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Type: Package
Title: Clinical and Laboratory Standards Institute (CLSI) EP15-A3 Calculations
Version: 0.1.0
Maintainer: Claucio Antonio Rank Filho <claucio.filho@hitechnologies.com.br>
Description: Calculations of "EP15-A3 document. A manual for user verification of precision and estimation of bias" CLSI (2014, ISBN:1-56238-966-1).
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
Depends: R (≥ 4.0)
Imports: stats, dplyr, tidyr
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
NeedsCompilation: no
Packaged: 2023-11-10 13:01:20 UTC; claucio
Author: Claucio Antonio Rank Filho [aut, cre]
Repository: CRAN
Date/Publication: 2023-11-10 19:43:23 UTC

Calculate bias validation interval

Description

Calculate bias validation interval

Usage

bias_validation_interval(TV, m, se_c)

Arguments

TV

True value

m

factor

se_c

SE Combined

Value

named list with the interval

Calculate the UVL factor

Description

Calculate the UVL factor

Usage

calculate_F_uvl(nsamp = 1, df, alpha = 0.05)

Arguments

nsamp

n samples in the study

df

degres of freedom

alpha

confidence level

Value

Uvl factor

Calculate ANOVA Results and Imprecision Estimates

Description

Calculate ANOVA Results and Imprecision Estimates

Usage

calculate_aov_infos(ep_15_table)

Arguments

ep_15_table

table generated from create_table_ep_15()

Value

Named list with ANOVA Results and Imprecision Estimates

Examples

calculate_aov_infos(create_table_ep_15(CLSIEP15::ferritin_long, data_type = 'long'))

Calculate bias interval from TV

Description

Calculate bias interval from TV

Usage

calculate_bias_interval(
  scenario,
  nrun,
  nrep,
  SWL,
  SR,
  nsamples,
  expected_mean,
  user_mean,
  ...
)

Arguments

scenario

Choosed scenario from section 3.3 of EP15-A3

nrun

Number of runs

nrep

number of repetitions per run (n0)

SWL

S within laboratory (obtained from anova)

SR

S repetability (obtained from anova)

nsamples

total number of samples tested usual 1

expected_mean

Expected mean or TV

user_mean

Mean of all samples (obtained from anova)

...

additional parameters necessary for processing the choosed scenario

Value

a named list with the defined mean, the interval significance (user mean should be in for approval), and total bias (user mean - TV)

Examples

calculate_bias_interval(scenario = 'E',
nrun = 7,
nrep = 5,
SWL = .042,
SR = .032,
nsamples = 2,
expected_mean = 1,
user_mean = .94
)

Calculate degres of freedom within-lab as specified in appendix B

Description

Calculate degres of freedom within-lab as specified in appendix B

Usage

calculate_dfWL(cvr_manufacture, cvwl_manufacture, k, n0, N)

Arguments

cvr_manufacture

CV repeatability informed by the manufacturer

cvwl_manufacture

CV within-lab informed by the manufacturer

k

the number of runs

n0

the “average” number of results per run

N

the total number of replicates

Value

dfwl

Calculate degrees of freedom of SE C (SE combined) given a selected scenario and additional parameters necessary for the scenario

Description

Calculate degrees of freedom of SE C (SE combined) given a selected scenario and additional parameters necessary for the scenario

Usage

calculate_df_combined(scenario, ...)

Arguments

scenario

Scenario (A, B, C, D, E)

...

additional parameters necessary for the scenario

Value

DF

Calculate M

Description

Calculate M

Usage

calculate_m(df, conf.level = 95, nsamples = 1)

Arguments

df

degrees of freedom

conf.level

confidence interval

nsamples

number of samples

Value

m factor

Calculate n0

Description

Calculate n0

Usage

calculate_n0(long_result_table)

Arguments

long_result_table

table generated by create_table_ep_15 function

Value

The n0 number which refers to Number of Results per Run

Calculate SE combined based on SE X and SE RM

Description

Calculate SE combined based on SE X and SE RM

Usage

calculate_se_c(se_x, se_rm)

Arguments

se_x

SE X

se_rm

SE RM

Value

SE C

Calculate SE RM given a scenario and a list of additional args that can change based on the selected scenario or sub scenario

Description

Calculate SE RM given a scenario and a list of additional args that can change based on the selected scenario or sub scenario

Usage

calculate_se_rm(scenario, additional_args)

Arguments

scenario

scenario (A, B, C, D, E)

additional_args

additional arguments list

Value

SE RM

Calculate SE RM for scenario A when f the manufacturer supplies an “expanded uncertainty” (abbreviated by uppercase “U”) for the TV and coverage e.g. 95 or 99,

Description

Calculate SE RM for scenario A when f the manufacturer supplies an “expanded uncertainty” (abbreviated by uppercase “U”) for the TV and coverage e.g. 95 or 99,

Usage

calculate_se_rm_a_Ucoverage(U, coverage)

Arguments

U

expanded uncertainty

coverage

coverage

Value

SE RM

Calculate SE RM for scenario A when f the manufacturer supplies an “expanded uncertainty” (abbreviated by uppercase “U”) for the TV and the “coverage factor” (abbreviated by “k”)

Description

Calculate SE RM for scenario A when f the manufacturer supplies an “expanded uncertainty” (abbreviated by uppercase “U”) for the TV and the “coverage factor” (abbreviated by “k”)

Usage

calculate_se_rm_a_Uk(U, k)

Arguments

U

expanded uncertainty

k

coverage factor

Value

SE RM

Calculate SE RM for scenario A when f the manufacturer supplies lower and upper limits and coverage confidence interval (95 or 99...)

