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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd-57-
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd:58:By default, the `Wald_test` function provides an F-type test with degrees of freedom estimated using the approximate Hotelling's $T^2_Z$ method. The test has less than 17 degrees of freedom, even though there are 152 independent studies in the data, and has a p-value that is not quite significant at conventional levels. The low degrees of freedom are a consequence of the fact that one of the levels of `evaluator independence` has only a few effect sizes in it:
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd-59-
##############################################
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd-84-
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd:85:Note that `coef_test` assumed that it should cluster based on `studyID`, which is the outer-most random effect in the metafor model. This can be specified explicitly by including the option `cluster = dropoutPrevention$studyID` in the call.
r-cran-clubsandwich-0.5.0/vignettes/meta-analysis-with-CRVE.Rmd-86-
##############################################
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd-117-However, using the CR2 variance estimator and Satterthwaite correction produces a p-value that is an order of magnitude larger (though still significant at the conventional 5% level).
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd:118:The degrees of freedom are just `r round(coef_test(lm_weighted, vcov = "CR2", cluster = MV_deaths$state, test = "Satterthwaite")["legal","df"], 1)`---drastically smaller than would be expected based on the number of clusters.
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd-119-
##############################################
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd-172-
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd:173:To conduct a joint test on the centered covariates, we can use the `Wald_test` function. The usual way to test this hypothesis would be to use the `CR1` variance estimator to calculate the robust Wald statistic, then use a $\chi^2_2$ reference distribution (or equivalently, compare a re-scaled Wald statistic to an $F(2,\infty)$ distribution). The `Wald_test` function reports the latter version:
r-cran-clubsandwich-0.5.0/vignettes/panel-data-CRVE.Rmd-174-
##############################################
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd-39-$$
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd:40:The regression coefficient vector is $\boldsymbol\beta$. In quite general terms, a set of constraints on the regression coefficient vector can be expressed in terms of a $q \times p$ matrix $\mathbf{C}$, where each row of $\mathbf{C}$ corresponds to one constraint. A joint null hypothesis is then $H_0: \mathbf{C} \boldsymbol\beta = \mathbf{0}$, where $\mathbf{0}$ is a $q \times 1$ vector of zeros.[^more-general]
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd-41-
##############################################
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd-83-```
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd:84:The result includes details about the form of `test` computed, the $F$-statistic, the numerator and denominator degrees of freedom used to compute the reference distribution, and the $p$-value corresponding to the specified null hypothesis. In this example, $p = `r format.pval(Wald_test(lm_trt, constraints = C_trt, vcov = V_trt)$p_val, digits = 3)`$, so we can rule out the null hypothesis that there are no differences in math performance across conditions.
r-cran-clubsandwich-0.5.0/vignettes/Wald-tests-in-clubSandwich.Rmd-85-
##############################################
r-cran-clubsandwich-0.5.0/R/gls.R-69- data
r-cran-clubsandwich-0.5.0/R/gls.R:70: } else eval(object$call$na.action)(data)
r-cran-clubsandwich-0.5.0/R/gls.R-71- }
##############################################
r-cran-clubsandwich-0.5.0/R/plm.R-88- mf <- match.call(plm::plm, call = obj$call, envir = envir)
r-cran-clubsandwich-0.5.0/R/plm.R:89: dat <- eval(mf$data, envir)
r-cran-clubsandwich-0.5.0/R/plm.R-90-
r-cran-clubsandwich-0.5.0/R/plm.R:91: index_names <- eval(mf$index)
r-cran-clubsandwich-0.5.0/R/plm.R-92-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd-57-
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd:58:By default, the `Wald_test` function provides an F-type test with degrees of freedom estimated using the approximate Hotelling's $T^2_Z$ method. The test has less than 17 degrees of freedom, even though there are 152 independent studies in the data, and has a p-value that is not quite significant at conventional levels. The low degrees of freedom are a consequence of the fact that one of the levels of `evaluator independence` has only a few effect sizes in it:
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd-59-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd-84-
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd:85:Note that `coef_test` assumed that it should cluster based on `studyID`, which is the outer-most random effect in the metafor model. This can be specified explicitly by including the option `cluster = dropoutPrevention$studyID` in the call.
r-cran-clubsandwich-0.5.0/inst/doc/meta-analysis-with-CRVE.Rmd-86-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd-117-However, using the CR2 variance estimator and Satterthwaite correction produces a p-value that is an order of magnitude larger (though still significant at the conventional 5% level).
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd:118:The degrees of freedom are just `r round(coef_test(lm_weighted, vcov = "CR2", cluster = MV_deaths$state, test = "Satterthwaite")["legal","df"], 1)`---drastically smaller than would be expected based on the number of clusters.
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd-119-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd-172-
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd:173:To conduct a joint test on the centered covariates, we can use the `Wald_test` function. The usual way to test this hypothesis would be to use the `CR1` variance estimator to calculate the robust Wald statistic, then use a $\chi^2_2$ reference distribution (or equivalently, compare a re-scaled Wald statistic to an $F(2,\infty)$ distribution). The `Wald_test` function reports the latter version:
r-cran-clubsandwich-0.5.0/inst/doc/panel-data-CRVE.Rmd-174-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd-39-$$
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd:40:The regression coefficient vector is $\boldsymbol\beta$. In quite general terms, a set of constraints on the regression coefficient vector can be expressed in terms of a $q \times p$ matrix $\mathbf{C}$, where each row of $\mathbf{C}$ corresponds to one constraint. A joint null hypothesis is then $H_0: \mathbf{C} \boldsymbol\beta = \mathbf{0}$, where $\mathbf{0}$ is a $q \times 1$ vector of zeros.[^more-general]
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd-41-
##############################################
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd-83-```
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd:84:The result includes details about the form of `test` computed, the $F$-statistic, the numerator and denominator degrees of freedom used to compute the reference distribution, and the $p$-value corresponding to the specified null hypothesis. In this example, $p = `r format.pval(Wald_test(lm_trt, constraints = C_trt, vcov = V_trt)$p_val, digits = 3)`$, so we can rule out the null hypothesis that there are no differences in math performance across conditions.
r-cran-clubsandwich-0.5.0/inst/doc/Wald-tests-in-clubSandwich.Rmd-85-
##############################################
r-cran-clubsandwich-0.5.0/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-clubsandwich-0.5.0/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-clubsandwich-0.5.0/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM