RadialMR

https://github.com/wspiller/radialmr
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### RadialMR Interactive Mode Notice Example

Source: https://github.com/wspiller/radialmr/blob/master/docs/LICENSE-text.html

An example of a short notice that a program, like RadialMR, should display when it starts in interactive mode. This notice informs users about the program's warranty status and redistribution terms.

```text
RadialMR  Copyright (C) 2017  WSpiller
This program comes with ABSOLUTELY NO WARRANTY; for details type \`show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type \`show c' for details.
```

--------------------------------

### Install RadialMR from GitHub

Source: https://github.com/wspiller/radialmr/blob/master/README.md

Installs the RadialMR package directly from its GitHub repository. This method requires the 'remotes' package and is useful for installing development versions or specific branches.

```r
install.packages("remotes")
remotes::install_github("WSpiller/RadialMR")
```

--------------------------------

### Install RadialMR from R-Universe

Source: https://github.com/wspiller/radialmr/blob/master/README.md

Installs the RadialMR package from the MRCIEU R-Universe repository. This is the recommended method for installing the latest stable version of the package.

```r
install.packages("RadialMR", repos = c("https://mrcieu.r-universe.dev", "https://cloud.r-project.org"))
```

--------------------------------

### Install RadialMR from GitHub

Source: https://github.com/wspiller/radialmr/blob/master/docs/index.html

Installs the RadialMR R package directly from its GitHub repository. This method is useful for installing the development version or if the R-Universe is not accessible. Requires the 'remotes' package.

```R
install.packages("remotes")
remotes::install_github("WSpiller/RadialMR")
```

--------------------------------

### Install RadialMR from R-Universe

Source: https://github.com/wspiller/radialmr/blob/master/docs/index.html

Installs the RadialMR R package from the MRCIEU R-Universe repository. This is the recommended installation method for accessing the latest stable version.

```R
install.packages("RadialMR", repos = c("https://mrcieu.r-universe.dev", "https://cloud.r-project.org"))
```

--------------------------------

### Example: Radial MR-Egger with MendelianRandomization data

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/egger_radial.html

Shows how to use the `egger_radial` function with data formatted by the `MendelianRandomization` package. This example requires the `MendelianRandomization` package to be installed and loaded.

```R
# Example using MendelianRandomization format data
if (require("MendelianRandomization", quietly = TRUE)) {
  # Placeholder for MendelianRandomization example
  # mr_input_data <- MendelianRandomization::mr_input(...)
  # egger_radial(r_input = mr_input_data, alpha = 0.05, weights = 2, summary = FALSE)
}

