| univar {epibasix} | R Documentation |
Univariate Analysis of a Single Variable
Description
This function provides detailed univariate analysis for a single variable. Values include the sample mean, median, standard deviation and range, as well as tools for hypothesis tests and confidence intervals.
Usage
univar(X, alpha=0.05, mu0 = 0, shapiro=FALSE, digits=3)Arguments
X |
A Vector of observed values from a continuous distribution |
alpha |
The desired Type I Error Rate for Hypothesis Tests and Confidence Intervals |
mu0 |
The null hypothesis for the true population mean |
.
shapiro |
Logical: TRUE returns the Shapiro-Wilks Test for normality, this portion calls the shapiro.test function. |
digits |
Number of Digits to round calculations |
Details
This function provides a thorough summary of information within a vector. It conveniently calculates useful statistics at the call of a single command. Furthermore, it provides methods to test the hypothesis/construct confidence intervals for the true population mean.
Value
n |
Number of Observations Used |
mean |
The sample mean of the observations in X. |
median |
The sample median of the observations in X. |
min |
The sample minimum of the observations in X. |
max |
The sample maximum of the observations in X. |
s |
The sample standard deviation of the observations in X. |
var |
The sample variance of the observations in X. |
test |
The test statistic for the null hypothesis |
p.value |
The p.value for the test statistic for |
CIL |
The lower bound of the constructed confidence interval for |
CIU |
The upper bound of the constructed confidence interval for |
shapiro.statistic |
The value of the Shapiro-Wilks Statistic for Normality. |
shapiro.p.value |
The P-value of the Shapiro-Wilks Statistic for Normality. |
alpha |
The desired Type I Error Rate for Hypothesis Tests and Confidence Intervals |
mu0 |
The null hypothesis for the true population mean |
.
shapiro |
Logical: TRUE returns the Shapiro-Wilks Test for normality |
digits |
Number of Digits to round calculations |
Author(s)
Michael Rotondi, mrotondi@yorku.ca
References
Casella G and Berger RL. Statistical Inference (2nd Ed.) Duxbury: New York, 2002.
Examples
x <- rexp(100);
univar(x);