Item-count technique/List experiment

Question

I would like to conduct multivariate regressions analyses of survey data with the item count technique using ictreg but without success as I keep seeing the following error message "Error in xtfrm.data.frame(x) : cannot xtfrm data frames". I am very new to R and not sure how to fix the issue.

Please find below the sample data on which I run the code on (only 20 observations, I don't have access to the remaining dataset), as well as the code.

structure(list(sex = structure(c(1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 1), label = "1=female)", format.stata = "%9.0g", labels = c(`0. Male` = 0, 
`1. Female` = 1), class = c("haven_labelled", "vctrs_vctr", "double"
)), age = structure(c(2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743), label = "Age (year)", format.stata = "%2.0f"), 
    P217_OCCUPATION = structure(c(9, 5, 7, 5, 5, 9, 5, 5, 5, 
    5, 8, 9, 9, 9, 5, 5, 8, 9, 5, 5), label = "P217 Occupation", format.stata = "%52.0f", labels = c(`1. LEGISLATORS,SENIOR OFFICIALS AND MANAGERS` = 1, 
    `2. PROFESSIONALS` = 2, `3. TECHNICIANS AND ASSOCIATE PROFESSIONALS` = 3, 
    `4. CLERKS` = 4, `5. SERVICE WORKERS AND SHOP AND MARKET SALES WORKERS` = 5, 
    `6. SKILLED AGRICULTURAL AND FISHERY WORKERS` = 6, `7. CRAFTS AND RELATED TRADES WORKERS` = 7, 
    `8. PLANT AND MACHINE OPERATORS ASSEMBLERS` = 8, `9. ELEMENTARY OCCUPATION` = 9, 
    `10. NOT STATED` = 10), class = c("haven_labelled", "vctrs_vctr", 
    "double")), industryGroup = structure(c(7, 5, 7, 5, 5, 7, 
    5, 7, 7, 7, 6, 6, 7, 8, 5, 7, 7, 7, 5, 5), label = "P3102 Industry Group", format.stata = "%45.0f", labels = c(`1. Agriculture, Hunting,.Forestry and Fishing` = 1, 
    `2. Mining and Quarrying` = 2, `3. Manufacturing` = 3, `4. Construction` = 4, 
    `5. Trade, Hotels & Restaurants` = 5, `6. Transport` = 6, 
    `7. Community & Personal Services` = 7, `8. Not Stated` = 8
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    ownership = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1), label = "What is the form of ownership of this activity/enterprise?", format.stata = "%46.0f", labels = c(`1.  Sole ownership or members of the household` = 1, 
    `2.  Partnership` = 2, `3.   Other /specify/` = 3, `9. Not stated` = 9
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    bookAcct = structure(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0), label = "Have book of account", format.stata = "%34.0f", labels = c(`1. Yes some accounts, but not full` = 1, 
    `2.  No, no accounts kept` = 2), class = c("haven_labelled", 
    "vctrs_vctr", "double")), easyAvoid = structure(c(1, 1, NA, 
    1, 0, 1, 1, 1, 1, 1, 1, 1, 0, NA, 0, 0, 0, 0, 1, 1), label = "Difficult evade (y/n)", format.stata = "%29.0f", labels = c(`1. Very easy` = 1, 
    `2. Easy` = 2, `3. Neither easy nor difficult` = 3, `4. Difficult` = 4, 
    `5. Very difficult` = 5, `98. .Don?t know` = 98, `99. Refuse to answer` = 99
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    easyComply = structure(c(0, 0, 0, 0, 1, 0, NA, 0, 0, 0, 1, 
    0, 0, NA, 0, 1, 0, 0, 0, 0), label = "Easy comply tax obl (y/n)", format.stata = "%29.0f", labels = c(`1. Very easy` = 1, 
    `2. Easy` = 2, `3. Neither easy nor difficult` = 3, `4. Difficult` = 4, 
    `5. Very difficult` = 5, `98. .Don?t know` = 98, `99. Refuse to answer` = 99
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    trustGVT = structure(c(0, 1, NA, 0, 0, 1, 1, 1, 1, 1, 0, 
    1, 1, 1, 1, 1, 0, 0, NA, 0), label = "Trust government (y/n)", format.stata = "%24.0f", labels = c(`1. A great deal of trust` = 1, 
    `2. Trust somewhat` = 2, `3. Trust just a little` = 3, `4. No trust at all` = 4, 
    `5. Have never heard of` = 5, `98. Don?t know` = 98, `99. Refuse to answer` = 99
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    moral0 = structure(c(1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1), label = "Should always pay taxes (y/n)", format.stata = "%46.0f", labels = c(`1. Agree much more with the FIRST statement` = 1, 
    `2. Agree a bit more with the FIRST statement` = 2, `3.  Agree a bit more with the SECOND statement` = 3, 
    `4. Agree much more with the SECOND statement` = 4, `5. Agree with neither statement` = 5, 