```

--------------------------------

### ivw_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/ivw_radial.html

Provides detailed documentation for the `ivw_radial` function, including its usage, parameters, return values, and an example.

```APIDOC
ivw_radial Function

Fits a radial inverse variance weighted (IVW) model using a range of weighting specifications. Outliers are determined with respect to their contribution to global heterogeneity, quantified by Cochran's Q-statistic, using a significance threshold specified by the user. The `ivw_radial` function returns an object of class "IVW", containing effect estimates, total estimated heterogeneity using Cochran's Q-statistic, the individual contribution to overall heterogeneity of each variant, and a data frame for used in the downstream plotting functions `plot_radial` and `plotly_radial`.

Usage:
    ivw_radial(r_input, alpha, weights, tol, summary)

Arguments:
    r_input: A formatted data frame using the `format_radial` function, or an object of class `MRInput` generated by `MendelianRandomization::mr_input` or a data.frame for a single exposure-outcome pair generated by `TwoSampleMR::harmonise_data`.
    alpha: A value specifying the statistical significance threshold for identifying outliers (`0.05` specifies a p-value threshold of 0.05).
    weights: A value specifying the inverse variance weights used to calculate IVW estimate and Cochran's Q statistic. By default modified second order weights are used, but one can choose to select first order (`1`), second order (`2`) or modified second order weights (`3`).
    tol: A value indicating the tolerance threshold for performing the iterative IVW approach. The value represents the minimum difference between the coefficients of the previous and current iterations required for a further iteration to be performed (default= `0.0001`).
    summary: A logical argument (`TRUE` or `FALSE`) indicating whether a summary of results and heterogeneity should be presented (default= `TRUE`).

Value:
An object of class "IVW" containing the following components:
    coef: The estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
    qstatistic: Cochran's Q statistic for overall heterogeneity.
    df: Degrees of freedom. This is equal to the number of variants -1 when fitting the radial IVW model.
    outliers: A data frame containing variants identified as outliers, with respective Q statistics, chi-squared tests and SNP identification.
    data: A data frame containing containing SNP IDs, inverse variance weights, the product of the inverse variance weight and ratio estimate for each variant, contribution to overall heterogeneity with corresponding p-value, and a factor indicator showing outlier status.
    confint: A vector giving lower and upper confidence limits for the radial IVW effect estimate.
    it.coef: The estimated iterative coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
    it.confint: A vector giving lower and upper confidence limits for the iterative radial IVW effect estimate.
    fe.coef: The estimated fixed effect exact coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
    fe.confint: A vector giving lower and upper confidence limits for the fixed effect exact radial IVW effect estimate.
    re.coef: The estimated random effect exact coefficient, its standard error, t-statistic and corresponding (two-sided) p-value.
    re.confint: A vector giving lower and upper confidence limits for the random effect exact radial IVW effect estimate.
    mf: The mean F statistic for the set of genetic variants, indicative of instrument strength.

Examples:
    # Example using format_radial data
    ldl.dat <- data_radial[data_radial[,10]<5e-8,]
    ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                              ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
    ivw_radial(ldl.fdat, 0.05, 1, 0.0001, TRUE)
    #> 
    #> Radial IVW
    #> 
    #>               Estimate  Std.Error   t value     Pr(>|t|)
    #> Effect (1st) 0.4874900 0.05830409  8.361163 6.210273e-17

```

--------------------------------

### Source File Notice Template for New Programs

Source: https://github.com/wspiller/radialmr/blob/master/docs/LICENSE-text.html

This template provides the standard notices to be attached to the start of each source file when distributing software under the GNU GPL. It includes copyright information, license terms, and warranty disclaimers.

```text
{one line to give the program's name and a brief idea of what it does.}
Copyright (C) 2017  {name of author}

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Example Usage of plotly_radial

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/plotly_radial.html

Demonstrates how to use the plotly_radial function with data processed by format_radial and ivw_radial. It shows data preparation and function call.

```R
ldl.dat <- data_radial[data_radial[,10]<5e-8,]
ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                          ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
ivw.object <- ivw_radial(ldl.fdat, 0.05, 1, 0.0001, TRUE)
plotly_radial(ivw.object)
```

--------------------------------

### R: Radial IVW Model Fitting

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/ivw_radial.html

Demonstrates fitting a radial IVW model using the `ivw_radial` function in R with example data.

```R
# Example using format_radial data
ldl.dat <- data_radial[data_radial[,10]<5e-8,]
ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                          ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
ivw_radial(ldl.fdat, 0.05, 1, 0.0001, TRUE)
```

--------------------------------

### Example: Radial MR-Egger with formatted data

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/egger_radial.html

Demonstrates how to use the `egger_radial` function with data pre-formatted by `format_radial`. This example filters data based on a p-value threshold and then applies the radial MR-Egger model.

```R
# Example using format_radial data
ldl.dat <- data_radial[data_radial[,10]<5e-8,]

ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                          ldl.dat[,15], ldl.dat[,21],
                          ldl.dat[,1])

egger_radial(ldl.fdat, 0.05, 1, TRUE)
#> 
#> Radial MR-Egger
#> 
#>               Estimate Std. Error   t value     Pr(>|t|)
#> (Intercept) -0.4303552  0.3244151 -1.326557 1.884296e-01
#> Wj           0.6129126  0.1109369  5.524877 3.988423e-07
#> 
#> Residual standard error: 1.537 on 80 degrees of freedom
#> 
#> F-statistic: 30.52 on 1 and 80 DF, p-value: 3.99e-07
#> Q-Statistic for heterogeneity: 188.9285 on 80 DF , p-value: 8.445607e-11
#> 
#>  Outliers detected 
#> 

```

--------------------------------

### R: Generate IVW Radial Object

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/plot_radial.html

Example code demonstrating how to prepare data using format_radial and then generate an IVW radial object using ivw_radial. This is a prerequisite for plotting with plot_radial.

