Error message: Error in approx(sp$y, sp$x, xout = cutoff) : need at least two non-NA values to interpolate In addition: Warning message: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) : collapsing to unique 'x' values

Acotas · December 30, 2022, 1:29am

I am getting the following error message when I try to find the odds ratio and upper and lower 95% CIs for my data. I ran the exact same programming for a second outcome and it worked completely fine. Here are the steps I took:

Data <- read.csv("C:/Users/kyley/OneDrive/Desktop/3 R Data Pain extra.csv")

Data$ï..Age <- as.numeric(Data$ï..Age)
Data$N.BMI <- as.factor(Data$N.BMI)
Data$N.Horm.Med.Use <- as.factor(Data$N.Horm.Med.Use)
Data$N.Age.menses <- as.factor(Data$N.Age.menses)
Data$N.Cycle.Regularity <- as.factor(Data$N.Cycle.Regularity)
Data$N.Pain <- as.factor(Data$N.Pain)
Data$N.Vape <- as.factor(Data$N.Vape)
Data$N.Alcohol <- as.factor(Data$N.Alcohol)
Data$N.Drug <- as.factor(Data$N.Drug)
Data$N.Sleep <- as.factor(Data$N.Sleep)
Data$N.Caffeine <- as.factor(Data$N.Caffeine)
Data$N.Stress <- as.factor(Data$N.Stress)
Data$N.Chem.Exposure <- as.factor(Data$N.Chem.Exposure)
Data$N.Smoking <- as.factor(Data$N.Smoking)
Data$N.Psych.Med <- as.factor(Data$N.Psych.Med)
Data$N.Relationship.status <- as.factor(Data$N.Relationship.status)
Data$N.Place.living <- as.factor(Data$N.Place.living)
Data$N.Pregnancy <- as.factor(Data$N.Pregnancy)
Data$N.Sexually.Active <- as.factor(Data$N.Sexually.Active)
Data$N.Days.between.cycles <- as.factor(Data$N.Days.between.cycles)
Data$N.Days.long.period <- as.factor(Data$N.Days.long.period)
Data$N.Mental.Health <- as.factor(Data$N.Mental.Health)

summary(Data)

library(MASS)
polrModel = polr(N.Pain~.,data=Data)

confint(polrModel)

(ctable <- coef(summary(polrModel)))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))

cbind(OR = exp(coef(polrModel)), exp(confint(polrModel)))

When I ran the above, it all worked perfectly. BUT, when I change it to my second outcome, I use the following information:

Data <- read.csv("C:/Users/kyley/OneDrive/Desktop/test.csv")

Data$ï..Age <- as.numeric(Data$ï..Age)
Data$N.BMI <- as.factor(Data$N.BMI)
Data$N.Horm.Med.Use <- as.factor(Data$N.Horm.Med.Use)
Data$N.Age.menses <- as.factor(Data$N.Age.menses)
Data$N.Cycle.Regularity <- as.factor(Data$N.Cycle.Regularity)
Data$N.Bleeding <- as.factor(Data$N.Bleeding)
Data$N.Vape <- as.factor(Data$N.Vape)
Data$N.Alcohol <- as.factor(Data$N.Alcohol)
Data$N.Drug <- as.factor(Data$N.Drug)
Data$N.Sleep <- as.factor(Data$N.Sleep)
Data$N.Caffeine <- as.factor(Data$N.Caffeine)
Data$N.Stress <- as.factor(Data$N.Stress)
Data$N.Chem.Exposure <- as.factor(Data$N.Chem.Exposure)
Data$N.Smoking <- as.factor(Data$N.Smoking)
Data$N.Psych.Med <- as.factor(Data$N.Psych.Med)
Data$N.Relationship.status <- as.factor(Data$N.Relationship.status)
Data$N.Place.living <- as.factor(Data$N.Place.living)
Data$N.Pregnancy <- as.factor(Data$N.Pregnancy)
Data$N.Sexually.Active <- as.factor(Data$N.Sexually.Active)
Data$N.Days.between.cycles <- as.factor(Data$N.Days.between.cycles)
Data$N.Days.long.period <- as.factor(Data$N.Days.long.period)
Data$N.Mental.Health <- as.factor(Data$N.Mental.Health)

summary(Data)

library(MASS)
polrModel = polr(N.Bleeding~.,data=Data)

confint(polrModel)

