Package 'RMediation'

Cumulative Distribution Function

Description

Generic function for computing cumulative distribution function.

Usage

cdf(object, ...)

Arguments

object	A distribution object.
...	Additional arguments passed to methods.

Value

A numeric vector of cumulative probabilities for the supplied quantiles, as returned by the dispatched method.

Examples

pn <- ProductNormal(mu = c(0.5, 0.3), Sigma = diag(2) * 0.1)
cdf(pn, q = 0)

---

Confidence Interval

Description

Generic function for computing confidence intervals.

Usage

ci(object, ...)

Arguments

object	A distribution object or numeric vector of means.
...	Additional arguments passed to methods.

Value

A confidence interval (numeric vector of lower/upper bounds, or a list) as returned by the dispatched method.

Examples

pn <- ProductNormal(mu = c(0.5, 0.3), Sigma = diag(2) * 0.1)
ci(pn)

---

Confidence Interval for MediationData Objects

Description

Computes confidence intervals for the indirect effect from a medfit MediationData object using RMediation's methods (DOP, Monte Carlo, etc.).

Usage

ci_mediation_data(object, level = 0.95, type = "dop", n.mc = 1e+05, ...)

ci_serial_mediation_data(object, level = 0.95, type = "MC", n.mc = 1e+05, ...)

Arguments

object	A MediationData object from the medfit package
level	Confidence level (default 0.95 for 95% CI)
type	Type of CI method: "dop" (Distribution of Product), "MC" (Monte Carlo), or "asymp" (asymptotic normal)
n.mc	Number of Monte Carlo samples (for type = "MC")
...	Additional arguments passed to underlying methods

Details

This method extracts the a and b path coefficients from the MediationData object, along with their standard errors and covariance, and computes confidence intervals using RMediation's methods.

Method Options

• "dop": Distribution of Product method. Uses the exact or approximate distribution of the product of two normal random variables. Recommended for most applications.

• "MC": Monte Carlo simulation. Samples from the joint distribution of a and b to estimate the CI. Use n.mc to control precision.

• "asymp": Asymptotic normal approximation using the delta method. Fast but may be inaccurate for small samples or non-normal distributions.

Value

A list with components:

CI	The confidence interval (lower, upper)
Estimate	Point estimate of indirect effect (a*b)
SE	Standard error of indirect effect
type	Method used for CI computation

See Also

ci for the generic function, MediationData for the input class, ProductNormal for the underlying distribution class

Examples

## Not run: 
library(medfit)
library(RMediation)

# Fit mediation models
fit_m <- lm(M ~ X + C, data = mydata)
fit_y <- lm(Y ~ X + M + C, data = mydata)

# Extract mediation structure
med_data <- extract_mediation(fit_m,
  model_y = fit_y,
  treatment = "X", mediator = "M"
)

# Compute CI using Distribution of Product
ci(med_data, type = "dop")

# Compute CI using Monte Carlo
ci(med_data, type = "MC", n.mc = 10000)

## End(Not run)

---

Distribution Quantile Function

Description

Compute quantiles for distribution objects. This function computes quantiles for product normal distributions, not data quantiles (use stats::quantile for data).

Usage

dist_quantile(object, ...)

Arguments

object	A distribution object (e.g., ProductNormal).
...	Additional arguments passed to methods.

Value

A numeric vector of quantiles for the supplied probabilities, as returned by the dispatched method.

Examples

pn <- ProductNormal(mu = c(0.5, 0.3), Sigma = diag(2) * 0.1)
dist_quantile(pn, p = 0.025)

---

Model-based Constrained Optimization (MBCO) Chi-squared Test

Description

This function computes asymptotic MBCO chi-squared test for a smooth function of model parameters including a function of indirect effects.

Usage

mbco(h0, h1, ...)

Arguments

h0	An OpenMx model estimated under a null hypothesis, which is a more constrained model
h1	An OpenMx model estimated under an alternative hypothesis, which is a less constrained model. This is usually a model hypothesized by a researcher.
...	Additional arguments.

