Warning message in R plm package regarding indexes that have the same length but not the same content

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I get a warning message from the plm package in R when I perform ´summary()´ of a model:

1: In Ops.pseries(y, bX) : indexes of pseries have same length but not same content: result was assigned first operand's index 2: In Ops.pseries(y, bX) : indexes of pseries have same length but not same content: result was assigned first operand's index

I used the following code:

library(dplyr)
library(lubridate)
library(plm)

data <- data.frame(ID = rep(c("123456", "234567", "345678", "456789", "567890", "678901", "789012", "890123", "901234","9012345"), each = 24),
                 month = rep(seq(dmy("01.01.2019"), dmy("01.12.2020"), by = "1 months"),10), group = rep(c(rep(T, 12), rep(F, 12)), 10),
                 temperature = runif(24*10, 0, 1)) %>% 
group_by(ID, group) %>% mutate(consumption = ifelse(group, runif(12, 1,2), runif(12,2,3))) 

pdata <- pdata.frame(x = data, index = c("ID", "month"))
model <- plm(formula = consumption ~  group + temperature, data = pdata, effect = "individual", model = "within") 
summary(model)
## Warnmeldungen:
## 1: In Ops.pseries(y, bX) :
##  indexes of pseries have same length but not same content: result was assigned first operand's index
## 2: In Ops.pseries(y, bX) :
##  indexes of pseries have same length but not same content: result was assigned first operand's index

My thought was that it could be one of the two indices. However, I get the same warning message when I either use "ID" or "month" as index.

An excerpt of the data feed in look like this: enter image description here

plm panel-data statistics r
2 Answers

It seems like plm or pdata.frame does not like some modifications injected into the data frame by some transformation you perform on the data prior to estimation.

Make sure to feed a clean data frame to pdata.frame like this and the code runs fine:

fdata <- data.frame(data)
pdata <- pdata.frame(x = fdata, index = c("ID", "month"))
model <- plm(formula = consumption ~  group + temperature, data = pdata, effect = "individual", model = "within") 
summary(model)

## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = consumption ~ group + temperature, data = pdata, 
##     effect = "individual", model = "within")
## 
## Balanced Panel: n = 10, T = 24, N = 240
## 
## Residuals:
##      Min.   1st Qu.    Median   3rd Qu.      Max. 
## -0.581113 -0.237459  0.031184  0.252256  0.541147 
## 
## Coefficients:
##              Estimate Std. Error  t-value Pr(>|t|)    
## groupTRUE   -1.020820   0.038559 -26.4743   <2e-16 ***
## temperature -0.029801   0.064738  -0.4603   0.6457    
## ---
## Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
## 
## Total Sum of Squares:    82.792
## Residual Sum of Squares: 20.318
## R-Squared:      0.75459
## Adj. R-Squared: 0.74275
## F-statistic: 350.521 on 2 and 228 DF, p-value: < 2.22e-16
user_46403460

Another way of solving the problem is to add ungroup().

So the following

group_by(ID, group) %>% mutate(consumption = ifelse(group, runif(12, 1,2), runif(12,2,3)))

should become

group_by(ID, group) %>% mutate(consumption = ifelse(group, runif(12, 1,2), runif(12,2,3))) %>% ungroup()
user_150309750
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