(ctable <- coef(summary(polrModel)))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))

cbind(OR = exp(coef(polrModel)), exp(confint(polrModel)))

When I try to run this, I get the following errors:

confint(polrModel)
Waiting for profiling to be done...
Re-fitting to get Hessian
Error in approx(sp$y, sp$x, xout = cutoff) :
need at least two non-NA values to interpolate
In addition: Warning message:
In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
collapsing to unique 'x' values

cbind(OR = exp(coef(polrModel)), exp(confint(polrModel)))
Waiting for profiling to be done...
Re-fitting to get Hessian
Error in approx(sp$y, sp$x, xout = cutoff) :
need at least two non-NA values to interpolate
In addition: Warning message:
In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
collapsing to unique 'x' values

The data in my csv file is exactly the same for all variables, except the variables Pain and Bleeding. Pain worked (pain variable present, bleeding variable absent) , but the file with Bleeding did not (bleeding variable present, pain variable absent).

With this in mind, I assume something must be wrong with my bleeding variable? But I can't see anything wrong with it and I am a complete beginner in R. The variable has 178 observations and have been coded to be either a 1, 2, or 3. There are no missing variables so the NA thing is confusing me.

Any suggestions would be extremely helpful!

Thanks!

technocrat · December 30, 2022, 3:02am

See the FAQ: How to do a minimal reproducible example reprex for beginners. It's difficult to analyze an error like this in the abstract. The best I can suggest is this post.

Acotas · December 31, 2022, 1:21am

Thanks for the suggestion. I followed the steps and was trying to make a smaller version of my data to reproduce the error, but unless I include all my rows and columns, I am not getting the same error and get different errors instead, or no errors at all. I am unsure if there are any other suggestions? Is it possible for me to share my entire data sheet of 178 rows and 22 columns so that someone could test the above code with the actual data sheet? I am unsure how else to get my data on here easily.

Acotas · December 31, 2022, 1:22am

I noticed that as I cut out columns, my P values are larger and as I add columns, the p values get exponentially smaller , so I wasn't sure if this could affect the ability to get an OR or CI somehow when numbers end with E-2, for example.

technocrat · December 31, 2022, 2:54am

Cut and paste the output of

dput(your data)

Acotas · December 31, 2022, 3:41am

This is what I get:

dput(Data)
structure(list(ï..Age = c(31, 18, 33, 35, 21, 22, 20, 23, 19,
19, 34, 19, 21, 20, 18, 18, 31, 19, 19, 35, 23, 20, 23, 25, 31,
22, 21, 22, 25, 28, 20, 25, 27, 24, 19, 31, 20, 19, 31, 24, 32,
21, 33, 23, 29, 26, 18, 29, 21, 26, 21, 23, 24, 31, 20, 25, 26,
26, 35, 27, 28, 34, 34, 23, 27, 29, 35, 25, 31, 32, 34, 31, 32,
20, 33, 24, 30, 24, 30, 20, 26, 32, 24, 24, 26, 22, 25, 24, 22,
33, 25, 26, 28, 27, 25, 24, 22, 29, 27, 24, 22, 32, 27, 23, 19,
27, 25, 26, 27, 28, 28, 24, 35, 29, 24, 26, 20, 26, 22, 22, 28,
22, 19, 27, 30, 26, 25, 31, 34, 23, 24, 24, 33, 20, 24, 35, 26,
35, 34, 24, 33, 25, 29, 24, 23, 32, 35, 19, 21, 32, 26, 27, 29,
23, 25, 26, 30, 29, 29, 25, 23, 29, 27, 25, 35, 27, 24, 26, 28,
30, 30, 35, 25, 24, 28, 25, 34, 31), N.BMI = structure(c(2L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 4L,
2L, 3L, 1L, 4L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 2L, 4L, 1L, 3L, 1L, 2L, 2L, 3L,
4L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 4L, 3L, 2L, 4L,
2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 1L, 1L,
2L, 4L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 2L, 1L, 2L, 2L,
3L, 4L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L,
2L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 3L,
2L, 4L, 4L, 2L, 4L, 2L, 3L, 3L, 1L, 2L, 2L, 4L, 4L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 1L, 2L,
3L), .Label = c("0", "1", "2", "3"), class = "factor"), N.Horm.Med.Use = structure(c(2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Age.menses = structure(c(1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Cycle.Regularity = structure(c(1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Bleeding = structure(c(3L,
1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 3L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L), .Label = c("1", "2", "3"), class = "factor"), N.Vape = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Alcohol = structure(c(1L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 3L,
3L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 1L,
2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L,
3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 1L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
3L), .Label = c("0", "1", "2"), class = "factor"), N.Drug = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L,
1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Sleep = structure(c(1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L,
1L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 3L,
3L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 3L,
2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Caffeine = structure(c(1L,
1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 1L,
2L, 2L, 2L, 4L, 3L, 1L, 2L, 4L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 2L,
2L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 4L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
2L, 3L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L,
1L, 1L, 3L, 3L, 2L, 4L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L,
3L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 3L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 3L, 1L, 2L, 2L,
2L), .Label = c("0", "1", "2", "3"), class = "factor"), N.Stress = structure(c(3L,
2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L,
2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
1L), .Label = c("1", "2", "3"), class = "factor"), N.Chem.Exposure = structure(c(1L,
1L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L,
3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 3L,
1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Smoking = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Psych.Med = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Relationship.status = structure(c(1L,
1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
3L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 1L,
3L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L,
3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L,
3L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L), .Label = c("0", "1", "2"), class = "factor"), N.Place.living = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L,
1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L,
3L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L,
1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Pregnancy = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L), .Label = c("0", "1"), class = "factor"), N.Sexually.Active = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L), .Label = c("0", "1"), class = "factor"), N.Days.between.cycles = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L), .Label = c("0", "1"), class = "factor"), N.Days.long.period = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Mental.Health = structure(c(2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L), .Label = c("0", "1"), class = "factor")), row.names = c(NA,
-178L), class = "data.frame")

