P122
入门：散点图、线性拟合、拟合参数slope
进阶：统计检验，多重矫正FDR
入门R代码
height <- c(176, 154, 138, 196, 132, 176, 181, 169, 150, 175) bodymass <- c(82, 49, 53, 112, 47, 69, 77, 71, 62, 78) plot(bodymass, height) plot(bodymass, height, pch = 16, cex = 1.3, col = "blue", main = "HEIGHT PLOTTED AGAINST BODY MASS", xlab = "BODY MASS (kg)", ylab = "HEIGHT (cm)")
进阶
eruption.lm = lm(eruptions ~ waiting, data=faithful) summary(eruption.lm) help(summary.lm)
Call:
lm(formula = eruptions ~ waiting, data = faithful)
Residuals:
Min 1Q Median 3Q Max
-1.2992 -0.3769 0.0351 0.3491 1.1933
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.87402 0.16014 -11.7 <2e-16 ***
waiting 0.07563 0.00222 34.1 <2e-16 ***
---
Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
Residual standard error: 0.497 on 270 degrees of freedom
Multiple R-squared: 0.811, Adjusted R-squared: 0.811
F-statistic: 1.16e+03 on 1 and 270 DF, p-value: <2e-16
Decide whether there is a significant relationship between the variables in the linear regression model of the data set faithful at .05 significance level.
NULL hypothesis: no relationship between x and y, so the slope is zero.
假设误差服从正态分布，基于零假设，我们要检验以下统计量是否显著。
统计量：(b-B)/sb follows a Student’s t distribution with n-2 degrees of freedom, where sb=s/√(∑(X-Mean(X))2) is the standard error of b.
medium专题
这个非常值得一看，回归里的系数和p-value分别是什么含义。
How to Interpret Regression Analysis Results: P-values and Coefficients
null hypothesis：coefficient is 0，如果p-value小于0.05，我们就可以拒绝零假设。
multiple testing
Benjamini and Hochberg‘s method
aggregated FDR
FDR with group info
Hu, James X., Hongyu Zhao, and Harrison H. Zhou. "False discovery rate control with groups." Journal of the American Statistical Association 105.491 (2010): 1215-1227.
待续~
regression | p-value | Simple (bivariate) linear model | 线性回归 | FDR | BH | R代码
原文：https://www.cnblogs.com/leezx/p/9121312.html