normalityAssessment can be used to assess whether a variable and the sampling distribution of its mean have an approximately normal distribution.
dataShape(
sampleVector,
na.rm = TRUE,
type = 2,
digits = 2,
conf.level = 0.95,
plots = TRUE,
xLabs = NA,
yLabs = NA,
qqCI = TRUE,
labelOutliers = TRUE,
sampleSizeOverride = NULL
)# S3 method for dataShape
print(x, digits = x$input$digits, extraNotification = TRUE, ...)
# S3 method for dataShape
pander(x, digits = x$input$digits, extraNotification = TRUE, ...)
normalityAssessment(
sampleVector,
samples = 10000,
digits = 2,
samplingDistColor = "#2222CC",
normalColor = "#00CC00",
samplingDistLineSize = 2,
normalLineSize = 1,
xLabel.sampleDist = NULL,
yLabel.sampleDist = NULL,
xLabel.samplingDist = NULL,
yLabel.samplingDist = NULL,
sampleSizeOverride = TRUE
)
# S3 method for normalityAssessment
print(x, ...)
# S3 method for normalityAssessment
pander(x, headerPrefix = "#####", suppressPlot = FALSE, ...)
samplingDistribution(
popValues = c(0, 1),
popFrequencies = c(50, 50),
sampleSize = NULL,
sampleFromPop = FALSE,
...
)
An object with several results, the most notably of which are:
Histogram of sample distribution
Shapiro-Wilk normality test of sample distribution
Anderson-Darling normality test of sample distribution
Kolmogorov-Smirnof normality test of sample distribution
Kurtosis for sample distribution
Skewness for sample distribution
Histogram of sampling distribution
Shapiro-Wilk normality test of sampling distribution
Anderson-Darling normality test of sampling distribution
Kolmogorov-Smirnof normality test of sampling distribution
Skewness and kurtosis for sampling distribution
Numeric vector containing the sample data.
Whether to remove missing data first.
Type of skewness and kurtosis to compute; either 1 (g1 and g2), 2 (G1 and G2), or 3 (b1 and b2). See Joanes & Gill (1998) for more information.
Number of digits to use when printing results.
Confidence of confidence intervals.
Whether to display plots.
The axis labels for the three plots (should be vectors of three elements; the first specifies the X or Y axis label for the rightmost plot (the histogram), the second for the middle plot (the QQ plot), and the third for the rightmost plot (the box plot).
Whether to show the confidence interval for the QQ plot.
Whether to label outliers with their row number in the box plot.
Whether to use the sample size of the sample as sample size for the sampling distribution, instead of the sampling distribution size. This makes sense, because otherwise, the sample size and thus sensitivity of the null hypothesis significance tests is a function of the number of samples used to generate the sampling distribution.
The object to print/pander.
Whether to be particularly informative.
Additional arguments are passed on, usually to the default methods.
Number of samples to use when constructing sampling distribution.
Color to use when drawing the sampling distribution.
Color to use when drawing the standard normal curve.
Size of the line used to draw the sampling distribution.
Size of the line used to draw the standard normal distribution.
Label of x axis of the distribution of the sample.
Label of y axis of the distribution of the sample.
Label of x axis of the sampling distribution.
Label of y axis of the sampling distribution.
A prefix to insert before the heading (e.g. to use Markdown headings).
Whether to suppress (TRUE) or print (FALSE) the plot.
The possible values (levels) of the relevant variable. For example, for a dichotomous variable, this can be "c(1:2)" (or "c(1, 2)"). Note that samplingDistribution is for manually specifying the frequency distribution (or proportions); if you have a vector with 'raw' data, just call normalityAssessment directly.
The frequencies corresponding to each value in popValues; must be in the same order! See the examples.
Size of the sample; the sum of the frequencies if not specified.
If true, the sample vector is created by sampling from the population information specified; if false, rep() is used to generate the sample vector. Note that is proportions are supplied in popFrequencies, sampling from the population is necessary!
samplingDistribution is a convenient wrapper for normalityAssessment that makes it easy to quickly generate a sample and sampling distribution from frequencies (or proportions).
dataShape computes the skewness and kurtosis.
normalityAssessment provides a number of normality tests and draws histograms of the sample data and the sampling distribution of the mean (most statistical tests assume the latter is normal, rather than the first; normality of the sample data guarantees normality of the sampling distribution of the mean, but if the sample size is sufficiently large, the sampling distribution of the mean is approximately normal even when the sample data are not normally distributed). Note that for the sampling distribution, the degrees of freedom are usually so huge that the normality tests, negligible deviations from normality will already result in very small p-values.
samplingDistribution makes it easy to quickly assess the distribution of a variables based on frequencies or proportions, and dataShape computes skewness and kurtosis.
### Note: the 'not run' is simply because running takes a lot of time,
### but these examples are all safe to run!
if (FALSE) {
normalityAssessment(rnorm(35));
### Create a distribution of three possible values and
### show the sampling distribution for the mean
popValues <- c(1, 2, 3);
popFrequencies <- c(20, 50, 30);
sampleSize <- 100;
samplingDistribution(popValues = popValues,
popFrequencies = popFrequencies,
sampleSize = sampleSize);
### Create a very skewed distribution of ten possible values
popValues <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
popFrequencies <- c(2, 4, 8, 6, 10, 15, 12, 200, 350, 400);
samplingDistribution(popValues = popValues,
popFrequencies = popFrequencies,
sampleSize = sampleSize, digits=5);
}
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