强制某些参数在lm()中具有正系数 [英] Force certain parameters to have positive coefficients in lm()

问题描述

我想知道如何约束 lm()中的某些参数以使其具有正系数.有一些软件包或函数(例如 display )可以使所有系数和截距为正.

I would like to know how to constrain certain parameters in lm() to have positive coefficients. There are a few packages or functions (e.g. display) that can make all coefficients, and the intercept, positive.

例如，在此示例中，我只想强制 x1 和 x2 具有正系数.

For instance, in this example, I would like to force only x1 and x2 to have positive coefficients.

    x1=c(NA,rnorm(99)*10)
    x2=c(NA,NA,rnorm(98)*10)
    x3=rnorm(100)*10
    y=sin(x1)+cos(x2)-x3+rnorm(100)

    lm(y~x1+x2+x3)

    Call:
      lm(formula = y ~ x1 + x2 + x3)       
    Coefficients:
      (Intercept)           x1           x2           x3  
    -0.06278      0.02261     -0.02233     -0.99626

我在包 nnls 中尝试了函数 nnnpls()，它可以轻松控制系数符号.不幸的是，由于数据中 NA 的问题，我无法使用它，因为此功能不允许 NA .

I have tried function nnnpls() in package nnls, it can control the coefficient sign easily. Unfortunately I can't use it due to issues with NAs in the data as this function doesn't allow NA.

我看到函数 glmc()可以用于施加约束，但是我无法使其正常工作.

I saw function glmc() can be used to apply constraints but I couldn't get it working.

有人可以让我知道我该怎么办吗?

Could someone let me know what should I do?

推荐答案

为此，您可以使用软件包 colf .目前，它提供了两个最小二乘非线性优化器，即 nls 或 nlxb :

You could use the package colf for this. It currently offers two least squares non linear optimizers, namely nls or nlxb:

library(colf)

colf_nlxb(y ~ x1 + x2 + x3, data = DF, lower = c(-Inf, 0, 0, -Inf))
#nlmrt class object: x 
#residual sumsquares =  169.53  on  98 observations
#    after  3    Jacobian and  3 function evaluations
#                name      coeff SEs tstat pval gradient JSingval
#1 param_X.Intercept. -0.0066952  NA    NA   NA   3.8118 103.3941
#2           param_x1  0.0000000  NA    NA   NA 103.7644  88.7017
#3           param_x2  0.0000000  NA    NA   NA   0.0000   9.8032
#4           param_x3 -0.9487088  NA    NA   NA 330.7776   0.0000

colf_nls(y ~ x1 + x2 + x3, data = DF, lower = c(-Inf, 0, 0, -Inf))
#Nonlinear regression model
#  model: y ~ param_X.Intercept. * X.Intercept. + param_x1 * x1 + param_x2 *        
#  x2 + param_x3 * x3
#   data: model_ingredients$model_data
#param_X.Intercept.           param_x1           param_x2           param_x3 
#           -0.0392             0.0000             0.0000            -0.9801 
# residual sum-of-squares: 159
# 
#Algorithm "port", convergence message: both X-convergence and relative convergence (5)

您可以设置下和/或上边界，以根据需要为每个系数指定极限.

You can set the lower and/or upper bounds to specify the limits as you like for each one of the coefficients.

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