABpv#> fv1 fv2 fv3 fv4 fv5 fv6 #> 3.301144e-02 9.911499e-01 2.861218e-01 3.501581e-01 4.298153e-01 1.294312e-01 #> fv7 fv8 fv9 fv10 fv11 fv12 #> 3.484147e-02 2.908584e-02 1.418045e-01 7.705731e-01 4.051792e-03 7.839975e-01 #> fv13 fv14 fv15 fv16 fv17 fv18 #> 2.985106e-01 2.235317e-01 1.021301e-03 6.531914e-01 1.272534e-02 1.886381e-03 #> fv19 fv20 fv21 fv22 fv23 fv24 #> 2.076070e-03 7.617339e-02 3.158509e-03 8.761060e-05 6.959489e-01 1.838333e-01 #> fv25 #> 5.882124e-08fit$FABci#> [,1] [,2] #> fv1 0.1142576 2.05898805 #> fv2 -1.9878811 0.20317560 #> fv3 -0.5918953 1.21079061 #> fv4 -0.7445656 1.19953879 #> fv5 -0.7867429 0.97627772 #> fv6 -0.3189098 1.71484591 #> fv7 -2.4743405 -0.06418161 #> fv8 0.1421588 2.01724496 #> fv9 -0.3267647 1.55028486 #> fv10 -1.2520181 0.48645769 #> fv11 0.5419691 2.32013537 #> fv12 -1.3668323 0.48314354 #> fv13 -0.7377862 1.43663812 #> fv14 -0.5114647 1.39081652 #> fv15 0.8010158 2.63237019 #> fv16 -1.2538535 1.35575830 #> fv17 0.2787870 1.83506839 #> fv18 0.7585767 2.75259070 #> fv19 0.6532015 2.41267292 #> fv20 -0.1384665 1.99340604 #> fv21 0.2956332 2.72607958 #> fv22 1.3294796 3.96144926 #> fv23 -0.6269868 1.19492796 #> fv24 -0.5162242 1.76613042 #> fv25 1.6665975 3.16807513#> #> Call: #> glm(formula = y ~ . + 0, family = ..1, data = as.data.frame(cbind(W, #> X))) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -2.55395 -0.87686 -0.05881 0.49388 2.00750 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z+bfab|) #> `(Intercept)` 0.94317 0.07717 12.222 < 2e-16 *** #> w1 0.83149 0.61437 1.353 0.17593 #> w2 -0.74838 0.57911 -1.292 0.19626 #> fv1 1.08662 0.59111 1.838 0.03301 * #> fv2 -1.17376 0.49488 -2.372 0.99115 #> fv3 0.30945 0.54794 0.565 0.28612 #> fv4 0.22745 0.59094 0.385 0.35016 #> fv5 0.09477 0.53588 0.177 0.42982 #> fv6 0.69797 0.61817 1.129 0.12943 #> fv7 -1.29761 0.71535 -1.814 0.03484 * #> fv8 1.07970 0.56994 1.894 0.02909 * #> fv9 0.61176 0.57054 1.072 0.14180 #> fv10 -0.38278 0.52842 -0.724 0.77057 #> fv11 1.43105 0.54048 2.648 0.00405 ** #> fv12 -0.44184 0.56231 -0.786 0.78400 #> fv13 0.34943 0.66093 0.529 0.29851 #> fv14 0.43964 0.57823 0.760 0.22353 #> fv15 1.71669 0.55665 3.084 0.00102 ** #> fv16 -0.24226 0.61496 -0.394 0.65319 #> fv17 1.05693 0.47301 2.234 0.01273 * #> fv18 1.75558 0.60609 2.897 0.00189 ** #> fv19 1.53294 0.53480 2.866 0.00208 ** #> fv20 0.92747 0.64800 1.431 0.07617 . #> fv21 1.70128 0.62299 2.731 0.00316 ** #> fv22 2.75408 0.73397 3.752 8.76e-05 *** #> fv23 0.28397 0.55378 0.513 0.69595 #> fv24 0.62495 0.69374 0.901 0.18383 #> fv25 2.41734 0.45635 5.297 5.88e-08 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> (Dispersion parameter for poisson family taken to be 1) #> #> Null deviance: 601.960 on 100 degrees of freedom #> Residual deviance: 99.218 on 72 degrees of freedom #> AIC: 405.32 #> #> Number of Fisher Scoring iterations: 5 #>