data("qualData") data("platelet") -# data(creatinine, package = "mcr") +data(creatinine, package = "mcr") data("calcium") data("ldlroc") data("PDL1RP") @@ -382,19 +382,51 @@,Estimating Regression coefficientmcreg
getCoefficientsandcalcBias. If you would like to utilize the entire functions inmcrpackage, just adding the specific package name in -front of each of them, likemcr::calcBias(), so that it +front of each of them, likemcr::calcBias(), so that it looks the function is called frommcrpackage. -Please to be noted that the
mcrpackage is not available -in CRAN, so themcregormcreg2function can -not be used temporarily.+#> Jackknife based calculation of standard error and confidence intervals according to Linnet's method. +printSummary(fit) +#> +#> +#> ------------------------------------------ +#> +#> Reference method: Method1 +#> Test method: Method2 +#> Number of data points: 120 +#> +#> ------------------------------------------ +#> +#> The confidence intervals are calculated with jackknife (Linnet's) method. +#> Confidence level: 95% +#> Error ratio: 1 +#> +#> ------------------------------------------ +#> +#> DEMING REGRESSION FIT: +#> +#> EST SE LCI UCI +#> Intercept 4.335885 1.568968372 1.2289002 7.442869 +#> Slope 1.012951 0.009308835 0.9945175 1.031386 +#> +#> +#> ------------------------------------------ +#> +#> JACKKNIFE SUMMARY +#> +#> EST Jack.Mean Bias Jack.SE +#> Intercept 4.335885 4.336377 4.918148e-04 1.568968372 +#> Slope 1.012951 1.012950 -1.876312e-06 0.009308835 +#> NULL +getCoefficients(fit) +#> EST SE LCI UCI +#> Intercept 4.335885 1.568968372 1.2289002 7.442869 +#> Slope 1.012951 0.009308835 0.9945175 1.031386# Deming regression -fit <- mcreg( +fit <- mcreg( x = platelet$Comparative, y = platelet$Candidate, error.ratio = 1, method.reg = "Deming", method.ci = "jackknife" ) -printSummary(fit) -getCoefficients(fit)
Estimating Bias in Medical Decision Level
@@ -405,15 +437,16 @@ Estimating Bias in Medical De
assay is 30, obviously this is a make-up number. Then you
can use the fit object above to estimate the bias using
calcBias function.
-
30, obviously this is a make-up number. Then you
can use the fit object above to estimate the bias using
calcBias function.
-Please to be noted that the mcr package is not available
-in CRAN, so the calcBias function can not be used
-temporarily.
# absolute bias.
-calcBias(fit, x.levels = c(30))
+calcBias(fit, x.levels = c(30))
+#> Level Bias SE LCI UCI
+#> X1 30 4.724429 1.378232 1.995155 7.453704
# proportional bias.
-calcBias(fit, x.levels = c(30), type = "proportional")
@@ -515,13 +548,31 @@
+#> sid x y
+#> 1 4 0.81 1.30
+#> 2 51 1.17 0.86
+#> 3 96 0.93 1.32
+#> 4 97 0.92 1.36
+#> 5 106 0.91 1.27
+#> 6 108 0.80 1.12
Detecting Outliers#> 4 10 16.4 10.8 # 4E approach -ba2 <- blandAltman(x = platelet$Comparative, y = platelet$Candidate) +ba2 <- blandAltman(x = creatinine$serum.crea, y = creatinine$plasma.crea) out2 <- getOutlier(ba2, method = "4E") -#> No outlier is detected. out2$stat -#> NULL +#> obs abs abs_limit_lr abs_limit_ur rel rel_limit_lr rel_limit_ur +#> 4 4 0.49 -0.2988882 0.3142586 0.4644550 -0.2748149 0.2734674 +#> 51 51 -0.31 -0.2988882 0.3142586 -0.3054187 -0.2748149 0.2734674 +#> 96 96 0.39 -0.2988882 0.3142586 0.3466667 -0.2748149 0.2734674 +#> 97 97 0.44 -0.2988882 0.3142586 0.3859649 -0.2748149 0.2734674 +#> 106 106 0.36 -0.2988882 0.3142586 0.3302752 -0.2748149 0.2734674 +#> 108 108 0.32 -0.2988882 0.3142586 0.3333333 -0.2748149 0.2734674 +#> Outlier +#> 4 TRUE +#> 51 TRUE +#> 96 TRUE +#> 97 TRUE +#> 106 TRUE +#> 108 TRUE out2$outmat -#> NULL
In addition, mcradds also provides outlier methods for
evaluating Reference Range, such as ‘Tukey’ and ‘Dixon’ that have been
wrapped in refInterval() function.
