Re: [R] plm issues: error for within or random, but not for pooling
Dear Giovanni Thank you for the quick reply and sorry for not being able to respond in kind: since our last e-mail we decided to change the way we measure the variables, and this took some time. I managed to track down the original issue, I think, to an improperly specified subset vector to the data=df[ , ] argument. I guess this would count as a user error. Working with plm I encountered some other potential issues: - [, var] subsetting: on my data the following works fine summary(ibes.kld.df.p[ , ]$ibes1.delta1y.diff) total sum of squares : 2472.4 id time 0.289638 0.032026 but the below takes 100% CPU for about a minute, and then fails. summary(ibes.kld.df.p[ , ibes1.delta1y.diff]) Error in substring(blanks, 1, pad) : invalid substring argument(s) I am not sure what characteristics of my data causes this (perhaps many NAs?), but I cannot reproduce a dummy example based on EmplUK: data(EmplUK, package = plm) E - pdata.frame(EmplUK, index = c(firm, year), drop.index = TRUE,row.names = TRUE) summary(E$emp) total sum of squares : 261540 id time 0.9807654 0.0091085 summary(E[, emp]) ##in the dummy, both ways of subsetting work fine total sum of squares : 261540 id time 0.9807654 0.0091085 - p.value of coef t test == p.value of regression F test (for pooling and within, but not for random): x.pool - try(plm(get(x.ibes.diff1) ~ get(x.kld.diff1), ibes.kld.df.p, model=pooling)) summary(x.pool); x.ibes.diff1; x.kld.diff1 Oneway (individual) effect Pooling Model Call: plm(formula = get(x.ibes.diff1) ~ get(x.kld.diff1), data = ibes.kld.df.p, model = pooling) Unbalanced Panel: n=2336, T=1-15, N=9330 Residuals : Min. 1st Qu. Median 3rd Qu.Max. -5.4500 -0.1500 0.0799 0.2100 4.0500 Coefficients : Estimate Std. Error t-value Pr(|t|) (Intercept) -0.1199 0.0056 -21.4 2e-16 *** get(x.kld.diff1) 0.0297 0.0165 1.80.071 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares:2720 Residual Sum of Squares: 2720 F-statistic: 3.25802 on 1 and 9328 DF, p-value: 0.0711 [1] ibes2.delta12y.diff [1] kld.delta1y_prod.diff x.fe - try(plm(get(x.ibes.diff1) ~ get(x.kld.diff1), ibes.kld.df.p, model=within)) summary(x.fe); x.ibes.diff1; x.kld.diff1 Oneway (individual) effect Within Model Call: plm(formula = get(x.ibes.diff1) ~ get(x.kld.diff1), data = ibes.kld.df.p, model = within) Unbalanced Panel: n=2336, T=1-15, N=9330 Residuals : Min. 1st Qu. Median 3rd Qu.Max. -4.1000 -0.1200 0.0121 0.1600 4.1300 Coefficients : Estimate Std. Error t-value Pr(|t|) get(x.kld.diff1) 0.0324 0.01661.950.051 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares:1790 Residual Sum of Squares: 1780 F-statistic: 3.80843 on 1 and 6993 DF, p-value: 0.051 [1] ibes2.delta12y.diff [1] kld.delta1y_prod.diff I suppose that this is OK, since for the pooling case I can confirm it with the simple lm(), but I am not sure that I understand why this happens? x.simp - try(lm(get(x.ibes.diff1) ~ get(x.kld.diff1), ibes.kld.df.p)) summary(x.simp); x.ibes.diff1; x.kld.diff1 Call: lm(formula = get(x.ibes.diff1) ~ get(x.kld.diff1), data = ibes.kld.df.p) Residuals: Min 1Q Median 3Q Max -5.4501 -0.1501 0.0799 0.2099 4.0499 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) -0.1199 0.0056 -21.4 2e-16 *** get(x.kld.diff1) 0.0297 0.0165 1.80.071 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.54 on 9328 degrees of freedom (3966 observations deleted due to missingness) Multiple R-squared: 0.000349, Adjusted R-squared: 0.000242 F-statistic: 3.26 on 1 and 9328 DF, p-value: 0.0711 [1] ibes2.delta12y.diff [1] kld.delta1y_prod.diff For random, the two are different: x.re - try(plm(get(x.ibes.diff1) ~ get(x.kld.diff1), ibes.kld.df.p, model=random)) summary(x.re); x.ibes.diff1; x.kld.diff1 Oneway (individual) effect Random Effect Model (Swamy-Arora's transformation) Call: plm(formula = get(x.ibes.diff1) ~ get(x.kld.diff1), data = ibes.kld.df.p, model = random) Unbalanced Panel: n=2336, T=1-15, N=9330 Effects: var std.dev share idiosyncratic 0.255 0.505 0.88 individual0.036 0.190 0.12 theta : Min. 1st Qu. MedianMean 3rd Qu.Max. 0.0639 0.1620 0.2340 0.2640 0.4060 0.4340 Residuals : Min. 1st Qu. Median Mean 3rd Qu. Max. -5.24000 -0.14300 0.06630 -0.00171 0.19700 3.79000 Coefficients : Estimate Std. Error t-value Pr(|t|) (Intercept) -0.115100.00708 -16.26 2e-16 *** get(x.kld.diff1) 0.029350.015921.840.065 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares:2420 Residual Sum of Squares: 2420 F-statistic: -0.417224 on 1 and 9328 DF, p-value: 1 [1]
