R:线性方程系统中的最小二乘估计
我将通过重复度量估算线性方程系统的一些参数。我的方程式看起来会如下:
variant1:
variant2:
至少10个值(重复测量;技术复制),每个和
。我想估计
和
resp。
。
此外,如果可能的话,我想知道这些估计的标准错误。
在R中,我的数据集看起来像这样(实际上我有:
i <- rep(1:3, each = 30)
j <- rep(rep(1:3, each = 10), 3)
K.i <- rep(c(6, 5, 10), each = 30) + rnorm(90)
K.ij <- K.i + rnorm(90)
# X_i, X_ij and x_ij should be 0 (since I assumed K_j being K_ij + normal noise
data <- cbind(i, j, K.i, K.ij)
如何估算预期参数值(最小化正方形的总和)以及在R中这些估计值的标准错误?
非常感谢您的帮助!
I'm going to estimate some parameters of linear equation systems with repeated measures. My equations will look like this:
Variant1:
Variant2:
At least 10 values (repeated measures; technical replicates) are known for every and
. I want to estimate the values for
and
resp.
.
Additionally I'd like to know the standard error of these estimates, if possible.
In R, my data set would look like this (in reality I have :
i <- rep(1:3, each = 30)
j <- rep(rep(1:3, each = 10), 3)
K.i <- rep(c(6, 5, 10), each = 30) + rnorm(90)
K.ij <- K.i + rnorm(90)
# X_i, X_ij and x_ij should be 0 (since I assumed K_j being K_ij + normal noise
data <- cbind(i, j, K.i, K.ij)
How can I estimate the expected parameter values (minimizing the sums of squares) and the standard errors of these estimates in R?
Thanks a lot in advance for your help!
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