Hypothesis of Pearson and SpearmanspearmanTest(x, y, h0 = 0.5, alternative = "greater")
#> $stat
#> cor lowerci upperci Z pval
-#> 0.6000000 -0.1150442 0.9824561 0.3243526 0.3728355
+#> 0.6000000 -0.1478261 0.9656153 0.3243526 0.3728355
#>
#> $method
#> [1] "Spearman's correlation"
@@ -621,8 +672,8 @@ Establishing Reference Range/Inter
#> N = 240
#> Outliers: NULL
#> Reference Interval: 9.04, 10.32
-#> RefLower Confidence Interval: 8.9798, 9.0975
-#> Refupper Confidence Interval: 10.2585, 10.3767
+#> RefLower Confidence Interval: 8.9799, 9.0977
+#> Refupper Confidence Interval: 10.2575, 10.3755
The first two methods are also accepted by NMPA guideline, but the
robust method is not recommended by NMPA because if you want to
establish a reference interval for your assay, you must collect the at
@@ -949,17 +1000,15 @@
Bland-Altman plotRegression plot
To generate the regression plot, you should create the object from
-mcreg() function and then call autoplot
+mcreg() function and then call autoplot
straightforward.
-Please to be noted that the mcr package is not available
-in CRAN, so the mcreg or mcreg2 function can
-not be used temporarily.
-fit <- mcreg2(
+fit <- mcreg(
x = platelet$Comparative, y = platelet$Candidate,
method.reg = "PaBa", method.ci = "bootstrap"
)
autoplot(fit)
+
More arguments can be used as shown below.
autoplot(
@@ -972,6 +1021,7 @@ Regression plot x.title = "Reference",
y.title = "Test"
)
+
diff --git a/articles/mcradds_files/figure-html/unnamed-chunk-33-1.png b/articles/mcradds_files/figure-html/unnamed-chunk-33-1.png
new file mode 100644
index 0000000..42ace42
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diff --git a/articles/mcradds_files/figure-html/unnamed-chunk-34-1.png b/articles/mcradds_files/figure-html/unnamed-chunk-34-1.png
new file mode 100644
index 0000000..dde42e8
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diff --git a/pkgdown.yml b/pkgdown.yml
index 6c2f046..8e7c2c7 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
pkgdown_sha: ~
articles:
mcradds: mcradds.html
-last_built: 2023-10-12T06:27Z
+last_built: 2023-10-12T09:43Z
urls:
reference: https://kaigu1990.github.io/mcradds/reference
article: https://kaigu1990.github.io/mcradds/articles
diff --git a/reference/Rplot003.png b/reference/Rplot003.png
index fb6df2a..186353b 100644
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diff --git a/reference/Rplot007.png b/reference/Rplot007.png
new file mode 100644
index 0000000..e1b3e18
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diff --git a/reference/Rplot008.png b/reference/Rplot008.png
new file mode 100644
index 0000000..bdc4c0d
Binary files /dev/null and b/reference/Rplot008.png differ
diff --git a/reference/autoplot-3.png b/reference/autoplot-3.png
index f517c59..6d4d183 100644
Binary files a/reference/autoplot-3.png and b/reference/autoplot-3.png differ
diff --git a/reference/autoplot-7.png b/reference/autoplot-7.png
new file mode 100644
index 0000000..b978b93
Binary files /dev/null and b/reference/autoplot-7.png differ
diff --git a/reference/autoplot-8.png b/reference/autoplot-8.png
new file mode 100644
index 0000000..28e0714
Binary files /dev/null and b/reference/autoplot-8.png differ
diff --git a/reference/autoplot.html b/reference/autoplot.html
index e00db28..378d07a 100644
--- a/reference/autoplot.html
+++ b/reference/autoplot.html
@@ -88,6 +88,28 @@ Usage
x.title = NULL,
y.title = NULL,
main.title = NULL
+)
+
+# S4 method for MCResult
+autoplot(
+ object,
+ color = "black",
+ fill = "lightgray",
+ size = 1.5,
+ shape = 21,
+ jitter = FALSE,
+ identity = TRUE,
+ identity.params = list(col = "gray", linetype = "dashed"),
+ reg = TRUE,
+ reg.params = list(col = "blue", linetype = "solid"),
+ equal.axis = FALSE,
+ legend.title = TRUE,
+ legend.digits = 2,
+ x.nbreak = NULL,
+ y.nbreak = NULL,
+ x.title = NULL,
+ y.title = NULL,
+ main.title = NULL
)
@@ -95,7 +117,7 @@ Usage
Arguments
- object
(BAsummary, MCResult)
input, depending on which function
-you have done, blandAltman() or mcreg().
blandAltman() or mcreg().
