Methods for renewal objects.
# S3 method for renewal
coef(object, ...)
# S3 method for renewal
vcov(object, ...)
# S3 method for renewal
residuals(object, type = c("pearson", "response", "prob"), ...)
# S3 method for renewal
residuals_plot(object, type = c("pearson", "response", "prob"), ...)
# S3 method for renewal
fitted(object, ...)
# S3 method for renewal
confint(
object,
parm,
level = 0.95,
type = c("asymptotic", "boot"),
bootType = c("norm", "bca", "basic", "perc"),
...
)
# S3 method for renewal
summary(object, ...)
# S3 method for renewal
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for summary.renewal
print(
x,
digits = max(3, getOption("digits") - 3),
width = getOption("width"),
...
)
# S3 method for renewal
model.matrix(object, ...)
# S3 method for renewal
logLik(object, ...)
# S3 method for renewal
nobs(object, ...)
# S3 method for renewal
extractAIC(fit, scale, k = 2, ...)
# S3 method for renewal
addBootSampleObject(object, ...)
# S3 method for renewal
df.residual(object, ...)an object from class "renewal".
further arguments for methods.
see the corresponding generics and section ‘Details’.
numeric width length.
same as in the generic.
Objects from class "renewal" represent fitted count renewal models and
are created by calls to "renewalCount()". There are methods for this class
for many of the familiar functions for interacting with fitted models.
fn <- system.file("extdata", "McShane_Wei_results_boot.RDS", package = "Countr")
object <- readRDS(fn)
class(object) # "renewal"
#> [1] "renewal"
coef(object)
#> scale_ scale_GERMAN scale_EDU scale_VOC scale_UNI
#> 1.397339743 -0.222677155 0.038579223 -0.173423930 -0.181808127
#> scale_CATH scale_PROT scale_MUSL scale_RURAL scale_YEAR_OF_B
#> 0.242045872 0.123221530 0.638803211 0.068074810 0.002307353
#> scale_AGEMARR shape_
#> -0.034029563 1.236232710
vcov(object)
#> scale_ scale_GERMAN scale_EDU scale_VOC
#> scale_ 0.0930236225 8.919340e-03 -8.165893e-03 1.153322e-03
#> scale_GERMAN 0.0089193396 5.214102e-03 -1.150253e-03 -4.339834e-04
#> scale_EDU -0.0081658934 -1.150253e-03 1.063042e-03 -2.814470e-04
#> scale_VOC 0.0011533223 -4.339834e-04 -2.814470e-04 1.935682e-03
#> scale_UNI 0.0256804922 2.754849e-03 -3.355547e-03 1.871018e-03
#> scale_CATH -0.0031003254 -1.579181e-03 1.237918e-04 5.534533e-05
#> scale_PROT -0.0027900298 -2.269655e-03 1.023422e-04 1.759421e-05
#> scale_MUSL -0.0057349764 1.042368e-03 1.377562e-04 1.867273e-04
#> scale_RURAL -0.0014521343 -2.639966e-04 9.503076e-05 3.333572e-04
#> scale_YEAR_OF_B -0.0001654313 -3.087628e-05 -1.915529e-06 2.043607e-05
#> scale_AGEMARR -0.0005939423 3.175688e-05 -1.743303e-05 -1.909649e-05
#> shape_ 0.0012043607 -1.032629e-04 2.449776e-05 -1.015040e-04
#> scale_UNI scale_CATH scale_PROT scale_MUSL
#> scale_ 2.568049e-02 -3.100325e-03 -2.790030e-03 -5.734976e-03
#> scale_GERMAN 2.754849e-03 -1.579181e-03 -2.269655e-03 1.042368e-03
#> scale_EDU -3.355547e-03 1.237918e-04 1.023422e-04 1.377562e-04
#> scale_VOC 1.871018e-03 5.534533e-05 1.759421e-05 1.867273e-04
#> scale_UNI 2.540730e-02 -1.041703e-05 2.833785e-05 -2.566270e-04
#> scale_CATH -1.041703e-05 4.999685e-03 4.460029e-03 3.044988e-03
#> scale_PROT 2.833785e-05 4.460029e-03 5.805663e-03 2.739216e-03
#> scale_MUSL -2.566270e-04 3.044988e-03 2.739216e-03 7.468544e-03
#> scale_RURAL 3.278856e-04 5.070440e-05 7.404498e-05 1.637396e-04
#> scale_YEAR_OF_B 4.751321e-05 2.372257e-07 -5.718801e-06 -1.559452e-07
#> scale_AGEMARR -6.938872e-05 -3.997862e-05 -2.326609e-05 3.817387e-05
#> shape_ -1.347961e-04 1.123694e-04 4.346523e-05 4.525415e-04
#> scale_RURAL scale_YEAR_OF_B scale_AGEMARR shape_
#> scale_ -1.452134e-03 -1.654313e-04 -5.939423e-04 1.204361e-03
