Error code with choiceAnalysis_settings
Posted: 19 Apr 2021, 17:49
I'm analysing RP data from two case studies, but am getting an error when it comes to "choiceAnalysis_settings".
keeps giving:
Error in "Gabon" + "Age" : non-numeric argument to binary operator
The variables are all numeric, I've checked multiple times. I've also tried different (also numeric) variables, and still get the same error. Any insight would be appreciated!
Full Code:
Code: Select all
#### ANALYSIS OF CHOICES
choiceAnalysis_settings <- list(
alternatives = c(farm=0, scare=1, kill=2, conserve=3),
avail = 1,
choiceVar = moddata3$Choice,
explanators = moddata3[,c("Gabon" + "Age" + "Female" + "Education" + "Farmer" +
"CropDam" + "meanequity" + "meantrust" + "meanfam")]
)Error in "Gabon" + "Age" : non-numeric argument to binary operator
The variables are all numeric, I've checked multiple times. I've also tried different (also numeric) variables, and still get the same error. Any insight would be appreciated!
Full Code:
Code: Select all
#### LOAD LIBRARY AND DEFINE CORE SETTINGS
### Clear memory
rm(list = ls())
### Initialise code
apollo_initialise()
### Set core controls
apollo_control = list(
modelName ="Apollo_synthesis",
modelDescr ="Analysise game synthesis with Apollo",
indivID ="HHID"
)
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS
mydata<-read_csv(here("dataset_2pt1.csv"))
## modify dataset
moddata <- mydata %>%
select(Treatments, Country, GameID, Round, Age, Education, Trust_comm_1, Trust_comm_2,
Equity_1, Equity_2, Rel1, Rel2, Rel3, Kill, Cons, HHID,
CC1, CC2, CC3, CC4, CC5, CC6, CC7, CC8, CC9,
decision_time, Animal_count, Exp_crop_damage, Prime_Occ, Gender, Goal_games) %>%
mutate(Farmer = ifelse(Prime_Occ %in% 1, 1, 0)) %>%
filter(Treatments!="P") %>%
mutate(CropDam = ifelse(Country %in% "Scotland" & Exp_crop_damage %in% 0-30, "1",
ifelse(Country %in% "Scotland" & Exp_crop_damage %in% 31-60, "2",
ifelse(Country %in% "Gabon" & Exp_crop_damage %in% 1, "1",
ifelse(Country %in% "Gabon" & Exp_crop_damage %in% 2, "2",
ifelse(Country %in% "Gabon" & Exp_crop_damage %in% 3, "3", "3")))))) %>%
mutate(Goal_games1 = ifelse(Goal_games %in% 1, "Win",
ifelse(Goal_games %in% 2, "Benefit group",
ifelse(Goal_games %in% 3, "Real life",
ifelse(Goal_games %in% 4, "Fun", "Other"))))) %>%
mutate(Treatments = ifelse(Treatments=="T2", "Baseline",
ifelse(Treatments=="T4", "Deterrents",
ifelse(Treatments=="T3", "Subsidy",
ifelse(Treatments=="T5", "Agglomeration", "")))),
Treatments=factor(Treatments, levels=c("Baseline",
"Deterrents",
"Subsidy",
"Agglomeration"))) %>%
na.omit() %>%
mutate(GameID=factor(GameID),
Country=factor(Country),
Gender=factor(Gender),
Goal_games=factor(Goal_games),
CropDam=as.numeric(CropDam),
meantrust = rowMeans(select(., Trust_comm_1, Trust_comm_2)),
meanequity = rowMeans(select(., Equity_1, Equity_2)),
meanfam = rowMeans(select(., Rel1, Rel2, Rel3))) %>%
group_by(Treatments, GameID, Round) %>%
mutate(Kill_mean = mean(Kill),
Kill_lag=lag(Kill_mean, 4, na.rm=TRUE),
Cons_mean =mean(Cons),
Cons_lag=lag(Cons, 4, na.rm=TRUE)) %>%
ungroup() %>%
mutate(Kill_lag=lag(Kill_mean, 4, na.rm=TRUE),
Cons_lag=lag(Cons, 4, na.rm=TRUE))
### combine the cell choice columns into a single column with separate rows for cell
moddata2 <- pivot_longer(moddata, CC1:CC9, names_to = "CC", values_to = "Choice")
moddata3 <- moddata2 %>%
select(Treatments, Country, GameID, Round, Age, Education, Kill, Cons, HHID, Farmer,
CC, Cons_mean, Cons_lag, Kill_lag, Kill_mean, meanequity, meanfam, meantrust, Choice,
decision_time, Animal_count, CropDam, Prime_Occ, Gender, Goal_games) %>%
mutate(Gabon = ifelse(Country=="Gabon", "1", "0")) %>%
mutate(Female = ifelse(Gender=="Female", "1", "0")) %>%
mutate(CC=factor(CC),
Gabon=as.numeric(Gabon),
Female=as.numeric(Female),
HHID=factor(HHID))
#### ANALYSIS OF CHOICES
choiceAnalysis_settings <- list(
alternatives = c(farm=0, scare=1, kill=2, conserve=3),
avail = 1,
choiceVar = moddata3$Choice,
explanators = moddata3[,c("Gabon" + "Age" + "Female" + "Education" + "Farmer" +
"CropDam" + "meanequity" + "meantrust" + "meanfam")]
)