TwoSampleMR 0.6.14

.github/workflows/check-full.yaml

@@ -29,13 +29,13 @@ jobs:
       matrix:
         config:
           - {os: macos-latest,   r: 'release'}
-          - {os: macos-13, r: 'release'}
           - {os: windows-latest, r: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
           - {os: ubuntu-latest,   r: 'oldrel-1'}
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: '4.3.2'}
-          - {os: ubuntu-24.04-arm, r: 'release', rspm: 'no' }
+          - {os: macos-13, r: 'release'}
+          # - {os: ubuntu-24.04-arm, r: 'release', rspm: 'no' }
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MRC Integrative
     Epidemiology Unit OpenGWAS Database
-Version: 0.6.13
+Version: 0.6.14
 Authors@R: c(
     person("Gibran", "Hemani", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),

NEWS.md

@@ -1,3 +1,10 @@
+# TwoSampleMR v0.6.14
+
+(Release date 2025-03-28)
+
+* Minor amends to the `mr_grip()` returned object names (thanks @fdudbridge)
+* Fixed some typos in the helpfiles and vignettes
+
 # TwoSampleMR v0.6.13
 
 (Release date 2025-03-26)

R/forest_plot2.R

@@ -438,7 +438,7 @@ forest_plot_names <- function(dat, section=NULL, bottom=TRUE)
 #'
 #' @param mr_res Results from [mr()].
 #' @param exponentiate Convert effects to OR? Default is `FALSE`.
-#' @param single_snp_method Which of the single SNP methosd to use when only 1 SNP was used to estimate the causal effect? The default is `"Wald ratio"`.
+#' @param single_snp_method Which of the single SNP methods to use when only 1 SNP was used to estimate the causal effect? The default is `"Wald ratio"`.
 #' @param multi_snp_method Which of the multi-SNP methods to use when there was more than 1 SNPs used to estimate the causal effect? The default is `"Inverse variance weighted"`.
 #' @param group_single_categories If there are categories with only one outcome, group them together into an "Other" group. The default is `TRUE`.
 #' @param by_category Separate the results into sections by category? The default is `TRUE`.

R/forest_plot_1-to-many.R

@@ -145,7 +145,7 @@ format_1_to_many <- function(mr_res, b="b",se="se",exponentiate=FALSE, ao_slc=FA
 #' @param sort_action Choose how to sort results.
 #' \itemize{
 #' \item `sort_action = 1`: sort results by effect size within groups. Use the group order supplied by the user.
-#' \item `sort_action = 2`: sort results by effect size and group. Overides the group ordering supplied by the user.
+#' \item `sort_action = 2`: sort results by effect size and group. Overrides the group ordering supplied by the user.
 #' \item `sort_action = 3`: group results for the same trait together (e.g. multiple results for the same trait from different MR methods).
 #' \item `sort_action = 4`: sort by decreasing effect size (largest effect size at top and smallest at bottom).
 #' \item `sort_action = 5`: sort by increasing effect size (smallest effect size at top and largest at bottom).

R/ld.R

@@ -31,7 +31,7 @@
 
 	if(!is.data.frame(dat))
 	{
-		stop("Expecting data frame returned from format_data")
+		stop("Expecting data frame returned from format_data().")
 	}
 
 	if("pval.exposure" %in% names(dat) && "pval.outcome" %in% names(dat))