technocrat · December 31, 2022, 9:31am

Thanks, that's very helpful—the error is reprodxucible. The error message, ultimately, traces to stats::regularize.values() by way of stats::approx() to a function defined there

r <- regularize.values(x, y, ties, missing(ties), na.rm = na.r

and an interval variable nx is set to the length of r$x in the return value or, if that value is NA to the number of rows. r$x is the number of unique values of x.

If there are no unique values of x the error is zero non-NA points, and if there is only one unique value of x the message seen from confint(polrModel) is seen

need at least two non-NA values to interpolate

See the source code.

Since we know from summary(Data) that each variable has more than one unique value and because the argument to confint is not Data but the polr object polrModel, the next step would be to identify the model term that is being collapsed to a single unique value.

I didn't make progress directly, so the next resort was the similar model function rms::lrm the results of which were identified to rms::validate that identified smoking as producing a singular information matrix. As an empiricist, I removed smoking from Data and got

library(MASS)

Data <- structure(list(ï..Age = c(
31, 18, 33, 35, 21, 22, 20, 23, 19,
19, 34, 19, 21, 20, 18, 18, 31, 19, 19, 35, 23, 20, 23, 25, 31,
22, 21, 22, 25, 28, 20, 25, 27, 24, 19, 31, 20, 19, 31, 24, 32,
21, 33, 23, 29, 26, 18, 29, 21, 26, 21, 23, 24, 31, 20, 25, 26,
26, 35, 27, 28, 34, 34, 23, 27, 29, 35, 25, 31, 32, 34, 31, 32,
20, 33, 24, 30, 24, 30, 20, 26, 32, 24, 24, 26, 22, 25, 24, 22,
33, 25, 26, 28, 27, 25, 24, 22, 29, 27, 24, 22, 32, 27, 23, 19,
27, 25, 26, 27, 28, 28, 24, 35, 29, 24, 26, 20, 26, 22, 22, 28,
22, 19, 27, 30, 26, 25, 31, 34, 23, 24, 24, 33, 20, 24, 35, 26,
35, 34, 24, 33, 25, 29, 24, 23, 32, 35, 19, 21, 32, 26, 27, 29,
23, 25, 26, 30, 29, 29, 25, 23, 29, 27, 25, 35, 27, 24, 26, 28,
30, 30, 35, 25, 24, 28, 25, 34, 31
), N.BMI = structure(c(
2L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 4L,
2L, 3L, 1L, 4L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 2L, 4L, 1L, 3L, 1L, 2L, 2L, 3L,
4L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 4L, 3L, 2L, 4L,
2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 1L, 1L,
2L, 4L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 2L, 1L, 2L, 2L,
3L, 4L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L,
2L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 3L,
2L, 4L, 4L, 2L, 4L, 2L, 3L, 3L, 1L, 2L, 2L, 4L, 4L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 1L, 2L,
3L
), .Label = c("0", "1", "2", "3"), class = "factor"), N.Horm.Med.Use = structure(c(
2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
2L
), .Label = c("0", "1", "2"), class = "factor"), N.Age.menses = structure(c(
1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L
), .Label = c("0", "1"), class = "factor"), N.Cycle.Regularity = structure(c(
1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L
), .Label = c("0", "1"), class = "factor"), N.Bleeding = structure(c(
3L,
1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 1L, 3L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 3L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L
), .Label = c("1", "2", "3"), class = "factor"), N.Vape = structure(c(
1L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), .Label = c("0", "1", "2"), class = "factor"), N.Alcohol = structure(c(
1L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 3L,
3L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 1L,
2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L,
3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 1L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
3L
), .Label = c("0", "1", "2"), class = "factor"), N.Drug = structure(c(
1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L,