[R] plm issues: error for within or random, but not for pooling
Dear all I am working on unbalanced panel data and I can readily fit a pooling model using plm(), but not a within or random model. Reproducing the examples in vignette(plm) and in the AER package I encountered no such issues. ##unfortunately I cannot disclose the data, and it is too big anyway dim(ibes.kld.exp.p[x.subs , ]) [1] 1318934 summary(ibes.kld.exp.p[x.subs , ]$ibes1y.meanest) total sum of squares : 28058 id time 0.752284 0.018656 summary(ibes.kld.exp.p[x.subs , ]$employee_kld) total sum of squares : 9146.5 id time 0.637098 0.073421 ##fitting a pooling model works OK x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=pooling) summary(x) Oneway (individual) effect Pooling Model Call: plm(formula = ibes1y.meanest ~ employee_kld, data = ibes.kld.exp.p[x.subs, ], model = pooling) Unbalanced Panel: n=3041, T=1-16, N=13189 Residuals : Min. 1st Qu. Median 3rd Qu.Max. -6.530 -0.871 -0.189 0.629 13.200 Coefficients : Estimate Std. Error t-value Pr(|t|) (Intercept)1.5607 0.0127 122.73 2e-16 *** employee_kld 0.1118 0.01527.35 2.2e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares:28100 Residual Sum of Squares: 27900 F-statistic: 53.954 on 1 and 13187 DF, p-value: 2.17e-13 plmtest(x, individual) Lagrange Multiplier Test - (Honda) data: ibes1y.meanest ~ employee_kld normal = 1675.7, p-value 2.2e-16 alternative hypothesis: significant effects ##fitting a within or random model fails x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=within) Error in Tapply.matrix(x, effect, mean, ...) : subscript out of bounds x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=random) Error in Tapply.matrix(x, effect, mean, ...) : subscript out of bounds Would this be an issue with my data (which is a bit specific, since employee_kld is categorical)? Or perhaps there is an issue in plm() for unbalanced data? Please let me know your opinion Liviu sessionInfo() R version 2.10.1 (2009-12-14) x86_64-pc-linux-gnu locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_GB.UTF-8LC_COLLATE=en_GB.UTF-8 [5] LC_MONETARY=C LC_MESSAGES=en_GB.UTF-8 [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] tcltk grid splines stats graphics grDevices utils [8] datasets methods base other attached packages: [1] RcmdrPlugin.sos_0.2-0tcltk2_1.1-1 RcmdrPlugin.Export_0.3-0 [4] Hmisc_3.7-0 xtable_1.5-6 Rcmdr_1.5-5 [7] car_1.2-16 ggplot2_0.8.5digest_0.4.2 [10] reshape_0.8.3plyr_0.1.9 proto_0.3-8 [13] plm_1.2-3sandwich_2.2-5 zoo_1.6-2 [16] MASS_7.3-5 Formula_0.2-0kinship_1.1.0-23 [19] lattice_0.18-3 nlme_3.1-96 survival_2.35-8 [22] fortunes_1.3-7 sos_1.2-4brew_1.0-3 [25] hints_1.0.1-1 loaded via a namespace (and not attached): [1] cluster_1.12.1 tools_2.10.1 -- Do you know how to read? http://www.alienetworks.com/srtest.cfm http://goodies.xfce.org/projects/applications/xfce4-dict#speed-reader Do you know how to write? http://garbl.home.comcast.net/~garbl/stylemanual/e.htm#e-mail __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R: plm issues: error for within or random, but not for pooling
Dear Liviu, it's difficult to tell without seeing the data. I might guess that you have some completely empty groups about which Tapply complains when doing the time-demeaning, but it would be just a guess. I realize you can't share the data in the present form, but may I suggest you try and subset your data in some random way, find a problematic subset (one which gives the error) then change labels and everything so that the data become unrecognizable, and send us that example? You can also randomly transform them, as this is likely to be a missing values issue. Giovanni -Messaggio originale- Da: Liviu Andronic [mailto:landronim...@gmail.com] Inviato: giovedì 4 febbraio 2010 12:32 A: r-help@r-project.org Help Cc: yves.croiss...