Merge pull request #2 from arcaldwell49/jamovi

Jamovi

.Rbuildignore

@@ -13,3 +13,14 @@
 ^vignettes/reanalysis_supp\.R$
 ^pwr_agree$
 ^codecov\.yml$
+^.*\.jmo$
+^build/js$
+^build/R$
+^build/R.*$
+^jamovi$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^paper\.md$
+^paper\.bib$
+^paper$

.gitignore

@@ -3,3 +3,5 @@
 .RData
 .Ruserdata
 inst/doc
+*.jmo
+^cran-comments\.md$

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: SimplyAgree
 Type: Package
-Title: Agreement, Consistency, and Reliability Calculations
+Title: Flexible and Robust Agreement and Reliability Analyses
 Version: 0.0.1
 Authors@R: person("Aaron", "Caldwell", email = "[email protected]",
                   role = c("aut", "cre"))
 Maintainer: Aaron Caldwell <[email protected]>
-Description: This package calculate absolute agreement between two continuous variables (agree_test), those with replicates (agree_reps), and with nested data (agree_reps and loa_mixed). Power calculations for Bland-Altman agreement limits can be calculated with the blandPowerCurve function. In addition, test-restest reliability can also be estimated (reli_stats).
+Description: Reliability and agreement analyses often have limited software support. Therefore, this package, and jamovi module, was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement. For robust analyses of agreement, boostrapped limits of agreement can also be calculated.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
@@ -22,7 +22,8 @@ Imports:
     tidyr,
     sjstats,
     stringr,
-    cccrm
+    cccrm,
+    jmvcore
 Suggests: 
     knitr,
     rmarkdown,

NAMESPACE

@@ -12,6 +12,9 @@ export(agree_reps)
 export(agree_test)
 export(blandPowerCurve)
 export(find_n)
+export(jmvagree)
+export(jmvagreemulti)
+export(jmvreli)
 export(loa_mixed)
 export(reli_stats)
 import(boot)

R/00jmv.R

@@ -0,0 +1,18 @@
+
+# This file is automatically generated, you probably don't want to edit this
+
+.jmvrefs <- list(
+    `R`=list(
+        `type`="software", 
+        `author`="R Core Team", 
+        `year`=2018, 
+        `title`="A Language and Envionment for Statistical Computing", 
+        `publisher`="[Computer software]. Retrieved from https://cran.r-project.org/", 
+        `url`="https://cran.r-project.org/"), 
+    `SimplyAgree`=list(
+        `type`="software", 
+        `author`="Caldwell, A.R.", 
+        `year`=2021, 
+        `title`="SimplyAgree: Flexible and Robust Agreement and Reliability Analyses", 
+        `publisher`="[R package]. Retrieved from https://github.com/arcaldwell49/SimplyAgree", 
+        `url`="https://github.com/arcaldwell49/SimplyAgree"))

R/agree_nest.R

@@ -20,12 +20,16 @@
 #'
 #' }
 
-#' @examples #to be added
-#'
+#' @examples
+#' data('reps')
+#' agree_nest(x = "x", y = "y", id = "id", data = reps, delta = 2)
 #' @section References:
 #' Zou, G. Y. (2013). Confidence interval estimation for the Bland–Altman limits of agreement with multiple observations per individual. Statistical methods in medical research, 22(6), 630-642.
+#'
 #' King, TS and Chinchilli, VM. (2001). A generalized concordance correlation coefficient for continuous and categorical data. Statistics in Medicine, 20, 2131:2147.
+#'
 #' King, TS; Chinchilli, VM; Carrasco, JL. (2007). A repeated measures concordance correlation coefficient. Statistics in Medicine, 26, 3095:3113.
+#'
 #' Carrasco, JL; Phillips, BR; Puig-Martinez, J; King, TS; Chinchilli, VM. (2013). Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Computer Methods and Programs in Biomedicine, 109, 293-304.
 #' @importFrom stats pnorm qnorm lm dchisq qchisq sd var
 #' @importFrom tidyselect all_of

