matrix_glmnet: Fit a glmnet model on Dataset object.
In surh/AMORglmnet: Plugin to use glmnet models with AMOR

Description Usage Arguments Details Value Author(s) Examples

This function takes a Dataset object and fits the specified glmnet model on each taxon. righn now only alpha = 0 (ie. ridge regularization) is supported. The function uses 10-fold cross-validation to select lambda, and permutation to obtain p-values.

matrix_glmnet(Dat, X = NULL, formula = NULL, family = "binomial",
  alpha = 0, glmnet.intercept = TRUE, glmnet.offset = NULL,
  method = "permutation", nperm = 1000, nboot = nperm, plot = FALSE,
  theme = theme_blackbox, verbose = TRUE)

`Dat`	A Dataset object.
`X`	A numeric matrix to be used as desing matrix for the model. If missing, the script will use the formula interface with the overparametrized_model.matrix function to obtain a valid design matrix.
`formula`	Right hand side of the formula to be used for the model. Variable names must correspond to header names in Map portion of the Dataset object. Ignored if X is passed. Note that offset is not supported.
`family`	The model family to be used. See glmnet for more help.
`alpha`	The elastic net parameter. 1 corresponds ot Lasso and 0 to Ridge, with intermediate values cirrespondig to mixtures between the two. This function only supports 0 for the moment and will use that value regardless of what the user supplies.
`glmnet.intercept`	Logical indicating whether to add an intercept. See intercept option in glmnet
`glmnet.offset`	Either a character indicating the name of the variable to be used as offset; or a numeric vector of length equal to the number of samples to be used directly as offset, see offset option in glmnet. Note that family multinomial takes a matrix as offset, while passing a vector of variable names should work, note that this has not been tested and might produce incorrect results. The function will print a warning if you try to do this.
`method`	Either "permuation" or "bootstrap". The default and preferred method is permutation.
`nperm`	Number of permutations to perform.
`nboot`	Number of bootstrap pseudo replicates to perform.
`plot`	Currently not implemented. The funciton contains code for plotting null distributions of parameters and observed values. Might eventually be an independent function.
`theme`	ggplot2 theme for plots.
`verbose`	Logical indicating if progress should be printed

Uses cv.glmnet with 10-fold cross-validation to pick the optiomal lambda value and then uses glmnet to fit each permuted dataset.

Returns a list with the following elements

`X`	Design matrix
`coeffcients`	Matrix of coefficients per taxon
`lambda`	Vector of lambda values per taxon
`method`	Method used for the replicates
`family`	Model family
`nreps`	Number of replicates performed
`call`	Function call
`formula`	model formula
`samples`	List of coefficiente matrices per OTU and replicate

Sur from Dangl Lab

# Load data
data(Rhizo)
data(Rhizo.map)

# Create dataset with the first 10 OTUS
Dat <- create_dataset(Rhizo[1:10,],Rhizo.map)

# Always set seed before using random numbers (like in permuations)
set.seed(743)
m1 <- matrix_glmnet(Dat = Dat, formula = ~ fraction + accession,
                    nperm = 100,family = "poisson")

# Use summary function to obtain statistics
m1.sum <- summary(m1)
# See distribution of pvalues behaves as expectee
hist(m1.sum$coefficients$p.value)

# Use bootstrap instead
m1 <- matrix_glmnet(Dat = Dat, formula = ~ fraction + accession,
                    nperm = 100,family = "poisson", method ="bootstrap")

# Use summary function to obtain statistics
m1.sum <- summary(m1)

# Visualizing coefficientw with ggplot requires a bit of rearrangement because
# the percent symbol in the column names causes trouble
dat <- subset(m1.sum$coefficients, Taxon == "OTU_14834")
dat$lower <- dat[,"2.5%"]
dat$upper <- dat[,"97.5%"]
p1 <- ggplot(dat,aes(x = lower, y = Variable)) +
  geom_segment(aes(xend = upper,yend = Variable)) +
  geom_vline(xintercept = 0, col = 'red') +
  theme_blackbox
p1