@let.ish-lyon.cnrs.fr; Millo Giovanni Oggetto: plm issues: error for within or random, but not for pooling Dear all I am working on unbalanced panel data and I can readily fit a pooling model using plm(), but not a within or random model. Reproducing the examples in vignette(plm) and in the AER package I encountered no such issues. ##unfortunately I cannot disclose the data, and it is too big anyway dim(ibes.kld.exp.p[x.subs , ]) [1] 1318934 summary(ibes.kld.exp.p[x.subs , ]$ibes1y.meanest) total sum of squares : 28058 id time 0.752284 0.018656 summary(ibes.kld.exp.p[x.subs , ]$employee_kld) total sum of squares : 9146.5 id time 0.637098 0.073421 ##fitting a pooling model works OK x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=pooling) summary(x) Oneway (individual) effect Pooling Model Call: plm(formula = ibes1y.meanest ~ employee_kld, data = ibes.kld.exp.p[x.subs, ], model = pooling) Unbalanced Panel: n=3041, T=1-16, N=13189 Residuals : Min. 1st Qu. Median 3rd Qu.Max. -6.530 -0.871 -0.189 0.629 13.200 Coefficients : Estimate Std. Error t-value Pr(|t|) (Intercept)1.5607 0.0127 122.73 2e-16 *** employee_kld 0.1118 0.01527.35 2.2e-13 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Total Sum of Squares:28100 Residual Sum of Squares: 27900 F-statistic: 53.954 on 1 and 13187 DF, p-value: 2.17e-13 plmtest(x, individual) Lagrange Multiplier Test - (Honda) data: ibes1y.meanest ~ employee_kld normal = 1675.7, p-value 2.2e-16 alternative hypothesis: significant effects ##fitting a within or random model fails x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=within) Error in Tapply.matrix(x, effect, mean, ...) : subscript out of bounds x - plm(ibes1y.meanest ~ employee_kld, ibes.kld.exp.p[x.subs , ], model=random) Error in Tapply.matrix(x, effect, mean, ...) : subscript out of bounds Would this be an issue with my data (which is a bit specific, since employee_kld is categorical)? Or perhaps there is an issue in plm() for unbalanced data? Please let me know your opinion Liviu sessionInfo() R version 2.10.1 (2009-12-14) x86_64-pc-linux-gnu locale: [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_GB.UTF-8LC_COLLATE=en_GB.UTF-8 [5] LC_MONETARY=C LC_MESSAGES=en_GB.UTF-8 [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] tcltk grid splines stats graphics grDevices utils [8] datasets methods base other attached packages: [1] RcmdrPlugin.sos_0.2-0tcltk2_1.1-1 RcmdrPlugin.Export_0.3-0 [4] Hmisc_3.7-0 xtable_1.5-6 Rcmdr_1.5-5 [7] car_1.2-16 ggplot2_0.8.5digest_0.4.2 [10] reshape_0.8.3plyr_0.1.9 proto_0.3-8 [13] plm_1.2-3sandwich_2.2-5 zoo_1.6-2 [16] MASS_7.3-5 Formula_0.2-0kinship_1.1.0-23 [19] lattice_0.18-3 nlme_3.1-96 survival_2.35-8 [22] fortunes_1.3-7 sos_1.2-4brew_1.0-3 [25] hints_1.0.1-1 loaded via a namespace (and not attached): [1] cluster_1.12.1 tools_2.10.1 -- Do you know how to read? http://www.alienetworks.com/srtest.cfm http://goodies.xfce.org/projects/applications/xfce4-dict#speed-reader Do you know how to write? http://garbl.home.comcast.net/~garbl/stylemanual/e.htm#e-mail Giovanni Millo Research Dept., Assicurazioni Generali SpA Via Machiavelli 4, 34132 Trieste (Italy) tel. +39 040 671184 fax +39 040 671160 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] plm Issues
Duh! Thanks and good advice. I was using 2.7.2 because it was, until recently, the latest version working with RPy (http://rpy.sourceforge.net/). Also didn't realize plm was still actively developed. Interesting that since plm now correctly handles diff and lag operations, it actually breaks with the behavior of lm: a=ts(c(1,2,4)) lm(a~diff(a)) Error in model.frame.default(formula = a ~ diff(a), drop.unused.levels = TRUE) : variable lengths differ (found for 'diff(a)') To regress a on its difference, one needs the more laborious: a=ts(c(1,2,4)) adata=as.data.frame(cbind(a,diff(a))) colnames(adata)=c(a,diffa) lm(a~diffa,data=adata) Call: lm(formula = a ~ diffa, data = adata) Coefficients: (Intercept)diffa 02 From the R help Fitting Linear ModelsUsing time series Considerable care is needed when using lm with time series. Unless na.action = NULL, the time series attributes are stripped from the variables before the regression is done. (This is necessary as omitting NAs would invalidate the time series attributes, and if NAs are omitted in the middle of the series the result would no longer be a regular time series.) Even if the time series attributes are retained, they are not used to line up series, so that the time shift of a lagged or differenced regressor would be ignored. It is good practice to prepare a data argument by ts.intersectts.union.html(..., dframe = TRUE), then apply a suitable na.action to that data frame and call lm with na.action = NULL so that residuals and fitted values are time series. On Sat, Jul 11, 2009 at 10:53 PM, milton ruser milton.ru...