R/agree_reps.R

@@ -20,12 +20,16 @@
 #'
 #' }
 
-#' @examples #to be added
-#'
+#' @examples
+#' #' data('reps')
+#' agree_reps(x = "x", y = "y", id = "id", data = reps, delta = 2)
 #' @section References:
 #' Zou, G. Y. (2013). Confidence interval estimation for the Bland–Altman limits of agreement with multiple observations per individual. Statistical methods in medical research, 22(6), 630-642.
+#'
 #' King, TS and Chinchilli, VM. (2001). A generalized concordance correlation coefficient for continuous and categorical data. Statistics in Medicine, 20, 2131:2147.
+#'
 #' King, TS; Chinchilli, VM; Carrasco, JL. (2007). A repeated measures concordance correlation coefficient. Statistics in Medicine, 26, 3095:3113.
+#'
 #' Carrasco, JL; Phillips, BR; Puig-Martinez, J; King, TS; Chinchilli, VM. (2013). Estimation of the concordance correlation coefficient for repeated measures using SAS and R. Computer Methods and Programs in Biomedicine, 109, 293-304.
 #' @importFrom stats pnorm qnorm lm anova dchisq qchisq sd var
 #' @importFrom tidyselect all_of

R/agree_test.R

@@ -20,7 +20,9 @@
 #'
 #' }
 
-#' @examples #to be added
+#' @examples
+#' data('reps')
+#' agree_test(x=reps$x, y=reps$y, delta = 2)
 #'
 #' @section References:
 #' Gwowen Shieh (2019): Assessing Agreement Between Two Methods of Quantitative Measurements: Exact Test Procedure and Sample Size Calculation, Statistics in Biopharmaceutical Research, <https://doi.org/10.1080/19466315.2019.1677495>
@@ -111,8 +113,8 @@ agree_test <- function(x,
   the_int <- summary(z)$coefficients[1,1]
   the_slope <-  summary(z)$coefficients[2,1]
   tmp.lm <- data.frame(the_int, the_slope)
-  scalemin = min(c(min(x),min(y)))
-  scalemax = max(c(max(x),max(y)))
+  scalemin = min(c(min(x, na.rm = TRUE),min(y, na.rm = TRUE)))
+  scalemax = max(c(max(x, na.rm = TRUE),max(y, na.rm = TRUE)))
 
   identity.plot = ggplot(ccc_res$df_diff,
                          aes(x = x, y = y)) +

R/blandPowerCurve.R

@@ -7,7 +7,7 @@
 #' @param conf.level the confidence level(s) required. Default is 95\%. More than one confidence level can be provided.
 #' @param agree.level the agreement level(s) required. Default is 95\%. The proportion of data that should lie between the thresholds, for 95\% limits of agreement this should be 0.95. More than one confidence level can be provided.
 
-#' @return a dataframe containing the power analysis results. The results can then be plotted with the plot.powerCurve function.
+#' @return A dataframe is returned containing the power analysis results. The results can then be plotted with the plot.powerCurve function.
 #' @section references:
 #' Lu, M. J., Zhong, W. H., Liu, Y. X., Miao, H. Z., Li, Y. C., & Ji, M. H. (2016). Sample Size for Assessing Agreement between Two Methods of Measurement by Bland-Altman Method. The international journal of biostatistics, 12(2), <https://doi.org/10.1515/ijb-2015-0039>
 #' @examples
@@ -23,6 +23,8 @@
 #' # Find at what N power of .8 is achieved
 #' find_n(powerCurve, power = .8)
 #' }
+#' # If the desired power is not found then
+#' ## Sample size range must be expanded
 #' @importFrom magrittr "%>%"
 #' @import dplyr
 #' @export

R/data.R

@@ -1,5 +1,5 @@
 #' @title temps
-#' @description A dataset from a study on the reliability of human body temperature at diferents times of day before and after exercise.
+#' @description A dataset from a study on the reliability of human body temperature at different times of day before and after exercise.
 #' @format A data frame with 60 rows and 10 variables:
 #' \describe{
 #'   \item{id}{Subject identifier}
@@ -14,7 +14,7 @@
 #'   \item{teso_delta}{Change in esophageal temperature}
 #'
 #' }
-#' @source \url{https://doi.org/10.1249/MSS.0000000000002575}
+#' @source Ravanelli N, Jay O. The Change in Core Temperature and Sweating Response during Exercise Are Unaffected by Time of Day within the Wake Period. Med Sci Sports Exerc. 2020 Dec 1. doi: 10.1249/MSS.0000000000002575. Epub ahead of print. PMID: 33273272.
 "temps"
 
