Package '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,
  res1,
  res2,
  by_used,
  sy_used,
  ny_used,
  max_nPC1 = NA,
  max_nPC2 = NA,
  X1Ymodel = NA,
  X2Ymodel = NA,
  basis = "eigenfunction"
)

Arguments

Gmatrix	Genetic instrument matrix from the exposure sample (N × J)
res1	FPCA result for exposure 1
res2	FPCA result for exposure 2
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_nPC1	Maximum number of principal components to retain for exposure 1 (NA = select automatically)
max_nPC2	Maximum number of principal components to retain for exposure 2 (NA = select automatically)
X1Ymodel	True effect model for X1 on Y (for simulation only)
X2Ymodel	True effect model for X2 on Y (for simulation only)
basis	Basis type for functional representation: "eigenfunction" or "polynomial"

Value

List with separate estimation results for both 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
)

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

---

Two-Sample Separate Univariable Functional MR

Description

Separate estimation for each exposure using outcome GWAS summary statistics. For single exposure: set G2 = NULL, by2 = NULL, sy2 = NULL.

Usage

fmvmr_separate_twosample(
  G1_exposure,
  G2_exposure = NULL,
  fpca_results,
  by_outcome1,
  by_outcome2 = NULL,
  sy_outcome1,
  sy_outcome2 = NULL,
  ny_outcome,
  max_nPC1 = NA,
  max_nPC2 = NA,
  true_effects = NULL,
  verbose = TRUE
)

Arguments

G1_exposure	Genetic instrument matrix from exposure 1 (N × J1)
G2_exposure	Genetic instrument matrix from exposure 2 (N × J2) or NULL for single exposure
fpca_results	List of 2 FPCA objects
by_outcome1	SNP-outcome betas for exposure 1 instruments
by_outcome2	SNP-outcome betas for exposure 2 instruments or NULL
sy_outcome1	Standard errors for exposure 1
sy_outcome2	Standard errors for exposure 2 or NULL
ny_outcome	Outcome GWAS sample size
max_nPC1	Maximum number of principal components to retain for exposure 1 (NA = automatically determined)
max_nPC2	Maximum number of principal components to retain for exposure 2 (NA = automatically determined)
true_effects	List containing true effects for exposure 1 and exposure 2 (simulation only)
verbose	Print progress messages and diagnostics during computation

Value

fmvmr_separate_twosample object

---

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_nPC1 = NA,
  max_nPC2 = NA,
  true_effects = NULL,
  verbose = TRUE
)

Arguments

G_exposure	Genetic instrument matrix from the exposure sample (N × J)
fpca_results	List of 2 FPCA objects
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_nPC1	Maximum number of principal components to retain for exposure 1 (NA = automatically determined)
max_nPC2	Maximum number of principal components to retain for exposure 2 (NA = automatically determined)
true_effects	List containing true effects for exposure 1 and exposure 2 (simulation only)
verbose	Print progress messages and diagnostics during computation

Value

fmvmr_twosample object

---

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,
  shared_effect = TRUE,
  separate_G = FALSE,
  shared_G_proportion = 0.15
)

Arguments

N	Sample size
J	Number of genetic instruments
ZXmodel	Model type (currently not used)
nSparse	Number of sparse observations per subject
NT	Number of points
TT	Max observation period
shared_effect	Whether X1 and X2 share confounding
separate_G	Whether to use separate instruments for each exposure
shared_G_proportion	Proportion of shared instruments (0-1)

Value

List with X1, X2 sparse data and genetic instruments

---

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,
  mediation_strength = 0.3,
  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
mediation_strength	Strength of mediation X1 -> X2 (default 0.3)
separate_G	Whether to use separate instruments for each exposure
shared_G_proportion	Proportion of shared instruments (0–1)
mediation_type	Character. Type of mediation effect: "linear" (default), "nonlinear", or "time_varying".

Value

List with same structure as getX_multi_exposure()

---

Generate outcome from exposures

Description

Generate outcome from exposures

Usage

getY_multi_exposure(
  RES,
  X1Ymodel = "1",
  X2Ymodel = "1",
  X1_effect = TRUE,
  X2_effect = TRUE,
  outcome_type = "continuous"
)

Arguments

RES	Output from getX_multi_exposure() or getX_multi_exposure_mediation()
X1Ymodel	Effect model for X1 (0-9)
X2Ymodel	Effect model for X2 (0-9)
X1_effect	Include X1 effect?
X2_effect	Include X2 effect?
outcome_type	"continuous" or "binary"

Value

Data frame with outcome Y

---

GMM estimation for continuous outcome

Description

GMM estimation for continuous outcome

Usage

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

---

Two-sample GMM

Description

Two-sample GMM

Usage

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

---

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)

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).