R/mr-grip.R

@@ -1,6 +1,6 @@
-#' MR-GRIP: a modified MR-Egger model with the Genotype Recoding Invariant Property
+#' MR-GRIP: a modified MR-Egger model with the Genotype Recoding Invariance Property
 #'
-#' This implements the modified MR-Egger model with the Genotype Recoding Invariant Property (MR-GRIP) due to Dudbridge and Bowden et al. (2025).
+#' This implements the modified MR-Egger model with the Genotype Recoding Invariance Property (MR-GRIP) due to Dudbridge and Bowden et al. (2025).
 #' It is well known that the results of MR-Egger are sensitive to which alleles are designated as the effect alleles.
 #' A pragmatic convention is to orient all SNPs to have positive effects on the exposure, which has some advantages in interpretation but also brings some philosophical limitations.
 #' The MR-GRIP model is a modification to the MR-Egger model in which each term is multiplied by the genotype-phenotype associations.
@@ -61,17 +61,17 @@ mr_grip <- function(b_exp, b_out, se_exp, se_out, parameters) {
   smod <- summary(mod)
   b <- stats::coefficients(smod)[2, 1]
   se <- stats::coefficients(smod)[2, 2]
-  b.adj <- NA
-  se.adj <- NA
-  pval.adj <- NA
+  b.wi <- NA
+  se.wi <- NA
+  pval.wi <- NA
   pval <- 2 * stats::pt(abs(b / se), length(b_exp) - 2L, lower.tail = FALSE)
   return(list(
     b = b,
     se = se,
     pval = pval,
-    b.adj = b.adj,
-    se.adj = se.adj,
-    pval.adj = pval.adj,
+    b.wi = b.wi,
+    se.wi = se.wi,
+    pval.wi = pval.wi,
     nsnp = length(b_exp),
     mod = smod,
     dat = dat

R/multivariable_mr.R

@@ -66,10 +66,10 @@ mv_extract_exposures <- function(id_exposure, clump_r2=0.001, clump_kb=10000, ha
 #' Note that you can provide an array of column names for each column, which is of length `filenames_exposure`
 #'
 #' @param filenames_exposure Filenames for each exposure dataset. Must have header with at least SNP column present. Following arguments are used for determining how to read the filename and clumping etc.
-#' @param sep Specify delimeter in file. The default is space, i.e. `sep=" "`. If length is 1 it will use the same `sep` value for each exposure dataset. You can provide a vector of values, one for each exposure dataset, if the values are different across datasets. The same applies to all dataset-formatting options listed below.
+#' @param sep Specify delimiter in file. The default is space, i.e. `sep=" "`. If length is 1 it will use the same `sep` value for each exposure dataset. You can provide a vector of values, one for each exposure dataset, if the values are different across datasets. The same applies to all dataset-formatting options listed below.
 #' @param phenotype_col Optional column name for the column with phenotype name corresponding the the SNP. If not present then will be created with the value `"Outcome"`. Default is `"Phenotype"`.
 #' @param snp_col Required name of column with SNP rs IDs. The default is `"SNP"`.
-#' @param beta_col Required for MR. Name of column with effect sizes. THe default is `"beta"`.
+#' @param beta_col Required for MR. Name of column with effect sizes. The default is `"beta"`.
 #' @param se_col Required for MR. Name of column with standard errors. The default is `"se"`.
 #' @param eaf_col Required for MR. Name of column with effect allele frequency. The default is `"eaf"`.
 #' @param effect_allele_col Required for MR. Name of column with effect allele. Must be "A", "C", "T" or "G". The default is `"effect_allele"`.

R/other_formats.R

@@ -72,7 +72,7 @@ dat_to_MRInput <- function(dat, get_correlations=FALSE, pop="EUR")
 
 #' Harmonise LD matrix against summary data
 #'
-#' LD matrix returns with rsid_ea_oa identifiers. Make sure that they are oriented to the same effect allele as the summary dataset. Summary dataset can be exposure dataset or harmonised dartaset.
+#' LD matrix returns with rsid_ea_oa identifiers. Make sure that they are oriented to the same effect allele as the summary dataset. Summary dataset can be exposure dataset or harmonised dataset.
 #'
 #' @param x Exposure dataset or harmonised dataset
 #' @param ld Output from [ld_matrix()]

