| 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