From google am trying to understand "simmer" package in R for discrete event simulation.
As per the example given in the link, am trying to create the simulator using create_simulator function. but am getting the below error
Code Used:
trajectory <- read.table(header=T, text= "event_id description resource amount duration successor 1 registration administration 1 runif(1,3,10) 2 2 intake nurse 1 runif(1,10,20) 3 3 consultation doctor 1 runif(1,5,15) NA" )
sim <- create_simulator(name = "SuperDuperSim") %>% add_trajectory(name = "simple_trajectory", trajectory_df = trajectory) %>% add_resource(name = "administration", capacity = 1) %>% add_resource(name = "nurse", capacity = 1) %>% add_resource(name = "doctor", capacity = 2) %>% add_entities_with_interval(n = 10, name_prefix = "patient", trajectory_name = "simple_trajectory", interval = "rnorm(1,10)") %>% replicator(15)
Error:
Error in eval(expr, envir, enclos) :
could not find function "create_simulator"
I have never used this package before but it appears to me that the create_simulator function does not exist in the current version of the package (the add_trajectory function doesn't seem to exist too). Information about the simmer package is available at https://github.com/r-simmer/simmer. More specifically, an introduction is given at: https://cran.r-project.org/web/packages/simmer/vignettes/A-introduction.html. It appears to me from this site that the creation of the simulator is now done by typing simmer(nameOfMySimulator).
Related
I am looking for some help in resolving an error using the partial least squares path modeling package ('plspm').
I can get results running a basic PLS-PM analysis but run into issues when using the grouping function, receiving the error message:
Error in if (w_dif < specs$tol || iter == specs$maxiter) break : missing value where TRUE/FALSE needed
I have no missing values and all variables have the proper classification. Elsewhere I read that there is a problem with processing observations with the exact same values across all variables, I have deleted those and still face this issue. I seem to be facing the issue only when I run the groups using the "bootstrap" method as well.
farmwood = read.csv("farmwood_groups(distance).csv", header = TRUE) %>%
slice(-c(119:123))
Control = c(0,0,0,0,0,0)
Normative = c(0,0,0,0,0,0)
B_beliefs = c(0,0,0,0,0,0)
P_control = c(1,0,0,0,0,0)
S_norm = c(0,1,0,0,0,0)
Behavior = c(0,0,1,1,1,0)
farmwood_path = rbind(Control, Normative, B_beliefs, P_control, S_norm, Behavior)
colnames(farmwood_path) = rownames(farmwood_path)
farmwood_blocks = list(14:18,20:23,8:13,24:27,19,4:7)
farmwood_modes = rep("A", 6)
farmwood_pls = plspm(farmwood, farmwood_path, farmwood_blocks, modes = farmwood_modes)
ames(farmwood)[names(farmwood) == "QB3"] <- "Distance"
farmwood$Distance <- as.factor(farmwood$Distance)
distance_boot = plspm.groups(farmwood_pls, farmwood$Distance, method = "bootstrap")
distance_perm = plspm.groups(farmwood_pls, farmwood$Distance, method = "permutation")
The data is contained here:
https://www.dropbox.com/s/8vewuupywpi1jkt/farmwood_groups%28distance%29.csv?dl=0
Any help would be appreciated. Thank you in advance
I am trying to calculate a Synthetic control using the tidysynth package. I'm fairly new to the package and the data but here is my code:
#Import data
synth <- read.csv("https://raw.githubusercontent.com/FDobkin/coal_paper/main/synth_data.csv")
#Convert year to date
synth$year <- strptime(synth$year, format = "%Y")
synth_out <- synth %>%
synthetic_control(
outcome=saleprice,
unit=fips,
time=year,
i_unit=47145,
i_time=2009-10-19,
generate_placebos=T
) %>%
generate_predictor(
time_window=2000-10-19:2009-10-19,
population = pop,
white = white_p,
age = age65p_p,
rucc = rucc_code,
income = median_income,
unemploy = unemprate,
laborforce = lfrate
) %>%
generate_weights(optimization_window = 2000-10-19:2009-10-19, # time to use in the optimization task
margin_ipop = .02,sigf_ipop = 7,bound_ipop = 6 # optimizer options
)
%>%
generate_control()
The error is:
Error in synth_method(treatment_unit_covariates = treatment_unit_covariates, :
Please specify only one treated unit.
