首先先用把lm的结果存储到“fit”变量中,然后summary(fit),即可得到参数、t检验和P值、拟合优度R方、F统计量和P值等,以R中自带的数据cars为例,里面speed和dist的回归为:
> fit<-lm(cars$speed~cars$dist)
> summary(fit)
输出结果为:
Call:
lm(formula = cars$speed ~ cars$dist)
Residuals:
Min 1Q Median 3Q Max
-7.5293 -2.1550 0.3615 2.4377 6.4179
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.28391 0.87438 9.474 1.44e-12 ***
cars$dist 0.16557 0.01749 9.464 1.49e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.156 on 48 degrees of freedom
Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12
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