Package 'mvfmr' reference manual

Title:	Functional Multivariable Mendelian Randomization
Description:	Implements Multivariable Functional Mendelian Randomization (MV-FMR) to estimate time-varying causal effects of multiple longitudinal exposures on health outcomes. Extends univariable functional Mendelian Randomisation (MR) (Tian et al., 2024 <doi:10.1002/sim.10222>) to the multivariable setting, enabling joint estimation of multiple time-varying exposures with pleiotropy and mediation scenarios. Key features include: (1) data-driven cross-validation for basis component selection, (2) handling of mediation pathways between exposures, (3) support for both continuous and binary outcomes using Generalized Method of Moments (GMM) and control function approaches, (4) one-sample and two-sample MR designs, (5) bootstrap inference and instrument diagnostics including Q-statistics for overidentification testing. Methods are described in Fontana et al. (2025) <doi:10.48550/arXiv.2512.19064>.
Authors:	Nicole Fontana [aut, cre], Francesca Ieva [aut, ths], Piercesare Secchi [aut, ths]
Maintainer:	Nicole Fontana <nicole.fontana@polimi.it>
License:	MIT + file LICENSE
Version:	0.2.0
Built:	2026-09-19 10:40:54 UTC
Source:	https://github.com/cran/mvfmr

mvfmr: Multivariable Functional Mendelian Randomization

Description

Implements Multivariable Functional Mendelian randomization to estimate time-varying causal effects of multiple correlated longitudinal exposures.

Author(s)

Nicole Fontana

Two-sample joint multivariable FMR (internal)

Description

Two-sample joint multivariable FMR (internal)

Usage

AUTOMATIC_Multi_FMVMR_twosample_simple(
  Gmatrix,
  res_list,
  by_used,
  sy_used,
  ny_used,
  max_nPC = NA,
  XYmodels = NA,
  basis = "eigenfunction"
)
AUTOMATIC_Multi_FMVMR_twosample_simple(
  Gmatrix,
  res_list,
  by_used,
  sy_used,
  ny_used,
  max_nPC = NA,
  XYmodels = NA,
  basis = "eigenfunction"
)

Arguments

Gmatrix

Genetic instrument matrix from the exposure sample (N × J)

res_list

List of length m of FPCA results, one per exposure

by_used

Vector of SNP-outcome effect estimates (betas) from the outcome GWAS, length J

sy_used

Vector of standard errors for SNP-outcome effects, length J

ny_used

Sample size of the outcome GWAS

max_nPC

Length-m vector: maximum number of principal components to retain per exposure (NA = select automatically)

XYmodels

Length-m vector: true effect model for each exposure on Y (for simulation only)

basis

Basis type for functional representation: "eigenfunction" or "polynomial"

Value

List with separate estimation results for each of the m exposures

Control function for logit model

Description

Control function for logit model

Usage

cf_logit(
  X,
  Y,
  Z,
  alpha = 1,
  nfolds = 10,
  standardize = TRUE,
  use_lasso = FALSE
)
cf_logit(
  X,
  Y,
  Z,
  alpha = 1,
  nfolds = 10,
  standardize = TRUE,
  use_lasso = FALSE
)

Arguments

X

Matrix of exposure principal components (N x K)

Y

Binary outcome vector (0/1, length N)

Z

Genetic instrument matrix (N x J)

alpha

Elastic net mixing parameter (1=lasso, 0=ridge)

nfolds

Number of cross-validation folds for lambda selection

standardize

Standardize variables before fitting

use_lasso

Use LASSO regularization in first stage. If FALSE, uses OLS.