    `888.  Refuse to answer` = 888, `999.  Don?t know` = 999), class = c("haven_labelled", 
    "vctrs_vctr", "double")), opinion1 = structure(c(1, 1, 0, 
    1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0), label = "Wrong not paying tax (y/n)", format.stata = "%27.0f", labels = c(`1.  Not wrong at all` = 1, 
    `2. Wrong but understandable` = 2, `3. Wrong and punishable` = 3, 
    `98. Don?t know` = 98, `99. Refuse to answer` = 99), class = c("haven_labelled", 
    "vctrs_vctr", "double")), opinion2 = structure(c(1, 1, 1, 
    0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, NA), label = "Tax auth right to make pay (y/n)", format.stata = "%29.0f", labels = c(`1.  Completely agree` = 1, 
    `2. Somewhat agree` = 2, `3. Neither agree nor disagree` = 3, 
    `4. Somewhat disagree` = 4, `5. Completely disagree` = 5, 
    `98. Don?t know` = 98, `99. Refuse to answer` = 99), class = c("haven_labelled", 
    "vctrs_vctr", "double")), moral1 = structure(c(0, 1, 1, 0, 
    1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, NA), label = "Refuse to pay until better service (y/n)", format.stata = "%29.0f", labels = c(`1.  Completely agree` = 1, 
    `2. Somewhat agree` = 2, `3. Neither agree nor disagree` = 3, 
    `4. Somewhat disagree` = 4, `5. Completely disagree` = 5, 
    `98. Don?t know` = 98, `99. Refuse to answer` = 99), class = c("haven_labelled", 
    "vctrs_vctr", "double")), direct_reporting = structure(c(0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, NA, 0, 0), label = "Do you always pay all taxes you owe to the government?", format.stata = "%19.0f", labels = c(No = 0, 
    Yes = 1), class = c("haven_labelled", "vctrs_vctr", "double"
    )), head = structure(c(0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
    0, 1, 1, 1, 1, 1, 1, 1), label = "Owner is HH head", format.stata = "%9.0g"), 
    norm = structure(c(0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 
    NA, 1, 1, 0, 0, 0, 0), label = "Social costs (y/n)", format.stata = "%27.0g", labels = c(`1. Very likely` = 1, 
    `2. Likely` = 2, `3. May or may not be likely` = 3, `4. Unlikely` = 4, 
    `5. Very unlikely` = 5, `98. Don?t know` = 98, `99. Refuse to answer` = 99
    ), class = c("haven_labelled", "vctrs_vctr", "double")), 
    treat = structure(c(0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 
    0, 1, 1, 0, 1, 0, 1), label = "RECODE of expgroup", format.stata = "%12.0g", labels = c(Control = 0, 
    Treatment = 1), class = c("haven_labelled", "vctrs_vctr", 
    "double")), married = structure(c(1, 1, 0, 0, 1, 1, 0, 0, 
    0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0), label = "Married", format.stata = "%9.0g"), 
    secondary = structure(c(0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 
    1, 1, 0, 0, 0, 0, 1, 0, 0), format.stata = "%9.0g"), log_hhsizeTot = structure(c(1.0986123085022, 
    1.0986123085022, 1.7917594909668, 0.6931471824646, 0.6931471824646, 
    1.0986123085022, 1.3862943649292, 0, 0, 1.3862943649292, 
    0.6931471824646, 1.3862943649292, 1.3862943649292, 1.3862943649292, 
    0.6931471824646, 1.3862943649292, 1.0986123085022, 0, 0, 
    1.3862943649292), format.stata = "%9.0g"), log_GROSS_VALUE = structure(c(11.1462001800537, 
    10.1105012893677, 9.07107830047607, 9.61580562591553, 9.50300979614258, 
    8.4990291595459, 11.0020999908447, 9.80972576141357, 9.9776668548584, 
    10.2691717147827, 11.0974102020264, 10.4744672775269, 9.95227813720703, 
    8.64822101593018, 10.1464338302612, 11.1844215393066, 10.5713167190552, 
    11.0974102020264, 8.69951438903809, 11.0974102020264), format.stata = "%9.0g"), 
    log_REVENUFROMSALE = structure(c(11.1418619155884, 10.1105012893677, 
    9.07107830047607, 9.61580562591553, 9.57498359680176, 8.4763708114624, 
    11.0020999908447, 9.80145454406738, 9.95418071746826, 10.2691717147827, 
    11.0974102020264, 10.4744672775269, 9.95227813720703, 8.64822101593018, 
    10.1345996856689, 11.1844215393066, 10.0858087539673, 10.8967390060425, 
    8.69951438903809, 11.0974102020264), format.stata = "%9.0g"), 
    y = structure(c(3, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 
    2, 1, 2, 2, 2, 3), format.stata = "%10.0g")), row.names = c(NA, 
-20L), class = c("tbl_df", "tbl", "data.frame"))

