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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-29-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:30:The data are lexical decision and word naming latencies for 300 words and 300 nonwords from 45 participants presented in Freeman et al. (2010). The 300 items in each `stimulus` condition were selected to form a balanced $2 \times 2$ design with factors neighborhood `density` (low versus high) and `frequency` (low versus high). The `task` was a between subjects factor: 25 participants worked on the lexical decision task and 20 participants on the naming task. After excluding erroneous responses each participants responded to between 135 and 150 words and between 124 and 150 nonwords. We analyzed log RTs which showed an approximately normal picture.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-31-
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-201-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:202:Before deciding what to do next, we take a look at the estimated random effect estimates. We do so for the model without any correlations. Note that for `afex` models we usually do not want to use the `summary` method as it prints the results on the level of the model coefficients and not model terms. But for the random effects we have to do so. However, we are only interested in the random effect terms so we only print those using `summary(model)$varcor`.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-203-
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-305-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:306:It is instructive to compare those results with results obtained using the comparatively 'worst' method for obtaining $p$-values implemented in `afex::mixed`, likelihood ratio tests. Likelihood ratio-tests should in principle deliver reasonable results for large data sets. A common rule of thumb is that the number of levels for each random effect grouping factor needs to be large, say above 50. Here, we have a very large number of items (`r length(levels(fhch$item))`), but not that many participants (`r length(levels(fhch$id))`). Thus, qualitative results should be the very similar, but it still is interesting to see exactly what happens. We therefore fit the final model using `method='LRT'`.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-307-
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-325-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:326:In terms of the significant findings, there are many that seem to be in line with the descriptive results described above. For example, the highly significant effect of `task:stimulus:frequency` with $F(1, 578.91) = 124.16$, $p < .001$, appears to be in line with the observation that the frequency effect appears to change its sign depending on the `task:stimulus` cell (with `nonword` and `naming` showing the opposite patterns than the other three conditions). Consequently, we start by investigating this interaction further below.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-327-
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-335-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:336:Our interest in the beginning is on the effect of `frequency` by `task:stimulus` combination. So let us first look at the estimated marginal means of this effect. In `emmeans` parlance these estimated means are called 'least-square means' because of historical reasons, but because of the lack of least-square estimation in mixed models we prefer the term estimated marginal means, or EMMs for short. Those can be obtained in the following way. To prevent `emmeans` from calculating the *df* for the EMMs (which can be quite costly), we use asymptotic *df*s (i.e., $z$ values and tests). `emmeans` requires to first specify the variable(s) one wants to treat as the effect variable(s) (here `frequency`) and then allows to specify condition variables.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-337-
##############################################
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-395-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:396:As the last example, let us take a look at the significant four-way interaction of `task:stimulus:density:frequency`, $F(1, 578.77) = 11.72$, $p < .001$. Here we might be interested in a slightly more difficult question namely whether the `density:frequency` interaction varies across `task:stimulus` conditions. If we again look at the figures above, it appears that there is a difference between `low:low` and `high:low` in the `nonword` and `lexdec` condition, but not in the other conditions.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-397-
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r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-435-
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd:436:In contrast to our expectation, the results show two significant effects and not only one. In line with our expectations, in the `nonword` and `lexdec` condition the EMM of `low:low` is smaller than the EMM for `high:low`, $z = -5.63$, $p < .0001$. However, in the `nonword` and `naming` condition we found the opposite pattern; the EMM of `low:low` is larger than the EMM for `high:low`, $z = 3.53$, $p = .003$. For all other effects $|z| < 1.4$, $p > .98$. In addition, there is no difference between `low:high` and `high:high` in any condition.
r-cran-afex-0.28-0/vignettes/afex_mixed_example.Rmd-437-
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r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-29-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:30:The data are lexical decision and word naming latencies for 300 words and 300 nonwords from 45 participants presented in Freeman et al. (2010). The 300 items in each `stimulus` condition were selected to form a balanced $2 \times 2$ design with factors neighborhood `density` (low versus high) and `frequency` (low versus high). The `task` was a between subjects factor: 25 participants worked on the lexical decision task and 20 participants on the naming task. After excluding erroneous responses each participants responded to between 135 and 150 words and between 124 and 150 nonwords. We analyzed log RTs which showed an approximately normal picture.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-31-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-201-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:202:Before deciding what to do next, we take a look at the estimated random effect estimates. We do so for the model without any correlations. Note that for `afex` models we usually do not want to use the `summary` method as it prints the results on the level of the model coefficients and not model terms. But for the random effects we have to do so. However, we are only interested in the random effect terms so we only print those using `summary(model)$varcor`.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-203-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-305-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:306:It is instructive to compare those results with results obtained using the comparatively 'worst' method for obtaining $p$-values implemented in `afex::mixed`, likelihood ratio tests. Likelihood ratio-tests should in principle deliver reasonable results for large data sets. A common rule of thumb is that the number of levels for each random effect grouping factor needs to be large, say above 50. Here, we have a very large number of items (`r length(levels(fhch$item))`), but not that many participants (`r length(levels(fhch$id))`). Thus, qualitative results should be the very similar, but it still is interesting to see exactly what happens. We therefore fit the final model using `method='LRT'`.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-307-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-325-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:326:In terms of the significant findings, there are many that seem to be in line with the descriptive results described above. For example, the highly significant effect of `task:stimulus:frequency` with $F(1, 578.91) = 124.16$, $p < .001$, appears to be in line with the observation that the frequency effect appears to change its sign depending on the `task:stimulus` cell (with `nonword` and `naming` showing the opposite patterns than the other three conditions). Consequently, we start by investigating this interaction further below.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-327-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-335-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:336:Our interest in the beginning is on the effect of `frequency` by `task:stimulus` combination. So let us first look at the estimated marginal means of this effect. In `emmeans` parlance these estimated means are called 'least-square means' because of historical reasons, but because of the lack of least-square estimation in mixed models we prefer the term estimated marginal means, or EMMs for short. Those can be obtained in the following way. To prevent `emmeans` from calculating the *df* for the EMMs (which can be quite costly), we use asymptotic *df*s (i.e., $z$ values and tests). `emmeans` requires to first specify the variable(s) one wants to treat as the effect variable(s) (here `frequency`) and then allows to specify condition variables.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-337-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-395-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:396:As the last example, let us take a look at the significant four-way interaction of `task:stimulus:density:frequency`, $F(1, 578.77) = 11.72$, $p < .001$. Here we might be interested in a slightly more difficult question namely whether the `density:frequency` interaction varies across `task:stimulus` conditions. If we again look at the figures above, it appears that there is a difference between `low:low` and `high:low` in the `nonword` and `lexdec` condition, but not in the other conditions.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-397-
##############################################
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-435-
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd:436:In contrast to our expectation, the results show two significant effects and not only one. In line with our expectations, in the `nonword` and `lexdec` condition the EMM of `low:low` is smaller than the EMM for `high:low`, $z = -5.63$, $p < .0001$. However, in the `nonword` and `naming` condition we found the opposite pattern; the EMM of `low:low` is larger than the EMM for `high:low`, $z = 3.53$, $p = .003$. For all other effects $|z| < 1.4$, $p > .98$. In addition, there is no difference between `low:high` and `high:high` in any condition.
r-cran-afex-0.28-0/inst/doc/afex_mixed_example.Rmd-437-
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r-cran-afex-0.28-0/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-afex-0.28-0/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-afex-0.28-0/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM