http://imgur.com/a/q4IdW "table"
Hi, I have a file that has coded complaints, you can see it in the link above, and I need to find a way to combine the 4 columns(primary issue, secondary issue, etc) so that I can then sum up all the issues together. it is possible for a complaint to have multiple issues, so that is why it is broken down like this, but for analysis purposes I want to treat all the issue columns as the same. I am very new to R so please try and speak in terms ill be able to understand or can google fairly quickly
> str(mydata)
'data.frame': 136 obs. of 25 variables:
$ ï..Issue.ID : Factor w/ 136 levels "CAO-2017-01",..: 20 21 22 23 24 25 26 27 28 29 ...
$ Reviewer.ID : Factor w/ 1 level "Vinokurov, A": 1 1 1 1 1 1 1 1 1 1 ...
$ Review.Date : Factor w/ 3 levels "6/30/2017","7/14/2017",..: 1 1 1 1 1 2 2 2 2 2 ...
$ CBA.ZIP.CODE : Factor w/ 61 levels "Allentown-Bethlehem-Easton, PA",..: 29 13 24 10 29 13 10 9 47 39 ...
$ Source.of.complaint : Factor w/ 7 levels "Advocate","Beneficiary",..: 7 7 3 7 6 7 2 3 3 3 ...
$ Primary.Issue.Category : Factor w/ 10 levels "Billing, coverage, coordination of benefits",..: 3 8 4 4 4 4 7 4 4 4 ...
$ Secondary.Issue.Category : Factor w/ 15 levels "","ABN issues ",..: 4 1 15 1 15 3 3 15 15 15 ...
$ Third.Issue.Category : Factor w/ 12 levels "","- Error -",..: 1 1 1 1 1 5 1 10 1 1 ...
$ Fourth.Issue.Category : Factor w/ 2 levels "","Low quantity/quality": 1 1 1 1 1 1 1 1 1 1 ...
$ Reviewer.Issue.Notes : logi NA NA NA NA NA NA ...
$ Primary.Equipment.Category : Factor w/ 13 levels "Commode chairs",..: 9 7 2 8 2 9 10 10 2 2 ...
$ Secondary.Equipment.Category : Factor w/ 7 levels "","- Error -",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Third.Equipment.Category : Factor w/ 10 levels "","- Error -",..: 1 1 1 1 1 2 1 2 1 1 ...
$ Fourth.Equipment.Category : logi NA NA NA NA NA NA ...
$ Reviewer.Equipment.Notes : logi NA NA NA NA NA NA ...
$ Primary.Resolution.Category : Factor w/ 16 levels "Beneficiary educated about DMEPOS\n",..: 9 12 15 12 5 14 13 9 5 10 ...
$ Secondary.Resolution.Category: Factor w/ 18 levels "","- Error -",..: 1 1 3 1 4 7 7 17 15 1 ...
$ Third.Resolution.Category : Factor w/ 8 levels "","Beneficiary educated about inquiry ",..: 1 1 1 1 3 1 1 1 1 1 ...
$ Fourth.Resolution.Category : logi NA NA NA NA NA NA ...
$ Reviewer.Resolution.Notes : logi NA NA NA NA NA NA ...
$ Future.Action : Factor w/ 4 levels "no","No","yes",..: 4 4 2 2 2 2 3 4 1 1 ...
$ Coder.1 : Factor w/ 2 levels "Briskin-Limehouse, A",..: 1 1 1 1 1 2 2 2 2 2 ...
$ Coder.1.Coded.Date : Factor w/ 4 levels "6/30/2017","7/13/2017",..: 1 1 1 1 1 2 2 2 2 2 ...
$ Coder.2 : Factor w/ 1 level "Aliu, F": 1 1 1 1 1 1 1 1 1 1 ...
$ Coder.2.Coded.Date : Factor w/ 7 levels "6/30/2017","7/12/2017",..: 1 1 1 1 1 2 3 3 3 3 ...