- ...
@@ -166,6 +188,35 @@ Arguments
+
+
+- reg
+(logical)
whether to add regression line where the slope and
+intercept are obtained from mcr::mcreg() function, default is TRUE.
- equal.axis
+(logical)
whether to adjust the ranges of x-axis and y-axis
+are identical. If equal.axis = TRUE, x-axis will be equal to y-axis.
- legend.title
+(logical)
whether to present the title in the legend.
- legend.digits
+(integer)
the number of digits after the decimal point
+in the legend.
See also
- h_difference() to see the type details.
+ h_difference() to see the type details.
+mcr::mcreg() to see the regression parameters.
@@ -231,6 +283,24 @@ Examples y.title = "Reference - Test"
)
+# Using the default arguments for regression plot
+data("platelet")
+fit <- mcreg(
+ x = platelet$Comparative, y = platelet$Candidate,
+ method.reg = "Deming", method.ci = "jackknife"
+)
+#> Jackknife based calculation of standard error and confidence intervals according to Linnet's method.
+autoplot(fit)
+
+
+# Only present the regression line and alter the color and shape.
+autoplot(fit,
+ identity = FALSE,
+ reg.params = list(col = "grey", linetype = "dashed"),
+ legend.title = FALSE,
+ legend.digits = 4
+)
+
mcreg() function and then call autoplot
+mcreg() function and then call autoplot
straightforward.mcr package is not available
-in CRAN, so the mcreg or mcreg2 function can
-not be used temporarily.
-fit <- mcreg2(
+fit <- mcreg(
x = platelet$Comparative, y = platelet$Candidate,
method.reg = "PaBa", method.ci = "bootstrap"
)
autoplot(fit)
autoplot(
@@ -972,6 +1021,7 @@ Regression plot x.title = "Reference",
y.title = "Test"
)
Usage
Arguments
- object
(BAsummary, MCResult)
input, depending on which function
-you have done, blandAltman() or mcreg().
blandAltman() or mcreg().
- ...
@@ -166,6 +188,35 @@ Arguments
+
+
+- reg
+(logical)
whether to add regression line where the slope and
+intercept are obtained from mcr::mcreg() function, default is TRUE.
- equal.axis
+(logical)
whether to adjust the ranges of x-axis and y-axis
+are identical. If equal.axis = TRUE, x-axis will be equal to y-axis.
- legend.title
+(logical)
whether to present the title in the legend.
- legend.digits
+(integer)
the number of digits after the decimal point
+in the legend.
See also
- h_difference() to see the type details.
+ h_difference() to see the type details.
+mcr::mcreg() to see the regression parameters.
@@ -231,6 +283,24 @@ Examples y.title = "Reference - Test"
)
+# Using the default arguments for regression plot
+data("platelet")
+fit <- mcreg(
+ x = platelet$Comparative, y = platelet$Candidate,
+ method.reg = "Deming", method.ci = "jackknife"
+)
+#> Jackknife based calculation of standard error and confidence intervals according to Linnet's method.
+autoplot(fit)
+
+
+# Only present the regression line and alter the color and shape.
+autoplot(fit,
+ identity = FALSE,
+ reg.params = list(col = "grey", linetype = "dashed"),
+ legend.title = FALSE,
+ legend.digits = 4
+)
+
(BAsummary, MCResult)
input, depending on which function
-you have done, blandAltman() or mcreg().
blandAltman() or mcreg().
Arguments
+
+
+- reg
+(logical)
whether to add regression line where the slope and
+intercept are obtained from mcr::mcreg() function, default is TRUE.
- equal.axis
+(logical)
whether to adjust the ranges of x-axis and y-axis
+are identical. If equal.axis = TRUE, x-axis will be equal to y-axis.
- legend.title
+(logical)
whether to present the title in the legend.
- legend.digits
+(integer)
the number of digits after the decimal point
+in the legend.
(logical)
whether to add regression line where the slope and
+intercept are obtained from mcr::mcreg() function, default is TRUE.
(logical)
whether to adjust the ranges of x-axis and y-axis
+are identical. If equal.axis = TRUE, x-axis will be equal to y-axis.
(logical)
whether to present the title in the legend.
(integer)
the number of digits after the decimal point
+in the legend.
See also
-h_difference() to see the type details.
h_difference() to see the type details.
mcr::mcreg() to see the regression parameters.