获得回归模型的统计量
You want the critical statistics and information regarding your regression, such as R2, the F statistic, confidence intervals for the coefficients, residuals, the ANOVA table,and so forth. 公卫人
Save the regression model in a variable, say m:
> m <- lm(y ~ u + v + w)
Then, use the following functions to extract regression statistics and information from 公卫人
the model:
anova(m)
ANOVA table
coefficients(m)
Model coefficients
coef(m)
Same as coefficients(m)
confint(m)
Confidence intervals for the regression coefficients 公卫人
deviance(m)
Residual sum of squares
effects(m)
Vector of orthogonal effects
fitted(m)
Vector of fitted y values
residuals(m)
Model residuals
resid(m)
Same as residuals(m)
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summary(m)
Key statistics, such as R2, the F statistic, and the residual standard error (σ)
vcov(m)
Variance-covariance matrix of the main parameters
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> m <- lm(y ~ u + v + w)
> summary(m)
Call:
lm(formula = y ~ u + v + w)
Residuals:
Min 1Q Median 3Q Max
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-3.3965 -0.9472 -0.4708 1.3730 3.1283
Coefficients:
Estimate Std. Error t value Pr(>|t|) 公卫家园
(Intercept) 1.4222 1.4036 1.013 0.32029
u 1.0359 0.2811 3.685 0.00106 ** 公卫百科
v 0.9217 0.3787 2.434 0.02211 *
w 0.7261 0.3652 1.988 0.05744 .
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---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.625 on 26 degrees of freedom 公卫家园
Multiple R-squared: 0.4981, Adjusted R-squared: 0.4402
F-statistic: 8.603 on 3 and 26 DF, p-value: 0.0003915
If you find it annoying to save the model in a variable, you are welcome to use one- 公卫家园
liners such as this:
> summary(lm(y ~ u + v + w))
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