Value

An object of class MBCOResult containing

statistic	asymptotic chi-squared test statistic value
df	chi-squared df
p_value	chi-squared p-value computed based on the method specified by the argument type
type	The type of test performed

Author(s)

Davood Tofighi [email protected]

Examples

## Not run: 
data(memory_exp)
memory_exp$x <- as.numeric(memory_exp$x) - 1 # manually creating dummy codes
endVar <- c("x", "repetition", "imagery", "recall")
manifests <- c("x", "repetition", "imagery", "recall")
full_model <- OpenMx::mxModel(
  "memory_example",
  type = "RAM",
  manifestVars = manifests,
  OpenMx::mxPath(
    from = "x",
    to = endVar,
    arrows = 1,
    free = TRUE,
    values = .2,
    labels = c("a1", "a2", "cp")
  ),
  OpenMx::mxPath(
    from = "repetition",
    to = "recall",
    arrows = 1,
    free = TRUE,
    values = .2,
    labels = "b1"
  ),
  OpenMx::mxPath(
    from = "imagery",
    to = "recall",
    arrows = 1,
    free = TRUE,
    values = .2,
    labels = "b2"
  ),
  OpenMx::mxPath(
    from = manifests,
    arrows = 2,
    free = TRUE,
    values = .8
  ),
  OpenMx::mxPath(
    from = "one",
    to = endVar,
    arrows = 1,
    free = TRUE,
    values = .1
  ),
  OpenMx::mxAlgebra(a1 * b1, name = "ind1"),
  OpenMx::mxAlgebra(a2 * b2, name = "ind2"),
  OpenMx::mxCI("ind1", type = "both"),
  OpenMx::mxCI("ind2", type = "both"),
  OpenMx::mxData(observed = memory_exp, type = "raw")
)
## Reduced  Model for indirect effect: a1*b1
null_model1 <- OpenMx::mxModel(
  model = full_model,
  name = "Null Model 1",
  OpenMx::mxConstraint(ind1 == 0, name = "ind1_eq0_constr")
)
full_model <- OpenMx::mxTryHard(full_model, checkHess = FALSE, silent = TRUE)
null_model1 <- OpenMx::mxTryHard(null_model1, checkHess = FALSE, silent = TRUE)
mbco(null_model1, full_model)

## End(Not run)

---

MBCO Result Class

Description

A class representing the results of a Model-Based Constrained Optimization (MBCO) test.

Usage

MBCOResult(
  statistic = integer(0),
  df = integer(0),
  p_value = integer(0),
  type = character(0)
)

Arguments

statistic	Numeric test statistic value.
df	Numeric degrees of freedom.
p_value	Numeric p-value.
type	Character string indicating the type of test.

Examples

res <- MBCOResult(statistic = 4.2, df = 1, p_value = 0.04, type = "asymp")

---

Confidence Interval for the Mediated Effect

Description

Produces confidence intervals for the mediated effect and the product of two normal random variables

Produces confidence intervals for the mediated effect and the product of two normal random variables

Usage

medci(
  mu.x,
  mu.y,
  se.x,
  se.y,
  rho = 0,
  alpha = 0.05,
  type = "dop",
  plot = FALSE,
  plotCI = FALSE,
  n.mc = 1e+05,
  ...
)

medci(
  mu.x,
  mu.y,
  se.x,
  se.y,
  rho = 0,
  alpha = 0.05,
  type = "dop",
  plot = FALSE,
  plotCI = FALSE,
  n.mc = 1e+05,
  ...
)

Arguments

mu.x	mean of $x$
mu.y	mean of $y$
se.x	standard error (deviation) of $x$
se.y	standard error (deviation) of $y$
rho	correlation between $x$ and $y$, where -1 <rho < 1. The default value is 0.
alpha	significance level for the confidence interval. The default value is .05.
type	method used to compute confidence interval. It takes on the values "dop" (default), "MC", "asymp" or "all"
plot	when TRUE, plots the distribution of n.mc data points from the distribution of product of two normal random variables using the density estimates provided by the function density. The default value is FALSE.
plotCI	when TRUE, overlays a confidence interval with error bars on the plot for the mediated effect. Note that to obtain the CI plot, one must also specify plot="TRUE". The default value is FALSE.
n.mc	when type="MC", n.mc determines the sample size for the Monte Carlo method. The default sample size is 1E5.
...	additional arguments to be passed on to the function.