>
What i got is: You have something like this:
issue_1 issue_2 issue_3 issue_4
person1 0 0 0 1
person2 1 1 0 1
person3 1 0 1 1
where 1 is presence of issue, and 0 the opposite, took from some survey.
would you like to show something like this?
Issue_1 appeared 2x
issue_2 appeared 1x
issue_4 appeared 3x
Could you check and answer again, please?
Please, use str(your_data) too, since you can't link us
Related
Hi I have a simple dataframe with this structure
> str(allvalues)
'data.frame': 150 obs. of 8 variables:
$ seriesId : Factor w/ 1 level "2021-02-28T00:00:00Z": 1 1 1 1 1 1 1 1 1 1 ...
$ forecastPoint : Factor w/ 30 levels "790","791","792",..: 1 2 3 4 5 6 7 8 9 10 ...
$ rowId : Factor w/ 30 levels "2021-03-01T00:00:00.000000Z",..: 1 2 3 4 5 6 7 8 9 10 ...
$ timestamp : Factor w/ 65 levels "1842.6640625",..: 7 8 9 11 14 4 1 16 12 18 ...
$ predictionValues: Factor w/ 1 level "total_visits (actual)": 1 1 1 1 1 1 1 1 1 1 ...
$ forecastDistance: Factor w/ 30 levels "1","10","11",..: 1 12 23 25 26 27 28 29 30 2 ...
$ prediction : num 2111 2130 2258 2276 2298 ...
$ scenario : Factor w/ 5 levels "0 0 10 10 10",..: 4 4 4 4 4 4 4 4 4 4 ...
and I want to group by "scenario" and sum "prediction"
but when I use
> allvalues %>% group_by(scenario) %>% summarise(cond_disp = sum(prediction))
cond_disp
1 351940.8
Is not grouping by scenarios, there should be 5 rows, each scenario and the sum
any help on what I am doing wrong?
I am analyzing this dataset it has numeric and factor variable. I would like to know the correlation so I can choose the best variables.
str(data)
$ Ag : num [1:1470] 41 49 37 33 27 32 59 30 38 36 ...
$ Ay : Factor w/ 2 levels "No","Yes": 2 1 2 1 1 1 1 1 1 1 ...
$ Bu : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 2 3 2 3 2 3 3 2 3 ...
$ Di : num [1:1470] 1 8 2 3 2 2 3 24 23 27 ...
$ Ed : num [1:1470] 2 1 2 4 1 2 3 1 3 3 ...
$ Ep : num [1:1470] 1 1 1 1 1 1 1 1 1 1 ...
$ Em : num [1:1470] 1 2 4 5 7 8 10 11 12 13 ...
$ Ge : Factor w/ 2 levels "Female","Male": 1 2 2 1 2 2 1 2 2 2 ...
$ Ho : num [1:1470] 94 61 92 56 40 79 81 67 44 94 ...
$ J1 : num [1:1470] 3 2 2 3 3 3 4 3 2 3 ...
$ J2 : num [1:1470] 2 2 1 1 1 1 1 1 3 2 ...
When I execute this(althought I want correlations of all data not only numeric) :
cor(data[sapply(data, is.numeric)])
I return this message:
Warning message:
In cor(data[sapply(data, is.numeric)]) :
the standard deviation is zero
It just politely lets you know that you set out to calculate correlation where one of the variables is constant. This often pointless.
Just filter that out aswell
x1 <- data[sapply(data,is.numeric)]
x2 <- x1[sapply(x1,sd)!=0]
cor(x2)
I just encountered a weird situation in RGui...I used the same script as always to get my data.frame into the right shape for ggplot2. So my data looks like the following:
time days treatment nucleic_acid habitat parallel disturbance variable cellcounts value
1 1 2 control dna water 1 none Proteobacteria batch 0.000000000
2 2 22 control dna water 1 none Proteobacteria batch 0.003586543
3 1 2 treated dna water 1 none Proteobacteria batch 0.000000000
4 2 22 treated dna biofilm 1 none Proteobacteria NA 0.000000000
'data.frame': 185648 obs. of 10 variables:
$ time : int 5 5 5 5 5 5 6 6 6 6 ...
