Version: 0.1.4
Date: 2018-05-10
Title: A Simple R Package for Classical Parametric Statistical Tests and Confidence Intervals in Large Samples
Author: Cqls Team
Maintainer: Pierre Lafaye de Micheaux <lafaye@unsw.edu.au>
Depends: R (≥ 1.8.0)
Description: One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://www.r-project.org
Packaged: 2018-05-10 09:59:30 UTC; lafaye
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2018-05-10 12:15:54 UTC

DIG NHLBI Teaching Dataset

Description

A clinical trial focused dataset was developed using the Digitalis Investigation Group (DIG). This dataset was designed to replicate the results found in the February 1997 New England Journal of Medicine article. Note that statistical processes such as permutations within treatment groups were used to completely anonymize the data; therefore, inferences derived from the teaching dataset may not be valid. The DIG Trial was a randomized, double-blind, multicenter trial with more than 300 centers in the United States and Canada participating. The purpose of the trial was to examine the safety and efficacy of Digoxin in treating patients with congestive heart failure in sinus rhythm. Data on 5281 male and 1519 female collected.

Format

This data frame contains the following columns:

ID

Patient ID

TRTMT

(0=Placebo, 1=Treatment)

AGE

Calculated: age at randomization

RACE

Q5: Race, 1=White 2=Nonwhite

SEX

(1 = male or 2 = female)

EJFPER

Q3: Ejection fraction (percent)

EJFMETH

Q3A: Ejection Fraction method

CHESTX

Q6: Chest X-ray (CT-Ratio)

BMI

Calculated: Body Mass Index (kg per M-squared)

KLEVEL

Q9A: Serum Potassium level

CREAT

Q9: Serum Creatinine (mg per dL)

DIGDOSER

Q10: Recommended Digoxin dose

CHFDUR

Q12: Duration of CHF (months)

RALES

Q13: Rales

ELEVJVP

Q14: Elevated jugular venous pressure

PEDEMA

Q15: Peripheral Edema

RESTDYS

Q16: Dyspnea at Rest

EXERTDYS

Q17: Dyspnea on Exertion

ACTLIMIT

Q18: Limitation of activity

S3

Q19: S3 Gallop

PULCONG

Q20: Pulmonary congestion

NSYM

Calculated: Sum of Q13-Q20, Y or N status

HEARTRTE

Q21: Heart Rate (beats per min)

DIABP

Q22: Diastolic BP (mmHg)

SYSBP

Q22: Sysolic BP (mmHg)

FUNCTCLS

Q23: NYHA Functional Class

CHFETIOL

Q24: CHF Etiology

PREVMI

Q25: Previous Myocardial Infarction

ANGINA

Q26: Current Angina

DIABETES

Q27: History of Diabetes

HYPERTEN

Q28: History of Hypertension

DIGUSE

Q29: Digoxin within past week

DIURETK

Q30: Potassium sparing Diuretics

DIURET

Q31: Other Diuretics

KSUPP

Q31A: Potassium supplements

ACEINHIB

Q32: Ace inhibitors

NITRATES

Q33: Nitrates

HYDRAL

Q34: Hydralazine

VASOD

Q35: Other Vasodilators

DIGDOSE

Q36: Dose of Digoxin per Placebo prescribed

CVD

Hosp: Cardiovascular Disease

CVDDAYS

Days randomization to First CVD Hosp

WHF

Hosp: Worsening Heart Failure

WHFDAYS

Days randomization to First WHF Hosp

DIG

Hosp: Digoxin Toxicity

DIGDAYS

Days rand. to First Digoxin Tox Hosp

MI

Hosp: Myocardial Infarction

MIDAYS

Days randomization to First MI Hosp

UANG

Hosp: Unstable Angina

UANGDAYS

Days rand. to First Unstable Angina Hosp

STRK

Hosp: Stroke

STRKDAYS

Days randomization to First Stroke Hosp

SVA

Hosp: Supraventricular Arrhythmia

SVADAYS

Days rand. to First SupraVent Arr. Hosp

VENA

Hosp: Ventricular Arrhythmia

VENADAYS

Days rand. to First Vent. Arr. Hosp

CREV

Hosp: Coronary Revascularization

CREVDAYS

Days rand. to First Cor. Revasc.

OCVD

Hosp: Other Cardiovascular Event

OCVDDAYS

Days rand. to First Other CVD Hosp

RINF

Hosp: Respiratory Infection

RINFDAYS

Days rand. to First Resp. Infection Hosp

OTH

Hosp: Other noncardiac, nonvascular

OTHDAYS

Days rand. to 1st Other Non CVD Hosp

HOSP

Hosp: Any Hospitalization

HOSPDAYS

Days randomization to First Any Hosp

NHOSP

Number of Hospitalizations

DEATH

Vital Status of Patient 1=Death 0=Alive

DEATHDAY

Days till last followup or death

REASON

Cause of Death

DWHF

Primary Endpt: Death or Hosp from HF

DWHFDAYS

Days rand. to death or Hosp from WHF

Source

NHLBI Teaching Dataset

References

The effect of digoxin on mortality and morbidity in patients with heart failure . The Digitalis Investigation Group. N En gl J Med. 1997 Feb 20;336(8):525-33

Examples

data(DIGdata)

Asymptotic tests

Description

Performs one and two sample asymptotic (no gaussian assumption on distribution) parametric tests on vectors of data.