---

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"),
  nPC1 = NA,
  max_nPC1 = NA,
  nPC2 = NA,
  max_nPC2 = 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 with two FPCA objects from fdapace (res1 and res2)
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)
nPC1	Fixed number of principal components to retain for exposure 1 (NA = select automatically)
max_nPC1	Maximum number of principal components to retain for exposure 1 (NA = automatically determined)
nPC2	Fixed number of principal components to retain for exposure 2 (NA = select automatically)
max_nPC2	Maximum number of principal components to retain for exposure 2 (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	List with true_effect1 and true_effect2 (simulation only)
X_true	List with X1_true and X2_true curves (simulation only)
verbose	Print progress and diagnostic messages during computation

Value

mvfmr object with:

• coefficients - Estimated beta coefficients

• vcov - Variance-covariance matrix

• effects - List with effect1 and effect2 curves

• nPC_used - Components selected (nPC1, nPC2)

• diagnostics - F-statistics, instrument diagnostics

• performance - MISE, coverage (if true effects provided)

---

Separate Univariable Functional Mendelian Randomization

Description

Separate Univariable Functional Mendelian Randomization

Usage

mvfmr_separate(
  G1,
  G2,
  fpca_results,
  Y,
  outcome_type = c("continuous", "binary"),
  method = c("gmm", "cf", "cf-lasso"),
  nPC1 = NA,
  max_nPC1 = NA,
  nPC2 = NA,
  max_nPC2 = NA,
  improvement_threshold = 0.001,
  bootstrap = FALSE,
  n_bootstrap = 100,
  n_cores = parallel::detectCores() - 1,
  true_effects = NULL,
  X_true = NULL,
  verbose = FALSE
)

Arguments

G1	Genetic instrument matrix for exposure 1
G2	Genetic instrument matrix for exposure 2, or NULL if only a single exposure is analyzed
fpca_results	List of FPCA objects
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)
nPC1	Fixed number of principal components to retain for exposure 1 (NA = select automatically)
max_nPC1	Maximum number of principal components to retain for exposure 1 (NA = automatically determined)
nPC2	Fixed number of principal components to retain for exposure 2 (NA = select automatically)
max_nPC2	Maximum number of principal components to retain for exposure 2 (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	List with true_effect1 and true_effect2 (simulation only)
X_true	List with X1_true and X2_true curves (simulation only)
verbose	Print progress and diagnostic messages during computation

Value

fmvmr_separate object

---

Separate univariable two-sample FMR (internal)

Description

Separate univariable two-sample FMR (internal)

Usage

Separate_Multi_FMVMR_twosample_simple(
  Gmatrix1,
  Gmatrix2 = NULL,
  res1,
  res2,
  by_used1,
  by_used2 = NULL,
  sy_used1,
  sy_used2 = NULL,
  ny_used,
  max_nPC1 = NA,
  max_nPC2 = NA,
  X1Ymodel = NA,
  X2Ymodel = NA,
  basis = "eigenfunction"
)

Arguments

Gmatrix1	Genetic instrument matrix from exposure 1 (N × J1)
Gmatrix2	Genetic instrument matrix from exposure 2 (N × J2) or NULL
res1	FPCA result for exposure 1
res2	FPCA result for exposure 2
by_used1	Vector of SNP-outcome effect estimates (betas for X1) from the outcome GWAS, length J
by_used2	Vector of SNP-outcome effect estimates (betas for X2) from the outcome GWAS, length J or NULL
sy_used1	Vector of standard errors for SNP-outcome effects for X1, length J
sy_used2	Vector of standard errors for SNP-outcome effects for X2, length J or NULL
ny_used	Sample size of the outcome GWAS
max_nPC1	Maximum number of principal components to retain for exposure 1 (NA = select automatically)
max_nPC2	Maximum number of principal components to retain for exposure 2 (NA = select automatically)
X1Ymodel	True effect model for X1 on Y (for simulation only)
X2Ymodel	True effect model for X2 on Y (for simulation only)
basis	Basis type for functional representation: "eigenfunction" or "polynomial"

Value

List with separate estimation results for both exposures

---