$ days : int 62 62 62 62 62 62 69 69 69 69 ...
$ treatment : Factor w/ 2 levels "control","treated": 2 2 2 1 1 1 2 2 2 1 ...
$ parallel : int 1 2 3 1 2 3 1 2 3 1 ...
$ nucleic_acid: Factor w/ 2 levels "cdna","dna": 1 1 1 1 1 1 1 1 1 1 ...
$ habitat : Factor w/ 2 levels "biofilm","water": 1 1 1 1 1 1 1 1 1 1 ...
$ cellcounts : Factor w/ 4 levels "batch","high",..: NA NA NA NA NA NA NA NA NA NA ...
$ disturbance : Factor w/ 3 levels "high","low","none": 3 3 3 3 3 3 3 3 3 3 ...
$ variable : Factor w/ 656 levels "Proteobacteria",..: 1 1 1 1 1 1 1 1 1 1 ...
$ value : num 0 0 0 0 0 0 0 0 0 0 ...
and I wanted aggregate to calculate the mean value of my up to 3 parallels:
df_mean<-aggregate(value~time+days+treatment+nucleic_acid+habitat+disturbance+variable+cellcounts, data = df, mean)
afterwards, the level "biofilm" in column "habitat" is lost.
df_mean<-droplevels(df_mean)
str(df_mean)
'data.frame': 44608 obs. of 9 variables:
$ time : int 1 2 1 2 1 2 1 2 1 2 ...
$ days : int 2 22 2 22 2 22 2 22 2 22 ...
$ treatment : Factor w/ 2 levels "control","treated": 1 1 2 2 1 1 2 2 1 1 ...
$ nucleic_acid: Factor w/ 2 levels "cdna","dna": 2 2 2 2 2 2 2 2 2 2 ...
$ habitat : Factor w/ 1 level "water": 1 1 1 1 1 1 1 1 1 1 ...
$ disturbance : Factor w/ 3 levels "high","low","none": 3 3 3 3 3 3 3 3 3 3 ...
$ variable : Factor w/ 656 levels "Proteobacteria",..: 1 1 1 1 2 2 2 2 3 3 ...
$ cellcounts : Factor w/ 4 levels "batch","high",..: 1 1 1 1 1 1 1 1 1 1 ...
$ value : num 0 0.00359 0 0 0 ...
So I spent a lot of time (I actually just realised this, there were many more issues that now all seem to be aggregate related) looking into this. I removed the column "cellcounts" and it worked. Interestingly, the columns "cellcounts" and "habitat" always carry in case of "biofilm" the same, therefore redundant, information ("biofilm" goes always with "NA"). Is this the cause? But it always worked before, so I don't get my head around this. Was there a change to the base::aggregate function or something like that? Do you have an explanation for me? I'm using R-3.4.0, other packages used are reshape, reshape2 and ggplot2
Thx a lot, a confused crazysantaclaus
The issue comes from the NA, maybe your file was loaded differently in the past and these were stored as strings instead of NA values ? Here's a way to solve it by setting them to a "NA" string:
levels(df$cellcounts) <- c(levels(df$cellcounts),"NA")
df$cellcounts[is.na(df$cellcounts)] <- "NA"
df_mean <- aggregate(value ~ time+days+treatment+nucleic_acid+habitat+disturbance+variable+cellcounts, data = df, mean,na.rm=TRUE)
df_mean<-droplevels(df_mean)
str(df_mean)
'data.frame': 4 obs. of 9 variables:
$ time : int 1 2 1 2
$ days : int 2 22 2 22
$ treatment : Factor w/ 2 levels "control","treated": 1 1 2 2
$ nucleic_acid: Factor w/ 1 level "dna": 1 1 1 1
$ habitat : Factor w/ 2 levels "biofilm","water": 2 2 2 1
$ disturbance : Factor w/ 1 level "none": 1 1 1 1
$ variable : Factor w/ 1 level "Proteobacteria": 1 1 1 1
$ cellcounts : Factor w/ 2 levels "batch","NA": 1 1 1 2
$ value : num 0 0.00359 0 0
data
df <- read.table(text=" time days treatment nucleic_acid habitat parallel disturbance variable cellcounts value
1 1 2 control dna water 1 none Proteobacteria batch 0.000000000
2 2 22 control dna water 1 none Proteobacteria batch 0.003586543
3 1 2 treated dna water 1 none Proteobacteria batch 0.000000000
4 2 22 treated dna biofilm 1 none Proteobacteria NA 0.000000000
",header=T)
This question already has answers here:
C5.0 decision tree - c50 code called exit with value 1
(6 answers)
Closed 6 years ago.