Description

Calculate SE RM for scenario A when f the manufacturer supplies lower and upper limits and coverage confidence interval (95 or 99...)

Usage

calculate_se_rm_a_lowerupper(upper, lower, coverage)

Arguments

upper

upper limit

lower

lower limit

coverage

coverage

Value

SE RM

Calculate SE RM for scenario A when “standard error” or “standard uncertainty” (abbreviated by lowercase “u”) or “combined standard uncertainty” (often denoted by “uC ”)

Description

Calculate SE RM for scenario A when “standard error” or “standard uncertainty” (abbreviated by lowercase “u”) or “combined standard uncertainty” (often denoted by “uC ”)

Usage

calculate_se_rm_a_u(u)

Arguments

u

“standard error” or “standard uncertainty” (abbreviated by lowercase “u”) or “combined standard uncertainty” (often denoted by “uC ”)

Value

SE RM

Calculate SE RM for scenario B or C If the reference material has a TV determined by PT or peer group results

Description

Calculate SE RM for scenario B or C If the reference material has a TV determined by PT or peer group results

Usage

calculate_se_rm_scenario_b_c(sd_rm, nlab)

Arguments

sd_rm

SD RM

nlab

number of lab or peer group results

Value

SE RM

Calculate SE RM for scenario D or E If the TV represents a conventional quantity value or When working with a commercial QC material supplied with a TV for which the standard error cannot be estimated

Description

Calculate SE RM for scenario D or E If the TV represents a conventional quantity value or When working with a commercial QC material supplied with a TV for which the standard error cannot be estimated

Usage

calculate_se_rm_scenario_d_e()

Value

SE RM

Calculate SE x

Description

Calculate SE x

Usage

calculate_se_x(nrun, nrep, SWL, SR)

Arguments

nrun

Run number

nrep

Number of repetitions per run n0

SWL

SWL from aov table

SR

SR from aov table

Value

SE X

Calculate upper verification limit

Description

Generic function for calculating UVL the return is a named list and cv_uvl_r and cv_uvl_wl depends on what is the input (S or CV) if the input is SR and SWL the returns is S

Usage

calculate_uvl_info(aov_return, nsamp = 1, cvr_or_sr, cvwl_or_swl)

Arguments

aov_return

Return of calculate_aov_info()

nsamp

number of samples in the experiment

cvr_or_sr

Desirable CV or S repetability

cvwl_or_swl

Desirable CV or S within-lab

Value

Named list with UVL params

Examples

 data <- create_table_ep_15(ferritin_wider)
 aov_t <- calculate_aov_infos(data)
 calculate_uvl_info(aov_t, nsamp = 5, cvr_or_sr = .43, cvwl_or_swl = .7)

Create table for precision calculations

Description

Create table for precision calculations

Usage

create_table_ep_15(data, data_type = "wider")

Arguments

data

a long or a wider data.frame with the same structure of CLSIEP15::ferritin_long or CLSIEP15::ferritin_wider

data_type

c('wider', 'long')

Value

a data.frame with renamed columns and structure adjustments

Examples

data <- create_table_ep_15(ferritin_long, data_type = "longer")

Reference of degrees of freedon based on tau given in the CLSI Manual

Description

Reference of degrees of freedon based on tau given in the CLSI Manual

Usage

dfc_references

Format

'dfc_references' A data frame with 390 rows and 4 columns:

tau

tau

df

degrees of freedon

labs

number of labs or peers

runs

number of runs

...

Source

CLSI EP15-A3

Ferrtin data used in CLSI document examples in wide format

Description

Ferrtin data used in CLSI document examples in wide format

Usage

ferritin_long

Format

'ferritin_long' A data frame with 25 rows and 3 columns:

rep

Repetition of sample

name

Run of the Runs obtained from 5 distinct days

value

result of the observation

...

Source

CLSI EP15-A3

Ferrtin data used in CLSI document examples in wide format

Description

Ferrtin data used in CLSI document examples in wide format

Usage

ferritin_wider

Format

'ferritin_wider' A data frame with 5 rows and 6 columns:

rep

Repetition of sample

Run_1, Run_2, Run_3, Run_4, Run_5

Runs from 5 distinct days

...

Source

CLSI EP15-A3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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