Value

List with gmm_est, gmm_se, variance_matrix, gmm_pval

Examples

set.seed(1)
n <- 200; J <- 5; K <- 2
Z <- matrix(rbinom(n * J, 2, 0.3), n, J)
X <- Z[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
lin_pred <- X %*% c(0.8, -0.4)
Y <- rbinom(n, 1, plogis(lin_pred))
fit <- cf_logit(X, Y, Z)
fit$gmm_est
set.seed(1)
n <- 200; J <- 5; K <- 2
Z <- matrix(rbinom(n * J, 2, 0.3), n, J)
X <- Z[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
lin_pred <- X %*% c(0.8, -0.4)
Y <- rbinom(n, 1, plogis(lin_pred))
fit <- cf_logit(X, Y, Z)
fit$gmm_est

Two-Sample Separate Univariable Functional MR

Description

Separate estimation for each exposure using outcome GWAS summary statistics.

Usage

fmvmr_separate_twosample(
  G_list,
  fpca_results,
  by_outcome_list,
  sy_outcome_list,
  ny_outcome,
  max_nPC = NA,
  true_effects = NULL,
  verbose = TRUE
)
fmvmr_separate_twosample(
  G_list,
  fpca_results,
  by_outcome_list,
  sy_outcome_list,
  ny_outcome,
  max_nPC = NA,
  true_effects = NULL,
  verbose = TRUE
)

Arguments

G_list

List of length m of genetic instrument matrices, one per exposure (N x J_k)

fpca_results

List of length m of FPCA objects, same length as G_list

by_outcome_list

List of length m of SNP-outcome beta vectors, one per exposure

sy_outcome_list

List of length m of SNP-outcome standard error vectors, one per exposure

ny_outcome

Outcome GWAS sample size

max_nPC

Maximum number of principal components to retain per exposure (length 1 or m; NA = automatically determined)

true_effects

Length-m vector of true effect model codes, one per exposure (simulation only)

verbose

Print progress messages and diagnostics during computation

Value

fmvmr_separate_twosample object

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
# Simulate outcome GWAS summary statistics for the two-sample design
by_outcome <- sapply(1:8, function(j) {
  coef(lm(outcome_data$Y ~ sim_data$details$G[, j]))[2]
})
sy_outcome <- sapply(1:8, function(j) {
  summary(lm(outcome_data$Y ~ sim_data$details$G[, j]))$coefficients[2, 2]
})
result <- fmvmr_separate_twosample(
  G_list = list(sim_data$details$G, sim_data$details$G),
  fpca_results = fpca_results,
  by_outcome_list = list(by_outcome, by_outcome),
  sy_outcome_list = list(sy_outcome, sy_outcome),
  ny_outcome = 60,
  max_nPC = c(2, 2),
  verbose = FALSE
)
result$exposures[[1]]$coefficients
set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
# Simulate outcome GWAS summary statistics for the two-sample design
by_outcome <- sapply(1:8, function(j) {
  coef(lm(outcome_data$Y ~ sim_data$details$G[, j]))[2]
})
sy_outcome <- sapply(1:8, function(j) {
  summary(lm(outcome_data$Y ~ sim_data$details$G[, j]))$coefficients[2, 2]
})
result <- fmvmr_separate_twosample(
  G_list = list(sim_data$details$G, sim_data$details$G),
  fpca_results = fpca_results,
  by_outcome_list = list(by_outcome, by_outcome),
  sy_outcome_list = list(sy_outcome, sy_outcome),
  ny_outcome = 60,
  max_nPC = c(2, 2),
  verbose = FALSE
)
result$exposures[[1]]$coefficients

Two-Sample Joint Multivariable Functional MR

Description

Joint estimation using outcome GWAS summary statistics. Simplified approach: only needs by, sy, ny (not individual outcome data).