 #' @rdname temps
@@ -25,7 +25,7 @@
 #' @format A data frame with 20 rows with 3 variables
 #' \describe{
 #'   \item{id}{Subject identifier}
-#'   \item{X}{X measurment}
+#'   \item{X}{X measurement}
 #'   \item{Y}{Y measurement}
 
 #'

R/jmvagree.b.R

@@ -0,0 +1,119 @@
+
+# This file is a generated template, your changes will not be overwritten
+
+jmvagreeClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Class(
+    "jmvagreeClass",
+    inherit = jmvagreeBase,
+    private = list(
+        .run = function() {
+
+            # `self$data` contains the data
+            # `self$options` contains the options
+            # `self$results` contains the results object (to populate)
+
+            if ( !is.null(self$options$method1) && !is.null(self$options$method2) ) {
+                # read the option values into shorter variable names
+                method1 <- self$options$method1
+                method2 <- self$options$method2
+
+                plotba <- self$results$plotba
+                plotcon <- self$results$plotcon
+
+                # get the data
+                data <- self$data
+
+                # convert to appropriate type
+                data[[method1]] <- jmvcore::toNumeric(data[[method1]])
+                data[[method2]] <- jmvcore::toNumeric(data[[method2]])
+
+                ciWidth = self$options$ciWidth
+                agreeWidth = self$options$agreeWidth
+                delta_val = self$options$testValue
+                res = agree_test(x = data[[method1]],
+                                 y = data[[method2]],
+                                 delta = delta_val,
+                                 conf.level = ciWidth,
+                                 agree.level = agreeWidth)
+
+                pr_res = paste0("Limit of Agreement = ", res$shieh_test$prop0*100, "%",
+                                "\n",
+                                "alpha =", (1-res$conf.level), "|", res$conf.level*100,"% Confidence Interval",
+                                "\n",
+                                "\n",
+                                "Shieh Test of Agreement",
+                                "\n",
+                                "Exact C.I.:"," [",round(res$shieh_test$lower.ci,4),", ",round(res$shieh_test$upper.ci, 4), "]",
+                                "\n",
+                                "\n",
+                                "Hypothesis Test: ",res$shieh_test$h0_test)
+
+                self$results$text$setContent(pr_res)
+                table1 <- self$results$blandtab
+                table1$setRow(rowNo=1, values=list(
+                    var="Mean Bias",
+                    estimate=res$loa$estimate[1],
+                    lowerci=res$loa$lower.ci[1],
+                    upperci=res$loa$upper.ci[1]
+                ))
+
+                table1$setRow(rowNo=2, values=list(
+                    var="Lower Limit of Agreement",
+                    estimate=res$loa$estimate[2],
+                    lowerci=res$loa$lower.ci[2],
+                    upperci=res$loa$upper.ci[2]
+                ))
+                table1$setRow(rowNo=3, values=list(
+                    var="Upper Limit of Agreement",
+                    estimate=res$loa$estimate[3],
+                    lowerci=res$loa$lower.ci[3],
+                    upperci=res$loa$upper.ci[3]
+                ))
+
+                table2 <- self$results$ccctab
+                table2$setRow(rowNo=1, values=list(
+                    var="CCC",
+                    estimate=res$ccc.xy$est.ccc[1],
+                    lowerci=res$ccc.xy$lower.ci[1],
+                    upperci=res$ccc.xy$upper.ci[1]
+                ))
+
+                plotba$setState(res)
+                plotcon$setState(res)
+            }
+
+
+
+            #citethis = paste0(
+            #    "Shieh (2019). Assessing Agreement Between Two Methods of Quantitative Measurements: Exact Test Procedure and Sample Size Calculation,
+            #    Statistics in Biopharmaceutical Research,
+            #    <https://doi.org/10.1080/19466315.2019.1677495>"
+            #)
+            #self$results$cites$setContent(citethis)
+        },
+        .plotba = function(image,...){
+
+            if (is.null(image$state))
+                return(FALSE)
+
+            plotpr = plot(image$state)
+
+
+            print(plotpr)
+
+            return(TRUE)
+
+        },
+        .plotcon = function(image,...){
+
+            if (is.null(image$state))
+                return(FALSE)
+
+            plotpr = image$state
+
+
+            print(plotpr$identity.plot)
+
+            return(TRUE)
+
+        })
+)