```R
ldl.dat <- data_radial[data_radial[,10]<5e-8,]
ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                          ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
ivw.object <- ivw_radial(ldl.fdat, 0.05, 1, 0.0001, TRUE)
```

--------------------------------

### Example: Radial MR-Egger with TwoSampleMR data

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/egger_radial.html

Illustrates fitting a radial MR-Egger model using data harmonized by the `TwoSampleMR` package. This involves extracting instruments, fetching outcome data, harmonizing them, and then passing to `egger_radial`.

```R
# Example using TwoSampleMR format data
if (FALSE) { # \dontrun{
if (require("TwoSampleMR", quietly = TRUE)) {
# Example with one exposure-outcome pair
bmi_exp_dat <- TwoSampleMR::extract_instruments(outcomes = 'ieu-a-2')
chd_out_dat <- TwoSampleMR::extract_outcome_data(
                                   snps = bmi_exp_dat$SNP,
                                   outcomes = 'ieu-a-7')
tsmar_dat <- TwoSampleMR::harmonise_data(exposure_dat = bmi_exp_dat,
                                       outcome_dat = chd_out_dat)
egger_radial(r_input = tsmrdat, alpha = 0.05,
             weights = 1, summary = TRUE)
}
} # }

```

--------------------------------

### Example: Using tsmr_to_rmr_format with TwoSampleMR

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/tsmr_to_rmr_format.html

Demonstrates how to use the tsmr_to_rmr_format function by first extracting instruments and outcome data using TwoSampleMR, then harmonising them, and finally converting the harmonised data to the RadialMR format.

```R
if (FALSE) { # \dontrun{
if (require("TwoSampleMR", quietly = TRUE)) {
  # Example with one exposure-outcome pair
  bmi_exp_dat <- TwoSampleMR::extract_instruments(outcomes = 'ieu-a-2')
  chd_out_dat <- TwoSampleMR::extract_outcome_data(
    snps = bmi_exp_dat$SNP,
    outcomes = 'ieu-a-7'
  )
  dat <- TwoSampleMR::harmonise_data(exposure_dat = bmi_exp_dat,
                                     outcome_dat = chd_out_dat)
  dat <- tsmr_to_rmr_format(dat)
  class(dat)
  head(dat)
}
} # }
```

--------------------------------

### MendelianRandomization Data Input and Radial IVW

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/ivw_radial.html

Shows how to prepare data for the MendelianRandomization package and run the Radial IVW (Inverse Variance Weighted) method. This example filters data based on p-value and uses specific beta and standard error columns.

```R
if (require("MendelianRandomization", quietly = TRUE)) {
    # Assuming 'data_radial' is a pre-loaded data frame
    # Filter data for SNPs with p-value < 5e-8
    dat <- data_radial[data_radial[,10] < 5e-8,]
    
    # Create MR input object
    mrdat <- MendelianRandomization::mr_input(bx = dat$ldlcbeta,
                                              bxse = dat$ldlcse,
                                              by = dat$chdbeta,
                                              byse = dat$chdse,
                                              snps = dat$rsid)
    
    # Run Radial IVW analysis
    ivw_radial(r_input = mrdat, alpha = 0.05,
               weights = 1, tol = 0.0001, summary = TRUE)
}
```

--------------------------------

### R: Generate MR-Egger Radial Object

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/plot_radial.html

Example code demonstrating how to generate an MR-Egger radial object using egger_radial, after preparing the data with format_radial. This object can then be used with the plot_radial function.

```R
egg.object <- egger_radial(ldl.fdat, 0.05, 1, TRUE)
```

--------------------------------

### plot_radial Function API Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/plot_radial.html

Provides the signature, parameter descriptions, return value, and usage examples for the plot_radial function. This function generates radial IVW and MR-Egger plots, allowing customization of scale, outlier display, and axis matching.

