summary.glmrob.RdThe summary method for class "glmrob"
summarizes robust fits of (currently only discrete) generalized linear
models.
# S3 method for class 'glmrob'
summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)
# S3 method for class 'glmrob'
vcov(object, ...)
# S3 method for class 'summary.glmrob'
print(x, digits = max(3, getOption("digits") - 3),
symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), ...)an object of class "glmrob", usually, a result of
a call to glmrob.
logical; if TRUE, the correlation matrix of
the estimated parameters is returned and printed.
logical. If TRUE, print the correlations in
a symbolic form (see symnum) rather than as numbers.
further arguments passed to or from other methods.
an object of class "summary.glrob".
the number of digits to use for printing.
logical indicating if the P-values should be visualized by so called “significance stars”.
summary.glmrob returns an object of class
"summary.glmrob".
Its print() method tries to be smart about formatting the
coefficients, standard errors, etc, and gives
“significance stars” if signif.stars is TRUE
(as per default when options where not changed).
The function summary.glmrob computes and returns a list
of summary statistics of the robustly fitted linear model given in
object. The following elements are in the list:
FIXME
glmrob; the generic summary and
also summary.glm.
data(epilepsy)
Rmod <- glmrob(Ysum ~ Age10 + Base4*Trt, family = poisson,
data = epilepsy, method= "Mqle")
ss <- summary(Rmod)
ss ## calls print.summary.glmrob()
#>
#> Call: glmrob(formula = Ysum ~ Age10 + Base4 * Trt, family = poisson, data = epilepsy, method = "Mqle")
#>
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 2.045101 0.152178 13.439 < 2e-16 ***
#> Age10 0.159912 0.046837 3.414 0.000640 ***
#> Base4 0.084966 0.004116 20.641 < 2e-16 ***
#> Trtprogabide -0.332755 0.086301 -3.856 0.000115 ***
#> Base4:Trtprogabide 0.011970 0.004903 2.441 0.014631 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> Robustness weights w.r * w.x:
#> 27 weights are ~= 1. The remaining 32 ones are summarized as
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0829 0.3443 0.5625 0.5384 0.7608 0.9638
#>
#> Number of observations: 59
#> Fitted by method ‘Mqle’ (in 14 iterations)
#>
#> (Dispersion parameter for poisson family taken to be 1)
#>
#> No deviance values available
#> Algorithmic parameters:
#> acc tcc
#> 0.0001 1.3450
#> maxit
#> 50
#> test.acc
#> "coef"
#>
str(ss) ## internal STRucture of summary object
#> List of 19
#> $ call : language glmrob(formula = Ysum ~ Age10 + Base4 * Trt, family = poisson, data = epilepsy, method = "Mqle")
#> $ terms :Classes 'terms', 'formula' language Ysum ~ Age10 + Base4 * Trt
#> .. ..- attr(*, "variables")= language list(Ysum, Age10, Base4, Trt)
#> .. ..- attr(*, "factors")= int [1:4, 1:4] 0 1 0 0 0 0 1 0 0 0 ...
#> .. .. ..- attr(*, "dimnames")=List of 2
#> .. .. .. ..$ : chr [1:4] "Ysum" "Age10" "Base4" "Trt"
#> .. .. .. ..$ : chr [1:4] "Age10" "Base4" "Trt" "Base4:Trt"
#> .. ..- attr(*, "term.labels")= chr [1:4] "Age10" "Base4" "Trt" "Base4:Trt"
#> .. ..- attr(*, "order")= int [1:4] 1 1 1 2
#> .. ..- attr(*, "intercept")= int 1
#> .. ..- attr(*, "response")= int 1
#> .. ..- attr(*, ".Environment")=<environment: 0x5655073c8150>
#> .. ..- attr(*, "predvars")= language list(Ysum, Age10, Base4, Trt)
#> .. ..- attr(*, "dataClasses")= Named chr [1:4] "numeric" "numeric" "numeric" "factor"
#> .. .. ..- attr(*, "names")= chr [1:4] "Ysum" "Age10" "Base4" "Trt"
#> $ family :List of 13
#> ..$ family : chr "poisson"
#> ..$ link : chr "log"
#> ..$ linkfun :function (mu)
#> ..$ linkinv :function (eta)
#> ..$ variance :function (mu)
#> ..$ dev.resids:function (y, mu, wt)
#> ..$ aic :function (y, n, mu, wt, dev)
#> ..$ mu.eta :function (eta)
#> ..$ initialize: expression({ if (any(y < 0)) stop("negative values not allowed for the 'Poisson' family") n <- rep.int(1, nobs| __truncated__
#> ..$ validmu :function (mu)
#> ..$ valideta :function (eta)
#> ..$ simulate :function (object, nsim)
#> ..$ dispersion: num 1
#> ..- attr(*, "class")= chr "family"
#> $ iter : int 14
#> $ control :List of 4
#> ..$ acc : num 1e-04
#> ..$ test.acc: chr "coef"
#> ..$ maxit : num 50
#> ..$ tcc : num 1.34
#> $ method : chr "Mqle"
#> $ residuals : Named num [1:59] -0.507 -0.447 -0.579 -0.816 1.549 ...
#> ..- attr(*, "names")= chr [1:59] "1" "2" "3" "4" ...
#> $ fitted.values: Named num [1:59] 16 15.8 13.1 16.3 44.6 ...
#> ..- attr(*, "names")= chr [1:59] "1" "2" "3" "4" ...
#> $ w.r : num [1:59] 1 1 1 1 0.868 ...
#> $ w.x : num [1:59] 1 1 1 1 1 1 1 1 1 1 ...
#> $ deviance : NULL
#> $ df.residual : NULL
#> $ null.deviance: NULL
#> $ df.null : NULL
#> $ df : NULL
#> $ aliased : Named logi [1:5] FALSE FALSE FALSE FALSE FALSE
#> ..- attr(*, "names")= chr [1:5] "(Intercept)" "Age10" "Base4" "Trtprogabide" ...
#> $ coefficients : num [1:5, 1:4] 2.045 0.16 0.085 -0.333 0.012 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:5] "(Intercept)" "Age10" "Base4" "Trtprogabide" ...
#> .. ..$ : chr [1:4] "Estimate" "Std. Error" "z value" "Pr(>|z|)"
#> $ dispersion : num 1
#> $ cov.scaled : num [1:5, 1:5] 0.023158 -0.006473 -0.000209 -0.00396 0.000064 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:5] "(Intercept)" "Age10" "Base4" "Trtprogabide" ...
#> .. ..$ : chr [1:5] "(Intercept)" "Age10" "Base4" "Trtprogabide" ...
#> - attr(*, "class")= chr "summary.glmrob"