R/read_data.R

@@ -5,10 +5,10 @@
 #'
 #' @param filename Filename. Must have header with at least SNP column present.
 #' @param snps SNPs to extract. If `NULL`, which the default, then doesn't extract any and keeps all.
-#' @param sep Specify delimeter in file. The default is space, i.e. `sep=" "`.
+#' @param sep Specify delimiter in file. The default is space, i.e. `sep=" "`.
 #' @param phenotype_col Optional column name for the column with phenotype name corresponding the the SNP. If not present then will be created with the value `"Outcome"`. Default is `"Phenotype"`.
 #' @param snp_col Required name of column with SNP rs IDs. The default is `"SNP"`.
-#' @param beta_col Required for MR. Name of column with effect sizes. THe default is `"beta"`.
+#' @param beta_col Required for MR. Name of column with effect sizes. The default is `"beta"`.
 #' @param se_col Required for MR. Name of column with standard errors. The default is `"se"`.
 #' @param eaf_col Required for MR. Name of column with effect allele frequency. The default is `"eaf"`.
 #' @param effect_allele_col Required for MR. Name of column with effect allele. Must be "A", "C", "T" or "G". The default is `"effect_allele"`.
@@ -64,7 +64,7 @@ read_outcome_data <- function(filename, snps=NULL, sep=" ", phenotype_col="Pheno
 #'
 #' @param filename Filename. Must have header with at least SNP column present.
 #' @param clump Whether to perform LD clumping with [clump_data()] on the exposure data. The default is `FALSE`.
-#' @param sep Specify delimeter in file. The default is a space, i.e. `" "`.
+#' @param sep Specify delimiter in file. The default is a space, i.e. `" "`.
 #' @param phenotype_col Optional column name for the column with phenotype name corresponding the the SNP. If not present then will be created with the value "Outcome". The default is `"Phenotype"`.
 #' @param snp_col Required name of column with SNP rs IDs. The default is `"SNP"`.
 #' @param beta_col Required for MR. Name of column with effect sizes. The default is `"beta"`.

R/steiger.R

@@ -9,7 +9,7 @@
 #' \describe{
 #' \item{vz}{Total volume of the error parameter space}
 #' \item{vz0}{Volume of the parameter space that gives the incorrect answer}
-#' \item{vz1}{Volume of the paramtere space that gives the correct answer}
+#' \item{vz1}{Volume of the parameter space that gives the correct answer}
 #' \item{sensitivity_ratio}{Ratio of vz1/vz0. Higher means inferred direction is less susceptible to measurement error}
 #' \item{pl}{plot of parameter space}
 #' }
@@ -92,7 +92,7 @@ steiger_sensitivity <- function(rgx_o, rgy_o, ...)
 #' \item{steiger_test_adj}{p-value for inference of direction of causality for given measurement error parameters}
 #' \item{vz}{Total volume of the error parameter space}
 #' \item{vz0}{Volume of the parameter space that gives the incorrect answer}
-#' \item{vz1}{Volume of the paramtere space that gives the correct answer}
+#' \item{vz1}{Volume of the parameter space that gives the correct answer}
 #' \item{sensitivity_ratio}{Ratio of vz1/vz0. Higher means inferred direction is less susceptible to measurement error}
 #' \item{sensitivity_plot}{Plot of parameter space of causal directions and measurement error}
 #' }
@@ -223,7 +223,7 @@ directionality_test <- function(dat)
 #' \item{steiger_test_adj}{p-value for inference of direction of causality for given measurement error parameters}
 #' \item{vz}{Total volume of the error parameter space}
 #' \item{vz0}{Volume of the parameter space that gives the incorrect answer}
-#' \item{vz1}{Volume of the paramtere space that gives the correct answer}
+#' \item{vz1}{Volume of the parameter space that gives the correct answer}
 #' \item{sensitivity_ratio}{Ratio of vz1/vz0. Higher means inferred direction is less susceptible to measurement error}
 #' \item{sensitivity_plot}{Plot of parameter space of causal directions and measurement error}
 #' }