```APIDOC
plot_radial(r_object, radial_scale, show_outliers, scale_match)

  A function for producing radial IVW and MR-Egger plots either individually or simultaneously. The function allows for a variety of aesthetic and scaling options, utilising the output from the IVW_radial and egger_radial functions.

  Arguments:
    r_object: An object of class "IVW" or "egger". For visualising both estimates simultaneously, both objects should be included as a vector c(A,B), where A and B denote the "IVW" and "egger" objects respectively.
    radial_scale: Indicates whether to produce a plot including a full radial scale (TRUE), or a scatterplot showing only the effect estimates (FALSE).
    show_outliers: Indicates whether display only the set of variants identified as outliers (TRUE) or the complete set of variants (FALSE). Note that when (show_outliers=TRUE), non-outlying variants further from the origin than the furthest outlier will cause an error message that one or more points have been omitted. These are non-outlying variants beyond the scale. If no outliers are present, a plot will be produced using the full set of variants, with an accompanying message indicating the absence of outliers.
    scale_match: Indicates whether x and y axes should have the same range(TRUE), or different ranges (FALSE). This improves the interpretation of the radial scale, and is set to FALSE when the radial scale is omitted from the plot.

  Value:
    A ggplot object containing a radial plot of either the IVW, MR-Egger, or both estimates simultaneously.

  Examples:
    ldl.dat <- data_radial[data_radial[,10]<5e-8,]
    ldl.fdat <- format_radial(ldl.dat[,6], ldl.dat[,9],
                              ldl.dat[,15], ldl.dat[,21], ldl.dat[,1])
    ivw.object <- ivw_radial(ldl.fdat, 0.05, 1, 0.0001, TRUE)
    plot_radial(ivw.object)
    
    egg.object <- egger_radial(ldl.fdat, 0.05, 1, TRUE)
    plot_radial(egg.object)
```

--------------------------------

### RadialMR data_radial Dataset Usage

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/data_radial.html

Demonstrates how to access and view the first few rows of the data_radial dataset, which contains Two Sample Summary GWAS data. This dataset is a data frame with 185 rows and 21 variables.

```R
head(data_radial)
#>         rsid a1 a2 chr      pos ldlcbeta hdlcbeta  tgbeta chdbeta  ldlcp2
#> 1 rs10903129  A  G   1 25641524   -0.033  -0.0009 -0.0080 -0.0120 4.0e-19
#> 2  rs4660293  A  G   1 39800767   -0.011   0.0350 -0.0200 -0.0110 1.4e-02
#> 3  rs1998013  T  C   1 55730618   -0.380   0.0350  0.0089 -0.1500 4.0e-67
#> 4 rs10493326  A  G   1 62725961    0.021  -0.0013  0.0310 -0.0087 6.0e-07
#> 5  rs4587594  A  G   1 62906518   -0.049  -0.0150 -0.0690  0.0170 3.0e-37
#> 6  rs6603981  T  C   1 92766395    0.034   0.0039  0.0072  0.0120 2.0e-14
#>    hdlcp2    tgp2 chdp2     ldlcz      ldlcse     hdlcz      hdlcse        tgz
#> 1 7.9e-01 1.7e-02  0.38  8.936966 0.003692528 0.2663106 0.003379512  2.3867077
#> 2 7.0e-19 1.0e-07  0.48  2.457263 0.004476525 8.8748941 0.003943709  5.3267239
#> 3 7.7e-02 6.6e-01  0.12 17.309336 0.021953470 1.7683644 0.019792300  0.4399132
#> 4 7.4e-01 1.0e-15  0.65  4.991217 0.004207391 0.3318533 0.003917393  8.0268589
#> 5 5.0e-05 3.0e-87  0.26 12.752994 0.003842235 4.0556270 0.003698565 19.7996112
#> 6 3.5e-01 7.6e-02  0.48  7.650628 0.004444080 0.9345893 0.004172956  1.7743819
#>          tgse      chdz      chdse
#> 1 0.003351898 0.8778963 0.01366904
#> 2 0.003754653 0.7063026 0.01557406
#> 3 0.020231265 1.5547736 0.09647707
#> 4 0.003862034 0.4537622 0.01917304
#> 5 0.003484917 1.1263911 0.01509245
#> 6 0.004172956 1.7743819 0.004172956
```

--------------------------------

### format_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Documentation for the format_radial function, likely used for data formatting within the RadialMR package.

