predictLars: Predict using Least Angle Regression (LARS) model

In adamlilith/enmSdm: Tools for Modeling Niches and Distributions of Species

Predict using Least Angle Regression (LARS) model

Description

This function makes calculates predictions from a grpregOverlap object constructed using the trainLars function.

Usage

predictLars(
  object,
  newdata,
  type = c("link", "response"),
  preds = NULL,
  failOnMissing = FALSE,
  ...
)

Arguments

object	Object of classes larsModel and cv.grpregOverlapMulti.
newdata	Data frame.
type	Character. Type of prediction to make: link produces predictions on the scale of the predictors, response produces predictions on the scale of the response (i.e., in the range [0, 1] if family = 'binomial' in trainLars).
preds	NULL or a character list. If NULL then all predictors (and their higher-order terms) will be used in the prediction. If a character vector that is a subset of the predictors used in training the LARS model, then the function yields partial predictions using only terms that include the specified variable(s) (plus any quadratic, cubic, interaction, and linear-by-quadratic terms, if the model was trained using them).
failOnMissing	Logical. If TRUE then if no variable(s) stated in pred occurs in the model then cause an error. Variables can be missing either because the user did not include them in the original model or because the variable(s) had no variability and so were removed automatically in the model training process. Note that if failOnMissing is TRUE and there is at least one variable in preds that occurs in the model then this function will still return values (it will not cause an error). If failOnMissing is FALSE (default) and no variables in preds occurs in the model then a warning will be produced and the returned value will be NULL.
...	Arguments to pass to predict.grpregOverlap.

Value

Numeric.

See Also

grpreg, grpregOverlap, predict.grpregOverlap, trainLars

Examples

# linear regression, a simulation demo
# (from grpregOverlap() function in the grpregOverlap package)
## Not run: 
set.seed(123)
X <- matrix(rnorm(n = 6*100), ncol = 6)
# true variables will be #1, #2, #5, and #6, plus
# the squares of #1 and #6, plus
# interaction between #1 and #6
# the cube of #5
imp <- c('x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x1_pow2', 'x6_pow2', 'x1_by_x6', 'x5_pow3')
betas <- c(5, 2, 0, 0, 1, -1, 8, 1, 2, -4)
names(betas) <- imp
y <- 0.5 + X %*% betas[1:6] + betas[7] * X[ , 1]^2 +
    betas[8] * X[ , 6]^2 + betas[9] * X[ , 1] * X[ , 6] + betas[10] * X[ , 5]^3
y <- as.integer(y >= 0)
X <- cbind(y, X)
X <- as.data.frame(X)
preds <- c('x1', 'x2', 'x3', 'x4', 'x5', 'x6')
names(X) <- c('y', preds)

# NOTE: default is to use nfolds=10 and a finer sequence of alphas
fit1 <- trainLars(data=X, penalty='cMCP', family='binomial', nfolds=3, alphas=c(0.01, 0.5, 1),
   quadratic=FALSE, cubic=FALSE, interaction=FALSE, interQuad=FALSE)
fit2 <- trainLars(data=X, penalty='cMCP', family='binomial', nfolds=3, alphas=c(0.01, 0.5, 1),
    quadratic=TRUE, cubic=FALSE, interaction=FALSE, interQuad=FALSE)
fit3 <- trainLars(data=X, penalty='cMCP', family='binomial', nfolds=3, alphas=c(0.01, 0.5, 1),
    quadratic=TRUE, cubic=TRUE, interaction=TRUE, interQuad=TRUE)

summary(fit1)
summary(fit2)
summary(fit3)

XX <- matrix(rnorm(n = 6*100), ncol = 6)
XX <- as.data.frame(XX)
names(XX) <- preds

# predictions using all variables
pred1 <- predictLars(fit1, XX, type='response')
pred2 <- predictLars(fit2, XX, type='response')
pred3 <- predictLars(fit3, XX, type='response')

# partial predictions examining effect of just x1 (plus any interactions)
pred1x1 <- predictLars(fit1, XX, type='response', preds='x1')
pred2x1 <- predictLars(fit2, XX, type='response', preds='x1')
pred3x1 <- predictLars(fit3, XX, type='response', preds='x1')

# partial predictions examining effect of just x4 (plus any interactions--
# note in reality x4 has no effect on y)
pred1x4 <- predictLars(fit1, XX, type='response', preds='x4')
pred2x4 <- predictLars(fit2, XX, type='response', preds='x4')
pred3x4 <- predictLars(fit3, XX, type='response', preds='x4')

par(mfrow=c(3, 3))
xlim <- c(0, 1)
breaks <- seq(0, 1, by=0.1)
hist(pred1, main='linear only', xlim=xlim, breaks=breaks)
hist(pred2, main='linear + quadratic', xlim=xlim, breaks=breaks)
hist(pred3, main='all terms', xlim=xlim, breaks=breaks)

hist(pred1x1, main='x1: linear only', xlim=xlim, breaks=breaks)
hist(pred2x1, main='x1: linear + quadratic', xlim=xlim, breaks=breaks)
hist(pred3x1, main='x1: all terms \n(linear, quad, cubic)', xlim=xlim, breaks=breaks)

hist(pred1x4, main='x4: linear only', xlim=xlim, breaks=breaks)
hist(pred2x4, main='x4: linear + quadratic', xlim=xlim, breaks=breaks)
hist(pred3x4, main='x4: all terms \n(linear, quad, cubic)', xlim=xlim, breaks=breaks)

# predictions using just x1 and x2
par(mfrow=c(1, 1))
pred3x1x2 <- predictLars(fit3, XX, type='response', preds=c('x1', 'x2'))
plot(pred3, pred3x1x2, xlim=c(0, 1), ylim=c(0, 1))

## End(Not run)

adamlilith/enmSdm documentation built on Jan. 6, 2023, 11 a.m.