1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), .Label = c("0", "1", "2"), class = "factor"), N.Sleep = structure(c(
1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L,
1L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 3L,
3L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 3L,
2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L
), .Label = c("0", "1", "2"), class = "factor"), N.Caffeine = structure(c(
1L,
1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 1L,
2L, 2L, 2L, 4L, 3L, 1L, 2L, 4L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 2L,
2L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 4L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
2L, 3L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L,
1L, 1L, 3L, 3L, 2L, 4L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L,
3L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 3L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 3L, 1L, 2L, 2L,
2L
), .Label = c("0", "1", "2", "3"), class = "factor"), N.Stress = structure(c(
3L,
2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L,
2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
1L
), .Label = c("1", "2", "3"), class = "factor"), N.Chem.Exposure = structure(c(
1L,
1L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L,
3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 3L,
1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 2L,
2L
), .Label = c("0", "1", "2"), class = "factor"), N.Smoking = structure(c(
1L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L
), .Label = c("0", "1", "2"), class = "factor"), N.Psych.Med = structure(c(
2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L
), .Label = c("0", "1"), class = "factor"), N.Relationship.status = structure(c(
1L,
1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
3L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 1L,
3L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L,
3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L,
3L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L
), .Label = c("0", "1", "2"), class = "factor"), N.Place.living = structure(c(
2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L,
1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L,
3L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L,
1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L
), .Label = c("0", "1", "2"), class = "factor"), N.Pregnancy = structure(c(
1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L
), .Label = c("0", "1"), class = "factor"), N.Sexually.Active = structure(c(
1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L
), .Label = c("0", "1"), class = "factor"), N.Days.between.cycles = structure(c(
1L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L
), .Label = c("0", "1"), class = "factor"), N.Days.long.period = structure(c(
1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), .Label = c("0", "1"), class = "factor"), N.Mental.Health = structure(c(
2L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L
), .Label = c("0", "1"), class = "factor")), row.names = c(
NA,
-178L
), class = "data.frame")

# some suggestions for names used during analysis;
# for presentation, more expansive descriptors
# can be used. Many typos will be saved
# for example, except for the first column
# all begin with N. If there are similar datasets
# with "M.BMI", the data frames could be named
# N_data and M_data with identical variable names
# which makes script and snippet reuse simpler

# save existing variable names for use in presentation
# materials possibly
header <- colnames(Data)
# shorter, lowercase names
colnames(Data) <- c("age","bmi","horm_med","at_menses","regularity","bleeding","vape","alcohol","drug","sleep","caffeine","stress","chem_expos","smoking","psych_med","relationship","place_living","pregnancy","sexually_active","between_cycles","length_period","mental_health")

