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.
来源：https://stackoverflow.com/questions/24224554/error-in-scale-default-length-of-center-must-equal-the-number-of-columns-of