Hi--I’m new to R. For a dissertation, my panel data is for 48 Sub-Saharan
countries (cross-sectional index=’i’) over 55 years 1960-2014 (time-series
index=’t’). The variables read into R from a text file are levels data. The
2SLS regression due to reverse causality will be based on change in the levels
data, so will need to difference the data grouped by cross-sectional index ‘i’.
There are nearly 50 total variables, but the model essentially will regress the
differenced Yit ~ X1it+X2it+X3it+X4it+X5it+X6it, with a dummy variable attached
to each of the change-X(s).
Due to missing data, R originally classified each X and Y variable as a
‘factor’, subsequently changed to ‘numeric’ via ‘as.numeric’ command.
However, when I write the following command for dplr solely to difference Yit
(=Yit-Yi[t-1]) mutated to new variable dYit, I receive error messages to the
effect that Yit and each of the X variables are ‘factors’.
>library (dplr)
>dt = CSUdata2 %>% group_by (i) %>% (dYit=Yit-lag(Yit))
‘CSUdata2’ is the object in which the tab-delimited text file dataset is
stored.
Questions:
Any idea why dplyr reads the variables as ‘factors’? A class(*) command per
variable shows R to know each Y and X as ‘numeric’.
Is the command to difference Yit done correctly? I plan to use the same
command for each variable requiring change until I understand the commands
better.
Thank you.
Sent from Windows Mail
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
