math

Rounding of Numbers

floor takes a single numeric argument x and returns a numeric vector containing the largest integers not greater than the corresponding elements of x.

Rounding of Numbers

ceiling takes a single numeric argument x and returns a numeric vector containing the smallest integers not less than the corresponding elements of x.

Rounding of Numbers

trunc takes a single numeric argument x and returns a numeric vector containing the integers formed by truncating the values in x toward 0.

Sign Function

sign returns a vector with the signs of the corresponding elements of x (the sign of a real number is 1, 0, or -1−1 if the number is positive, zero, or negative, respectively). Note that sign does not operate on complex vectors.

Finite, Infinite and NaN Numbers

is.finite and is.infinite return a vector of the same length as x, indicating which elements are finite (not infinite and not missing) or infinite. Inf And -Inf are positive And negative infinity whereas NaN means 'Not a Number’. (These apply to numeric values and real and imaginary parts of complex values but not to values of integer vectors.) Inf and NaN are reserved words in the R language.

Finite, Infinite and NaN Numbers

is.finite and is.infinite return a vector of the same length as x, indicating which elements are finite (not infinite and not missing) or infinite.

Finite, Infinite and NaN Numbers

is.finite and is.infinite return a vector of the same length as x, indicating which elements are finite (not infinite and not missing) or infinite.

Lagged Differences

Returns suitably lagged and iterated differences.

rounds the values in its first argument to the specified number of decimal places (default 0). See 'Details' about "round to even" when rounding off a 5.

computes logarithms, by default natural logarithms, log10 computes common (i.e., base 10) logarithms, and log2 computes binary (i.e., base 2) logarithms. The general form log(x, base) computes logarithms with base base.

Logarithms and Exponentials

log2 computes binary (i.e., base 2) logarithms.

Logarithms and Exponentials

log10 computes common (i.e., base 10) logarithms

evaluate the cosine alpha

Product of Vector Elements prod returns the product of all the values present in its arguments.

Sum of Vector Elements

sum returns the sum of all the values present in its arguments.

Logarithms and Exponentials

computes the exponential function.

Arithmetic Mean

Weighted Arithmetic Mean

Compute a weighted mean.

Median Value

Compute the sample median.

abs(x) computes the absolute value of x

relative standard deviation

Relative standard deviation is a common formula used in statistics and probability theory to determine a standardized measure of the ratio of the standard deviation to the mean. This formula is useful in various situations including when comparing your own data to other related data and in financial settings such as the stock market. Relative standard deviation, which also may be referred to as RSD or the coefficient of variation, is used to determine if the standard deviation of a set of data is small or large when compared to the mean. In other words, the relative standard deviation can tell you how precise the average of your results is. This formula is most frequently used in chemistry, statistics and other math-related settings but can also be used in the business world when assessing finances and the stock market. The relative standard deviation Of a Set Of data can be depicted As either a percentage Or As a number. The higher the relative standard deviation, the more spread out the results are from the mean Of the data. On the other hand, a lower relative standard deviation means that the measurement Of data Is more precise.

Standard Deviation

This function computes the standard deviation of the values in x. If na.rm is TRUE then missing values are removed before computation proceeds.

Pearson Correlation Testing in R Programming

Correlation is a statistical measure that indicates how strongly two variables are related. It involves the relationship between multiple variables as well. For instance, if one is interested to know whether there is a relationship between the heights of fathers and sons, a correlation coefficient can be calculated to answer this question. Generally, it lies between -1 and +1. It is a scaled version of covariance and provides the direction and strength of a relationship. this function measure a Parametric Correlation – Pearson correlation(r): It measures a linear dependence between two variables (x and y) is known as a parametric correlation test because it depends on the distribution of the data. Pearson Rank Correlation is a parametric correlation. The Pearson correlation coefficient is probably the most widely used measure for linear relationships between two normal distributed variables and thus often just called "correlation coefficient".

Test for Association/Correlation Between Paired Samples

Test for association between paired samples, using one of Pearson's product moment correlation coefficient, Kendall's auτ or Spearman's \rhoρ.

set.seed is the recommended way to specify seeds.

get a random number value between [0,1].

runif generates random deviates.

The Exponential Distribution

Density, distribution function, quantile function and random generation for the exponential distribution with rate rate (i.e., mean 1/rate).

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.

Random Samples and Permutations

sample takes a sample of the specified size from the elements of x using either with or without replacement.

grouping data input by given numeric tolerance

Correlation, Variance and Covariance (Matrices)

var, cov and cor compute the variance of x and the covariance or correlation of x and y if these are vectors. If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.

Correlation, Variance and Covariance (Matrices)

var, cov and cor compute the variance of x and the covariance or correlation of x and y if these are vectors. If x and y are matrices then the covariances (or correlations) between the columns of x and the columns of y are computed.

Function to calculate the Least Common Multiple (LCM)

finds root of specific degree of number.