Nothing
# This file is a generated template, your changes will not be overwritten
jmvdemingClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Class(
"jmvdemingClass",
inherit = jmvdemingBase,
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$method2
# get the data
data <- self$data
# convert to appropriate type
data[[method1]] <- jmvcore::toNumeric(data[[method1]])
data[[method2]] <- jmvcore::toNumeric(data[[method2]])
df = data
colnames(df) = c("method1", "method2")
plotcon <- self$results$plotcon
plotcheck <- self$results$plotcheck
ciWidth = self$options$ciWidth/100
delta_val = self$options$testValue
res = dem_reg(x = "method1",
y = "method2",
data = df,
error.ratio = delta_val,
weighted = self$options$weighted,
conf.level = ciWidth)
res$call$conf.level = ciWidth
res$call$weighted = self$options$weighted
#res$call$agree.level = agreeWidth
#res$call$delta = delta_val
#res$call$prop_bias = prop_bias
pr_res1 = res$call
if(self$options$weighted){
pr_res = paste0("Weighted Deming Regression with ", self$options$ciWidth, "% Confidence Intervals")
} else {
pr_res = paste0("Deming Regression with ", self$options$ciWidth, "% Confidence Intervals")
}
self$results$text$setContent(pr_res)
table1 <- self$results$demtab
table1$setRow(rowNo=1, values=list(
var="Intercept",
estimate=res$model$coef[1],
se = res$model$se[1],
df = res$model$df[1],
lowerci=res$model$lower.ci[1],
upperci=res$model$upper.ci[1]
))
table1$setRow(rowNo=2, values=list(
var="Slope",
estimate=res$model$coef[2],
se = res$model$se[2],
df = res$model$df[2],
lowerci=res$model$lower.ci[2],
upperci=res$model$upper.ci[2]
))
plotcon$setState(res)
plotcheck$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$setContent(citethis)
},
.plotcon = function(image,ggtheme,...){
if (is.null(image$state))
return(FALSE)
plotpr = plot(image$state) +
labs(x = self$options$xlabel,
y = self$options$ylabel) +
ggtheme
# set transparency
#theme(
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# legend.key = element_rect(colour = "transparent", fill = "transparent"),
# panel.background = element_rect(fill = "transparent",colour = NA),
# plot.background = element_rect(fill = "transparent",colour = NA)
#)
print(plotpr)
return(TRUE)
},
.plotcheck = function(image,ggtheme,...){
if (is.null(image$state))
return(FALSE)
plotpr = check(image$state) #+
# set transparency
#ggtheme
#theme(strip.text = element_text(face = "bold", size = 11),
# legend.text = element_text(face = "bold", size = 11),
# legend.title = element_text(face = "bold", size = 11),
# axis.text.x = element_text(face = "bold", size = 11),
# axis.text.y = element_text(face = "bold", size = 11),
# axis.title.x = element_text(face = "bold", size = 11),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# panel.background = element_rect(fill = "transparent",colour = NA),
# plot.background = element_rect(fill = "transparent",colour = NA),
# legend.background = element_rect(fill = "transparent",colour = NA))
print(plotpr)
return(TRUE)
})
)
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