Predict Methods for Poisson NMF and Multinomial Topic Model

Predict loadings based on previously fit Poisson NMF, or predict topic proportions based on previously fit multinomial topic model. This can be thought of as projecting data points onto a previously estimated set of factors fit$F.

# S3 method for poisson_nmf_fit
predict(object, newdata, numiter = 20, ...)

# S3 method for multinom_topic_model_fit
predict(object, newdata, numiter = 20, ...)

Arguments

object

An object of class “poisson_nmf_fit” or “multinom_topic_model_fit”.

newdata

An optional counts matrix. If omitted, the loadings estimated in the original data are returned.

numiter

The number of updates to perform.

...

Additional arguments passed to fit_poisson_nmf.

Value

A loadings matrix with one row for each data point and one column for each topic or factor. For predict.multinom_topic_model_fit, the output can also be interpreted as a matrix of estimated topic proportions, in which L[i,j] is the proportional contribution of topic j to data point i.

See also

fit_poisson_nmf

Examples

# \donttest{
# Simulate a 175 x 1,200 counts matrix.
set.seed(1)
dat <- simulate_count_data(175,1200,k = 3)

# Split the data into training and test sets.
train <- dat$X[1:100,]
test <- dat$X[101:175,]

# Fit a Poisson non-negative matrix factorization using the
# training data.
fit <- init_poisson_nmf(train,F = dat$F,init.method = "random")
fit <- fit_poisson_nmf(train,fit0 = fit)
#> Fitting rank-3 Poisson NMF to 100 x 1200 dense matrix.
#> Running 100 SCD updates, without extrapolation (fastTopics 0.6-147).

# Compare the estimated loadings in the training data against the
# loadings used to simulate these data.
Ltrain <- predict(fit)
plot(dat$L[1:100,],Ltrain,pch = 20,col = "darkblue")
abline(a = 0,b = 1,col = "magenta",lty = "dotted",
       xlab = "true",ylab = "estimated")


# Next, predict loadings in unseen (test) data points, and compare
# these predictions against the loadings that were used to simulate
# the test data.
Ltest <- predict(fit,test)
#> Fitting rank-3 Poisson NMF to 75 x 1200 dense matrix.
#> Running 20 SCD updates, without extrapolation (fastTopics 0.6-147).
plot(dat$L[101:175,],Ltest,pch = 20,col = "darkblue",
     xlab = "true",ylab = "estimated")
abline(a = 0,b = 1,col = "magenta",lty = "dotted")


# Simulate a 175 x 1,200 counts matrix.
set.seed(1)
dat <- simulate_multinom_gene_data(175,1200,k = 3)

# Split the data into training and test sets.
train <- dat$X[1:100,]
test <- dat$X[101:175,]

# Fit a topic model using the training data.
fit <- init_poisson_nmf(train,F = dat$F,init.method = "random")
fit <- fit_poisson_nmf(train,fit0 = fit)
#> Fitting rank-3 Poisson NMF to 100 x 1200 dense matrix.
#> Running 100 SCD updates, without extrapolation (fastTopics 0.6-147).
fit <- poisson2multinom(fit)

# Compare the estimated topic proportions in the training data against
# the topic proportions used to simulate these data.
Ltrain <- predict(fit)
plot(dat$L[1:100,],Ltrain,pch = 20,col = "darkblue")
abline(a = 0,b = 1,col = "magenta",lty = "dotted",
       xlab = "true",ylab = "estimated")


# Next, predict loadings in unseen (test) data points, and compare
# these predictions against the loadings that were used to simulate
# the test data.
Ltest <- predict(fit,test)
#> Fitting rank-3 Poisson NMF to 75 x 1200 dense matrix.
#> Running 20 SCD updates, without extrapolation (fastTopics 0.6-147).
plot(dat$L[101:175,],Ltest,pch = 20,col = "darkblue",
     xlab = "true",ylab = "estimated")
abline(a = 0,b = 1,col = "magenta",lty = "dotted")

# }