Details

This function returns a confidence interval at significance level $\alpha$ for the mediated effect (product of two normal random variables). To obtain a confidence interval using a specific method, the argument type should be specified. The default is type="dop", which uses the code we wrote in R to implement the distribution of product of the coefficients method described by Meeker and Escobar (1994) to evaluate the CDF of the distribution of product. type="MC" uses the Monte Carlo approach to compute the confidence interval (Tofighi & MacKinnon, 2011). type="asymp" produces the asymptotic normal confidence interval. Note that except for the Monte Carlo method, the standard error for the indirect effect is based on the analytical results by Craig (1936):

$SE = \sqrt{\sigma_y^2 \mu_x^2 + \sigma_x^2 \mu_y^2 + 2\mu_x\mu_y\rho\sigma_x\sigma_y + \sigma_x^2\sigma_y^2 + \sigma_x^2\sigma_y^2\rho^2}$

where $\sigma_x$ and $\sigma_y$ are the standard errors of $x$ and $y$, respectively. In addition, the estimate of the indirect effect is $\mu_x\mu_y + \sigma_{xy}$, where $\sigma_{xy}$ is the covariance between $x$ and $y$. The argument type="all" prints confidence intervals using all available methods.

This function returns a ($1-\alpha$)% confidence interval for the mediated effect (product of two normal random variables). To obtain a confidence interval using a specific method, the argument type should be specified. The default is type="dop", which uses the code we wrote in R to implement the distribution of product of the coefficients method described by Meeker and Escobar (1994) to evaluate the CDF of the distribution of product. type="MC" uses the Monte Carlo approach to compute the confidence interval (Tofighi & MacKinnon, 2011). type="asymp" produces the asymptotic normal confidence interval. Note that except for the Monte Carlo method, the standard error for the indirect effect is based on the analytical results by Craig (1936):

$\sqrt(se.y^2 \mu.x^2+se.x^2 \mu.y^2+2 \mu.x \mu.y \rho se.x se.y+ se.x^2 se.y^2+se.x^2 se.y^2 \rho^2)$

. In addition, the estimate of indirect effect is $\mu.x \mu.y +\sigma.xy$; type="all" prints confidence intervals using all four options.

Value

A vector of lower confidence limit and upper confidence limit. When type is "prodclin" (default), "DOP", "MC" or "asymp", medci returns a list that contains:

CI	a vector of lower and upper confidence limits (at significance level $\alpha$),
Estimate	a point estimate of the quantity of interest,
SE	standard error of the quantity of interest,
MC Error	When type="MC", error of the Monte Carlo estimate.

Note that when type="all", medci returns a list of four objects, each of which a list that contains the results produced by each method as described above.

A vector of lower confidence limit and upper confidence limit. When type is "prodclin" (default), "DOP", "MC" or "asymp", medci returns a list that contains:

(\eqn{1-\alpha})% CI	a vector of lower and upper confidence limits,
Estimate	a point estimate of the quantity of interest,
SE	standard error of the quantity of interest,
MC Error	When type="MC", error of the Monte Carlo estimate.

Note that when type="all", medci returns a list of four objects, each of which a list that contains the results produced by each method as described above.

Author(s)

Davood Tofighi [email protected]

References

Craig, C. C. (1936). On the frequency function of $xy$. The Annals of Mathematical Statistics, 7, 1–15.

MacKinnon, D. P., Fritz, M. S., Williams, J., and Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39, 384–389.

Meeker, W. and Escobar, L. (1994). An algorithm to compute the CDF of the product of two normal random variables. Communications in Statistics: Simulation and Computation, 23, 271–280.

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

Craig, C. C. (1936). On the frequency function of $xy$. The Annals of Mathematical Statistics, 7, 1–15.

MacKinnon, D. P., Fritz, M. S., Williams, J., and Lockwood, C. M. (2007). Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods, 39, 384–389.

Meeker, W. and Escobar, L. (1994). An algorithm to compute the CDF of the product of two normal random variables. Communications in Statistics: Simulation and Computation, 23, 271–280.

