当您在 Eviews 中进行回归时,您会得到如下统计数据面板:
R中有没有一种方法可以让我在一个列表中获得所有/大部分关于R回归的统计数据?
请参阅summary
,它将为大多数类别的回归对象生成摘要。
例如,来自help(glm)
:
> clotting <- data.frame(
+ u = c(5,10,15,20,30,40,60,80,100),
+ lot1 = c(118,58,42,35,27,25,21,19,18),
+ lot2 = c(69,35,26,21,18,16,13,12,12))
> summary(glm(lot1 ~ log(u), data = clotting, family = Gamma))
Call:
glm(formula = lot1 ~ log(u), family = Gamma, data = clotting)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.04008 -0.03756 -0.02637 0.02905 0.08641
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0165544 0.0009275 -17.85 4.28e-07 ***
log(u) 0.0153431 0.0004150 36.98 2.75e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.002446059)
Null deviance: 3.51283 on 8 degrees of freedom
Residual deviance: 0.01673 on 7 degrees of freedom
AIC: 37.99
Number of Fisher Scoring iterations: 3
R 对 GUI 程序的最大优势通常是函数的输出是可用的。所以你可以这样做:
> s = summary(glm(lot1 ~ log(u), data = clotting, family = Gamma))
> s$coefficients[1,]
Estimate Std. Error t value Pr(>|t|)
-1.655438e-02 9.275466e-04 -1.784749e+01 4.279149e-07
> s$cov.scaled
(Intercept) log(u)
(Intercept) 8.603427e-07 -3.606457e-07
log(u) -3.606457e-07 1.721915e-07
获取 t 和 p 以及所有参数或缩放协方差矩阵。但是请务必阅读文档以了解摘要方法,以确保您得到了您认为得到的结果。有时,返回对象中的内容可能会在转换后的比例上进行计算,并在打印对象时以未转换的比例呈现。
但是请注意,您似乎作为示例显示的是 ARIMA 模型,并且Rsummary
中的对象没有很好的功能arima
:
> m = arima(lh, order = c(1,0,1))
> summary(m)
Length Class Mode
coef 3 -none- numeric
sigma2 1 -none- numeric
var.coef 9 -none- numeric
mask 3 -none- logical
loglik 1 -none- numeric
aic 1 -none- numeric
arma 7 -none- numeric
residuals 48 ts numeric
call 3 -none- call
series 1 -none- character
code 1 -none- numeric
n.cond 1 -none- numeric
model 10 -none- list
这只是包含这些元素的列表对象的默认摘要。简单地打印它可以让你得到一些东西:
> m
Call:
arima(x = lh, order = c(1, 0, 1))
Coefficients:
ar1 ma1 intercept
0.4522 0.1982 2.4101
s.e. 0.1769 0.1705 0.1358
sigma^2 estimated as 0.1923: log likelihood = -28.76, aic = 65.52
如果 m 是您lm
生成的模型,只需执行以下操作:summary(m)
获取所有这些模型统计信息和数字。