Panel data: dealing with lagging and binary dependent variable with plm

Question

I am attempting to run a pooled logistic regression with panel data and a binary dependent variable. Since I wanted to lag some of the variables, I used the plm package to create them. When I tried to do it other ways, I ran into problems. I can't use lag or embed, because it is panel data.

hybridsubsidies <-pdata.frame(reduced, c("state","year"))

lagee<-(lag(hybridsubsidies$eespending,1))
lagratio<-(lag(hybridsubsidies$ratio, 1))
laggopvote<-(lag(hybridsubsidies$gopvote, 1))
laggasoline<-(lag(hybridsubsidies$gasoline, 1))

I wanted to put all the variables into the original data frame (hybridsubsidies) before I ran the pooled analysis. I'm pretty sure I don't need to, but I'm a visual person, and would like to verify the format of the data is appropriate before running any analysis.

From the output below, it looks like everything is done correctly.

head(lag(hybridsubsidies$eespending,1))

ALABAMA-1999 ALABAMA-2000 ALABAMA-2001 ALABAMA-2002 ALABAMA-2003 ALABAMA-2004

     NA        58294        55378        26982        28264         2566 

head(hybridsubsidies$eespending)

ALABAMA-1999 ALABAMA-2000 ALABAMA-2001 ALABAMA-2002 ALABAMA-2003 ALABAMA-2004

  58294        55378        26982        28264         2566        26906 

My problem is that when I try and assign this lag variable as a vector in the data frame, this way,

hybridsubsidies$lagee<-(lag(hybridsubsidies$eespending,1))

it does so(when I call the names in the dataframe, they are included), but then I can no longer view the dataframe. R says to me:

Error in edit.data.frame(get(subx, envir = parent), title = subx, ...) : can only handle vector and factor elements

How can I solve this so that I can view the data frame before I run the analysis? I want to look at it, since it looks like I will have to use glm instead of plm (pooling) for this analysis since the dependent variable is a binary variable and plm does not support these d.v.'s

This has been giving me problems for awhile now.

col1 ST YR EELAG EE

[1,] 1 1 NA 58294

[2,] 1 2 58294 55378

[3,] 1 3 55378 26982

[4,] 1 4 26982 28264

[5,] 1 5 28264 2566

[6,] 1 6 2566 26906

[7,] 1 7 26906 29466

[8,] 2 1 NA 355

[9,] 2 2 355 259

[10,] 2 3 259 224

[11,] 2 4 224 217

[12,] 2 5 217 241

[13,] 2 6 241 231

[14,] 2 7 231 231

[15,] 3 1 NA 5111

[16,] 3 2 5111 3753

[17,] 3 3 3753 2211

[18,] 3 4 2211 1452

[19,] 3 5 1452 2913

[20,] 3 6 2913 3128

[21,] 3 7 3128 7132

[22,] 4 1 NA 1597

[23,] 4 2 1597 905

Answer 1

lag returns a time series object. Does

hybridsubsidies$lagee<-(as.vector(lag(hybridsubsidies$eespending,1)))

work?