Cross-Validated Predictions from GEE
Calculates predictions from generalized estimating equations and internally cross-validates them using the logarithmic, quadratic and spherical proper scoring rules; Kung-Yee Liang and Scott L. Zeger (1986) <doi:10.1093/biomet/73.1.13>.
cvGEE: Cross-Validated Predictions from GEE
cvGEE calculates cross-validated versions of the logarithmic, quadratic and spherical scoring rules based on generalized estimating equations.
The package presumes that the GEE has been solved using the
geeglm() function of the geepack.
family = gaussian()only the quadratic rule is available calculated as the squared prediction error; lower values indicate a better predictive ability.
family = binomial()and dichotomous outcome data the probabilities for the two categories are calculated from the Bernoulli probability mass function. For
family = binomial()and binomial data the probabilities for each possible response are calculated from a beta-binomial distribution with variance set equal to the variance from the corresponding quasi-likelihood behind the GEE. Likewise for
family = poisson()the probabilities for the number of events up to a particular maximum (set using the
max_countargument) are calculated using the negative binomial distribution with variance set equal to the variance from the corresponding quasi-likelihood behind the GEE. For these families all three scoring rules are available, with higher values in each rule indicating better predictive ability.
We compare a linear and a nonlinear GEE for the dichotomized version of the serum bilirubin biomarker from the PBC dataset:
library("geepack") library("cvGEE") library("splines") library("lattice") pbc2$serBilirD <- as.numeric(pbc2$serBilir > 1.2) gm1 <- geeglm(serBilirD ~ year * drug, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") gm2 <- geeglm(serBilirD ~ ns(year, 3, Boundary.knots = c(0, 10)) * drug, family = binomial(), data = pbc2, id = id, corstr = "exchangeable") plot_data <- cv_gee(gm1, return_data = TRUE) plot_data$linear <- plot_data$.score plot_data$non_linear <- unlist(cv_gee(gm2)) xyplot(linear + non_linear ~ year | .rule, data = plot_data, type = "smooth", auto.key = TRUE, layout = c(3, 1), scales = list(y = list(relation = "free")), xlab = "Follow-up time (years)", ylab = "Scoring Rules")
The development version of the package can be installed from GitHub using the devtools package:
and with vignettes
devtools::install_github("drizopoulos/cvGEE", build_vignettes = TRUE)
Functions in cvGEE
|aids||Didanosine versus Zalcitabine in HIV Patients|
|cvGEE||Proper Scoring Rules for Generalized Estimating Equations|
|pbc2||Mayo Clinic Primary Biliary Cirrhosis Data|
|cv_gee||Proper Scoring Rules for Generalized Estimating Equations|
Vignettes of cvGEE
Last month downloads
|License||GPL (>= 3)|
|Packaged||2019-07-20 18:16:39 UTC; drizo|
|Date/Publication||2019-07-23 14:52:05 UTC|
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