The error seems to becoming from the generate_weights() statement. I am specifying the specific county that is receiving the treatment in the synthetic_control() statement. What is the error noting is wrong?
When trying to use the cSPADE algorithm from the arulesSequences package in R I always receive the following error:
Error in makebin(data, file) : 'sid' invalid (order)
I think the problem is related to the arrangement of the data somehow but I really don't know what the problem may be.
A similar problem was presented here but the solution that I tried to incorporate by using
arrange(sessions#itemsetInfo,sequenceID,eventID)
did not change anything. Thanks for your help!
#This is how I pre-processed the data
load("example.Rda")
example.Rda %>% head(5) %>% knitr::kable()
str(example.Rda)
example.Rda = as.data.frame(example.Rda)
sessions <- as(example.Rda %>% transmute(items = question_type), "transactions")
transactionInfo(sessions)$sequenceID <- example.Rda$user_id
transactionInfo(sessions)$eventID <- example.Rda$item_id
itemLabels(sessions) <- str_replace_all(itemLabels(sessions), "items=", "")
inspect(head(sessions,10))
arrange(sessions#itemsetInfo,sequenceID,eventID)
str(sessions)
View(sessions)
#This is the definition of the itemsets after which the error occurs
itemsets <- cspade(sessions,
parameter = list(support = 0.001),
control = list(verbose = FALSE))
I'm solving the exercises from book Forecasting:Principles and Practice 3rd edition
On chapter 7 ex 1 I want to filter Jan 2014 month from tsibbledata:vic_elec and summarise data by day, here's the code :
jan14_vic_elec <- vic_elec %>%
filter(yearmonth(Time) == yearmonth("2014 Jan")) %>%
index_by(Date = as_date(Time)) %>%
summarise(
Demand = sum(Demand),
Temperature = max(Temperature)
)
This chunk on the filter() functions gives an error :
Error: Problem with filter() input ..1. i Input ..1 is
yearmonth(Time) == yearmonth("2014 Jan"). x function
'Rcpp_precious_remove' not provided by package 'Rcpp'
Can somebody help ?
Open a new r window, and do this.
It should work!
Main issue is you have some package clashes. Hence start in a new window
library(fpp3)
jan14_vic_elec <- vic_elec %>%
filter(yearmonth(Time) == yearmonth("2014 Jan")) %>%
index_by(Date = as_date(Time)) %>%
summarise(
Demand = sum(Demand),
Temperature = max(Temperature)
)
everything good?
During that week I spent time writing a script that even this morning seemed to work. but then I tried to run it again and exactly in a part that uses the function "summarize" of the package dplyr appears an error that I had never seen.
Below is an excerpt of the code I used and the error on the console:
library(tidyverse)
a <- c(1,0,1,1,0,1,1,1,1,0,0)
b <-c( 0.9157101,
0.4854955,
0.8853174,
0.4373646,
0.3855175,
0.8603407,
0.9193342,
0.4693117,
0.9849855,
0.4458159,
0.4379776)
c <- c(8,2,7,1,0,6,8,1,9,1,1)
treated_data <- data.frame(Risk = a ,
Model_Predicted = b,
Grupo = c)
calculo <- treated_data %>% group_by(Grupo) %>% summarise(Quantidade = n(),
Non_event = sum(Risk),
Event = n() - sum(Risk))
Console Result:
---------------------------------------------------------
Error in n() : argument "vec" is missing, with no default
---------------------------------------------------------