Usage

fmvmr_twosample(
  G_exposure,
  fpca_results,
  by_outcome,
  sy_outcome,
  ny_outcome,
  max_nPC = NA,
  true_effects = NULL,
  verbose = TRUE
)
fmvmr_twosample(
  G_exposure,
  fpca_results,
  by_outcome,
  sy_outcome,
  ny_outcome,
  max_nPC = NA,
  true_effects = NULL,
  verbose = TRUE
)

Arguments

G_exposure

Genetic instrument matrix from the exposure sample (N × J)

fpca_results

List of length m of FPCA objects, one per exposure

by_outcome

Vector of SNP-outcome effect estimates (betas) from the outcome GWAS, length J

sy_outcome

VVector of standard errors for SNP-outcome effects, length J

ny_outcome

Sample size of the outcome GWAS

max_nPC

Maximum number of principal components to retain per exposure (length 1 or m; NA = automatically determined)

true_effects

Length-m vector of true effect model codes, one per exposure (simulation only)

verbose

Print progress messages and diagnostics during computation

Value

fmvmr_twosample object

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
# Simulate outcome GWAS summary statistics for the two-sample design
by_outcome <- sapply(1:8, function(j) {
  coef(lm(outcome_data$Y ~ sim_data$details$G[, j]))[2]
})
sy_outcome <- sapply(1:8, function(j) {
  summary(lm(outcome_data$Y ~ sim_data$details$G[, j]))$coefficients[2, 2]
})
result <- fmvmr_twosample(
  G_exposure = sim_data$details$G,
  fpca_results = fpca_results,
  by_outcome = by_outcome,
  sy_outcome = sy_outcome,
  ny_outcome = 60,
  max_nPC = c(2, 2),
  verbose = FALSE
)
coef(result)
set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
# Simulate outcome GWAS summary statistics for the two-sample design
by_outcome <- sapply(1:8, function(j) {
  coef(lm(outcome_data$Y ~ sim_data$details$G[, j]))[2]
})
sy_outcome <- sapply(1:8, function(j) {
  summary(lm(outcome_data$Y ~ sim_data$details$G[, j]))$coefficients[2, 2]
})
result <- fmvmr_twosample(
  G_exposure = sim_data$details$G,
  fpca_results = fpca_results,
  by_outcome = by_outcome,
  sy_outcome = sy_outcome,
  ny_outcome = 60,
  max_nPC = c(2, 2),
  verbose = FALSE
)
coef(result)

Generate multi-exposure data with genetic instruments

Description

Generate multi-exposure data with genetic instruments

Usage

getX_multi_exposure(
  N = 10000,
  J = 30,
  ZXmodel = "A",
  nSparse = 10,
  NT = 1000,
  TT = 50,
  n_exposures = 2,
  shared_effect = TRUE,
  separate_G = FALSE,
  shared_G_proportion = 0.15
)
getX_multi_exposure(
  N = 10000,
  J = 30,
  ZXmodel = "A",
  nSparse = 10,
  NT = 1000,
  TT = 50,
  n_exposures = 2,
  shared_effect = TRUE,
  separate_G = FALSE,
  shared_G_proportion = 0.15
)

Arguments

N

Sample size

J

Number of genetic instruments (per exposure, if separate_G = TRUE)

ZXmodel

Model type (currently not used)

nSparse

Number of sparse observations per subject

NT

Number of points

TT

Max observation period

n_exposures

Number of exposures to simulate (m)

shared_effect

Whether all exposures share the same time-varying confounding

separate_G

Whether to use separate instruments for each exposure

shared_G_proportion

Proportion of shared instruments (0-1)

Value

List with per-exposure sparse data and genetic instruments

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 50, J = 8, nSparse = 5, n_exposures = 2)
length(sim_data$exposures)
dim(sim_data$details$G)
set.seed(1)
sim_data <- getX_multi_exposure(N = 50, J = 8, nSparse = 5, n_exposures = 2)
length(sim_data$exposures)
dim(sim_data$details$G)

Generate multi-exposure mediation data with genetic instruments

Description

Generate multi-exposure mediation data with genetic instruments

Usage

getX_multi_exposure_mediation(
  N = 10000,
  J = 30,
  ZXmodel = "A",
  nSparse = 10,
  n_exposures = 2,
  mediation_strength = NULL,
  separate_G = FALSE,
  shared_G_proportion = 0,
  mediation_type = "linear"
)
getX_multi_exposure_mediation(
  N = 10000,
  J = 30,
  ZXmodel = "A",
  nSparse = 10,
  n_exposures = 2,
  mediation_strength = NULL,
  separate_G = FALSE,
  shared_G_proportion = 0,
  mediation_type = "linear"
)

