统计余弦分析,

发布于 2024-09-02 01:25:19 字数 462 浏览 10 评论 0原文

嘿,我正在尝试计算统计中的余弦分析,但我不知道如何做到这一点。我需要计算昼夜节律数据的 MESOR、AMPLITUDE 和 ACROPHASE。

http://www.wepapers.com/Papers/73565/Cosinor_analysis_of_accident_risk_using__SPSS%27s_regression_procedures。 ppt

有一个链接显示了如何做到这一点,公式等,但它并没有给我太多帮助。有谁知道它的代码,无论是在 statistica 还是 SPSS 中?

我真的需要完成这件事,因为这是一篇重要的论文

Hey i am trying to calculate a cosinor analysis in statistica but am at a loss as to how to do so. I need to calculate the MESOR, AMPLITUDE, and ACROPHASE of ciracadian rhythm data.

http://www.wepapers.com/Papers/73565/Cosinor_analysis_of_accident_risk_using__SPSS%27s_regression_procedures.ppt

there is a link that shows how to do it, the formulas and such, but it has not given me much help. Does anyone know the code for it, either in statistica or SPSS??

I really need to get this done because it is for an important paper

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木格 2024-09-09 01:25:19

我没有 SPSS 或 Statistica,所以我无法告诉您确切的“按此按钮”类型的步骤,但这也许会有所帮助。

余弦分析正在用已知周期拟合余弦(或正弦)曲线。主要思想是,如果周期已知,则拟合余弦函数的非线性问题可以简化为参数呈线性的问题。我假设你的经期T=24小时。

  1. 您应该已经有两个变量:进行测量的时间和测量的(当然,这些变量可能被称为其他名称)。
  2. 现在创建两个新变量:SinTime = sin(2 x pi x Time / 24) 和 CosTime = cos(2 x pi x >时间 / 24) - 这在您链接的演示文稿的第 11 页中进行了描述(x 是乘法)。如果精确值不是内置的,请使用 pi=3.1415。
  3. 运行多元线性回归,以 Value 作为结果,以 SinTimeCosTime 作为两个预测变量。您应该得到它们系数的估计值,我们将其称为AB
  4. 回归模型的截距项是 MESOR。
  5. 振幅为 sqrt(A^2 + B^2) [A 平方加 B 平方的平方根]
  6. ACROPHASE 为 arctan(- B / A),其中 arctan 是 tan 的反函数。最后两个公式来自演示文稿的第 14 页。
  7. 回归模型还应该为您提供 R 平方值,以了解 24 小时昼夜节律模式与数据的拟合程度,以及测试是否存在 24 小时周期的昼夜节律成分的总体 p 值。
  8. 人们可以使用标准误差传播公式获得幅度和相位的标准误差,但这未包含在演示中。

I don't have SPSS or Statistica, so I can't tell you the exact "push-this-button" kind of steps, but perhaps this will help.

Cosinor analysis is fitting a cosine (or sine) curve with a known period. The main idea is that the non-linear problem of fitting a cosine function can be reduced to a problem that is linear in its parameters if the period is known. I will assume that your period T=24 hours.

  1. You should already have two variables: Time at which the measurement is taken, and Value of the measurement (these, of course, might be called something else).
  2. Now create two new variables: SinTime = sin(2 x pi x Time / 24) and CosTime = cos(2 x pi x Time / 24) - this is desribed on p.11 of the presentation you linked (x is multiplication). Use pi=3.1415 if the exact value is not built-in.
  3. Run multiple linear regression with Value as outcome and SinTime and CosTime as two predictors. You should get estimates of their coefficients, which we will call A and B.
  4. The intercept term of the regression model is the MESOR.
  5. The AMPLITUDE is sqrt(A^2 + B^2) [square root of A squared plus B squared]
  6. The ACROPHASE is arctan(- B / A), where arctan is the inverse function of tan. The last two formulas are from p.14 of the presentation.
  7. The regression model should also give you an R-squared value to see how well the 24 hour circadian pattern fits the data, and an overall p-value that tests for the presence of a circadian component with period 24 hrs.
  8. One can get standard errors on amplitude and phase using standard error propogation formulas, but that is not included in the presentation.
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