我从systemfit()得到了结果,并尝试将每个方程的系数可视化。
我使用coefplot()并避免使用“$”,我写了coefplot(summary(fitsur)[["eq"]][1])
。
但是,我仍然得到错误“$ operator对原子向量无效”。
你能帮我避免这个错误吗?
fitsur <- systemfit(list(EQ1 = rp1, EQ2 = rp2, EQ3 = rp3, EQ4 = rp6), data=data)
> summary(fitsur)[["eq"]][1]
[[1]]
OLS estimates for 'EQ1' (equation 1)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.081244665 0.820103650 -2.53778 0.0114454 *
ID110 0.110331812 0.068478736 1.61118 0.1077442
ID130 0.022217695 0.024279274 0.91509 0.3605675
FPP01 -0.022953616 0.032193881 -0.71298 0.4761763
DEP04 0.005921742 0.002822725 2.09788 0.0363958 *
Inc_Consensus 2.004021829 0.932511596 2.14906 0.0320889 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.483239 on 522 degrees of freedom
Number of observations: 535 Degrees of Freedom: 522
SSR: 1148.399037 MSE: 2.199998 Root MSE: 1.483239
Multiple R-Squared: 0.069067 Adjusted R-Squared: 0.047666
> coefplot(summary(fitsur)[["eq"]][1])
Error: $ operator is invalid for atomic vectors
答案 0 :(得分:2)
在我看来,coefplot(fitsur$eq[[1]])
可能会解决您的问题。
以下是可重现的例子:
library(systemfit)
library(coefplot)
# this paragraph was borrowed from the systemfit manual
data( "Kmenta" )
eqDemand <- consump ~ price + income
eqSupply <- consump ~ price + farmPrice + trend
system <- list( demand = eqDemand, supply = eqSupply )
## OLS estimation
fitols <- systemfit( system, data=Kmenta )
names(fitsur) #
# [1] "eq" "call" "coefficients" "coefCov" "residCovEst"
# [6] "residCov" "method" "rank" "df.residual" "iter"
# [11] "control" "panelLike"
str(fitsur$eq,list.len=2)
# List of 2
# $ :List of 14
# ..$ eqnNo : int 1
# ..$ eqnLabel : chr "demand"
# .. [list output truncated]
# ..- attr(*, "class")= chr "systemfit.equation"
# $ :List of 14
# ..$ eqnNo : int 2
# ..$ eqnLabel : chr "supply"
# .. [list output truncated]
# ..- attr(*, "class")= chr "systemfit.equation"
coefplot(fitsur$eq[[1]])
# Hit <Return> to see next plot:
# ...shows the plot...