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

See Also

qprodnormal pprodnormal ci RMediation-package

Examples

## Example 1
res <- medci(
  mu.x = .2, mu.y = .4, se.x = 1, se.y = 1, rho = 0, alpha = .05,
  type = "dop", plot = TRUE, plotCI = TRUE
)
## Example 2
res <- medci(
  mu.x = .2, mu.y = .4, se.x = 1, se.y = 1, rho = 0,
  alpha = .05, type = "all", plot = TRUE, plotCI = TRUE
)
## Example 1
res <- medci(
  mu.x = .2, mu.y = .4, se.x = 1, se.y = 1, rho = 0, alpha = .05,
  type = "dop", plot = TRUE, plotCI = TRUE
)
## Example 2
res <- medci(
  mu.x = .2, mu.y = .4, se.x = 1, se.y = 1, rho = 0,
  alpha = .05, type = "all", plot = TRUE, plotCI = TRUE
)

---

Memory Experiment Data Description from MacKinnon et al., 2018

Description

Data were obtained from eight replicated experiments. The data were collected on the first day of class as part of the first Dr. MacKinnon's (2018) classroom teaching. The pedagogical value of the experiment was that students would have first-hand knowledge of the experiment thereby increasing their understanding of course concepts. Permission to use the data was obtained from the university Institutional Review Board.

Usage

data(memory_exp)

Format

A data frame with 369 rows and 5 variables:

study

Replication ID, ranges from 1 to 8

repetition

Use of repetition rehearsal technique, ranges from 1 to 12

recall

Total words recalled out of 20 words, ranges from 3 to 20

imagery

Use of imagery rehearsal technique on a 1 to 9 scale

x

A factor with two levels: "repetition" (primary rehearsal) or "imagery" (secondary rehearsal)

Note

If you use the data set, please cite the original article by MacKinnon et al. (2018) cited below.

Source

doi:10.1037/met0000174.supp

References

MacKinnon, D. P., Valente, M. J., & Wurpts, I. C. (2018). Benchmark validation of statistical models: Application to mediation analysis of imagery and memory. Psychological Methods, 23, 654–671. doi:10.1037/met0000174

---

Probability (percentile) for the Monte Carlo Sampling Distribution of a nonlinear function of coefficients estimates

Description

This function returns a probability corresponding to the quantile q.

Usage

pMC(q, mu, Sigma, quant, lower.tail = TRUE, n.mc = 1e+05, ...)

Arguments

q	quantile
mu	a vector of means (e.g., coefficient estimates) for the normal random variables. A user can assign a name to each mean value, e.g., mu=c(b1=.1,b2=3); otherwise, the coefficient names are assigned automatically as follows: b1,b2,....
Sigma	either a covariance matrix or a vector that stacks all the columns of the lower triangle variance–covariance matrix one underneath the other.
quant	quantity of interest, which is a nonlinear/linear function of the model parameters. Argument quant is a formula that must start with the symbol "tilde" (~): e.g., ~b1*b2*b3*b4. The names of coefficients must conform to the names provided in the argument mu or to the default names, i.e., b1,b2,....
lower.tail	logical; if TRUE (default), the probability is $P[quant < q]$; otherwise, $P[quant > q]$
n.mc	Monte Carlo sample size (default: 1e5). Larger values provide more precision but take longer to compute.
...	additional arguments.

Value

scalar probability value.

Author(s)

Davood Tofighi [email protected]

References

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

Examples

pMC(.2,
  mu = c(b1 = 1, b2 = .7, b3 = .6, b4 = .45), Sigma = c(.05, 0, 0, 0, .05, 0, 0, .03, 0, .03),
  quant = ~ b1 * b2 * b3 * b4
)

---

Percentile for the Distribution of Product of Two Normal Variables

Description

Generates percentiles (100 based quantiles) for the distribution of product of two normal random variables and the mediated effect

Usage

pprodnormal(
  q,
  mu.x,
  mu.y,
  se.x = 1,
  se.y = 1,
  rho = 0,
  lower.tail = TRUE,
  type = "dop",
  n.mc = 1e+05
)

Arguments

q	quantile or value of the product
mu.x	mean of $x$
mu.y	mean of $y$
se.x	standard error (deviation) of $x$
se.y	standard error (deviation) of $y$
rho	correlation between $x$ and $y$, where -1 <rho < 1. The default value is 0.
lower.tail	logical; if TRUE (default), the probability is $P(XY \le q)$; otherwise, $P(XY > q)$
type	method used to compute confidence interval. It takes on the values "dop" (default), "MC", or "all"
n.mc	Monte Carlo sample size when type="MC" (default: 1e5). Larger values provide more precision but take longer to compute.

Details

This function returns the percentile (probability) and the associated error for the distribution of product of mediated effect (two normal random variables). To obtain a percentile using a specific method, the argument type should be specified. The default method is type="dop", which is based on the method described by Meeker and Escobar (1994) to evaluate the CDF of the distribution of product of two normal random variables. type="MC" uses the Monte Carlo approach (Tofighi & MacKinnon, 2011). type="all" prints percentiles using all three options. For the method type="dop", the error is the modulus of absolute error for the numerical integration (for more information see Meeker and Escobar, 1994). For type="MC", the error refers to the Monte Carlo error.