```R
format_radial()

# Description:
# format_radial

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### RadialMR Package Core Functions

Source: https://github.com/wspiller/radialmr/blob/master/docs/index.html

Provides an overview of the main functions within the RadialMR R package for Mendelian Randomization analysis. These functions handle data preparation, model fitting (IVW and MR-Egger), and plot generation.

```APIDOC
RadialMR Package Functions:

1. format_radial(data_frame)
   - Description: Converts a data frame containing summary data into a standard format suitable for radial analyses.
   - Parameters:
     - data_frame: A data frame containing summary statistics (e.g., SNP, effect estimates, standard errors).
   - Returns: A formatted data frame ready for radial regression.

2. ivw_radial(..., weights = c("first", "second", "modified_second"), ...)
   - Description: Fits a radial inverse variance weighted (IVW) model. Supports first order, second order, or modified second order weights. It provides an effect estimate and can identify outliers using Cochran’s Q-statistic. Includes iterative and exact IVW estimation.
   - Parameters:
     - ...: Arguments passed to the underlying model fitting functions.
     - weights: Specifies the weighting scheme for the IVW model (e.g., "first", "second", "modified_second").
   - Returns: An object containing the IVW model results, including effect estimates and outlier statistics.

3. egger_radial(..., weights = c("first", "second", "modified_second"), ...)
   - Description: Fits a radial MR-Egger model. Similar to ivw_radial, it supports different weighting schemes and identifies outliers using Rucker’s Q-statistic.
   - Parameters:
     - ...: Arguments passed to the underlying model fitting functions.
     - weights: Specifies the weighting scheme for the MR-Egger model.
   - Returns: An object containing the MR-Egger model results.

4. plotly_radial(..., radial_results)
   - Description: Produces interactive radial plots using the 'plotly' library, corresponding to the output of 'ivw_radial' and 'egger_radial' functions.
   - Parameters:
     - radial_results: The output object from 'ivw_radial' or 'egger_radial'.
     - ...: Additional arguments for plot customization.
   - Returns: An interactive plotly object.

5. plot_radial(..., radial_results, scale_options, aesthetic_options)
   - Description: Produces static radial plots. Offers various scaling and aesthetic options, allowing visualization of IVW estimates, MR-Egger estimates, or both simultaneously.
   - Parameters:
     - radial_results: The output object from 'ivw_radial' or 'egger_radial'.
     - scale_options: Options for scaling the plot.
     - aesthetic_options: Options for plot aesthetics (e.g., colors, labels).
   - Returns: A static plot object (e.g., ggplot2 object).
```

--------------------------------

### data_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Provides documentation for the data_radial function, which is associated with sample summary GWAS data published in Do et al (2014).

```R
data_radial()

# Description:
# Two Sample Summary GWAS data published in Do et al (2014).

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### plotly_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Documentation for the plotly_radial function, likely used for creating interactive plots with Plotly for RadialMR analysis.

```R
plotly_radial()

# Description:
# plotly_radial

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### Radial IVW Function Usage with TwoSampleMR Data

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/ivw_radial.html

Illustrates the usage of the `ivw_radial` function with harmonized data obtained from the TwoSampleMR package. It specifies parameters like alpha, weights, tolerance, and requests a summary output.

```R
# Assuming 'tsmrdat' is harmonised data from TwoSampleMR
# ivw_radial function is called with specific parameters
ivw_radial(r_input = tsmrdat, alpha = 0.05,
           weights = 1, tol = 0.0001, summary = TRUE)
```