I'm getting error while working on C5.0 with Mushroom Data set. I've factored the target class and there are no missing values.
f <-file("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data", open="r")
data <- read.table(f, sep=",", header=F)
str(data)
gives
'data.frame': 8124 obs. of 23 variables:
$ V1 : Factor w/ 2 levels "e","p": 2 1 1 2 1 1 1 1 2 1 ...
$ V2 : Factor w/ 6 levels "b","c","f","k",..: 6 6 1 6 6 6 1 1 6 1 ...
$ V3 : Factor w/ 4 levels "f","g","s","y": 3 3 3 4 3 4 3 4 4 3 ...
$ V4 : Factor w/ 10 levels "b","c","e","g",..: 5 10 9 9 4 10 9 9 9 10 ...
$ V5 : Factor w/ 2 levels "f","t": 2 2 2 2 1 2 2 2 2 2 ...
$ V6 : Factor w/ 9 levels "a","c","f","l",..: 7 1 4 7 6 1 1 4 7 1 ...
$ V7 : Factor w/ 2 levels "a","f": 2 2 2 2 2 2 2 2 2 2 ...
$ V8 : Factor w/ 2 levels "c","w": 1 1 1 1 2 1 1 1 1 1 ...
$ V9 : Factor w/ 2 levels "b","n": 2 1 1 2 1 1 1 1 2 1 ...
$ V10: Factor w/ 12 levels "b","e","g","h",..: 5 5 6 6 5 6 3 6 8 3 ...
$ V11: Factor w/ 2 levels "e","t": 1 1 1 1 2 1 1 1 1 1 ...
$ V12: Factor w/ 5 levels "?","b","c","e",..: 4 3 3 4 4 3 3 3 4 3 ...
$ V13: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ V14: Factor w/ 4 levels "f","k","s","y": 3 3 3 3 3 3 3 3 3 3 ...
$ V15: Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ V16: Factor w/ 9 levels "b","c","e","g",..: 8 8 8 8 8 8 8 8 8 8 ...
$ V17: Factor w/ 1 level "p": 1 1 1 1 1 1 1 1 1 1 ...
$ V18: Factor w/ 4 levels "n","o","w","y": 3 3 3 3 3 3 3 3 3 3 ...
$ V19: Factor w/ 3 levels "n","o","t": 2 2 2 2 2 2 2 2 2 2 ...
$ V20: Factor w/ 5 levels "e","f","l","n",..: 5 5 5 5 1 5 5 5 5 5 ...
$ V21: Factor w/ 9 levels "b","h","k","n",..: 3 4 4 3 4 3 3 4 3 3 ...
$ V22: Factor w/ 6 levels "a","c","n","s",..: 4 3 3 4 1 3 3 4 5 4 ...
$ V23: Factor w/ 7 levels "d","g","l","m",..: 6 2 4 6 2 2 4 4 2 4 ...
and when i run
C5.model <- C5.0(data[1:4000,-1],data[1:4000,1],trials = 3)
gives
c50 code called exit with value 1
I had no clue how to find this. Any idea on debugging is appreciated
Edit1 : Error is same but solution is different.
Note: When i changed the data set, it is working.
f <-file("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data", open="r")
data <- read.table(f, sep=",", header=F)
str(data)
pacman::p_load(C50)
C5.model <- C5.0(data[1:10000,c(2:16,18:23)],data[1:10000,1],trials = 3,na.action = na.pass)
Column 17 was the cause of this problem as it had no identifying variation.
