sys = tfest(data,np) estimates a continuous-time transfer function sys using the time-domain or frequency-domain data data and containing np poles. The number of zeros in sys is max(np-1,0).
时间: 2024-03-28 16:38:07 浏览: 103
是的,您说的是关于MATLAB中tfest函数的描述,其中np是传递函数模型中极点的数量,sys是估计得到的传递函数模型对象。在这个描述中,还提到了一个重要的信息,即传递函数模型的零点数量是max(np-1,0)。这个公式的意思是,如果np-1大于0,则传递函数模型中的零点数量为np-1,否则没有零点。这个公式的原理是基于传递函数模型的极点-零点分解,其中极点和零点的数量之差是1。因此,如果有n个极点,那么最多有n-1个零点,如果n-1小于等于0,则没有零点。在使用tfest函数时,需要合理选择np的值,以平衡传递函数模型的复杂度和准确性。
相关问题
Write MATLAB code that implements the parametric bootstrap.Test it using the forearm data.Assume that the normal distribu-tion is a reasonable model for the data.Use your code to get abootstrap estimate of the standard error and the bias of the coeffi-cie
Sure, here is the MATLAB code that implements the parametric bootstrap:
```matlab
% Load forearm data
load forearm
% Define function that fits linear regression model and returns coefficient estimates
fit_lm = @(x, y) [ones(size(x)), x] \ y;
% Define number of bootstrap samples
B = 1000;
% Define empty arrays to store bootstrap estimates
beta0_boot = zeros(B, 1);
beta1_boot = zeros(B, 1);
% Compute original coefficient estimates
beta_orig = fit_lm(forearm(:, 1), forearm(:, 2));
% Compute standard deviation of residuals
resid_std = std(forearm(:, 2) - beta_orig(1) - beta_orig(2) * forearm(:, 1));
% Perform bootstrap
for i = 1:B
% Generate bootstrap sample
y_boot = beta_orig(1) + beta_orig(2) * forearm(:, 1) + randn(size(forearm(:, 1))) * resid_std;
% Fit linear regression model to bootstrap sample
beta_boot = fit_lm(forearm(:, 1), y_boot);
% Store bootstrap estimates
beta0_boot(i) = beta_boot(1);
beta1_boot(i) = beta_boot(2);
end
% Compute bootstrap standard errors and bias
beta0_se = std(beta0_boot);
beta1_se = std(beta1_boot);
beta0_bias = mean(beta0_boot) - beta_orig(1);
beta1_bias = mean(beta1_boot) - beta_orig(2);
% Display results
disp(['Bootstrap standard error of intercept: ', num2str(beta0_se)]);
disp(['Bootstrap standard error of slope: ', num2str(beta1_se)]);
disp(['Bootstrap bias of intercept: ', num2str(beta0_bias)]);
disp(['Bootstrap bias of slope: ', num2str(beta1_bias)]);
```
This code fits a linear regression model to the forearm data using the `fit_lm` function, which simply calls the backslash operator to perform the least squares optimization. It then defines the number of bootstrap samples `B`, and initializes empty arrays to store the bootstrap estimates.
The code then computes the standard deviation of the residuals of the original model, which is used to generate the bootstrap samples. It then iterates over `B` bootstrap samples, generating a new set of observations by adding Gaussian noise to the original predictions based on the standard deviation of the residuals. It fits a new linear regression model to each bootstrap sample, and stores the resulting coefficient estimates.
Finally, the code computes the standard errors and bias of the bootstrap estimates, and displays the results.
We additionally performed genome-wide MR analyses using Causal Analysis Using Summary Effect estimates (CAUSE),孟德尔随机化安装CAUSE包
(version 1.3.0)in R software (version 4.0.2). We obtained the summary statistics for the exposure and outcome from large-scale GWAS datasets, and used the two-sample MR approach to estimate the causal effect of the exposure on the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis, and performed sensitivity analyses using the weighted median and MR-Egger methods to assess the robustness of the results. We also checked for potential pleiotropy using the MR-PRESSO method. The significance threshold was set at a Bonferroni-corrected p-value of 0.05/number of tests performed.