summary(Data)
#>       age        bmi     horm_med at_menses regularity bleeding vape    alcohol
#>  Min.   :18.00   0: 12   0:115    0:113     0:138      1:128    0:156   0:28   
#>  1st Qu.:23.00   1:100   1: 57    1: 65     1: 40      2: 36    1:  8   1:61   
#>  Median :26.00   2: 41   2:  6                         3: 14    2: 14   2:89   
#>  Mean   :26.21   3: 25                                                         
#>  3rd Qu.:30.00                                                                 
#>  Max.   :35.00                                                                 
#>  drug    sleep   caffeine stress  chem_expos smoking psych_med relationship
#>  0:132   0:101   0:38     1: 37   0: 44      0:120   0:149     0:74        
#>  1: 28   1: 61   1:78     2:122   1:107      1: 55   1: 29     1: 9        
#>  2: 18   2: 16   2:57     3: 19   2: 27      2:  3             2:95        
#>                  3: 5                                                      
#>                                                                            
#>                                                                            
#>  place_living pregnancy sexually_active between_cycles length_period
#>  0:98         0:139     0: 45           0:154          0:161        
#>  1:61         1: 39     1:133           1: 24          1: 17        
#>  2:19                                                               
#>                                                                     
#>                                                                     
#>                                                                     
#>  mental_health
#>  0: 71        
#>  1:107        
#>               
#>               
#>               
#> 
# for reasons explained in the narrative
# a model that omits the smoking variable
# does not produce the error

polrModel = polr(bleeding~.,data=Data[-14])
confint(polrModel)
#> Waiting for profiling to be done...
#> 
#> Re-fitting to get Hessian
#>                        2.5 %      97.5 %
#> age              -0.22062704  0.04200941
#> bmi1             -3.13057222 -0.02298817
#> bmi2             -2.20830952  1.19419249
#> bmi3             -2.17081176  1.74613460
#> horm_med1         0.11222653  2.11067991
#> horm_med2        -2.43571533  2.17996759
#> at_menses1       -1.39101280  0.53289325
#> regularity1       0.73923102  3.20719625
#> vape1             0.43934323  6.33365053
#> vape2            -5.23363827 -0.44013515
#> alcohol1         -3.82464921 -1.05599542
#> alcohol2         -2.51857478 -0.06657700
#> drug1            -8.61093282 -2.86060333
#> drug2            -1.89529722  0.89779188
#> sleep1           -1.65943985  0.46027756
#> sleep2           -1.42526028  1.80139273
#> caffeine1        -1.25549974  1.19227726
#> caffeine2        -0.71189232  1.81186162
#> caffeine3        -6.48423583  1.02937729
#> stress2          -3.26245872 -0.57693306
#> stress3          -2.45871611  0.94560447
#> chem_expos1      -1.87258021  0.26972738
#> chem_expos2      -2.14045896  0.88728422
#> psych_med1       -1.25946562  1.41941451
#> relationship1    -1.28451399  2.59178288
#> relationship2    -1.39793666  1.10499033
#> place_living1    -0.99812484  0.91680394
#> place_living2    -4.78273854 -0.31551395
#> pregnancy1       -2.12635182  0.90751048
#> sexually_active1 -0.54449347  2.38304285
#> between_cycles1  -2.83820672  0.26819997
#> length_period1    2.52966590  6.02665954
#> mental_health1   -0.01271768  2.23225749

^{Created on 2022-12-31 by the reprex package (v2.0.1)}

Acotas · December 31, 2022, 10:39pm

Thanks so much for figuring out what was causing the issue. When I run it without smoking, I get extremely different upper and lower 95% CIs than what you came up with. For instance, vape 1 you got 0.43934323 6.33365053, but when I ran it I got 1.551687776 563.20885582 with the same numbers. Unsure if the code I am using is wrong?

Do you know why smoking is causing this result? When I ran the exact same numbers but excluded bleeding and instead included pain, I was able to run it fine, including smoking.