### Load the list experiment package (including required sandwich package)
library(sandwich)
library(list)
# Load package allowing to upload .dta
## First foreign to pull .dta
library(foreign) 
## Then install haven package to upload .dta from >=Stata 13 versions
install.packages("readstata13")
library(readstata13)

myTryCatch <- function(expr) {
  warn <- err <- NULL
  value <- withCallingHandlers(
    tryCatch(expr, error=function(e) {
      err <<- e
      NULL
    }), warning=function(w) {
      warn <<- w
      invokeRestart("muffleWarning")
    })
  list(value=value, warning=warn, error=err)
}

## ####
## Table 1 - "Observed Data from the List Experiment"

rm(list=ls())
require(list)

## Load .dta data (change directory)
mydata <- readstata13::read.dta13("XXX.dta")
## Drop observations with missing data from key variables
### define key vars
key_vars <- c("easyAvoid", "easyComply", "trustGVT", "norm", "sex", "age", "head", "ownership", "bookAcct", "married", "secondary","P217_OCCUPATION", "log_hhsizeTot", "log_GROSS_VALUE","log_REVENUFROMSALE")
### use complete.cases(dat[key_vars]) - gives a boolean vector that allows to subset the rows
dat <- mydata[complete.cases(mydata[key_vars]), ]  ##  subset rows for complete cases in key_vars

## make factor
dat <- transform(dat, P217_OCCUPATION=as.factor(P217_OCCUPATION))

# Count the number of rows kept
nrow(dat)

table(dat$y, dat$treat)

## ####
## Table 2 - "Estimated Coefficients from the Logistic Regression Models"

### Fit standard design ML model
# Since the calls to list::ictreg() are identical except the model formulae, list()
## put formulae in named list
frml <- list(
  ## ...
  noboundary0=y ~ easyAvoid + easyComply + trustGVT + norm + sex + age + 
    head + ownership + bookAcct + married + secondary + P217_OCCUPATION +
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE,
  ## Enforcement + Trust
  noboundary1=y ~ easyAvoid + trustGVT,
  ## Enforcement + Facilitation + Trust
  noboundary2=y ~ easyAvoid + easyComply + trustGVT,
  ## Enforcement + Trust + Social norm
  noboundary3=y ~ easyAvoid + trustGVT + norm,
  ## Enforcement + Facilitation + Trust + Social norm
  noboundary4=y ~ easyAvoid + easyComply + trustGVT + norm,
  ## ...
  noboundary5=y ~  ~ easyAvoid + easyComply + trustGVT + sex + age + head + 
    ownership + bookAcct + married + secondary + P217_OCCUPATION + 
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE,
  ## ...
  noboundary6=y ~ easyAvoid + trustGVT + norm + sex + age + head + ownership + 
    bookAcct + married + secondary + P217_OCCUPATION + log_hhsizeTot +
    log_GROSS_VALUE + log_REVENUFROMSALE,
  ## ...
  noboundary7=y ~ easyAvoid + easyComply + trustGVT + norm + sex + age + head + 
    ownership + bookAcct + married + secondary + P217_OCCUPATION + 
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE
)