Value

An object of the type list that contains the following values:

p	probability (percentile) corresponding to quantile q
error	estimate of the absolute error

Author(s)

Davood Tofighi [email protected]

References

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

See Also

medci RMediation-package

Examples

pprodnormal(q = 0, mu.x = .5, mu.y = .3, se.x = 1, se.y = 1, rho = 0, type = "all")

---

Cumulative Distribution Function for the Product of Three Normal Variables

Description

Computes P(X1 * X2 * X3 <= q) where ⁠(X1, X2, X3)⁠ follows a trivariate normal distribution with mean vector mean and covariance matrix cov. Z is marginalized analytically through the conditional Gaussian structure ⁠Z | (X, Y)⁠, and the remaining two-dimensional integral is evaluated with adaptive cubature (dimension reduction; see Tofighi, 2026).

Usage

pprodnormal3(q, mean, cov, method = "hcubature", tol = 1e-06)

p_prod3(...)

Arguments

q	Numeric scalar quantile.
mean	Numeric vector of means of length 3.
cov	3x3 covariance matrix. Must be positive-definite, except that a single zero-variance component is permitted (the problem then reduces to the two-variable product-normal case). Perfect correlation between two components (⁠|rho| = 1⁠) or an indefinite matrix is rejected.
method	Integration method. Currently only "hcubature" is supported.
tol	Numeric tolerance passed to the integration routine; must be strictly positive.
...	Additional arguments (unused; present for the p_prod3 deprecated alias).

Value

Probability P(X1 * X2 * X3 <= q) as a numeric scalar in ⁠[0, 1]⁠.

Note

p_prod3() is a superseded alias for pprodnormal3(), kept for backward compatibility; use pprodnormal3() in new code.

Note

Numerical accuracy can degrade as the standardized ⁠(X, Y)⁠ correlation approaches +-1 (the bivariate block becomes ill-conditioned). For a fully degenerate point mass (all variances zero) with zero means, q == 0 returns 0.5 by the mid-distribution convention rather than 0 or 1.

See Also

pprodnormal() and qprodnormal() for the two-variable product- normal CDF/quantile; ProductNormal3 for the corresponding S7 class.

Examples

Sigma <- diag(3)
pprodnormal3(q = 0, mean = c(0, 0, 0), cov = Sigma)

---

ProductNormal Class

Description

Represents the distribution of the product of normal random variables.

Usage

ProductNormal(mu = integer(0), Sigma = integer(0))

Arguments

mu	Numeric vector of means.
Sigma	Covariance matrix.

Examples

pn <- ProductNormal(mu = c(0.5, 0.3), Sigma = diag(2) * 0.1)

---

Enhanced ProductNormal constructor with better validation

Description

Enhanced ProductNormal constructor with better validation

Usage

ProductNormal2(mu, Sigma, validate = TRUE)

Arguments

mu	Numeric vector of means
Sigma	Covariance matrix
validate	Whether to run additional validation (default: TRUE)

Examples

pn <- ProductNormal2(mu = c(0.5, 0.3), Sigma = diag(2) * 0.1)

---

ProductNormal3 Class

Description

Represents the distribution of the product of three normal random variables. Intended for sequential indirect effects of the form a1 * a2 * b, where ⁠(a1, a2, b)⁠ is asymptotically trivariate normal.

Usage

ProductNormal3(mu = integer(0), Sigma = integer(0), method = character(0))

Arguments

mu	Numeric vector of means of length 3: c(a1_hat, a2_hat, b_hat).
Sigma	3x3 asymptotic covariance matrix of ⁠(a1, a2, b)⁠.
method	Integration method. Currently only "hcubature" is supported.

Examples

obj <- ProductNormal3(
  mu = c(0.5, 0.3, 0.2),
  Sigma = diag(3),
  method = "hcubature"
)
obj
cdf(obj, q = 1)
## Not run: 
confint(obj, level = 0.95)

## End(Not run)

---

Quantile for the Monte Carlo Sampling Distribution of a nonlinear function of coefficients estimates

Description

This function returns a quantile corresponding to the probability p.