dput(Data)
structure(list(ï..Age = c(31, 18, 33, 35, 21, 22, 20, 23, 19,
19, 34, 19, 21, 20, 18, 18, 31, 19, 19, 35, 23, 20, 23, 25, 31,
22, 21, 22, 25, 28, 20, 25, 27, 24, 19, 31, 20, 19, 31, 24, 32,
21, 33, 23, 29, 26, 18, 29, 21, 26, 21, 23, 24, 31, 20, 25, 26,
26, 35, 27, 28, 34, 34, 23, 27, 29, 35, 25, 31, 32, 34, 31, 32,
20, 33, 24, 30, 24, 30, 20, 26, 32, 24, 24, 26, 22, 25, 24, 22,
33, 25, 26, 28, 27, 25, 24, 22, 29, 27, 24, 22, 32, 27, 23, 19,
27, 25, 26, 27, 28, 28, 24, 35, 29, 24, 26, 20, 26, 22, 22, 28,
22, 19, 27, 30, 26, 25, 31, 34, 23, 24, 24, 33, 20, 24, 35, 26,
35, 34, 24, 33, 25, 29, 24, 23, 32, 35, 19, 21, 32, 26, 27, 29,
23, 25, 26, 30, 29, 29, 25, 23, 29, 27, 25, 35, 27, 24, 26, 28,
30, 30, 35, 25, 24, 28, 25, 34, 31), N.BMI = structure(c(2L,
2L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 4L,
2L, 3L, 1L, 4L, 2L, 2L, 2L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 4L, 2L, 4L, 1L, 3L, 1L, 2L, 2L, 3L,
4L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 4L, 3L, 2L, 4L,
2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 1L, 1L,
2L, 4L, 3L, 4L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 4L, 2L, 1L, 2L, 2L,
3L, 4L, 2L, 2L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 3L, 3L,
2L, 4L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 3L,
2L, 4L, 4L, 2L, 4L, 2L, 3L, 3L, 1L, 2L, 2L, 4L, 4L, 3L, 2L, 2L,
2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 1L, 2L,
3L), .Label = c("0", "1", "2", "3"), class = "factor"), N.Horm.Med.Use = structure(c(2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 3L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 3L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Age.menses = structure(c(1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Cycle.Regularity = structure(c(1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Pain = structure(c(3L,
2L, 2L, 2L, 4L, 2L, 3L, 4L, 4L, 4L, 4L, 1L, 3L, 2L, 2L, 3L, 1L,
3L, 3L, 2L, 2L, 4L, 2L, 4L, 2L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L,
1L, 3L, 4L, 4L, 3L, 2L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 2L, 2L, 4L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 2L, 3L, 3L, 4L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 4L, 3L, 4L, 1L,
3L, 2L, 2L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 4L, 3L, 3L, 2L, 3L,
2L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 2L, 2L, 4L, 2L, 3L, 3L, 2L, 3L,
2L, 2L, 1L, 3L, 2L, 4L, 2L, 3L, 2L, 3L, 4L, 1L, 3L, 1L, 2L, 4L,
2L, 2L, 2L, 2L, 3L, 3L, 2L, 4L, 3L, 1L, 3L, 1L, 2L, 3L, 4L, 3L,
2L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 4L, 3L,
3L, 3L, 3L, 2L, 2L, 3L, 2L, 2L, 2L, 4L, 2L, 3L, 2L, 2L, 3L, 2L,
2L), .Label = c("0", "1", "2", "3"), class = "factor"), N.Vape = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
3L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Alcohol = structure(c(1L,
2L, 2L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 2L,
2L, 2L, 2L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 2L,
2L, 3L, 3L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 3L,
3L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 1L,
2L, 2L, 1L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 2L, 2L,
3L, 3L, 1L, 3L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 3L, 3L, 2L, 3L, 3L,
2L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 1L, 2L,
3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
3L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L,
3L, 3L, 1L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 2L,
3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
3L), .Label = c("0", "1", "2"), class = "factor"), N.Drug = structure(c(1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 2L,
1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 1L,
1L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 3L, 1L, 2L, 1L, 3L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Sleep = structure(c(1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 1L, 1L,
1L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 3L, 2L, 2L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 3L,
3L, 2L, 2L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 3L,
2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Caffeine = structure(c(1L,
1L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 1L,
2L, 2L, 2L, 4L, 3L, 1L, 2L, 4L, 2L, 1L, 3L, 3L, 3L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 1L, 2L,
2L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 3L, 2L, 3L, 3L, 2L,
1L, 4L, 3L, 1L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 1L, 2L,
2L, 3L, 2L, 1L, 2L, 1L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L,
1L, 1L, 3L, 3L, 2L, 4L, 3L, 1L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L,
3L, 1L, 2L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 3L, 2L, 2L, 2L, 1L, 3L,
3L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 3L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 2L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 3L,
2L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 3L, 1L, 2L, 2L,
2L), .Label = c("0", "1", "2", "3"), class = "factor"), N.Stress = structure(c(3L,
2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 3L, 2L, 3L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 3L,
2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
1L), .Label = c("1", "2", "3"), class = "factor"), N.Chem.Exposure = structure(c(1L,
1L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 3L,
3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 3L, 3L,
2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 3L,
1L, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 3L, 1L, 1L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Smoking = structure(c(1L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L), .Label = c("0", "1", "2"), class = "factor"), N.Psych.Med = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L), .Label = c("0", "1"), class = "factor"), N.Relationship.status = structure(c(1L,
1L, 1L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 2L, 1L, 1L, 2L, 1L, 1L,
3L, 2L, 1L, 1L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 3L,
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 1L,
3L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 3L,
3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 2L, 3L, 3L,
1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 3L,
3L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L,
3L), .Label = c("0", "1", "2"), class = "factor"), N.Place.living = structure(c(2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 3L,
1L, 2L, 3L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L,
3L, 2L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 2L, 2L,
1L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L), .Label = c("0", "1", "2"), class = "factor"), N.Pregnancy = structure(c(1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
1L), .Label = c("0", "1"), class = "factor"), N.Sexually.Active = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
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technocrat · December 31, 2022, 10:45pm