# and lapply() over it. For cases ictreg fails in an interation (which would stop the loop)
# provide NA as a substitute using tryCatch

## run regressions
library(list)

fits <- lapply(frml, \(x) {
  tryCatch(ictreg(x, treat="treat", J=4, data=dat, method="lm", 
                  overdispersed=FALSE),
           error=\(e) NA)
})

# fits is now a list that contains regression results
names(fits)
# [1] "noboundary0" "noboundary1" "noboundary2" "noboundary3" "noboundary4"
# [6] "noboundary5" "noboundary6" "noboundary7"

# which is easily accessible
fits$noboundary1   

summary(fits$noboundary1)

sink('foo.txt')  ## open sink
lapply(frml, \(x) {
  +   myTryCatch(ictreg(x, treat="treat", J=4, data=dat, method="lm", overdispersed=FALSE))
  })
sink()  ## close sink

Answer 1

Tried your approach but couldn't reproduce the error. Maybe it's due to using the haven package which produces some weird labeled format that is tedious to handle, strongly suggest using readstata13::read.dta13("D:/XXXXX.dta") instead.

At least I can show you a better approach to filter out missings and how to avoid too much repetitive code.

For filter out missings we can use complete.cases(), but actually we don't need it, since ictreg() will probably take care of missings itself by list-wise deletion.

## define key vars
key_vars <- c("easyAvoid", "easyComply", "trustGVT", "norm", "sex", "age", 
              "head", "ownership", "bookAcct", "married", "secondary",
              "P217_OCCUPATION", "log_hhsizeTot", "log_GROSS_VALUE",
              "log_REVENUFROMSALE")

complete.cases(dat[key_vars]) now gives a boolean vector that allows to subset the rows.

#dat <- dat[complete.cases(dat[key_vars]), ]  ##  subset rows for complete cases in key_vars

## make factor
dat <- transform(dat, P217_OCCUPATION=as.factor(P217_OCCUPATION))  

Since the calls to list::ictreg() are identical except the model formulae, we could list() these,

## put formulae in named list
frml <- list(
  ## ...
  noboundary0=y ~ easyAvoid + easyComply + trustGVT + norm + sex + age + 
    head + ownership + bookAcct + married + secondary + P217_OCCUPATION +
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE,
  ## Enforcement + Trust
  noboundary1=y ~ easyAvoid + trustGVT,
  ## Enforcement + Facilitation + Trust
  noboundary2=y ~ easyAvoid + easyComply + trustGVT,
  ## Enforcement + Trust + Social norm
  noboundary3=y ~ easyAvoid + trustGVT + norm,
  ## Enforcement + Facilitation + Trust + Social norm
  noboundary4=y ~ easyAvoid + easyComply + trustGVT + norm,
  ## ...
  noboundary5=y ~  ~ easyAvoid + easyComply + trustGVT + sex + age + head + 
    ownership + bookAcct + married + secondary + P217_OCCUPATION + 
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE,
  ## ...
  noboundary6=y ~ easyAvoid + trustGVT + norm + sex + age + head + ownership + 
    bookAcct + married + secondary + P217_OCCUPATION + log_hhsizeTot +
    log_GROSS_VALUE + log_REVENUFROMSALE,
  ## ...
  noboundary7=y ~ easyAvoid + easyComply + trustGVT + norm + sex + age + head + 
    ownership + bookAcct + married + secondary + P217_OCCUPATION + 
    log_hhsizeTot + log_GROSS_VALUE + log_REVENUFROMSALE
)

and lapply() over it. For cases ictreg fails in an iteration (which would stop the loop), we can provide NA as a substitute using tryCatch.