Arguments

N

Sample size

J

Number of genetic instruments per exposure

ZXmodel

Model type (currently not used, kept for compatibility)

nSparse

Number of sparse observations per subject

n_exposures

Number of exposures to simulate (m)

mediation_strength

m x m numeric matrix of pairwise mediation strengths: entry [j, k] (with j < k) is the strength with which exposure j mediates its effect onto exposure k, generated later in the sequence. Must be strictly upper triangular (entries with j >= k must be 0). Default: NULL, i.e. no mediation (all-zero matrix).

separate_G

Whether to use separate instruments for each exposure

shared_G_proportion

Proportion of shared instruments (0-1)

mediation_type

Character, or m x m character matrix mirroring mediation_strength: type of mediation effect for each pair, one of "linear" (default), "nonlinear", or "time_varying".

Value

List with same structure as getX_multi_exposure()

Examples

set.seed(1)
# Exposure 1 mediates onto exposure 2 with strength 0.3
mediation_strength <- matrix(c(0, 0, 0.3, 0), 2, 2)
sim_data <- getX_multi_exposure_mediation(
  N = 50, J = 8, nSparse = 5, n_exposures = 2,
  mediation_strength = mediation_strength
)
length(sim_data$exposures)
set.seed(1)
# Exposure 1 mediates onto exposure 2 with strength 0.3
mediation_strength <- matrix(c(0, 0, 0.3, 0), 2, 2)
sim_data <- getX_multi_exposure_mediation(
  N = 50, J = 8, nSparse = 5, n_exposures = 2,
  mediation_strength = mediation_strength
)
length(sim_data$exposures)

Generate outcome from exposures

Description

Generate outcome from exposures

Usage

getY_multi_exposure(
  RES,
  XYmodels = NULL,
  X_effects = NULL,
  outcome_type = "continuous"
)
getY_multi_exposure(
  RES,
  XYmodels = NULL,
  X_effects = NULL,
  outcome_type = "continuous"
)

Arguments

RES

Output from getX_multi_exposure() or getX_multi_exposure_mediation()

XYmodels

Length-m vector of effect models per exposure, one of '0'-'9' (default: '1' for all)

X_effects

Length-m logical vector: include each exposure's effect? (default: TRUE for all)

outcome_type

"continuous" or "binary"

Value

Data frame with outcome Y

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 50, J = 8, nSparse = 5, n_exposures = 2)
dat <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"), outcome_type = "continuous")
head(dat$Y)
set.seed(1)
sim_data <- getX_multi_exposure(N = 50, J = 8, nSparse = 5, n_exposures = 2)
dat <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"), outcome_type = "continuous")
head(dat$Y)

GMM estimation for continuous outcome

Description

GMM estimation for continuous outcome

Usage

gmm_lm_onesample(X, Y, Z, beta0 = NA)
gmm_lm_onesample(X, Y, Z, beta0 = NA)

Arguments

X

Matrix of exposure principal components (N x K)

Y

Outcome vector (length N)

Z

Genetic instrument matrix (N x J)

beta0

Initial values for beta (default NA, uses zero initialization)

Value

List with gmm_est, gmm_se, variance_matrix, gmm_pval, Q_stat, Q_pval

Examples

set.seed(1)
n <- 200; J <- 5; K <- 2
Z <- matrix(rbinom(n * J, 2, 0.3), n, J)
X <- Z[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
Y <- as.numeric(X %*% c(1, -0.5) + rnorm(n))
fit <- gmm_lm_onesample(X, Y, Z)
fit$gmm_est
set.seed(1)
n <- 200; J <- 5; K <- 2
Z <- matrix(rbinom(n * J, 2, 0.3), n, J)
X <- Z[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
Y <- as.numeric(X %*% c(1, -0.5) + rnorm(n))
fit <- gmm_lm_onesample(X, Y, Z)
fit$gmm_est

