Basic

• https://en.wikipedia.org/wiki/Lasso_(statistics). It has a discussion when two covariates are highly correlated. For example if gene ${\displaystyle i}$ and gene ${\displaystyle j}$ are identical, then the values of ${\displaystyle \beta _{j}}$ and ${\displaystyle \beta _{k}}$ that minimize the lasso objective function are not uniquely determined. Elastic Net has been designed to address this shortcoming.
• Strongly correlated covariates have similar regression coefficients, is referred to as the grouping effect. From the wikipedia page "one would like to find all the associated covariates, rather than selecting only one from each set of strongly correlated covariates, as lasso often does. In addition, selecting only a single covariate from each group will typically result in increased prediction error, since the model is less robust (which is why ridge regression often outperforms lasso)".
• Glmnet Vignette. It tries to minimize ${\displaystyle RSS(\beta )+\lambda [(1-\alpha )\|\beta \|_{2}^{2}/2+\alpha \|\beta \|_{1}]}$. The elastic-net penalty is controlled by ${\displaystyle \alpha }$, and bridge the gap between lasso (${\displaystyle \alpha =1}$) and ridge (${\displaystyle \alpha =0}$). Following is a CV curve (adaptive lasso) using the example from glmnet(). Two vertical lines are indicated: left one is lambda.min (that gives minimum mean cross-validated error) and right one is lambda.1se (the most regularized model such that error is within one standard error of the minimum). For the tuning parameter ${\displaystyle \lambda }$,
• The larger the ${\displaystyle \lambda }$, more coefficients are becoming zeros (think about coefficient path plots) and thus the simpler (more regularized) the model.
• If ${\displaystyle \lambda }$ becomes zero, it reduces to the regular regression and if ${\displaystyle \lambda }$ becomes infinity, the coefficients become zeros.
• In terms of the bias-variance tradeoff, the larger the ${\displaystyle \lambda }$, the higher the bias and the lower the variance of the coefficient estimators.
set.seed(1010)
n=1000;p=100
nzc=trunc(p/10)
x=matrix(rnorm(n*p),n,p)
beta=rnorm(nzc)
fx= x[,seq(nzc)] %*% beta
eps=rnorm(n)*5
y=drop(fx+eps)
px=exp(fx)
px=px/(1+px)
ly=rbinom(n=length(px),prob=px,size=1)