## run regressions
library(list)

fits <- lapply(frml, \(x) {
  tryCatch(ictreg(x, treat="treat", J=4, data=dat, method="lm", 
                  overdispersed=FALSE),
           error=\(e) NA)
})

fits is now a list that contains regression results,

names(fits)
# [1] "noboundary0" "noboundary1" "noboundary2" "noboundary3" "noboundary4"
# [6] "noboundary5" "noboundary6" "noboundary7"

easily accessible using e.g.

fits$noboundary1    
# Item Count Technique Regression 
# 
# Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
#     overdispersed = FALSE)
# 
# Coefficient estimates
# 
# sensitive.(Intercept)   sensitive.easyAvoid    sensitive.trustGVT 
#           -0.23076923           -0.03846154           -0.03846154 
#   control.(Intercept)     control.easyAvoid      control.trustGVT 
#            2.23076923            0.53846154           -0.46153846 
# 
# Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.

summary(fits$noboundary1)
# Item Count Technique Regression 
# 
# Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
#     overdispersed = FALSE)
# 
# Sensitive item 
#                 Est.    S.E.
# (Intercept) -0.23077 0.70346
# easyAvoid   -0.03846 0.76846
# trustGVT    -0.03846 0.76846
# 
# Control items 
#                 Est.    S.E.
# (Intercept)  2.23077 0.61947
# easyAvoid    0.53846 0.64029
# trustGVT    -0.46154 0.64029

Note that I used method='lm', because 'ml' failed, probably due to insufficient data in the example. So, not sure where exactly your error occurs, maybe in etable(fits$noboundary1) but couldn't find that function.

Edit

Do get a log file of what's happening or what didn't work respectively during computing the regressions, you could use user2161065's nice myTryCatch() function. sink the output in a .txt file.

> sink('foo.txt')  ## open sink
> lapply(frml, \(x) {
+   myTryCatch(ictreg(x, treat="treat", J=4, data=dat, method="lm", 
+                   overdispersed=FALSE))
+ })
> sink()  ## close sink

During sink, console output is directed to the designated file. If you won't get any console output anymore, while playing with sink(), the connection just didn't close properly, so don't panic and read this answer. .pdf might be prettier at first glance, but copying code from it is a pain so recommending .txt.

Produced foo.txt looks like this:

$noboundary0
$noboundary0$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

       sensitive.(Intercept)          sensitive.easyAvoid         sensitive.easyComply           sensitive.trustGVT 
                 0.531999537                           NA                           NA                           NA 
              sensitive.norm                sensitive.sex                sensitive.age               sensitive.head 
                          NA                           NA                           NA                           NA 
         sensitive.ownership           sensitive.bookAcct            sensitive.married          sensitive.secondary 
                          NA                           NA                           NA                           NA 
  sensitive.P217_OCCUPATION7   sensitive.P217_OCCUPATION8   sensitive.P217_OCCUPATION9      sensitive.log_hhsizeTot 
                          NA                           NA                           NA                           NA 
   sensitive.log_GROSS_VALUE sensitive.log_REVENUFROMSALE          control.(Intercept)            control.easyAvoid 
                          NA                           NA                 -5.293434378                  0.403108983 
          control.easyComply             control.trustGVT                 control.norm                  control.sex 
                -1.745441164                  1.131363593                 -0.703881540                 -0.006762457 
                 control.age                 control.head            control.ownership             control.bookAcct 
                          NA                 -0.114450154                           NA                  2.996656209 
             control.married            control.secondary     control.P217_OCCUPATION7     control.P217_OCCUPATION8 
                -0.344308843                 -0.588201709                           NA                  2.444489359 
    control.P217_OCCUPATION9        control.log_hhsizeTot      control.log_GROSS_VALUE   control.log_REVENUFROMSALE 
                 0.746024481                  0.002783119                 -3.068078812                  3.741720690 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary0$warning
<simpleWarning in meatHC(x, type = type, omega = omega): HC3 covariances become numerically unstable if hat values are close to 1 as for observations 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, ...>

$noboundary0$error
NULL


$noboundary1
$noboundary1$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

sensitive.(Intercept)   sensitive.easyAvoid    sensitive.trustGVT   control.(Intercept)     control.easyAvoid 
          -0.28076923            0.21153846            0.06153846            2.23076923            0.53846154 
     control.trustGVT 
          -0.46153846 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary1$warning
NULL

$noboundary1$error
NULL


$noboundary2
$noboundary2$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

sensitive.(Intercept)   sensitive.easyAvoid  sensitive.easyComply    sensitive.trustGVT   control.(Intercept) 
          -0.13866397            0.02732794                    NA           -0.14372470            2.23076923 
    control.easyAvoid    control.easyComply      control.trustGVT 
           0.53846154           -0.07894737           -0.46153846 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary2$warning
NULL

