HMM text recognition in R depmixs4 - r

Question

I'm wondering how I would utilize the depmixs4 package for R to run HMM on a dataset. What functions would I use so I get a classification of a testing data set?
I have a file of training data, a file of label data, and a test data.
Training data consists of 4620 rows. Each row has 1079 values. These values are 83 windows with 13 values per window so in otherwords the 1079 is data that is made up of 83 states and each category has 13 observations. Each of these rows with 1079 values is a spoken word so it have 4620 utterances. But in total the data only has 7 distinct words. each of these distinct words have 660 different utterances hence the 4620 rows of words.
So we have words (0-6)
The label file is a list where each row is labeled 0-6 corresponding to what word they are. For example row 300 is labeled 2, row 450 is labeled 6 and 520 is labeled 0.
The test file contains about 5000 rows structured exactly like the training data except there are no labels assocaiated with it.
I want to use HMM to using the training data to classify the test data.
How would I use depmixs4 to output a classification of my test data?
I'm looking at :
depmix(response, data=NULL, nstates, transition=~1, family=gaussian(),
prior=~1, initdata=NULL, respstart=NULL, trstart=NULL, instart=NULL,
ntimes=NULL,...)
but I don't know what response refers to or any of the other parameters.

Answer 1

Here's a quick, albeit incomplete, test to get you started, if only to familiarize you with the basic outline. Please note that this is a toy example and it merely scratches the surface for HMM design/analysis. The vignette for the depmixs4 package, for instance, offers quite a lot of context and examples. Meanwhile, here's a brief intro.
Let's say that you wanted to investigate if industrial production offers clues about economic recessions. First, let's load the relevant packages and then download the data from the St. Louis Fed:
library(quantmod)
library(depmixS4)
library(TTR)
fred.tickers <-c("INDPRO")
getSymbols(fred.tickers,src="FRED")
Next, transform the data into rolling 1-year percentage changes to minimize noise in the data and convert data into data.frame format for analysis in depmixs4:
indpro.1yr <-na.omit(ROC(INDPRO,12))
indpro.1yr.df <-data.frame(indpro.1yr)
Now, let's run a simple HMM model and choose just 2 states--growth and contraction. Note that we're only using industrial production to search for signals:
model <- depmix(response=INDPRO ~ 1,
family = gaussian(),
nstates = 2,
data = indpro.1yr.df ,
transition=~1)
Now let's fit the resulting model, generate posterior states
for analysis, and estimate probabilities of recession. Also, we'll bind the data with dates in an xts format for easier viewing/analysis. (Note the use of set.seed(1), which is used to create a replicable starting value to launch the modeling.)
set.seed(1)
model.fit <- fit(model, verbose = FALSE)
model.prob <- posterior(model.fit)
prob.rec <-model.prob[,2]
prob.rec.dates <-xts(prob.rec,as.Date(index(indpro.1yr)),
order.by=as.Date(index(indpro.1yr)))
Finally, let's review and ideally plot the data:
head(prob.rec.dates)
[,1]
1920-01-01 1.0000000
1920-02-01 1.0000000
1920-03-01 1.0000000
1920-04-01 0.9991880
1920-05-01 0.9999549
1920-06-01 0.9739622
High values (>0.80 ??) indicate/suggest that the economy is in recession/contraction.
Again, a very, very basic introduction, perhaps too basic. Hope it helps.