Usage

qMC(p, mu, Sigma, quant, n.mc = 1e+05, ...)

Arguments

p	probability.
mu	a vector of means (e.g., coefficient estimates) for the normal random variables. A user can assign a name to each mean value, e.g., mu=c(b1=.1,b2=3); otherwise, the coefficient names are assigned automatically as follows: b1,b2,....
Sigma	either a covariance matrix or a vector that stacks all the columns of the lower triangle variance–covariance matrix one underneath the other.
quant	quantity of interest, which is a nonlinear/linear function of the model parameters. Argument quant is a formula that must start with the symbol "tilde" (~): e.g., ~b1*b2*b3*b4. The names of coefficients must conform to the names provided in the argument mu or to the default names, i.e., b1,b2,....
n.mc	Monte Carlo sample size (default: 1e5). Larger values provide more precision but take longer to compute.
...	additional arguments.

Value

scalar quantile value.

Author(s)

Davood Tofighi [email protected]

References

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

Examples

qMC(.05,
  mu = c(b1 = 1, b2 = .7, b3 = .6, b4 = .45), Sigma = c(.05, 0, 0, 0, .05, 0, 0, .03, 0, .03),
  quant = ~ b1 * b2 * b3 * b4
)

---

Quantile for the Distribution of Product of Two Normal Variables

Description

Generates quantiles for the distribution of product of two normal random variables

Usage

qprodnormal(
  p,
  mu.x,
  mu.y,
  se.x,
  se.y,
  rho = 0,
  lower.tail = TRUE,
  type = "dop",
  n.mc = 1e+05
)

Arguments

p	probability
mu.x	mean of $x$
mu.y	mean of $y$
se.x	standard error (deviation) of $x$
se.y	standard error (deviation) of $y$
rho	correlation between $x$ and $y$, where -1 <rho < 1. The default value is 0.
lower.tail	logical; if TRUE (default), the probability is $P(XY \le q)$; otherwise, $P(XY > q)$
type	method used to compute confidence interval. It takes on the values "dop" (default), "MC", or "all"
n.mc	Monte Carlo sample size when type="MC" (default: 1e5). Larger values provide more precision but take longer to compute.

Details

This function returns a quantile and the associated error (accuracy) corresponding the requested percentile (probability) p of the distribution of product of mediated effect (product of two normal random variables). To obtain a quantile using a specific method, the argument type should be specified. The default method is type="dop", which uses the method described by Meeker and Escobar (1994) to evaluate the CDF of the distribution of product of two normal variables. type="MC" uses the Monte Carlo approach (Tofighi & MacKinnon, 2011). type="all" prints quantiles using all three options. For the method type="dop", the error is the modulus of absolute error for the numerical integration (for more information see Meeker and Escobar, 1994). For type="MC", the error refers to the Monte Carlo error.

Value

An object of the type list that contains the following values:

q	quantile corresponding to probability p
error	estimate of the absolute error

Author(s)

Davood Tofighi [email protected]

References

Tofighi, D. and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692–700. doi:10.3758/s13428-011-0076-x

See Also

medci RMediation-package

Examples

## lower tail
qprodnormal(
  p = .1, mu.x = .5, mu.y = .3, se.x = 1, se.y = 1, rho = 0,
  lower.tail = TRUE, type = "all"
)
## upper tail
qprodnormal(
  p = .1, mu.x = .5, mu.y = .3, se.x = 1, se.y = 1, rho = 0,
  lower.tail = FALSE, type = "all"
)

---

Tidy generic function

Description

Tidy generic function

Usage

tidy(x, ...)

Arguments

x	An object to tidy
...	Additional arguments passed to methods

Value

A tibble or data.frame with tidy output

Examples

## Not run: 
tidy(mbco_result)

## End(Not run)

---

Creates a data.frame for a log-likelihood object

Description

Creates a data.frame for a log-likelihood object

Usage

## S3 method for class 'logLik'
tidy(x, ...)

Arguments

x	x A log-likelihood object, typically returned by logLik.
...	Additional arguments (not used)

Value

A data.frame with columns:

term

The term name

estimate

The log-likelihood value

df

The degrees of freedom

Author(s)

Davood Tofighi [email protected]

See Also

logLik

Examples

fit <- lm(mpg ~ wt, data = mtcars)
logLik_fit <- logLik(fit)
tidy(logLik_fit)

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