## Full lasso
set.seed(999)
cv.full <- cv.glmnet(x, ly, family='binomial', alpha=1, parallel=TRUE)
plot(cv.full)  # cross-validation curve and two lambda's
plot(glmnet(x, ly, family='binomial', alpha=1), xvar="lambda", label=TRUE) # coefficient path plot
plot(glmnet(x, ly, family='binomial', alpha=1))  # L1 norm plot
log(cv.full$lambda.min) # -4.546394 log(cv.full$lambda.1se) # -3.61605
sum(coef(cv.full, s=cv.full$lambda.min) != 0) # 44 ## Ridge Regression to create the Adaptive Weights Vector set.seed(999) cv.ridge <- cv.glmnet(x, ly, family='binomial', alpha=0, parallel=TRUE) wt <- 1/abs(matrix(coef(cv.ridge, s=cv.ridge$lambda.min)
[, 1][2:(ncol(x)+1)] ))^1 ## Using gamma = 1, exclude intercept
## Adaptive Lasso using the 'penalty.factor' argument
set.seed(999)
cv.lasso <- cv.glmnet(x, ly, family='binomial', alpha=1, parallel=TRUE, penalty.factor=wt)
# defautl type.measure="deviance" for logistic regression
plot(cv.lasso)
log(cv.lasso$lambda.min) # -2.995375 log(cv.lasso$lambda.1se) # -0.7625655
sum(coef(cv.lasso, s=cv.lassolambda.min) != 0) # 34  Lambda ${\displaystyle \lambda }$ Mixing parameter ${\displaystyle \alpha }$ cva.glmnet() from the glmnetUtils package to choose both the alpha and lambda parameters via cross-validation, following the approach described in the help page for cv.glmnet. Underfittig, overfitting and relaxed lasso Plots library(glmnet) data(QuickStartExample) cvfit = cv.glmnet(x, y) fit = glmnet(x, y) oldpar <- par(mfrow=c(2,2)) plot(cvfit) # mse vs log(lambda) plot(fit) # coef vs L1 norm plot(fit, xvar = "lambda", label = TRUE) # coef vs log(lambda) plot(fit, xvar = "dev", label = TRUE) # coef vs Fraction Deviance Explained par(oldpar)  print() method ?print.glmnet and ?print.cv.glmnet Extract/compute deviance deviance(fitted_glmnet_object) coxnet.deviance(pred = NULL, y, x = 0, offset = NULL, weights = NULL, beta = NULL)  cv.glmnet Usage cv.glmnet(x, y, weights = NULL, offset = NULL, lambda = NULL, type.measure = c("default", "mse", "deviance", "class", "auc", "mae", "C"), nfolds = 10, foldid = NULL, alignment = c("lambda", "fraction"), grouped = TRUE, keep = FALSE, parallel = FALSE, gamma = c(0, 0.25, 0.5, 0.75, 1), relax = FALSE, trace.it = 0, ...)  type.measure parameter (loss to use for CV): • default = deviance which uses squared-error for gaussian models (a.k.a type.measure="mse"), logistic and poisson regresion, and partial-likelihood for the Cox model. PS: for binary response data I found that type='class' gives a discontinuous CV curve while 'deviance' give a smooth CV curve. • mse or mae (mean absolute error) can be used by all models except the "cox"; they measure the deviation from the fitted mean to the response • class applies to binomial and multinomial logistic regression only, and gives misclassification error. • auc is for two-class logistic regression only, and gives area under the ROC curve. • C is Harrel's concordance measure, only available for cox models No need for glmnet if we have run cv.glmnet https://stats.stackexchange.com/a/77549 Do not supply a single value for lambda. Supply instead a decreasing sequence of lambda values. glmnet relies on its warms starts for speed, and its often faster to ﬁt a whole path than compute a single ﬁt. cv.glmnet in cox model Note that the y-axis on the plot depends on the type.measure parameter. It is not the objective function used to find the estimator. It is not always partial likelihood device has a largest value at a large lambda. In the following two plots, the first one is from the glmnet vignette and the 2nd one is from the coxnet vignette. Relaxed fit and ${\displaystyle \gamma }$ parameter Relaxed fit: Take a glmnet fitted object, and then for each lambda, refit the variables in the active set without any penalization. Suppose the glmnet fitted linear predictor at ${\displaystyle \lambda }$ is ${\displaystyle {\hat {\eta }}_{\lambda }(x)}$ and the relaxed version is ${\displaystyle {\tilde {\eta }}_{\lambda }(x)}$. We also allow for shrinkage between the two: {\displaystyle {\begin{aligned}{\tilde {\eta }}_{\lambda ,\gamma }=\gamma {\hat {\eta }}_{\lambda }(x)+(1-\gamma ){\tilde {\eta }}_{\lambda }(x).\end{aligned}}} ${\displaystyle \gamma \in [0,1]}$ is an additional tuning parameter which can be selected by cross validation. The debiasing will potentially improve prediction performance, and CV will typically select a model with a smaller number of variables. The default behavior of extractor functions like predict and coef, as well as plot will be to present results from the glmnet fit (not cv.glmnet), unless a value of ${\displaystyle \gamma }$ is given different from the default value ${\displaystyle \gamma =1}$. Question: how does cv.glmnet() select ${\displaystyle \gamma }$ parameter? Ans: it depends on the parameter type.measure in cv.glmnet.  library(glmnet) data(QuickStartExample) fitr=glmnet(x,y, relax=TRUE) set.seed(1) cfitr=cv.glmnet(x,y,relax=TRUE) c(fitrlambda.min, fitr$lambda.1se) # [1] 0.08307327 0.15932708 str(cfitr$relaxed) plot(cfitr),oldpar <- par(mfrow=c(1,3), mar = c(5,4,6,2)) plot(fitr, main = expression(gamma == 1)) plot(fitr,gamma=0.5, main = expression(gamma == .5)) plot(fitr,gamma=0, main = expression(gamma == 0)) par(oldpar)  Special cases: ${\displaystyle \gamma =1}$: only regularized fit, no relaxed fit. ${\displaystyle \gamma =0}$: only relaxed fit; a faster version of forward stepwise regression. set.seed(1) cfitr2=cv.glmnet(x,y,gamma=0,relax=TRUE) # default gamma = c(0, 0.25, 0.5, 0.75, 1) plot(cfitr2) c(cfitr2$lambda.min, cfitr2$lambda.1se) # [1] 0.08307327 0.15932708 str(cfitr2$relaxed)  Computation time beta1 = 2; beta2 = -1 lambdaT = .002 # baseline hazard lambdaC = .004 # hazard of censoring set.seed(1234) x1 = rnorm(n) x2 = rnorm(n) # true event time T = Vectorize(rweibull)(n=1, shape=1, scale=lambdaT*exp(-beta1*x1-beta2*x2)) # No censoring event2 <- rep(1, length(T)) system.time(fit <- cv.glmnet(x, Surv(T,event2), family = 'cox')) # user system elapsed # 4.701 0.016 4.721 system.time(fitr <- cv.glmnet(x, Surv(T,event2), family = 'cox', relax= TRUE)) # user system elapsed # 161.002 0.382 161.573  predict() and coef() methods ?predict.glmnet OR ?coef.glmnet OR ?coef.relaxed. Similar to other predict methods, this functions predicts fitted values, logits, coefficients and more from a fitted "glmnet" object. ## S3 method for class 'glmnet' predict(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) ## S3 method for class 'relaxed' predict(object, newx, s = NULL, gamma = 1, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...) ## S3 method for class 'glmnet' coef(object, s = NULL, exact = FALSE, ...)  ?predict.cv.glmnet OR ?coef.cv.glmnet OR ?coef.cv.relaxed. This function makes predictions from a cross-validated glmnet model, using the stored "glmnet.fit" object, and the optimal value chosen for lambda (and gamma for a 'relaxed' fit). ## S3 method for class 'cv.glmnet' predict(object, newx, s = c("lambda.1se", "lambda.min"), ...) ## S3 method for class 'cv.relaxed' predict(object, newx, s = c("lambda.1se", "lambda.min"), gamma = c("gamma.1se", "gamma.min"), ...)  Cindex Usage: Cindex(pred, y, weights = rep(1, nrow(y)))  assess.glmnet Usage: assess.glmnet(object, newx = NULL, newy, weights = NULL, family = c("gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian"), ...) confusion.glmnet(object, newx = NULL, newy, family = c("binomial", "multinomial"), ...) roc.glmnet(object, newx = NULL, newy, ...)  ridge regression # example from SGL package set.seed(1) n = 50; p = 100; size.groups = 10 X = matrix(rnorm(n * p), ncol = p, nrow = n) beta = (-2:2) y = X[,1:5] %*% beta + 0.1*rnorm(n) data = list(x = X, y = y) cvfit <- cv.glmnet(X, y, alpha = 0) plot(cvfit) o <- coef(cvfit, lambda = cvfit$lambda.min) %>% drop()

