predict.glmGamPoi: Predict 'link' or 'response' values for Gamma-Poisson GLMs

In const-ae/glmGamPoi: Fit a Gamma-Poisson Generalized Linear Model

Predict 'link' or 'response' values for Gamma-Poisson GLMs

Description

Predict mu (i.e., type = "response") or log(mu) (i.e., type = "link") from a 'glmGamPoi' fit (created by glm_gp(...)) with the corresponding estimate of the standard error. If newdata is NULL, mu is returned for the original input data.

Usage

## S3 method for class 'glmGamPoi'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  se.fit = FALSE,
  offset = mean(object$Offset),
  on_disk = NULL,
  verbose = FALSE,
  ...
)

Arguments

object	a glmGamPoi fit object (produced by glm_gp()).
newdata	a specification of the new data for which the expression for each gene is predicted. newdata should be a data.frame if the original fit was specified with a formula, provide a data.frame with one column for each variable in the formula. For example, if glm_gp(se, design = ~ age + batch + treatment), then the data.frame needs a age, batch, and treatment column that contain the same data types as the original fit. vector if the original fit was specified using a vector, you need to again provide a vector with the same format. matrix if newdata is a matrix, it is applied directly as Mu <- exp(object$Beta %*% t(newdata) + object$offset_matrix). So make sure, that it is constructed correctly. NULL if newdata is NULL, the predicted values for the original input data are returned.
type	either 'link' or 'response'. The default is 'link', which returns the predicted values before the link function (exp()) is applied. Thus, the values can be positive and negative numbers. However, often the predicted values are easier to interpret after the link function is applied (i.e., type = "response"), because then the values are on the same scale as the original counts.
se.fit	boolean that indicates if in addition to the mean the standard error of the mean is returned.
offset	count models (in particular for sequencing experiments) usually have a sample specific size factor (⁠offset = log(size factor)⁠). It defines how big we expect the predicted results are. If newdata is NULL, the offset is ignored, because the predict() returns a result based on the pre-calculated object$Mu. If newdata is not NULL, by default the offset is mean(object$Offset), which puts the in the same size as the average sample.
on_disk	a boolean that indicates if the results are HDF5Matrix's from the HDF5Array package. If newdata is NULL, on_disk is ignored. Otherwise, if on_disk = NULL, the result is calculated on disk depending if offset is stored on disk.
verbose	a boolean that indicates if information about the individual steps are printed while predicting. Default: FALSE.
...	currently ignored.

Details

For se.fit = TRUE, the function sticks very close to the behavior of stats::predict.glm() for fits from MASS::glm.nb().

Note: If type = "link", the results are computed using the natural logarithm as the link function. This differs from the lfc estimate provided by test_de, which are on the log2 scale.

Value

If se.fit == FALSE, a matrix with dimensions ⁠nrow(object$data) x nrow(newdata)⁠.
If se.fit == TRUE, a list with three entries

fit

the predicted values as a matrix with dimensions ⁠nrow(object$data) x nrow(newdata)⁠. This is what would be returned if se.fit == FALSE.

se.fit

the associated standard errors for each fit. Also a matrix with dimensions ⁠nrow(object$data) x nrow(newdata)⁠.

residual.scale

Currently fixed to 1. In the future, this might become the values from object$overdispersion_shrinkage_list$ql_disp_shrunken.

See Also

stats::predict.lm() and stats::predict.glm()

Examples

 set.seed(1)
 # The simplest example
 y <- rnbinom(n = 10, mu = 3, size = 1/2.4)
 fit <- glm_gp(y, size_factors = FALSE)
 predict(fit, type = "response")
 predict(fit, type = "link", se.fit = TRUE)


 # Fitting a whole matrix
 model_matrix <- cbind(1, rnorm(5))
 true_Beta <- cbind(rnorm(n = 30), rnorm(n = 30, mean = 3))
 sf <- exp(rnorm(n = 5, mean = 0.7))
 model_matrix
 Y <- matrix(rnbinom(n = 30 * 5, mu = sf * exp(true_Beta %*% t(model_matrix)), size = 1/2.4),
             nrow = 30, ncol = 5)
 fit <- glm_gp(Y, design = model_matrix, size_factors = sf, verbose = TRUE)

 head(predict(fit, type = "response"))
 pred <- predict(fit, type = "link", se.fit = TRUE, verbose = TRUE)
 head(pred$fit)
 head(pred$se.fit)


 # Fitting a model with covariates
 data <- data.frame(fav_food = sample(c("apple", "banana", "cherry"), size = 50, replace = TRUE),
                    city = sample(c("heidelberg", "paris", "new york"), size = 50, replace = TRUE),
                    age = rnorm(n = 50, mean = 40, sd = 15))
 Y <- matrix(rnbinom(n = 4 * 50, mu = 3, size = 1/3.1), nrow = 4, ncol = 50)
 fit <- glm_gp(Y, design = ~ fav_food + city + age, col_data = data)
 predict(fit)[, 1:3]

 nd <- data.frame(fav_food = "banana", city = "paris", age = 29)
 predict(fit, newdata = nd)

 nd <- data.frame(fav_food = "banana", city = "paris", age = 29:40)
 predict(fit, newdata = nd, se.fit = TRUE, type = "response")

const-ae/glmGamPoi documentation built on July 25, 2024, 10:22 p.m.