#> scale_GERMAN -2.639966e-04 -3.087628e-05 3.175688e-05 -1.032629e-04
#> scale_EDU 9.503076e-05 -1.915529e-06 -1.743303e-05 2.449776e-05
#> scale_VOC 3.333572e-04 2.043607e-05 -1.909649e-05 -1.015040e-04
#> scale_UNI 3.278856e-04 4.751321e-05 -6.938872e-05 -1.347961e-04
#> scale_CATH 5.070440e-05 2.372257e-07 -3.997862e-05 1.123694e-04
#> scale_PROT 7.404498e-05 -5.718801e-06 -2.326609e-05 4.346523e-05
#> scale_MUSL 1.637396e-04 -1.559452e-07 3.817387e-05 4.525415e-04
#> scale_RURAL 1.456890e-03 4.237730e-06 -1.537028e-05 4.570599e-05
#> scale_YEAR_OF_B 4.237730e-06 5.677948e-06 -4.288778e-06 -9.240759e-07
#> scale_AGEMARR -1.537028e-05 -4.288778e-06 4.272941e-05 -1.697867e-05
#> shape_ 4.570599e-05 -9.240759e-07 -1.697867e-05 1.136108e-03
## Pearson residuals: rescaled by sd
head(residuals(object, "pearson"))
#> 1 2 3 4 5 6
#> -0.4536889 0.2666367 -0.2879260 1.4626630 -0.3689430 -0.4503339
## response residuals: not rescaled
head(residuals(object, "response"))
#> 1 2 3 4 5 6
#> -0.6359564 0.3731793 -0.3861650 1.8671934 -0.5056416 -0.6306918
head(fitted(object))
#> [1] 2.635956 2.626821 2.386165 2.132807 2.505642 2.630692
## loglik, nobs, AIC, BIC
c(loglik = as.numeric(logLik(object)), nobs = nobs(object),
AIC = AIC(object), BIC = BIC(object))
#> loglik nobs AIC BIC
#> -2077.005 1243.000 4178.010 4239.513
asym <- se.coef(object, , "asymptotic")
boot <- se.coef(object, , "boot")
cbind(asym, boot)
#> asym boot
#> scale_ 0.304997742 0.331438080
#> scale_GERMAN 0.072208742 0.077987820
#> scale_EDU 0.032604326 0.034450773
#> scale_VOC 0.043996381 0.045988517
#> scale_UNI 0.159396681 0.158987448
#> scale_CATH 0.070708448 0.070752807
#> scale_PROT 0.076194899 0.072959615
#> scale_MUSL 0.086420737 0.094049819
#> scale_RURAL 0.038169230 0.039366247
#> scale_YEAR_OF_B 0.002382845 0.002482896
#> scale_AGEMARR 0.006536774 0.006872492
#> shape_ 0.033706207 0.044842338
## CI for coefficients
asym <- confint(object, type = "asymptotic")
## Commenting out for now, see the nite in the code of confint.renewal():
## boot <- confint(object, type = "boot", bootType = "norm")
## list(asym = asym, boot = boot)
summary(object)
#>
#> Call:
#> renewal(formula = form, data = data, dist = "weibull")
#>
#> Pearson residuals:
#> Min 1Q Median 3Q Max
#> -2.66294 -0.73038 -0.09425 0.49688 6.73987
#> Inter-arrival dist.: weibull
#> Links: scale: link log, shape: link log
#>
#> Count model coefficients
#> Estimate Std. Error z value Pr(>|z|)
#> scale_ 1.397340 0.304998 4.581 4.62e-06 ***
#> scale_GERMAN -0.222677 0.072209 -3.084 0.002044 **
#> scale_EDU 0.038579 0.032604 1.183 0.236708
#> scale_VOC -0.173424 0.043996 -3.942 8.09e-05 ***
#> scale_UNI -0.181808 0.159397 -1.141 0.254036
#> scale_CATH 0.242046 0.070708 3.423 0.000619 ***
#> scale_PROT 0.123222 0.076195 1.617 0.105838
#> scale_MUSL 0.638803 0.086421 7.392 1.45e-13 ***
#> scale_RURAL 0.068075 0.038169 1.783 0.074505 .
#> scale_YEAR_OF_B 0.002307 0.002383 0.968 0.332885
#> scale_AGEMARR -0.034030 0.006537 -5.206 1.93e-07 ***
#> shape_ 1.236233 0.033706 36.677 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Number of iterations in nlminb optimization: 52
#>
#> Execution time 371
#> Log-likelihood: -2077.0049 on 12 Df
print(object)
#>
#> Call:
#> renewal(formula = form, data = data, dist = "weibull")
#>
#> Count model coefficients (inter-arrival weibull with scale: link log, shape: link log):
#> scale_ scale_GERMAN scale_EDU scale_VOC
#> 1.397340 -0.222677 0.038579 -0.173424
#> scale_UNI scale_CATH scale_PROT scale_MUSL
#> -0.181808 0.242046 0.123222 0.638803
#> scale_RURAL scale_YEAR_OF_B scale_AGEMARR shape_
#> 0.068075 0.002307 -0.034030 1.236233
#>
#> Log-likelihood: -2077.0049 on 12 Df
## see renewal_methods
## see renewal_methods