sum(o != 0) # [1] 101.
# Too biased.
o[1:10]
#   (Intercept)            V1            V2            V3            V4
# -3.269401e-01 -2.253226e-36 -8.900799e-37  5.198885e-37  1.311976e-36
#            V5            V6            V7            V8            V9
#  1.873125e-36  1.582532e-37  2.085781e-37  4.732839e-37  2.997614e-37

y_predicted <- predict(cvfit, s = cvfit$lambda.min, newx = X) # Sum of Squares Total and Error sst <- sum((y - mean(y))^2) sse <- sum((y_predicted - y)^2) # R squared rsq <- 1 - sse / sst rsq # 0.46 library(SGL) # sparse group lasso set.seed(1) index <- ceiling(1:p / size.groups) cvFit = cvSGL(data, index, type = "linear", alpha=.95) # this alpha is the default plot(cvFit) cvFit$fit$beta[, 20] # 20th lambda gives smallest negative log likelihood # identify correct predictors # [1] -10.942712 -6.167799 0.000000 6.595406 14.442019 0.000000 ... set.seed(1) cvFit2 = cvSGL(data, index, type = "linear", alpha=0) plot(cvFit2) cvFit2$fit$beta[, 20] # [1] -10.8417371 -6.5251240 0.2476438 6.7223001 14.1605263 0.2149542 # [7] 0.2481450 0.1404282 0.1799818 0.3784596 0.0000000 0.0000000 ...  • Tikhonov regularization (ridge regression). It was used to handle ill-posed/overfitting situation. Ridge regression shrinks the coefficients by a uniform factor of ${\displaystyle {\displaystyle (1+N\lambda )^{-1}}{\displaystyle (1+N\lambda )^{-1}}}$ and does not set any coefficients to zero. • cvSGL • How and when: ridge regression with glmnet. On training data, ridge regression fits less well than the OLS but the parameter estimate is more stable. So it does better in prediction because it is less sensitive to extreme variance in the data such as outliers. Group lasso • pcLasso: Principal Components Lasso package • pclasso paper, slides, Blog • Each feature must be assigned to a group • It allows to assign each feature to groups (including overlapping). library(pcLasso) set.seed(1) n = 50; p = 100; size.groups = 10 index <- ceiling(1:p / size.groups) X = matrix(rnorm(n * p), ncol = p, nrow = n) beta = (-2:2) y = X[,1:5] %*% beta + 0.1*rnorm(n) groups <- vector("list", 3) for (k in 1:2) { groups[[k]] <- 5 * (k-1) + 1:5 } groups[[3]] <- 11:p cvfit <- cv.pcLasso(X, y, ratio = 0.8, groups = groups) plot(cvfit) pred.pclasso <- predict(cvfit, xnew = X, s = "lambda.min") mean((y-pred.pclasso)^2) # [1] 1.956387 library(SGL) index <- ceiling(1:p / size.groups) data = list(x = X, y = y) set.seed(1) cvFit = cvSGL(data, index, type = "linear") Fit = SGL(data, index, type = "linear") # SGL() uses a different set of lambdas than cvSGL() does # After looking at cvFit$lambdas; Fit$lambdas # I should pick the last lambda pred.SGL <- predictSGL(Fit, X, length(Fit$lambdas))
mean((y-pred.SGL)^2)    # [1] 0.146027

