：）我之前写了一个R函数，它将计算任意顺序的最小二乘多项式，以适应我放入其中的任何数据。 “LeastSquaresDegreeN.R”代码有效，因为我可以重现我之前获得的结果。但是，当我尝试将新数据放入其中时，我收到“不一致的参数”错误。
"Error in Conj(t(Q))%*%t(b) : non-conformable arguments"
一个非常简单的数据示例：
t <- seq(1,100,1)
fifthDegree <- t^5
LeastSquaresDegreeN(t,fifthDegree,5)
此应输出并绘制多项式f（t）= t ^ 5（最多为舍入误差）。 但是，即使我明确地制作了这些向量，我也会收到“不一致的参数”错误：
t <- as.vector(t)
fifthDegree <- as.vector(fifthDegree)
LeastSquaresDegreeN(t,fifthDegree,5)
我也尝试过对这些载体进行转置 - 但没有任何作用。
当然，解决方案非常简单。救命！？谢谢！
这是功能：
LeastSquaresDegreeN <- function(t, b, deg)
{
# Usage: t is independent variable vector, b is function data
# i.e., b = f(t)
# deg is desired polynomial order
# deg <- deg + 1 is a little adjustment to make the R loops index correctly.
deg <- deg + 1
t <- t(t)
dataSize <- length(b)
A <- mat.or.vec(dataSize, deg) # Built-in R function to create zero
# matrix or zero vector of arbitrary size
# Given basis phi(z) = 1 + z + z^2 + z^3 + ...
# Define matrix A
for (i in 0:deg-1) {
A[1:dataSize,i+1] = t^i
}
# Compute QR decomposition of A. Pull Q and R out of QRdecomp
QRdecomp <- qr(A)
Q <- qr.Q(QRdecomp, complete=TRUE)
R <- qr.R(QRdecomp, complete=TRUE)
# Perform Q^* b^T (Conjugate transpose of Q)
c <- Conj(t(Q))%*%t(b)
# Find x. R isn't square - so we have to use qr.solve
x <- qr.solve(R, c)
# Create xPlot (which is general enough to plot any degree
# polynomial output)
xPlot = x[1,1]
for (i in 1:deg-1){
xPlot = xPlot + x[i+1,1]*t^i
}
# Now plot it. Least squares "l" plot first, then the points in red.
plot(t, xPlot, type='l', xlab="independent variable t", ylab="function values f(t)", main="Data Plotted with Nth Degree Least Squares Polynomial", col="blue")
points(t, b, col="red")
} # End