--------------------------------

### Create Radial Plots with RadialMR

Source: https://github.com/wspiller/radialmr/blob/master/README.md

The `plot_radial()` function generates static radial plots. It offers various scaling and aesthetic options, allowing visualization of IVW estimates, MR-Egger estimates, or both simultaneously.

```APIDOC
plot_radial(results, ...)
  - Generates static radial plots for MR analysis.
  - Can display IVW, MR-Egger, or both estimates with customizable aesthetics.
  - Parameters:
    - results: Output object from `ivw_radial()` or `egger_radial()`, or a list containing both.
    - ...: Additional arguments for plot scaling and aesthetics.
  - Returns: A graphical plot object.
```

--------------------------------

### mrinput_to_rmr_format Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Converts an MRInput object from the MendelianRandomization package to the RadialMR rmr_format class. This function facilitates data compatibility between packages.

```R
mrinput_to_rmr_format(mrinput_object)

# Description:
# Convert an object of class MRInput from the MendelianRandomization package to the RadialMR rmr_format class.

# Parameters:
# - mrinput_object: An object of class MRInput from the MendelianRandomization package.

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### plot_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Documentation for the plot_radial function, used for generating plots related to RadialMR analysis.

```R
plot_radial()

# Description:
# plot_radial

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### ivw_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Documentation for the ivw_radial function, likely related to Inverse Variance Weighted methods in Mendelian Randomization.

```R
ivw_radial()

# Description:
# ivw_radial

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### egger_radial Function API

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/egger_radial.html

Details the signature, arguments, and return values of the `egger_radial` function for fitting radial MR-Egger models. It supports various weighting schemes and outlier detection.

```APIDOC
egger_radial(r_input, alpha, weights, summary)

Fits a radial MR-Egger model using first order, second order, or modified second order weights. Outliers are identified using a significance threshold specified by the user. The function returns an object of class "egger", containing regression estimates, a measure of total heterogeneity using Rucker's Q statistic, the individual contribution to overall heterogeneity of each variant, and a data frame for use in constructing the radial plot.

Arguments:
  r_input: A formatted data frame using the `format_radial` function, or an object of class `MRInput` generated by `MendelianRandomization::mr_input` or a data.frame for a single exposure-outcome pair generated by `TwoSampleMR::harmonise_data`.
  alpha: A value specifying the statistical significance threshold for identifying outliers (e.g., `0.05`).
  weights: A value specifying the inverse variance weights used to calculate the MR-Egger estimate and Rucker's Q statistic. Options are first order (`1`), second order (`2`), or modified second order weights (`3`, default).
  summary: A logical argument (`TRUE` or `FALSE`) indicating whether a summary of results and heterogeneity should be presented (default = `TRUE`).

Value:
  An object of class "egger" containing:
  - coef: A matrix with intercept and slope coefficients, standard errors, t-statistics, and p-values.
  - qstatistic: Rucker's Q statistic for overall heterogeneity.
  - df: Degrees of freedom for the model.
  - outliers: A data frame of identified outliers with their Q statistics and tests.
  - data: A data frame with SNP IDs, weights, heterogeneity contributions, and outlier status.
  - confint: Confidence limits for the radial MR-Egger effect estimate.
```

--------------------------------

### Fit Radial IVW Model in RadialMR

Source: https://github.com/wspiller/radialmr/blob/master/README.md

The `ivw_radial()` function fits a radial inverse variance weighted (IVW) model. It supports first, second, or modified second-order weights and can identify outliers using Cochran's Q-statistic. It also includes iterative and exact IVW estimation methods.

```APIDOC
ivw_radial(data, ...)
  - Fits a radial Inverse Variance Weighted (IVW) Mendelian randomization model.
  - Supports various weighting schemes (1st, 2nd, modified 2nd order) and iterative/exact estimation.
  - Parameters:
    - data: A formatted data frame from `format_radial()`.
    - ...: Additional arguments for model fitting.
  - Returns: An object containing the IVW estimate, standard error, and outlier statistics (e.g., Cochran's Q).
```

--------------------------------

### Format Data for RadialMR Analysis

Source: https://github.com/wspiller/radialmr/blob/master/README.md

The `format_radial()` function is used to convert a data frame containing summary data into a specific format required for radial analyses within the RadialMR package.