Usage

asymp.test(x,...)
## Default S3 method:
asymp.test(x, y = NULL,
parameter = c("mean", "var", "dMean", "dVar", "rMean", "rVar"),
alternative = c("two.sided", "less", "greater"),
reference = 0, conf.level = 0.95, rho = 1, ...)
## S3 method for class 'formula'
asymp.test(formula, data, subset, na.action, ...)

Arguments

x

a (non-empty) numeric vector of data values.

y

an optional (non-empty) numeric vector of data values.

parameter

a character string specifying the parameter under testing, must be one of "mean", "var", "dMean" (default), "dVar", "rMean", "rVar"

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

reference

a number indicating the reference value of the parameter (difference or ratio true value for two sample test)

conf.level

confidence level of the interval.

rho

optional parameter (only used for parameters "dMean" and "dVar") for penalization (or enhancement) of the contribution of the second parameter.

formula

a formula of the form lhs ~ rhs where lhs is a numeric variable giving the data values and rhs a factor with two levels giving the corresponding groups.

data

an optional matrix or data frame (or similar: see model.frame) containing the variables in the formula formula. By default the variables are taken from environment(formula).

subset

an optional vector specifying a subset of observations to be used.

na.action

a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action").

...

further arguments to be passed to or from methods.

Details

Asymptotic parametric test and confidence intervals are based on the following unified statistic :

\frac{\hat{\theta}(Y)-\theta}{\hat{\sigma_{\hat{\theta}}(Y)}}

which asymptotically follows a N(0,1).

\theta stands for the parameter under testing (mean/variance, difference/ratio of means or variances).

The term \hat{\sigma_{\hat{\theta}}(Y)} is calculated by the ad-hoc seTheta function (see seMean).

Value

A list with class "htest" containing the following components:

statistic

the value of the unified \theta statistic.

p.value

the p-value for the test.

conf.int

a confidence interval for the parameter appropriate to the specified alternative hypothesis.

estimate

the estimated parameter depending on whether it wasa one-sample test or a two-sample test (in which case the estimated parameter can be a difference/ratio in means/variances).

null.value

the specified hypothesized value of parameter depending on whether it was a one-sample test or a two-sample test.

alternative

a character string describing the alternative hypothesis.

method

a character string indicating what type of asymptotictest was performed.

data.name

a character string giving the name(s) of the data.

Author(s)

J.-F. Coeurjolly, R. Drouilhet, P. Lafaye de Micheaux, J.-F. Robineau

References

C oeurjolly, J.F. Drouilhet, R. Lafaye de Micheaux, P. Robineau, J.F. (2009) asympTest: a simple R package for performing classical parametric statistical tests and confidence intervals in large samples, The R Journal

See Also

t.test, var.test for normal distributed data.

Examples

## one sample
x <- rnorm(70, mean = 1, sd = 2)
asymp.test(x)
asymp.test(x,par="mean",alt="g")
asymp.test(x,par="mean",alt="l",ref=2)
asymp.test(x,par="var",alt="g")
asymp.test(x,par="var",alt="l",ref=2)
## two samples
y <- rnorm(50, mean = 2, sd = 1)
asymp.test(x,y)
asymp.test(x,y,"rMean","l",.75)
asymp.test(x,y,"dMean","l",0,rho=.75)
asymp.test(x,y,"dVar")
## Formula interface
asymp.test(uptake~Type,data=CO2)

se functions

Description

se functions compute the Standard Error of respectively mean, variance, difference of means, of variances and ratio of means and variances.

Usage

seMean(x,...)
## Default S3 method:
seMean(x,...)
seVar(x,...)
## Default S3 method:
seVar(x,...)
seDMean(x,...)
## Default S3 method:
seDMean(x, y, rho = 1, ...)
seDMeanG(x,...)
## Default S3 method:
seDMeanG(x, y,...)
seDVar(x,...)
## Default S3 method:
seDVar(x, y, rho = 1, ...)
seRMean(x,...)
## Default S3 method:
seRMean(x, y, r0,...)
seRVar(x,...)
## Default S3 method:
seRVar(x, y, r0,...)

Arguments

x

a (non-empty) numeric vector of data values.

y

an optional (non-empty) numeric vector of data values.

rho

optional parameter for penalization (or enhancement) of the contribution of the second parameter.

r0

an optional parameter for ratio of means (seRMean) or variances (seRVar). It acts as parameter r in seDMean and seDVar. Defaults are mean(x)/mean(y) in seRMean and var(x)/var(y) for seRVar.

...

further arguments to be passed to or from methods.

Details

se functions performs classical standard error estimation for parameters mean, variance, difference of means or variances, ratio of means or variances.

Value

Return the value of the estimated standard error for the corresponding parameter.

Author(s)

J.-F. Coeurjolly, R. Drouilhet, P. Lafaye de Micheaux, J.-F. Robineau

References

Coeurjolly, J.F. Drouilhet, R. Lafaye de Micheaux, P. Robineau, J.F. (2008) asympTest: a simple R package for performing classical parametric statistical tests and confidence intervals in large samples, The R Journal

See Also

asymp.test that used estimated standard error for asymptotic parametric tests.

Examples

x <- rnorm(70, mean = 1, sd = 2)
y <- rnorm(50, mean = 2, sd = 1)
## mean statistic 
asymp.test(x)$stat
mean(x)/seMean(x)
## variance statistic
asymp.test(x,param="var",alt="l",param0=2)$stat
(var(x)-2)/seVar(x)
## difference of means statistic
asymp.test(x,y)$stat
(mean(x)-mean(y))/seDMean(x,y)