the argument inference = FALSE and to.remap = FALSE leads to and error in plot(eventstudy)

[Toodlepoodle]

While using the eventstudy package if we put inference = FALSE it leads to an error when trying to use the plot() function within the package
Here is a MWE for the issue:

library(zoo)
library(eventstudies)

time.dt  <- data.frame(name=c("A","B"),
                       when=as.Date(c("2014-01-01","2014-01-01")))


gdp.dt  <- data.frame(state=rep(c("A","B"),each=5),
                      year=as.Date(rep(c("2012-01-01","2013-01-01","2014-01-01","2015-01-01","2016-01-01"),each = 1)),
                      gdp= c(100,100,100,160,165,50,50,50,80,85))

dtlist <- split(gdp.dt, gdp.dt$state)

z_list <- lapply(dtlist, function(x) zoo(x$gdp, order.by = x$year))

exp.zoo  <- do.call(merge, z_list)

### eventstudy without levels,with original values
es <- eventstudies::eventstudy(firm.returns = exp.zoo,
         event.list = time.dt,
         event.window = 2,
         is.levels=FALSE,
         type = "None",
         to.remap = FALSE,
         inference = TRUE
         )
pdf("eventstudy_inference_true.pdf")
plot(es)
dev.off()
### eventstudy with levels, percentage change 
es_inf_false <- eventstudies::eventstudy(firm.returns = exp.zoo,
         event.list = time.dt,
         event.window = 2,
         is.levels=FALSE,
         type = "None",
         to.remap = FALSE,
         inference = FALSE
     )
plot(es_inf_false)

eventstudy_inference_true.pdf
The attached file is the plot created when we keep inference = TRUE
In the following case where inference = FALSE we get the following error

Error in xy.coords(x, y, xlabel, ylabel, log) : 
  'x' and 'y' lengths differ

The eventstudy value in the case of inference = FALSE (i.e es_inf_false) comes out to be

     1  2
-1 100 50
0  100 50
1  160 80
2  165 85

[Toodlepoodle]

While using the eventstudy package only if we put inference = FALSE and to.remap = FALSE it leads to an error when trying to use the plot() function within the package
Following is an MWE of the issue:

library(zoo)
library(eventstudies)

time.dt  <- data.frame(name=c("A","B"),
                       when=as.Date(c("2014-01-01","2014-01-01")))


gdp.dt  <- data.frame(state=rep(c("A","B"),each=5),
                      year=as.Date(rep(c("2012-01-01","2013-01-01","2014-01-01","2015-01-01","2016-01-01"),each = 1)),
                      gdp= c(100,100,100,160,165,50,50,50,80,85))

dtlist <- split(gdp.dt, gdp.dt$state)

z_list <- lapply(dtlist, function(x) zoo(x$gdp, order.by = x$year))

exp.zoo  <- do.call(merge, z_list)

### eventstudy without levels,with original values
es <- eventstudies::eventstudy(firm.returns = exp.zoo,
         event.list = time.dt,
         event.window = 2,
         is.levels=FALSE,
         type = "None",
         to.remap = TRUE,
         inference = TRUE
         )
pdf("eventstudy_inference_true.pdf")
plot(es)
dev.off()
### eventstudy with levels, percentage change 
es_inf <- eventstudies::eventstudy(firm.returns = exp.zoo,
         event.list = time.dt,
         event.window = 2,
         is.levels=FALSE,
         type = "None",
         to.remap = TRUE,
         inference = FALSE
     )
plot(es_inf)

### eventstudy ; to.remap = FALSE , inference = FALSE
es_inf_false <- eventstudies::eventstudy(firm.returns = exp.zoo,
         event.list = time.dt,
         event.window = 2,
         is.levels=FALSE,
         type = "None",
         to.remap = FALSE,
         inference = FALSE
         )
plot(es_inf_false)

[Toodlepoodle]