library(ggplot2)
library(tidyr)
dat <- tibble(y=y, SGL=pred.SGL, pclasso=pred.pclasso) %>% gather("method", "predict", 2:3)

ggplot(dat, aes(x=y, y=predict, color=method)) + geom_point(shape=1)


penalty.factor

The is available in glmnet() but not in cv.glmnet().

• Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties, Fan & Li (2001) JASA
• Adaptive Lasso: What it is and how to implement in R. Adaptive lasso weeks to minimize ${\displaystyle RSS(\beta )+\lambda \sum _{1}^{p}{\hat {\omega }}_{j}|\beta _{j}|}$ where ${\displaystyle \lambda }$ is the tuning parameter, ${\displaystyle {\hat {\omega }}_{j}={\frac {1}{(|{\hat {\beta }}_{j}^{ini}|)^{\gamma }}}}$ is the adaptive weights vector and ${\displaystyle {\hat {\beta }}_{j}^{ini}}$ is an initial estimate of the coefficients obtained through ridge regression. Adaptive Lasso ends up penalizing more those coefficients with lower initial estimates. ${\displaystyle \gamma }$ is a positive constant for adjustment of the adaptive weight vector, and the authors suggest the possible values of 0.5, 1 and 2.
• When n goes to infinity, ${\displaystyle {\hat {\omega }}_{j}|\beta _{j}|}$ converges to ${\displaystyle I(\beta _{j}\neq 0)}$. So the adaptive Lasso procedure can be regarded as an automatic implementation of best-subset selection in some asymptotic sense.
• What is the oracle property of an estimator? An oracle estimator must be consistent in 1) variable selection and 2) consistent parameter estimation.
• (Linear regression) The adaptive lasso and its oracle properties Zou (2006, JASA)
• (Cox model) Adaptive-LASSO for Cox's proportional hazard model by Zhang and Lu (2007, Biometrika)
• When the LASSO fails???. Adaptive lasso is used to demonstrate its usefulness.

Survival data

data(CoxExample)
dim(x) # 1000 x 30
ind.train <- 1:nrow(x)/2
cv.fit <- cv.glmnet(x[ind.train, ], y[ind.train, ], family="cox")
coef(cv.fit, s = "lambda.min")
# 30 x 1, coef(...) is equivalent to predict(type="coefficients",...)
pr <- predict(cv.fit, x[-ind.train, ], type = "link", s = "lambda.min")
# the default option for type is 'link' which gives the linear predictors for "cox" models.
pr2 <- x[-ind.train, ] %*% coef(cv.fit, s = "lambda.min")
all.equal(pr[, 1], pr2[, 1]) # [1] TRUE
class(pr) # [1] "matrix"
class(pr2) # [1] "dgeMatrix"
# we can also use predict() with glmnet object
pr22 <- predict(fit, x[-ind.train, ], type='link', s = cv.fit\$lambda.min)
all.equal(pr[, 1], pr22[, 1]) # [1] TRUE

pr3 <- predict(cv.fit, x[-ind.train, ], type = "response", s = "lambda.min")
range(pr3)  # relative risk [1]  0.05310623 19.80143519


Timing

nvec <- c(100, 400)
pvec <- c(1000, 5000)
for(n in nvec)
for(p in pvec) {
nzc = trunc(p/10)  # 1/10 variables are non-zeros
x = matrix(rnorm(n * p), n, p)
beta = rnorm(nzc)
fx = x[, seq(nzc)] %*% beta/3
hx = exp(fx)
ty = rexp(n, hx)
tcens = rbinom(n = n, prob = 0.3, size = 1)  # censoring indicator
y = cbind(time = ty, status = 1 - tcens)  # y=Surv(ty,1-tcens) with library(survival)
foldid = sample(rep(seq(10), length = n))
o <- system.time(fit1_cv <- cv.glmnet(x, y, family = "cox", foldid = foldid) )
cat(sprintf("n=%1d, p=%5d, time=%8.3f\n", n, p, o['elapsed']))
}


Running on core i7 2.6GHz, macbook pro 2018. Unit is seconds.

p ∖ n 100 400
1000 5 58
5000 9 96

Xeon E5-2680 v4 @ 2.40GHz

p ∖ n 100 400
1000 7 98
5000 13 125