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Maintainer: helixcn <zhangjl@ibcas.ac.cn>
Description: To give the exactly results of linear regression.
License: GNU 2 or later
LazyLoad: yes
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\name{ linmod}
\Rdversion{ 1.1}
\alias{ linmod}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
linear regression
}
\description{
to give the more exactly results of linear regression
}
\usage{
linmod(x,源码 y)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{ x}{
a numeric design matrix for the model
}
\item{ y}{
a numeric vector of responses
}
}
\details{
%% ~~ If necessary, more details than the description above ~~
}
\value{
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{ comp1 }{ Description of 'comp1'}
%% \item{ comp2 }{ Description of 'comp2'}
%% ...
}
\references{
Friedrich Leisch, Creating R Packages: A Tutorial
}
\author{
helixcn
}
\note{
Please read Friedrich Leisch,
}
%% ~Make other sections like Warning with \section{ Warning }{ ....} ~
\seealso{
%% ~~objects to See Also as \code{ \link{ help}}, ~~~
}
\examples{
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## The function is currently defined as
function (x, y)
{
qx <- qr(x)
coef <- solve.qr(qx, y)
df <- nrow(x) - ncol(x)
sigma2 <- sum((y - x \%*\% coef)^2)/df
vcov <- sigma2 * chol2inv(qx$qr)
colnames(vcov) <- rownames(vcov) <- colnames(x)
list(coefficients = coef, vcov = vcov, sigma = sqrt(sigma2),
df = df)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\keyword{ ~kwd1 }
\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line
2ï¼linmod-package.Rd
\name{ linmod-package}
\Rdversion{ 1.1}
\alias{ linmod-package}
\alias{ linmod}
\docType{ package}
\title{ Linear Regression Modification}
\description{ to Give the more exactly output of linear regression rather than R default}
\details{
\tabular{ ll}{
Package: \tab linmod\cr
Type: \tab Package\cr
Version: \tab 1.0\cr
Date: \tab --\cr
License: \tab GNU 2.0 or later\cr
LazyLoad: \tab yes\cr
}
~~The aim of the package was to give the more exactly output of linear regression~~ linmod~~
}
\author{ helixcn
Maintainer: helixcn <helixcn@.com>}
\references{
Friedrich Leisch,,Creating R Packages: A Tutorial
}
\seealso{ lm}
\examples{
data(cats, package="MASS")
mod1 <- linmod(Hwt~Bwt*Sex, data=cats)
mod1
summary(mod1)
}
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[1]/post/5cfeda9d3dc
作者:Michael Notter
Python机器学习系列拟合和回归傻傻分不清?一文带你彻底搞懂它
在Python机器学习的探索中,理解拟合与回归这两个概念至关重要。分析它们虽然都涉及数据与模型的源码关联,但有着明确的分析差异。拟合是源码ubuntu xenial源码安装个广义概念,涵盖了将离散数据点通过线性或非线性方式映射到一条曲线的分析ag亚博源码整个过程,旨在减小数据点与拟合曲线的源码偏差。回归分析则是分析拟合的一种具体实现,它探究变量间的源码定量关系,以建立模型。分析
回归拟合则根据复杂度分为几个类别。源码一元线性回归如np.polyfit方法,分析通过求解系数来拟合数据,源码分享旅游APP源码如法一中的分析[8., -.],sklearn的源码LinearRegression方法也得到类似结果。曲线拟合如curve_fit则适用于非线性模型,如法三中的星凌社区源码[8., -.]。一元多项式回归,如2次多项式,np.polyfit、sklearn的宜昌直播系统源码LinearRegression和curve_fit都能得到系数,只是形式不同。
对于更复杂的函数拟合,如指数函数,curve_fit依然是首选,如得到的最佳系数[2., 0.]。这些方法不仅适用于一元,也是多元回归建模的基础。想深入了解数据集和源码的朋友,可以随时联系作者获取更多信息。
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