Need to update the documentation:
While using the eventstudies package - If we put inference = FALSE and to.remap = FALSE the output we get is the just values for each unique id, at each event time. No further function is performed on the values and the mean at each event time is not calculated.This is why it leads to an error when the plot function is called.
The fact that this particular combination of arguments results in just physical date being converted to event dates and nothing else is not mentioned in the documentation.
Following is an MWE of the issue

# Loading relevant packages
library(zoo)
library(tidyverse)
library(dplyr)
library(eventstudies)

### Creating zoo object for performing eventstudy
Godda <- c(0.2194192, 0.2441143, 0.2222089, 0.2684767, 0.3066328, 0.3778628, 0.4764268, 0.6418671, 0.9171428, 1.0000000)
Durg <- c(0.9304014, 0.9425652, 0.9726595, 0.9823686, 0.8884803, 0.8591888, 0.9447556, 0.9688862, 0.9470548, 1.0000000)
Deen_Bandhu <- c(0.6331619, 0.7526251, 0.8107307, 0.7813707, 0.8360146, 0.8880688, 0.8970987, 0.9737993, 0.9030149, 1.0000000)
Panipat <- c(0.8869552, 1.0000000, 0.9307962, 0.8718149, 0.8376134, 0.8404316, 0.8766181, 0.9448039, 0.8654426, 0.8146210)
Meramandali <- c(0.8433920, 0.7743240, 0.8377193, 1.0000000, 0.6348767, 0.6395256, 0.6178923, 0.5794285, 0.5949299, 0.6380138)
# Creating the date vector
dates <- as.Date(c("2012-01-01", "2013-01-01", "2014-01-01", "2015-01-01", "2016-01-01",
                   "2017-01-01", "2018-01-01", "2019-01-01", "2020-01-01", "2021-01-01"))
# Creating the zoo object
project_radiance_zoo <- zoo(cbind(Godda, Durg, Deen_Bandhu, Panipat, Meramandali), order.by = dates)

### Creating the event date dataframe
name <- c("Godda", "Durg", "Deen_Bandhu", "Panipat", "Meramandali")
when <- c("2017-08-31", "2017-03-31", "2015-10-25", "2015-04-17", "2015-02-12")
# create the dataframe
event_date <- data.frame(name,when)
event_date$when <- as.Date(event_date$when)

### Eventstudies

# Case - I to.remap = TRUE , remap = "cumsum"  inference = TRUE
es1 <- eventstudy(firm.returns = project_radiance_zoo,
                 event.list = event_date,
                 event.window = 3,
                 type = "None",
                 to.remap = TRUE,
                 remap = "cumsum",
                 is.levels = TRUE, 
                 inference = TRUE,
                 inference.strategy = "bootstrap")

es1

# Case - II to.remap = TRUE , remap = "cumprod"  inference = TRUE
es2 <- eventstudy(firm.returns = project_radiance_zoo,
                 event.list = event_date,
                 event.window = 3,
                 type = "None",
                 to.remap = TRUE,
                 remap = "cumprod",
                 is.levels = TRUE, 
                 inference = TRUE,
                 inference.strategy = "bootstrap")

es2

# Case - III to.remap = TRUE , remap = "cumprod"  inference = FALSE
es3 <- eventstudy(firm.returns = project_radiance_zoo,
                 event.list = event_date,
                 event.window = 3,
                 type = "None",
                 to.remap = TRUE,
                 remap = "cumprod",
                 is.levels = TRUE, 
                 inference = FALSE)

es3

# Case - IV to.remap = FALSE ,  inference = TRUE
es4 <- eventstudy(firm.returns = project_radiance_zoo,
                 event.list = event_date,
                 event.window = 3,
                 type = "None",
                 to.remap = FALSE,
                 is.levels = TRUE, 
                 inference = TRUE,
                 inference.strategy = "bootstrap")

es4

# Case - V to.remap = FALSE ,  inference = FALSE
es5 <- eventstudy(firm.returns = project_radiance_zoo,
                 event.list = event_date,
                 event.window = 3,
                 type = "None",
                 to.remap = FALSE,
                 is.levels = TRUE, 
                 inference = FALSE,
                 inference.strategy = "bootstrap")

es5
plot(es5)