Two-sample GMM

Description

Two-sample GMM

Usage

gmm_twosample_simple(bx, by, sy, ny)
gmm_twosample_simple(bx, by, sy, ny)

Arguments

bx

Matrix J x K of first-stage coefficients (SNP -> PC associations)

by

Vector length J of outcome GWAS betas

sy

Vector length J of outcome GWAS standard errors

ny

Outcome GWAS sample size

Value

List with gmm_est, gmm_se, variance_matrix, gmm_pval, Q_stat, Q_df, Q_pval

Examples

set.seed(1)
J <- 10; K <- 2
bx <- matrix(rnorm(J * K, sd = 0.3), J, K)
by <- bx %*% c(0.5, -0.2) + rnorm(J, sd = 0.05)
sy <- runif(J, 0.02, 0.05)
fit <- gmm_twosample_simple(bx, by, sy, ny = 50000)
fit$gmm_est
set.seed(1)
J <- 10; K <- 2
bx <- matrix(rnorm(J * K, sd = 0.3), J, K)
by <- bx %*% c(0.5, -0.2) + rnorm(J, sd = 0.05)
sy <- runif(J, 0.02, 0.05)
fit <- gmm_twosample_simple(bx, by, sy, ny = 50000)
fit$gmm_est

Calculate F-statistics and Q-statistic for instrument strength (internal)

Description

Calculate F-statistics and Q-statistic for instrument strength (internal)

Usage

IS(J, K, PC, datafull, Y = NULL)
IS(J, K, PC, datafull, Y = NULL)

Arguments

J

Number of genetic instruments

K

Number of exposures

PC

Vector of indices indicating which columns in datafull correspond to the principal components

datafull

Data frame containing instruments (first J columns) and principal components (subsequent columns) [G, X]

Y

Optional outcome vector; if provided, Q-statistic for overidentification is calculated)

Value

Matrix with columns: PC (component index), RR (R-squared), FF (F-statistic), cFF (conditional F-statistic). If Y is provided, additional columns: Qvalue (Hansen's J overidentification test statistic), df (degrees of freedom for Q-test), pvalue (p-value for Q-test from chi-squared distribution).

Examples

set.seed(1)
n <- 200; J <- 5; K <- 2
G <- matrix(rbinom(n * J, 2, 0.3), n, J)
PCmat <- G[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
Y <- as.numeric(PCmat %*% c(1, -0.5) + rnorm(n))
fstats <- IS(J = J, K = K, PC = 1:K, datafull = cbind(G, PCmat), Y = Y)
fstats
set.seed(1)
n <- 200; J <- 5; K <- 2
G <- matrix(rbinom(n * J, 2, 0.3), n, J)
PCmat <- G[, 1:K] + matrix(rnorm(n * K, sd = 0.5), n, K)
Y <- as.numeric(PCmat %*% c(1, -0.5) + rnorm(n))
fstats <- IS(J = J, K = K, PC = 1:K, datafull = cbind(G, PCmat), Y = Y)
fstats

Joint Multivariable Functional Mendelian Randomization

Description

Joint Multivariable Functional Mendelian Randomization

Usage

mvfmr(
  G,
  fpca_results,
  Y,
  outcome_type = c("continuous", "binary"),
  method = c("gmm", "cf", "cf-lasso"),
  nPC = NA,
  max_nPC = NA,
  improvement_threshold = 0.001,
  bootstrap = FALSE,
  n_bootstrap = 100,
  n_cores = parallel::detectCores() - 1,
  true_effects = NULL,
  X_true = NULL,
  verbose = FALSE
)
mvfmr(
  G,
  fpca_results,
  Y,
  outcome_type = c("continuous", "binary"),
  method = c("gmm", "cf", "cf-lasso"),
  nPC = NA,
  max_nPC = NA,
  improvement_threshold = 0.001,
  bootstrap = FALSE,
  n_bootstrap = 100,
  n_cores = parallel::detectCores() - 1,
  true_effects = NULL,
  X_true = NULL,
  verbose = FALSE
)

