beta.mm: Compute the least-squares slope estimate for simple linear...
In mdedge/stfspack: Functions to Support Statistical Thinking from Scratch
Description
Usage
Arguments
Value
Examples
Description
Given two numeric vectors, computes a least-squares slope estimate for the model y = a + b*x + disturbance. lm() also returns this number, but beta.mm() is faster, which is advantageous for simulating bootstrapping or permutation testing.
Usage
1
beta.mm(x, y)
Arguments
x
A numeric vector containing entries for the independent variable.
y
A numeric vector containing entries for the dependent variable, must be the same length as x.
Value
The least-squares slope estimate (i.e. the estimate of b) for the model y = a + b*x + disturbance.
Examples
1
mdedge/stfspack documentation built on May 9, 2019, 8:17 a.m.
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Description Usage Arguments Value Examples
Description
Given two numeric vectors, computes a least-squares slope estimate for the model y = a + b*x + disturbance. lm() also returns this number, but beta.mm() is faster, which is advantageous for simulating bootstrapping or permutation testing.
Usage
1 | beta.mm(x, y)
|
Arguments
x |
A numeric vector containing entries for the independent variable. |
y |
A numeric vector containing entries for the dependent variable, must be the same length as x. |
Value
The least-squares slope estimate (i.e. the estimate of b) for the model y = a + b*x + disturbance.
Examples
1 |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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