summary.lmFAB.Rdsummary method for class lmFAB
# S3 method for lmFAB summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)
| object | an object of class |
|---|---|
| correlation | see |
| symbolic.cor | see |
| ... | see |
A list of summary statistics of the fitted linear model
A mod of summary.lm 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<-rnorm(n,lp) # fit model fit<-lmFAB(y~w1+w2,X,~v)#> #> 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$FABpv#> fv1 fv2 fv3 fv4 fv5 fv6 #> 0.9648761744 0.1769855064 0.0434419005 0.9509301028 0.1834120523 0.6121279024 #> fv7 fv8 fv9 fv10 fv11 fv12 #> 0.3330693409 0.2915015548 0.2480613538 0.0399971632 0.0730291266 0.7874723942 #> fv13 fv14 fv15 fv16 fv17 fv18 #> 0.6662131325 0.0713603082 0.6953131564 0.2237296715 0.3164595744 0.0168584012 #> fv19 fv20 fv21 fv22 fv23 fv24 #> 0.0179217789 0.1261205120 0.2887643318 0.0867330870 0.2877226520 0.0000607045 #> fv25 #> 0.4552802290fit$FABci#> [,1] [,2] #> fv1 -2.97671852 0.0675694 #> fv2 -0.69158116 2.4483476 #> fv3 0.07068395 3.4542586 #> fv4 -0.05222870 2.9726579 #> fv5 -0.73484679 2.4925812 #> fv6 -1.70809660 1.6349929 #> fv7 -1.37330471 2.3370765 #> fv8 -1.10635978 2.1994969 #> fv9 -0.85663720 2.0481377 #> fv10 0.11160544 3.5010549 #> fv11 -0.20026881 3.1851233 #> fv12 -2.18866139 1.2157252 #> fv13 -2.24666657 1.3235498 #> fv14 -0.19112315 3.2578697 #> fv15 -1.86703623 0.9883930 #> fv16 -0.82282090 2.2136070 #> fv17 -1.00213743 1.8112431 #> fv18 0.49455912 3.7906426 #> fv19 0.49708338 3.9925819 #> fv20 -0.50737677 2.7904561 #> fv21 -1.06412224 2.1386227 #> fv22 -0.23781209 2.4765893 #> fv23 -0.95380126 1.9248873 #> fv24 1.39424850 5.5476417 #> fv25 -1.32338583 1.5152253#> #> Call: #> lm(formula = y ~ . + 0, data = as.data.frame(cbind(W, X))) #> #> Residuals: #> Min 1Q Median 3Q Max #> -1.92204 -0.62701 -0.03975 0.63206 2.34158 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t+bfab|) #> `(Intercept)` 1.12796 0.11935 9.451 3.04e-14 *** #> w1 -1.01945 0.88345 -1.154 0.2523 #> w2 -1.35505 0.96708 -1.401 0.1655 #> fv1 -1.56129 0.85926 -1.817 0.9649 #> fv2 0.87838 0.95307 0.922 0.1770 #> fv3 1.76247 1.02703 1.716 0.0434 * #> fv4 1.49051 0.89976 1.657 0.9509 #> fv5 0.87887 0.97963 0.897 0.1834 #> fv6 -0.24997 0.88518 -0.282 0.6121 #> fv7 0.48189 1.12623 0.428 0.3331 #> fv8 0.54657 1.00344 0.545 0.2915 #> fv9 0.59575 0.88170 0.676 0.2481 #> fv10 1.80633 1.02881 1.756 0.0400 * #> fv11 1.49243 1.02758 1.452 0.0730 . #> fv12 -0.71091 0.89710 -0.792 0.7875 #> fv13 -0.46156 1.08368 -0.426 0.6662 #> fv14 1.53337 1.04689 1.465 0.0714 . #> fv15 -0.43932 0.86672 -0.507 0.6953 #> fv16 0.69539 0.92166 0.755 0.2237 #> fv17 0.40455 0.85396 0.474 0.3165 #> fv18 2.14260 1.00047 2.142 0.0169 * #> fv19 2.24483 1.06100 2.116 0.0179 * #> fv20 1.14154 1.00100 1.140 0.1261 #> fv21 0.53725 0.97214 0.553 0.2888 #> fv22 1.11939 0.82391 1.359 0.0867 . #> fv23 0.48554 0.87378 0.556 0.2877 #> fv24 3.94001 0.97594 4.037 6.07e-05 *** #> fv25 0.09592 0.86161 0.111 0.4553 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 0.9648 on 72 degrees of freedom #> Multiple R-squared: 0.7217, Adjusted R-squared: 0.6135 #> F-statistic: 6.669 on 28 and 72 DF, p-value: 4.385e-11 #>