Your script ran for me without error under the following configuration:
> sessionInfo()
R version 2.9.1 (2009-06-26)
i386-pc-mingw32
locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] plm_1.1-4 sandwich_2.2-1 zoo_1.5-6 MASS_7.2-47
[5] Formula_0.1-3 kinship_1.1.0-22 lattice_0.17-25 nlme_3.1-92
[9] survival_2.35-4
loaded via a namespace (and not attached):
[1] grid_2.9.1 tools_2.9.1
I suggest you upgrade to R 2.9.1.
By the way, I did not know about "plm". To find that function, I
proceeded as follows:
library(RSiteSearch)
plm. <- RSiteSearch.function('plm')
HTML(plm.)
This identified 82 different help pages in 11 different packages; 38
matches were found in the "plm" package, which contained a function
called "plm". I assume this is the one you were using.
Hope this helps.
Spencer Graves
Damien Moore wrote:
Hi List
I'm having difficulty understanding how plm should work with dynamic
formulas. See the commands and output below on a standard data set. Notice
that the first summary(plm(...)) call returns the same result as the second
(it shouldn't if it actually uses the lagged variable requested). The third
call results in error (trying to use diff'ed variable in regression)
Other info: I'm running R 2.7.2 on WinXP
cheers
*>data("Gasoline",package="Ecdat")
Gasoline_plm<-plm.data(Gasoline,c("country","year"))
pdim(Gasoline_plm)
**Balanced Panel: n=18, T=19, N=342
*
*>summary(plm(lgaspcar~lincomep,data=Gasoline_plm**))
**Oneway (individual) effect Within Model
Call:
plm(formula = lgaspcar ~ lincomep, data = Gasoline_plm)
Balanced Panel: n=18, T=19, N=342
Residuals :
Min. 1st Qu. Median 3rd Qu. Max.
-0.40100 -0.08410 -0.00858 0.08770 0.73400
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
lincomep -0.76183 0.03535 -21.551 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 17.061
Residual Sum of Squares: 6.9981
Multiple R-Squared: 0.58981
F-statistic: 464.442 on 323 and 1 DF, p-value: 0.036981
**> summary(plm(lgaspcar~lag(lincomep),data=Gasoline_plm))
**Oneway (individual) effect Within Model
Call:
plm(formula = lgaspcar ~ lag(lincomep), data = Gasoline_plm)
Balanced Panel: n=18, T=19, N=342
Residuals :
Min. 1st Qu. Median 3rd Qu. Max.
-0.40100 -0.08410 -0.00858 0.08770 0.73400
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
lag(lincomep) -0.76183 0.03535 -21.551 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 17.061
Residual Sum of Squares: 6.9981
Multiple R-Squared: 0.58981
F-statistic: 464.442 on 323 and 1 DF, p-value: 0.036981
*
*>summary(plm(lgaspcar~diff(lincomep),data=Gasoline_plm))*
*Error in model.frame.default(formula = lgaspcar ~ diff(lincomep), data =
mydata, :
variable lengths differ (found for 'diff(lincomep)')
*
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and provide commented, minimal, self-contained, reproducible code.
