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absz.density.ratio()
|
Perform kernel estimates of two densities of absolute z-statistics
and their ratio. |
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absz.density()
|
Density estimates for absolute z-statistics assuming that z-statistics are symmetrically distributed around 0 |
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as.perc()
|
Convert numbers like 0.421 to 42.1% |
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deround.z.density.adjust()
|
Draw derounded z assuming missing digits of mu and sigma are uniformly distributed, but adjust for estimated density of z using rejection sampling |
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deround.z.uniform()
|
Draw derounded z assuming missing digits of mu and sigma are uniformly distributed |
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dsr.ab.df()
|
Create an ab.df for the dsr approach |
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dsr.mark.obs()
|
Finds observations in dat for which we shall perform dsr adjustment |
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make.z.pdf()
|
Compute a normalized pdf from a vector of z-statistics |
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min(<max.z>)
|
Compute minimum and maximum possible values of z given rounded mu and sigma |
|
num.deci()
|
Get the number of significand digits of a floating point number
using the character presentation of those numbers of R |
|
num.sig.digits()
|
Get the number of significand digits of a floating point number
using the character presentation of those numbers of R |
|
rightmost.sig.digit()
|
Get the last significant digit(s) of a floating point number |
|
rounding.risk.s.thresholds()
|
Compute thresholds for the significant s of the
reported standard deviation such that we can rule-out
the errors: misclassification, wrong inclusion, wrong exclusion |
|
rounding.risks()
|
Assess for observations with reported z-statistic z and
a signficand of s for the standard error whether it is at risk of
the errors:
misclassification, wrong inclusion, wrong exclusion |
|
rounding.risks.summary()
|
Summary statistics for rounding risks for different thresholds |
|
sample.uniform.z.deround()
|
Sample derounded z from the uniformely derounded distributon for a given
single value of mu and sigma |
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set.last.digit.zero()
|
Sets the last digit of a number x to zero |
|
significand()
|
Get the significands of a numeric vector using
the character presentation of those numbers of R |
|
stat_abszdensity()
|
ggplot2 density lines for absolute z-statistics assuming that they are symmetrically distributed around 0 |
|
study.with.derounding()
|
Analysis with derounded z-statistics for different window half-widths around z0 |
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window(<binom.test.2s>)
|
Apply on windows two sided binomiminal test with H0: z = z0 |
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window(<binom.test>)
|
Apply on windows one-sided binomiminal test with H0: z <= z0 |
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window(<t.ci>)
|
Window function returning estimated probability that a z-statistic is
above a threshold z0 in a window with half-width h around z0 and t-test
confidence intervals |