Fredrik Karlsson - 7 months ago 27

R Question

This question may be too package specific, but I would value input on what can be wrong in my use of the

`predict`

The procedure I'm using is the following:

`require(penalized)`

# neg contains negative data

# pos contains positive data

Now, the procedure below aims to construct comparable (balanced in terms os positive and negative cases) training and validation data sets.

`# 50% negative training set`

negSamp <- neg %>% sample_frac(0.5) %>% as.data.frame()

# Negative validation set

negCompl <- neg[setdiff(row.names(neg),row.names(negSamp)),]

# 50% positive training set

posSamp <- pos %>% sample_frac(0.5) %>% as.data.frame()

# Positive validation set

posCompl <- pos[setdiff(row.names(pos),row.names(posSamp)),]

# Combine sets

validat <- rbind(negSamp,posSamp)

training <- rbind(negCompl,posCompl)

Ok, so here we now have two comparable sets.

`[1] FALSE TRUE`

> dim(training)

[1] 1061 381

> dim(validat)

[1] 1060 381

> identical(names(training),names(validat))

[1] TRUE

I fit the model to the training set without a problem (and I've tried using a range of Lambda1 values here). But, fitting the model to the validation data set fails, with a just odd error description.

`> fit <- penalized(VoiceTremor,training[-1],data=training,lambda1=40,standardize=TRUE)`

# nonzero coefficients: 13

> fit2 <- predict(fit, penalized=validat[-1], data=validat)

Error in .local(object, ...) :

row counts of "penalized", "unpenalized" and/or "data" do not match

Just to make sure that this is not due to some NA's in the data set:

`> identical(validat,na.omit(validat))`

[1] TRUE

Oddly enough, I may generate some new data that is comparable to the proper data set:

`data.frame(VoiceTremor="NVT",matrix(rnorm(380000),nrow=1000,ncol=380) ) -> neg`

data.frame(VoiceTremor="VT",matrix(rnorm(380000),nrow=1000,ncol=380) ) -> pos

> dim(pos)

[1] 1000 381

> dim(neg)

[1] 1000 381

and run the procedure above, and then the second fit works!

How come? What could be wrong with my second (not training) data set?

Answer

Ok,

I found the solution to this problem. The problem was in my finding of complementary data sets.

```
neg[setdiff(row.names(neg),row.names(negSamp)),]
```

does not do the right thing, but

```
neg %>%
rownames_to_column() %>%
filter(! rowname %in% row.names(negSamp)) %>%
column_to_rownames() %>% data.frame()
```

does. With this change, along with using `data.frame`

instead of `as.data.frame`

then it all works.