```APIDOC
format_radial(data)
  - Converts summary data into a format suitable for radial MR analyses.
  - Parameters:
    - data: A data frame containing summary-level genetic association data.
  - Returns: A formatted data frame ready for radial regression functions.
```

--------------------------------

### Create Interactive Radial Plots with Plotly

Source: https://github.com/wspiller/radialmr/blob/master/README.md

The `plotly_radial()` function generates interactive radial plots. These plots visualize the results from `ivw_radial()` and `egger_radial()` functions, allowing for dynamic exploration of the MR analysis.

```APIDOC
plotly_radial(ivw_results, egger_results, ...)
  - Produces interactive radial plots using the plotly library.
  - Visualizes results from IVW and MR-Egger radial models.
  - Parameters:
    - ivw_results: Output object from `ivw_radial()`.
    - egger_results: Output object from `egger_radial()`.
    - ...: Additional arguments for plot customization.
  - Returns: An interactive plotly object.
```

--------------------------------

### egger_radial Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Documentation for the egger_radial function, likely related to Mendelian Randomization analysis.

```R
egger_radial()

# Description:
# egger_radial

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### tsmr_to_rmr_format Function Documentation

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/index.html

Converts data from TwoSampleMR::harmonise_data() to the RadialMR rmr_format class. This function is useful for integrating data from TwoSampleMR.

```R
tsmr_to_rmr_format(tsmr_data)

# Description:
# Convert a data.frame containing a single exposure - outcome pair generated by TwoSampleMR::harmonise_data() to the RadialMR rmr_format class.

# Parameters:
# - tsmr_data: A data.frame generated by TwoSampleMR::harmonise_data() for a single exposure-outcome pair.

# Note: This entry is based on the provided text which lists the function name and a brief description. 
# Full parameter, return, and usage details are not available in the input text.
```

--------------------------------

### Perform Radial MR-Egger Analysis

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/egger_radial.html

This R code snippet demonstrates how to perform a Radial Mendelian Randomization (MR) analysis. It first filters the input data based on a p-value threshold, then prepares the data for MR analysis using the `mr_input` function from the `MendelianRandomization` package, and finally runs the MR-Egger regression using `egger_radial`. The output includes regression coefficients, standard errors, and heterogeneity statistics.

```R
dat <- data_radial[data_radial[,10] < 5e-8,]
mrdat <- MendelianRandomization::mr_input(
    bx = dat$ldlcbeta,
    bxse = dat$ldlcse,
    by = dat$chdbeta,
    byse = dat$chdse,
    snps = dat$rsid
)
eggers_result <- egger_radial(r_input = mrdat, alpha = 0.05, weights = 1, summary = TRUE)
print(eggers_result)
```

--------------------------------

### mrinput_to_rmr_format: Convert MRInput to rmr_format

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/mrinput_to_rmr_format.html

This function converts an object of class MRInput, generated by the MendelianRandomization package, into the rmr_format class used by RadialMR. It creates a data.frame suitable for radial MR analyses. The input requires an MRInput object, and the output is a data.frame with the 'rmr_format' class.

```APIDOC
mrinput_to_rmr_format(dat)

  Converts an object of class MRInput from the MendelianRandomization package to the RadialMR rmr_format class.

  Arguments:
    dat: Object from MendelianRandomization::mr_input.

  Value:
    Object of class rmr_format, the RadialMR format.

  Examples:
    if (require("MendelianRandomization", quietly = TRUE)) {
      dat <- data_radial[data_radial[,10] < 5e-8,]
      dat <- MendelianRandomization::mr_input(bx = dat$ldlcbeta,
                                            bxse = dat$ldlcse,
                                            by = dat$chdbeta,
                                            byse = dat$chdse,
                                            snps = dat$rsid)
      dat <- mrinput_to_rmr_format(dat)
      head(dat)
      class(dat)
    }
```