I am using mboost package to do some classification. Here is the code
library('mboost')
load('so-data.rdata')
model <- glmboost(is_exciting~., data=training, family=Binomial())
pred <- predict(model, newdata=test, type="response")
But R complains when doing prediction that
Error in scale.default(X, center = cm, scale = FALSE) :
length of 'center' must equal the number of columns of 'x'
The data (training and test) can be downloaded here (7z, zip).
What is the reason of the error and how to get rid of it? Thank you.
UPDATE:
> str(training)
'data.frame': 439599 obs. of 24 variables:
$ is_exciting : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_state : Factor w/ 52 levels "AK","AL","AR",..: 15 5 5 23 47 5 44 42 42 5 ...
$ school_charter : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_magnet : Factor w/ 2 levels "f","t": 1 1 1 1 2 1 1 1 1 1 ...
$ school_year_round : Factor w/ 2 levels "f","t": 1 1 1 1 1 2 1 1 1 2 ...
$ school_nlns : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_charter_ready_promise : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ teacher_prefix : Factor w/ 6 levels "","Dr.","Mr.",..: 5 5 3 5 6 5 6 6 5 6 ...
$ teacher_teach_for_america : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 2 1 2 1 ...
$ teacher_ny_teaching_fellow : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ primary_focus_subject : Factor w/ 28 levels "","Applied Sciences",..: 19 17 18 18 10 4 17 17 18 17 ...
$ primary_focus_area : Factor w/ 8 levels "","Applied Learning",..: 6 5 5 5 5 4 5 5 5 5 ...
$ secondary_focus_subject : Factor w/ 28 levels "","Applied Sciences",..: 28 18 17 19 26 18 18 28 24 25 ...
$ secondary_focus_area : Factor w/ 8 levels "","Applied Learning",..: 7 5 5 6 8 5 5 7 7 4 ...
$ resource_type : Factor w/ 7 levels "","Books","Other",..: 4 4 2 5 5 2 2 5 5 5 ...
$ poverty_level : Factor w/ 4 levels "high poverty",..: 2 2 4 2 1 2 2 1 2 1 ...
$ grade_level : Factor w/ 5 levels "","Grades 3-5",..: 5 5 2 5 5 2 3 2 4 2 ...
$ fulfillment_labor_materials : num 30 35 35 30 30 35 30 35 35 35 ...
$ total_price_excluding_optional_support: num 1274 477 892 548 385 ...
$ total_price_including_optional_support: num 1499 562 1050 645 453 ...
$ students_reached : int 31 20 250 36 19 28 90 21 60 56 ...
$ eligible_double_your_impact_match : Factor w/ 2 levels "f","t": 1 2 1 2 1 2 1 1 1 1 ...
$ eligible_almost_home_match : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 2 2 1 1 ...
$ essay_length : int 236 285 194 351 383 273 385 437 476 159 ...
> str(test)
'data.frame': 44772 obs. of 23 variables:
$ school_state : Factor w/ 51 levels "AK","AL","AR",..: 22 35 11 46 5 35 11 28 28 10 ...
$ school_charter : Factor w/ 2 levels "f","t": 1 1 1 1 2 1 1 1 1 1 ...
$ school_magnet : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_year_round : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_nlns : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ school_charter_ready_promise : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ teacher_prefix : Factor w/ 6 levels "","Dr.","Mr.",..: 3 5 6 6 3 5 5 5 3 5 ...
$ teacher_teach_for_america : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ teacher_ny_teaching_fellow : Factor w/ 2 levels "f","t": 1 2 1 1 1 1 1 1 1 1 ...
$ primary_focus_subject : Factor w/ 28 levels "","Applied Sciences",..: 5 16 17 17 18 11 16 17 2 17 ...
$ primary_focus_area : Factor w/ 8 levels "","Applied Learning",..: 2 4 5 5 5 2 4 5 6 5 ...
$ secondary_focus_subject : Factor w/ 28 levels "","Applied Sciences",..: 25 1 19 1 17 9 17 11 1 1 ...
