{stats} R# Documentation

stats

require(R);

#' The R Stats Package
imports "stats" from "Rlapack";

The R Stats Package

R statistical functions, This package contains functions for statistical calculations and random number generation. For a complete list of functions, use library(help = "stats").

The R Stats Package

R statistical functions, This package contains functions for statistical calculations and random number generation. For a complete list of functions, use library(help = "stats").



.NET clr function exports
combin

calculates C(n, k).
pnorm

The Normal Distribution Density, distribution function, quantile function and random generation for the normal distribution with mean equal to mean and standard deviation equal to sd.
dnorm
p.adjust

Adjust P-values for Multiple Comparisons

Given a set of p-values, returns p-values adjusted using one of several methods.
ecdf

Empirical Cumulative Distribution Function

Compute an empirical cumulative distribution function, with several methods for plotting, printing and computing with such an “ecdf” object.
CDF

Empirical Cumulative Distribution Function

Compute an empirical cumulative distribution function
spline

Interpolating Splines
tabulate.mode

Average by removes outliers
prcomp

Principal Components Analysis

Performs a principal components analysis on the given data matrix and returns the results as an object of class prcomp. The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix. This is generally the preferred method for numerical accuracy. The print method for these objects prints the results in a nice format and the plot method produces a scree plot.

Unlike princomp, variances are computed With the usual divisor N - 1. Note that scale = True cannot be used If there are zero Or constant (For center = True) variables.
as.dist
corr

matrix correlation
corr_sign
corr.test

Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame. Although the cor function finds the correlations for a matrix, it does not report probability values. cor.test does, but for only one pair of variables at a time. corr.test uses cor to find the correlations for either complete or pairwise data and reports the sample sizes and probability values as well. For symmetric matrices, raw probabilites are reported below the diagonal and correlations adjusted for multiple comparisons above the diagonal. In the case of different x and ys, the default is to adjust the probabilities for multiple tests. Both corr.test and corr.p return raw and adjusted confidence intervals for each correlation.
quantile

Sample Quantiles

The generic function quantile produces sample quantiles corresponding to the given probabilities. The smallest observation corresponds to a probability of 0 and the largest to a probability of 1.
median
level

get quantile levels
dist

Distance Matrix Computation

This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.
t.test

Student's t-Test Performs one and two sample t-tests on vectors of data.
fisher.test

Fisher's Exact Test for Count Data Performs Fisher's exact test for testing the null of independence of rows and columns in a contingency table with fixed marginals.
chisq.test

Pearson's Chi-squared Test for Count Data

chisq.test performs chi-squared contingency table tests and goodness-of-fit tests.
moran.test

Calculate Moran's I quickly for point data test spatial cluster via moran index
mantel.test

The Mantel test, named after Nathan Mantel, is a statistical test of the correlation between two matrices. The matrices must be of the same dimension; in most applications, they are matrices of interrelations between the same vectors of objects. The test was first published by Nathan Mantel, a biostatistician at the National Institutes of Health, in 1967.[1] Accounts of it can be found in advanced statistics books (e.g., Sokal & Rohlf 1995[2]).
lowess
var.test

F Test to Compare Two Variances

Performs an F test to compare the variances of two samples from normal populations.
aov

Fit an Analysis of Variance Model

Fit an analysis of variance model by a call to lm for each stratum.
filterMissing

set the NA, NaN, Inf value to the default value
opls
plsda

Partial Least Squares Discriminant Analysis

plsda is used to calibrate, validate and use of partial least squares discrimination analysis (PLS-DA) model.
z

z-score
chi_square

The chiSquare method is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories. It takes a double input x and an integer freedom for degrees of freedom as inputs. It returns the Chi Squared result.
gamma.cdf
iqr_outliers

check of the outliers via IQR method
poisson_disk

Fast Poisson Disk Sampling in Arbitrary Dimensions. Robert Bridson. ACM SIGGRAPH 2007
[Document Index]