$noboundary2$error
NULL


$noboundary3
$noboundary3$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

sensitive.(Intercept)   sensitive.easyAvoid    sensitive.trustGVT        sensitive.norm   control.(Intercept) 
           -0.2459459             0.3027027            -0.6243243             0.7108108             2.3000000 
    control.easyAvoid      control.trustGVT          control.norm 
            0.4000000             0.3000000            -0.9000000 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary3$warning
<simpleWarning in meatHC(x, type = type, omega = omega): HC3 covariances become numerically unstable if hat values are close to 1 as for observations 12>

$noboundary3$error
NULL


$noboundary4
$noboundary4$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

sensitive.(Intercept)   sensitive.easyAvoid  sensitive.easyComply    sensitive.trustGVT        sensitive.norm 
          -0.26969697            0.05454545                    NA           -1.57272727            1.83939394 
  control.(Intercept)     control.easyAvoid    control.easyComply      control.trustGVT          control.norm 
           2.30000000            0.40000000           -0.69696970            0.30000000           -0.90000000 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary4$warning
<simpleWarning in meatHC(x, type = type, omega = omega): HC3 covariances become numerically unstable if hat values are close to 1 as for observations 12, 16>

$noboundary4$error
NULL


$noboundary5
$noboundary5$value
NULL

$noboundary5$warning
NULL

$noboundary5$error
<simpleError in terms.formula(formula, data = data): invalid model formula>


$noboundary6
$noboundary6$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

       sensitive.(Intercept)          sensitive.easyAvoid           sensitive.trustGVT               sensitive.norm 
                   -5.769248                    21.525008                   -11.931336                           NA 
               sensitive.sex                sensitive.age               sensitive.head          sensitive.ownership 
                          NA                           NA                           NA                           NA 
          sensitive.bookAcct            sensitive.married          sensitive.secondary   sensitive.P217_OCCUPATION7 
                          NA                           NA                           NA                           NA 
  sensitive.P217_OCCUPATION8   sensitive.P217_OCCUPATION9      sensitive.log_hhsizeTot    sensitive.log_GROSS_VALUE 
                          NA                           NA                           NA                           NA 
sensitive.log_REVENUFROMSALE          control.(Intercept)            control.easyAvoid             control.trustGVT 
                          NA                   -36.698978                   -12.387545                     8.379496 
                control.norm                  control.sex                  control.age                 control.head 
                   -2.324243                    -2.867994                           NA                    -1.132987 
           control.ownership             control.bookAcct              control.married            control.secondary 
                          NA                    -1.160721                     9.735517                    -4.499469 
    control.P217_OCCUPATION7     control.P217_OCCUPATION8     control.P217_OCCUPATION9        control.log_hhsizeTot 
                          NA                    -6.525690                     2.207788                    -8.630112 
     control.log_GROSS_VALUE   control.log_REVENUFROMSALE 
                   -2.935581                     7.647998 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary6$warning
<simpleWarning in meatHC(x, type = type, omega = omega): HC3 covariances become numerically unstable if hat values are close to 1 as for observations 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, ...>

$noboundary6$error
NULL


$noboundary7
$noboundary7$value

Item Count Technique Regression 

Call: ictreg(formula = x, data = dat, treat = "treat", J = 4, method = "lm", 
    overdispersed = FALSE)

Coefficient estimates

       sensitive.(Intercept)          sensitive.easyAvoid         sensitive.easyComply           sensitive.trustGVT 
                 0.531999537                           NA                           NA                           NA 
              sensitive.norm                sensitive.sex                sensitive.age               sensitive.head 
                          NA                           NA                           NA                           NA 
         sensitive.ownership           sensitive.bookAcct            sensitive.married          sensitive.secondary 
                          NA                           NA                           NA                           NA 
  sensitive.P217_OCCUPATION7   sensitive.P217_OCCUPATION8   sensitive.P217_OCCUPATION9      sensitive.log_hhsizeTot 
                          NA                           NA                           NA                           NA 
   sensitive.log_GROSS_VALUE sensitive.log_REVENUFROMSALE          control.(Intercept)            control.easyAvoid 
                          NA                           NA                 -5.293434378                  0.403108983 
          control.easyComply             control.trustGVT                 control.norm                  control.sex 
                -1.745441164                  1.131363593                 -0.703881540                 -0.006762457 
                 control.age                 control.head            control.ownership             control.bookAcct 
                          NA                 -0.114450154                           NA                  2.996656209 
             control.married            control.secondary     control.P217_OCCUPATION7     control.P217_OCCUPATION8 
                -0.344308843                 -0.588201709                           NA                  2.444489359 
    control.P217_OCCUPATION9        control.log_hhsizeTot      control.log_GROSS_VALUE   control.log_REVENUFROMSALE 
                 0.746024481                  0.002783119                 -3.068078812                  3.741720690 

Number of control items J set to 4. Treatment groups were indicated by '' and '' and the control group by ''.