--------------------------------

### Fit Radial MR-Egger Model in RadialMR

Source: https://github.com/wspiller/radialmr/blob/master/README.md

The `egger_radial()` function fits a radial MR-Egger model. It uses first, second, or modified second-order weights and identifies outliers using Rucker's Q-statistic.

```APIDOC
egger_radial(data, ...)
  - Fits a radial MR-Egger Mendelian randomization model.
  - Supports various weighting schemes (1st, 2nd, modified 2nd order).
  - Parameters:
    - data: A formatted data frame from `format_radial()`.
    - ...: Additional arguments for model fitting.
  - Returns: An object containing the MR-Egger estimate, standard error, and outlier statistics (e.g., Rucker's Q).
```

--------------------------------

### RadialMR data_radial Dataset Variable Descriptions

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/data_radial.html

Provides a detailed breakdown of the variables included in the data_radial dataset. Each variable is described along with its data type and meaning, covering genetic variant identifiers, alleles, chromosomal location, position, and association estimates (beta, p-value, z-score, standard error) for LDL-C, HDL-C, triglycerides, and CHD.

```APIDOC
data_radial:
  Description: A data frame containing summary GWAS data for 185 independent SNPs.
  Variables:
    rsid: The identification number for each variant.
    a1: The reference allele for each variant.
    a2: The other allele for each variant.
    chr: The chromosome number in which each variant is located.
    pos: The genomic position for the genetic variant relative to chromosome number.
    ldlcbeta: The association estimate for the genetic variant obtained by regressing LDL-C upon the genetic variant.
    hdlcbeta: The association estimate for the genetic variant obtained by regressing HDL-C upon the genetic variant.
    tgbeta: The association estimate for the genetic variant obtained by regressing triglycerides upon the genetic variant.
    chdbeta: The association estimate for CHD obtained by regressing CHD upon the genetic variant.
    ldlcp2: The p-value corresponding to association estimate ldlcbeta.
    hdlcp2: The p-value corresponding to association estimate hdlcbeta.
    tgp2: The p-value corresponding to association estimate tgbeta.
    chdp2: The p-value corresponding to association estimate chdbeta.
    ldlcz: The z-score corresponding to association estimate ldlcbeta.
    ldlcse: The standard error corresponding to association estimate ldlcbeta.
    hdlcz: The z-score corresponding to association estimate hdlcbeta.
    hdlcse: The standard error corresponding to association estimate hdlcbeta.
    tgz: The z-score corresponding to association estimate tgbeta.
    tgse: The standard error corresponding to association estimate tgbeta.
    chdz: The z-score corresponding to association estimate chdbeta.
    chdse: The standard error corresponding to association estimate chdbeta.
```

--------------------------------

### TwoSampleMR Data Extraction and Harmonization

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/ivw_radial.html

Demonstrates how to extract genetic instruments for an exposure and outcome data, then harmonizes them for MR analysis using the TwoSampleMR package. This is a prerequisite step before running MR models.

```R
if (require("TwoSampleMR", quietly = TRUE)) {
    # Example with one exposure-outcome pair
    bmi_exp_dat <- TwoSampleMR::extract_instruments(outcomes = 'ieu-a-2')
    chd_out_dat <- TwoSampleMR::extract_outcome_data(
                                   snps = bmi_exp_dat$SNP,
                                   outcomes = 'ieu-a-7')
    tsmrdat <- TwoSampleMR::harmonise_data(exposure_dat = bmi_exp_dat,
                                       outcome_dat = chd_out_dat)
    # The harmonised data (tsmrdat) can then be used for MR analysis
}
```

--------------------------------

### plotly_radial Function API

Source: https://github.com/wspiller/radialmr/blob/master/docs/reference/plotly_radial.html

Generates interactive radial IVW and MR-Egger plots. It takes an object from IVW_radial or egger_radial and generates a plotly object. Hovering over data points shows SNP IDs.

```APIDOC
plotly_radial(r_object, TEST)

Arguments:
  r_object: An object of class "IVW" or "egger".
  TEST: Logical; indicating whether testing the function

Value:
A plotly object containing a radial plot of either the IVW or MR-Egger estimates. Hovering the mouse over individual datapoints will highlight the corresponding SNP identification number for that observation.

References:
Bowden, J., et al., Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International Journal of Epidemiology, 2018. 47(4): p. 1264-1278.
```
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