R中的错误:参数不一致.不对? [英] Error in R: nonconformable arguments. Not true?

问题描述

这是我的代码:

    #define likelihood function (including an intercept/constant in the function.)
lltobit <- function(b,x,y) {
  sigma <-  b[3]
  y  <- as.matrix(y)
  x <- as.matrix(x)
  vecones <- rep(1,nrow(x)) 
  x <- cbind(vecones,x)
  bx <- x %*% b[1:2] 
  d <- y != 0 
  llik <- sum(d * ((-1/2)*(log(2*pi) + log(sigma^2) + ((y - bx)/sigma)^2)) 
              + (1-d) * (log(1 - pnorm(bx/sigma))))
  return(-llik)
}

n <- nrow(censored) #define number of variables 
y <- censored$y #define y and x for easier use
x1 <- as.matrix(censored$x)
x <-  cbind(rep(1,n),x1) #include constant/intercept 
bols <- (solve(t(x) %*% x)) %*% (t(x) %*% y) #compute ols estimator (XX) -1 XY
init <- rbind(as.matrix(bols[1:nrow(bols)]),1) #initial values 

init

tobit1 <- optim(init, lltobit, x=x, y=y, hessian=TRUE, method="BFGS")

其中被检查的是我的数据表，包括y的200个(被检查的)值和x的200个值.

where censored is my data table, including 200 (censored) values of y and 200 values of x.

一切正常，但是运行optim命令时，出现以下错误:

Everything works, but when running the optim command, i get the following error:

tobit1 <- optim(init, lltobit, x=x, y=y, hessian=TRUE, method="BFGS")
Error in x %*% b[1:2] : non-conformable arguments

我知道这是什么意思，但是由于x是200 x 2的矩阵，而b [1:2]是2 x 1的向量，那出了什么问题?我尝试同时转置初始值向量和初始值向量，但没有任何效果.谁能帮我?

I know what it means, but since x is a 200 by 2 matrix, and b[1:2] a vector of 2 by 1, what goes wrong? I tried transposing both, and also the initial values vector, but nothing works. Can anyone help me?

推荐答案

我今天偶然发现了一个类似的问题(即使一切似乎都正常，不一致的参数"错误)，而我的解决方案是针对以下问题的基本规则矩阵乘法:即左矩阵的列数量必须与右行的数量相同strong>矩阵=我必须在乘法方程式中切换顺序. 换句话说，在矩阵乘法中(不同于普通乘法)，A %*% B与B %*% A不同.

I stumbled upon a similar problem today ("non-conformable arguments" error, even though everything seemed OK), and solution in my case was in basic rules for matrix-multiplication: i.e. number of columns of the left matrix must be the same as the number of rows of the right matrix = I had to switch order in multiplication equation. In other words, in matrix multiplication (unlike ordinary multiplication), A %*% B is not the same as B %*% A.

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