$noboundary7$warning
<simpleWarning in meatHC(x, type = type, omega = omega): HC3 covariances become numerically unstable if hat values are close to 1 as for observations 1, 2, 4, 5, 6, 8, 9, 10, 11, 12, ...>

$noboundary7$error
NULL

---

Data:

> dput(dat)
structure(list(sex = c(1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 1, 0, 1), age = c(2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743, 2.40000009536743, 2.40000009536743, 
2.40000009536743, 2.40000009536743), P217_OCCUPATION = structure(c(4L, 
1L, 2L, 1L, 1L, 4L, 1L, 1L, 1L, 1L, 3L, 4L, 4L, 4L, 1L, 1L, 3L, 
4L, 1L, 1L), levels = c("5", "7", "8", "9"), class = "factor"), 
    industryGroup = c(7, 5, 7, 5, 5, 7, 5, 7, 7, 7, 6, 6, 7, 
    8, 5, 7, 7, 7, 5, 5), ownership = c(1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), bookAcct = c(0, 0, 
    0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), easyAvoid = c(1, 
    1, NA, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, NA, 0, 0, 0, 0, 1, 1
    ), easyComply = c(0, 0, 0, 0, 1, 0, NA, 0, 0, 0, 1, 0, 0, 
    NA, 0, 1, 0, 0, 0, 0), trustGVT = c(0, 1, NA, 0, 0, 1, 1, 
    1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, NA, 0), moral0 = c(1, 1, 
    0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), opinion1 = c(1, 
    1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0), 
    opinion2 = c(1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
    1, 1, 1, 0, NA), moral1 = c(0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 
    0, 1, 1, 1, 1, 1, 0, 0, 0, NA), direct_reporting = c(0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, NA, 0, 0), head = c(0, 
    0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1), 
    norm = c(0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, NA, 1, 1, 
    0, 0, 0, 0), treat = c(0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 
    0, 0, 1, 1, 0, 1, 0, 1), married = c(1, 1, 0, 0, 1, 1, 0, 
    0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0), secondary = c(0, 
    0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0), 
    log_hhsizeTot = c(1.0986123085022, 1.0986123085022, 1.7917594909668, 
    0.6931471824646, 0.6931471824646, 1.0986123085022, 1.3862943649292, 
    0, 0, 1.3862943649292, 0.6931471824646, 1.3862943649292, 
    1.3862943649292, 1.3862943649292, 0.6931471824646, 1.3862943649292, 
    1.0986123085022, 0, 0, 1.3862943649292), log_GROSS_VALUE = c(11.1462001800537, 
    10.1105012893677, 9.07107830047607, 9.61580562591553, 9.50300979614258, 
    8.4990291595459, 11.0020999908447, 9.80972576141357, 9.9776668548584, 
    10.2691717147827, 11.0974102020264, 10.4744672775269, 9.95227813720703, 
    8.64822101593018, 10.1464338302612, 11.1844215393066, 10.5713167190552, 
    11.0974102020264, 8.69951438903809, 11.0974102020264), log_REVENUFROMSALE = c(11.1418619155884, 
    10.1105012893677, 9.07107830047607, 9.61580562591553, 9.57498359680176, 
    8.4763708114624, 11.0020999908447, 9.80145454406738, 9.95418071746826, 
    10.2691717147827, 11.0974102020264, 10.4744672775269, 9.95227813720703, 
    8.64822101593018, 10.1345996856689, 11.1844215393066, 10.0858087539673, 
    10.8967390060425, 8.69951438903809, 11.0974102020264), y = c(3, 
    2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 1, 2, 2, 2, 3)), class = "data.frame", row.names = c(NA, 
-20L))