Arguments

G

Genetic instrument matrix (N x J)

fpca_results

List of length m of FPCA objects from fdapace, one per exposure

Y

Outcome vector

outcome_type

Type of outcome: "continuous" for numeric outcomes, "binary" for 0/1 outcomes

method

Estimation method: "gmm" (Generalized Method of Moments), "cf" (control function), or "cf-lasso" (control function with Lasso)

nPC

Fixed number of principal components to retain per exposure (length 1 or m; NA = select automatically)

max_nPC

Maximum number of principal components to retain per exposure (length 1 or m; NA = automatically determined)

improvement_threshold

Minimum cross-validation improvement required to add an additional principal component

bootstrap

Whether to compute confidence intervals using bootstrap resampling

n_bootstrap

Number of bootstrap replicates (only used if bootstrap = TRUE)

n_cores

Number of CPU cores to use for parallel computations

true_effects

Length-m vector of true effect model codes, one per exposure (simulation only)

X_true

Length-m list of true X curves, one per exposure (simulation only)

verbose

Print progress and diagnostic messages during computation

Value

mvfmr object with:

coefficients - Estimated beta coefficients
vcov - Variance-covariance matrix
effects - List of length m with the estimated time-varying effect curves
nPC_used - Components selected per exposure
diagnostics - F-statistics, instrument diagnostics
performance - MISE, coverage (if true effects provided)

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
result <- mvfmr(
  G = sim_data$details$G,
  fpca_results = fpca_results,
  Y = outcome_data$Y,
  max_nPC = c(2, 2),
  n_cores = 1,
  verbose = FALSE
)
coef(result)
set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
result <- mvfmr(
  G = sim_data$details$G,
  fpca_results = fpca_results,
  Y = outcome_data$Y,
  max_nPC = c(2, 2),
  n_cores = 1,
  verbose = FALSE
)
coef(result)

Separate Univariable Functional Mendelian Randomization

Description

Separate Univariable Functional Mendelian Randomization

Usage

mvfmr_separate(
  G_list,
  fpca_results,
  Y,
  outcome_type = c("continuous", "binary"),
  method = c("gmm", "cf", "cf-lasso"),
  nPC = NA,
  max_nPC = NA,
  improvement_threshold = 0.001,
  bootstrap = FALSE,
  n_bootstrap = 100,
  n_cores = parallel::detectCores() - 1,
  true_effects = NULL,
  X_true = NULL,
  verbose = FALSE
)
mvfmr_separate(
  G_list,
  fpca_results,
  Y,
  outcome_type = c("continuous", "binary"),
  method = c("gmm", "cf", "cf-lasso"),
  nPC = NA,
  max_nPC = NA,
  improvement_threshold = 0.001,
  bootstrap = FALSE,
  n_bootstrap = 100,
  n_cores = parallel::detectCores() - 1,
  true_effects = NULL,
  X_true = NULL,
  verbose = FALSE
)

Arguments

G_list

List of length m of genetic instrument matrices, one per exposure. Use a list of length 1 to analyze a single exposure.

fpca_results

List of length m of FPCA objects, same length as G_list

Y

Outcome vector

outcome_type

Type of outcome: "continuous" for numeric outcomes, "binary" for 0/1 outcomes

method

Estimation method: "gmm" (Generalized Method of Moments), "cf" (control function), or "cf-lasso" (control function with Lasso)

nPC

Fixed number of principal components to retain per exposure (length 1 or m; NA = select automatically)

max_nPC

Maximum number of principal components to retain per exposure (length 1 or m; NA = automatically determined)

improvement_threshold

Minimum cross-validation improvement required to add an additional principal component

bootstrap

Whether to compute confidence intervals using bootstrap resampling

n_bootstrap

Number of bootstrap replicates (only used if bootstrap = TRUE)

n_cores

Number of CPU cores to use for parallel computations

true_effects

Length-m vector of true effect model codes, one per exposure (simulation only)