It is not the values of the data, per se, but the fact that somewhere in the model of the data there is a calculation that gives rise to a singular matrix and when the selection of response variable changes, the model changes, too. So I’m not surprised by the contrasting results.

Acotas · December 31, 2022, 10:49pm

Oh, okay. I am not sure I entirely understand, but if there is no way around it besides removing smoking, I will have to consider that. Also, when I run the data you used without smoking, I get extremely different upper and lower 95% CIs than what you came up with. For instance, vape 1 you got 0.43934323 6.33365053, but when I ran it I got 1.551687776 563.20885582 with the same numbers. Unsure if the code I am using is wrong?

technocrat · December 31, 2022, 11:32pm

I thought it possible that I may have munged Data, but rechecking still yields

N.Vape1                 0.43934323  6.33365053

The only code is

polrModel = polr(N.Bleeding~.,data=Data[-14])
confint(polrModel)

and that's what you're using, right? Try running from a fresh session to check?

Acotas · January 1, 2023, 2:59am

Hello,

When I use your code, I get the same thing for CIs. However, I also need the odds ratio and p values. So this was the code I was using that I was getting weird results from:

library(MASS)
polrModel = polr(N.Bleeding~.,data=Data)

(ctable <- coef(summary(polrModel)))
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
(ctable <- cbind(ctable, "p value" = p))

cbind(OR = exp(coef(polrModel)), exp(confint(polrModel)))

technocrat · January 1, 2023, 8:04am

Isn't that just the difference between

confint(polrModel)

and

exp(confint(polrModel))

Acotas · January 1, 2023, 9:28pm

Hello,

Sorry, I am a bit confused. Do you mean that OR is just the difference between confit(polrModel) and exp(confit(polrModel)) ?

When I use your
polrModel = polr(N.Bleeding~.,data=Data[-14])
confint(polrModel)

I get the right CIs that you have, but when I then use
cbind(OR = exp(coef(polrModel)), exp(confint(polrModel)))

to get the OR, it changes the CIs so that they are wrong again. So I'm not sure if my OR calculations are wrong.

technocrat · January 1, 2023, 10:03pm

Isn't the one CI around the estimates of the model and the other around the natural logs of the estimates? The latter is the CI expressed in log units.

This site has some helpful material

One way to calculate a p-value in this case is by comparing the t-value against the standard normal distribution, like a z test. Of course this is only true with infinite degrees of freedom, but is reasonably approximated by large samples, becoming increasingly biased as sample size decreases. This approach is used in other software packages such as Stata and is trivial to do. First we store the coefficient table, then calculate the p-values and combine back with the table. \dots

We can also get confidence intervals for the parameter estimates. These can be obtained either by profiling the likelihood function or by using the standard errors and assuming a normal distribution. Note that profiled CIs are not symmetric (although they are usually close to symmetric). If the 95% CI does not cross 0, the parameter estimate is statistically significant. \dots

The coefficients from the model can be somewhat difficult to interpret because they are scaled in terms of logs. Another way to interpret logistic regression models is to convert the coefficients into odds ratios. To get the OR and confidence intervals, we just exponentiate the estimates and confidence intervals.

system · January 22, 2023, 10:03pm

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