CODEWITHSUNDEEP
r
user2407064
user2407064

Reputation: 55

Accessing class values in R's poLCA

I am trying my hand at learning Latent Component Analysis, while also learning R. I'm using the poLCA package, and am having a bit of trouble accessing the attributes. I can run the sample code just fine:

ds = read.csv("http://www.math.smith.edu/r/data/help.csv")
ds = within(ds, (cesdcut = ifelse(cesd>20, 1, 0)))

library(poLCA)
res2 = poLCA(cbind(homeless=homeless+1, 
    cesdcut=cesdcut+1, satreat=satreat+1, 
    linkstatus=linkstatus+1) ~ 1, 
    maxiter=50000, nclass=3, 
    nrep=10, data=ds)

but in order to make this more useful, I'd like to access the attributes within the objects created by the poLCA class as such: 

attr(res2, 'Nobs')
attr(res2, 'maxiter')

but they both come up as 'Null'. I expect Nobs to be 453 (determined by the function) and maxiter to be 50000 (dictated by my input value).

I'm sure I'm just being naive, but I could use any help available. Thanks a lot!

Upvotes: 1

Views: 677

Answers (1)

Hong Ooi
Hong Ooi

Reputation: 57696

Welcome to R. You've got the model-fitting syntax right, in that you can get a model out (don't know how latent component analysis works, so can't speak to the statistical validity of your result). However, you've mixed up the different ways in which R can store information pertaining to a model.

poLCA returns an object of class poLCA, which is

a list containing the following elements:

(. . .)

Nobs number of fully observed cases (less than or equal to N).

maxiter maximum number of iterations through which the estimation algorithm was set to run.

Since it's a list, you can extract individual elements from your model object using the $ operator:

res2$Nobs      # number of observations
res2$maxiter   # maximum iterations

In some cases, there might be extractor functions to get this information without having to do low-level indexing. For example, many model-fitting functions will have a fitted method, which pulls out the vector of fitted values on the training data; and similarly residuals pulls out the vector of residuals. You should check whether there are such extractor functions provided by the poLCA package and use them if possible; that way, you're not making assumptions about the structure of the model object that might be broken in the future.

This is distinct to getting the attributes of an object, which is what you use attr for. Attributes in R are what you might call metadata: they contain R-specific information about an object itself, rather than information about whatever it is the object relates to. Examples of common attributes include class (the class of an object), dim (the dimensions of an array or matrix), names (names of individual elements of a vector/list/array) and so on.

Upvotes: 3

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