$ secondary_focus_area : Factor w/ 8 levels "","Applied Learning",..: 4 1 6 1 5 6 5 2 1 1 ...
$ resource_type : Factor w/ 7 levels "","Books","Other",..: 5 5 5 2 5 6 4 5 5 4 ...
$ poverty_level : Factor w/ 4 levels "high poverty",..: 1 2 4 4 1 2 2 2 1 2 ...
$ grade_level : Factor w/ 5 levels "","Grades 3-5",..: 4 3 3 5 4 5 5 4 3 5 ...
$ fulfillment_labor_materials : num 30 30 30 30 30 30 30 30 30 30 ...
$ total_price_excluding_optional_support: num 2185 149 1017 156 860 ...
$ total_price_including_optional_support: num 2571 175 1197 183 1012 ...
$ students_reached : int 200 110 10 22 180 51 30 15 260 20 ...
$ eligible_double_your_impact_match : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
$ eligible_almost_home_match : Factor w/ 2 levels "f","t": 2 1 1 1 1 1 1 1 2 1 ...
$ essay_length : int 221 137 313 243 373 344 304 431 231 173 ...
> summary(model)
Generalized Linear Models Fitted via Gradient Boosting
Call:
glmboost.formula(formula = is_exciting ~ ., data = training, family = Binomial())
Negative Binomial Likelihood
Loss function: {
f <- pmin(abs(f), 36) * sign(f)
p <- exp(f)/(exp(f) + exp(-f))
y <- (y + 1)/2
-y * log(p) - (1 - y) * log(1 - p)
}
Number of boosting iterations: mstop = 100
Step size: 0.1
Offset: -1.197806
Coefficients:
NOTE: Coefficients from a Binomial model are half the size of coefficients
from a model fitted via glm(... , family = 'binomial').
See Warning section in ?coef.mboost
(Intercept) school_stateDC
-0.5250166130 0.0426909965
school_stateIL school_chartert
0.0084191638 0.0729272310
teacher_prefixMrs. teacher_prefixMs.
-0.0181489492 0.0438425925
teacher_teach_for_americat resource_typeBooks
0.2593005345 0.0046126706
resource_typeTechnology fulfillment_labor_materials
-0.0313904871 0.0120086140
eligible_double_your_impact_matcht eligible_almost_home_matcht
-0.0316376431 -0.0522717398
essay_length
0.0004993224
attr(,"offset")
[1] -1.197806
Selection frequencies:
fulfillment_labor_materials teacher_teach_for_americat
0.24 0.15
essay_length school_chartert
0.15 0.09
teacher_prefixMs. resource_typeTechnology
0.08 0.07
eligible_double_your_impact_matcht eligible_almost_home_matcht
0.07 0.07
teacher_prefixMrs. school_stateDC
0.04 0.02
school_stateIL resource_typeBooks
0.01 0.01
I also tried glm but it said
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) :
factor teacher_prefix has new levels
But I don't see any new levels in the teacher_prefix variable:
> levels(training$teacher_prefix)
[1] "" "Dr." "Mr." "Mr. & Mrs." "Mrs." "Ms."
> levels(test$teacher_prefix)
[1] "" "Dr." "Mr." "Mr. & Mrs." "Mrs." "Ms."
Actually, the problems with glmboost and glm are related. There are problems with your teacher_prefix variable.
As the glm example points out, there are levels that are in test that are not in training (kind of). While both factors have the same levels(), the training set has no observations where teacher_prefix=="" but test does. Compare
table(test$teacher_prefix)
table(training$teacher_prefix)
So glm is actually giving the more accurate, helpful error message. The problem is the same with glmboost although it isn't as direct about saying it.
Doing this seemed to "fix" it
test2 <- subset(test, teacher_prefix %in% c("Dr.","Mr.","Mrs.","Ms."))
test2$teacher_prefix <- droplevels(test2$teacher_prefix)
pred <- predict(model, newdata=test2, type="response")
We just get rid of the unused levels and then do the standard prediction.