@gmail.comwrote: The first think one need to do when has a so old version, is update it :-) After, if the problem remain, try get help with the colleagues. best milton On Thu, Jul 9, 2009 at 10:58 AM, Damien Moore damienlmo...@gmail.comwrote: 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 -0.761830.03535 -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) -0.761830.03535 -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)') * [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.htmlhttp://www.r-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] plm Issues
The dyn and dynlm packages can handle time series in lm and glm. (dyn can also handle many additional regression functions as well) In the case of dyn just write dyn$lm instead of lm like this: library(dyn) a - ts(c(1, 2, 4)) dyn$lm(a ~ diff(a)) Call: lm(formula = dyn(a ~ diff(a))) Coefficients: (Intercept) diff(a) 02 On Mon, Jul 13, 2009 at 10:17 AM, Damien Mooredamienlmo...@gmail.com wrote: Duh! Thanks and good advice. I was using 2.7.2 because it was, until recently, the latest version working with RPy (http://rpy.sourceforge.net/). Also didn't realize plm was still actively developed. Interesting that since plm now correctly handles diff and lag operations, it actually breaks with the behavior of lm: a=ts(c(1,2,4)) lm(a~diff(a)) Error in model.frame.default(formula = a ~ diff(a), drop.unused.levels = TRUE) : variable lengths differ (found for 'diff(a)') To regress a on its difference, one needs the more laborious: a=ts(c(1,2,4)) adata=as.data.frame(cbind(a,diff(a))) colnames(adata)=c(a,diffa) lm(a~diffa,data=adata) Call: lm(formula = a ~ diffa, data = adata) Coefficients: (Intercept) diffa 0 2 From the R help Fitting Linear ModelsUsing time series Considerable care is needed when using lm with time series. Unless na.action = NULL, the time series attributes are stripped from the variables before the regression is done. (This is necessary as omitting NAs would invalidate the time series attributes, and if NAs are omitted in the middle of the series the result would no longer be a regular time series.) Even if the time series attributes are retained, they are not used to line up series, so that the time shift of a lagged or differenced regressor would be ignored. It is good practice to prepare a data argument by ts.intersectts.union.html(..., dframe = TRUE), then apply a suitable na.action to that data frame and call lm with na.action = NULL so that residuals and fitted values are time series. On Sat, Jul 11, 2009 at 10:53 PM, milton ruser milton.ru...@gmail.comwrote: The first think one need to do when has a so old version, is update it :-) After, if the problem remain, try get help with the colleagues. best milton On Thu, Jul 9, 2009 at 10:58 AM, Damien Moore damienlmo...@gmail.comwrote: 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)') * [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.htmlhttp://www.r-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the
Re: [R] plm Issues
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 -0.761830.03535 -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) -0.761830.03535 -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)')
*
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] plm Issues
The first think one need to do when has a so old version, is update it :-) After, if the problem remain, try get help with the colleagues. best milton On Thu, Jul 9, 2009 at 10:58 AM, Damien Moore damienlmo...@gmail.comwrote: 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 -0.761830.03535 -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) -0.761830.03535 -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)') * [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.htmlhttp://www.r-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] plm Issues
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 -0.761830.03535 -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) -0.761830.03535 -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)') * [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