X_true

Length-m list of true X curves, one per exposure (simulation only)

verbose

Print progress and diagnostic messages during computation

Value

mvfmr_separate object

Examples

set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
result <- mvfmr_separate(
  G_list = list(sim_data$details$G, sim_data$details$G),
  fpca_results = fpca_results,
  Y = outcome_data$Y,
  max_nPC = c(2, 2),
  n_cores = 1,
  verbose = FALSE
)
coef(result, exposure = 1)
set.seed(1)
sim_data <- getX_multi_exposure(N = 60, J = 8, nSparse = 5, n_exposures = 2)
outcome_data <- getY_multi_exposure(sim_data, XYmodels = c("2", "8"))
fpca_results <- lapply(sim_data$exposures, function(exp_k) {
  fdapace::FPCA(exp_k$Ly_sim, exp_k$Lt_sim,
                list(dataType = "Sparse", error = TRUE, verbose = FALSE))
})
result <- mvfmr_separate(
  G_list = list(sim_data$details$G, sim_data$details$G),
  fpca_results = fpca_results,
  Y = outcome_data$Y,
  max_nPC = c(2, 2),
  n_cores = 1,
  verbose = FALSE
)
coef(result, exposure = 1)

Separate univariable two-sample FMR (internal)

Description

Separate univariable two-sample FMR (internal)

Usage

Separate_Multi_FMVMR_twosample_simple(
  Gmatrix_list,
  res_list,
  by_used_list,
  sy_used_list,
  ny_used,
  max_nPC = NA,
  XYmodels = NA,
  basis = "eigenfunction"
)
Separate_Multi_FMVMR_twosample_simple(
  Gmatrix_list,
  res_list,
  by_used_list,
  sy_used_list,
  ny_used,
  max_nPC = NA,
  XYmodels = NA,
  basis = "eigenfunction"
)

Arguments

Gmatrix_list

List of length m of genetic instrument matrices, one per exposure (N x J_k)

res_list

List of length m of FPCA results, one per exposure

by_used_list

List of length m of SNP-outcome effect estimate vectors, one per exposure

sy_used_list

List of length m of SNP-outcome standard error vectors, one per exposure

ny_used

Sample size of the outcome GWAS

max_nPC

Length-m vector: maximum number of principal components to retain per exposure (NA = select automatically)

XYmodels

Length-m vector: true effect model for each exposure on Y (for simulation only)

basis

Basis type for functional representation: "eigenfunction" or "polynomial"

Value

List with separate estimation results for each of the m exposures

Package 'mvfmr'

Help Index

mvfmr: Multivariable Functional Mendelian Randomization

Description

Author(s)

Two-sample joint multivariable FMR (internal)

Description

Usage

Arguments

Value

Control function for logit model

Description

Usage

Arguments

Value

Examples

Two-Sample Separate Univariable Functional MR

Description

Usage

Arguments

Value

Examples

Two-Sample Joint Multivariable Functional MR

Description

Usage

Arguments

Value

Examples

Generate multi-exposure data with genetic instruments

Description

Usage

Arguments

Value

Examples

Generate multi-exposure mediation data with genetic instruments

Description

Usage

Arguments

Value

Examples

Generate outcome from exposures

Description

Usage

Arguments

Value

Examples

GMM estimation for continuous outcome

Description

Usage

Arguments

Value

Examples

Two-sample GMM

Description

Usage

Arguments

Value

Examples

Calculate F-statistics and Q-statistic for instrument strength (internal)

Description

Usage

Arguments

Value

Examples

Joint Multivariable Functional Mendelian Randomization

Description

Usage

Arguments

Value

Examples

Separate Univariable Functional Mendelian Randomization

Description

Usage

Arguments

Value

Examples

Separate univariable two-sample FMR (internal)

Description

Usage

Arguments