From 966da79af48c371c05dd8011702ef2bd3b1d1e03 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 1 Mar 2018 10:35:09 +0100 Subject: Static documentation rebuilt using current pkgdown --- docs/reference/calplot.lm-1.png | Bin 0 -> 69416 bytes docs/reference/calplot.lm.html | 93 ++++++++++++++------------ docs/reference/chemCal-package.html | 23 ++++--- docs/reference/din32645-1.png | Bin 0 -> 66191 bytes docs/reference/din32645.html | 31 +++++---- docs/reference/index.html | 128 ++++++++++++++++++++++-------------- docs/reference/inverse.predict.html | 85 ++++++++++++++---------- docs/reference/lod.html | 89 +++++++++++++++---------- docs/reference/loq.html | 110 ++++++++++++++++++------------- docs/reference/massart97ex1.html | 15 ++++- docs/reference/massart97ex3-1.png | Bin 0 -> 51775 bytes docs/reference/massart97ex3.html | 34 ++++++---- 12 files changed, 367 insertions(+), 241 deletions(-) create mode 100644 docs/reference/calplot.lm-1.png create mode 100644 docs/reference/din32645-1.png create mode 100644 docs/reference/massart97ex3-1.png (limited to 'docs/reference') diff --git a/docs/reference/calplot.lm-1.png b/docs/reference/calplot.lm-1.png new file mode 100644 index 0000000..c90b25f Binary files /dev/null and b/docs/reference/calplot.lm-1.png differ diff --git a/docs/reference/calplot.lm.html b/docs/reference/calplot.lm.html index 5a33491..910e4ea 100644 --- a/docs/reference/calplot.lm.html +++ b/docs/reference/calplot.lm.html @@ -18,19 +18,29 @@ + + + + + + + - + + + @@ -77,45 +87,46 @@ as confidence, and, for unweighted regression, prediction bands.

- 
calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"),
+    
calplot(object, xlim = c("auto", "auto"), ylim = c("auto", "auto"),
     xlab = "Concentration", ylab = "Response", alpha=0.05, varfunc = NULL)

     
     
 Arguments

-    

-      
object

-      

-    A univariate model object of class lm or 
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
object
A univariate model object of class lm or 
     rlm 
-    with model formula y ~ x or y ~ x - 1.
-  
-      
xlim

-      

-    The limits of the plot on the x axis.
-  

-      
ylim

-      

-    The limits of the plot on the y axis.
-  

-      
xlab

-      

-    The label of the x axis.
-  

-      
ylab

-      

-    The label of the y axis.
-  

-      
alpha

-      

-    The error tolerance level for the confidence and prediction bands. Note that this 
+    with model formula y ~ x or y ~ x - 1.
xlim
The limits of the plot on the x axis.
ylim
The limits of the plot on the y axis.
xlab
The label of the x axis.
ylab
The label of the y axis.
alpha
The error tolerance level for the confidence and prediction bands. Note that this 
     includes both tails of the Gaussian distribution, unlike the alpha and beta parameters
-    used in lod (see note below).
-  
-      
varfunc

-      

-    The variance function for generating the weights in the model.
-    Currently, this argument is ignored (see note below).
-  

-    
+    used in lod (see note below).
varfunc
The variance function for generating the weights in the model.
+    Currently, this argument is ignored (see note below).

     
     
Value

 
@@ -129,16 +140,16 @@
   for the data, for which responses should be predicted. Prediction intervals
   using weights e.g. from a variance function are currently not supported by
   the internally used function predict.lm, therefore,
-  calplot does not draw prediction bands for such models.

-    
It is possible to compare the calplot prediction bands with the
-  lod values if the lod() alpha and beta parameters are
+  calplot does not draw prediction bands for such models.
  
+
It is possible to compare the calplot prediction bands with the
+  lod values if the lod() alpha and beta parameters are
   half the value of the calplot() alpha parameter.

     
 
     
Examples

     
data(massart97ex3)
 m <- lm(y ~ x, data = massart97ex3)
-calplot(m)

+calplot(m)
@@ -167,7 +178,7 @@
-

Site built with pkgdown.

+

Site built with pkgdown.

diff --git a/docs/reference/chemCal-package.html b/docs/reference/chemCal-package.html index b720fc9..ee92d84 100644 --- a/docs/reference/chemCal-package.html +++ b/docs/reference/chemCal-package.html @@ -6,8 +6,7 @@ - - — chemCal-package • chemCal +Calibration functions for analytical chemistry — chemCal-package • chemCal @@ -19,19 +18,28 @@ + + + + + + + - + + + @@ -70,18 +78,17 @@
-

-

+

Calibration functions for analytical chemistry

-

See ../DESCRIPTION

+

See ../DESCRIPTION

Details

-

There is a package vignette located in ../doc/chemCal.pdf.

+

There is a package vignette located in ../doc/chemCal.pdf.

@@ -105,7 +112,7 @@
-

Site built with pkgdown.

+

Site built with pkgdown.

diff --git a/docs/reference/din32645-1.png b/docs/reference/din32645-1.png new file mode 100644 index 0000000..d04e5fe Binary files /dev/null and b/docs/reference/din32645-1.png differ diff --git a/docs/reference/din32645.html b/docs/reference/din32645.html index 65a9268..5e2bf2d 100644 --- a/docs/reference/din32645.html +++ b/docs/reference/din32645.html @@ -18,19 +18,28 @@ + + + + + + + - + + + @@ -76,7 +85,7 @@

Sample dataset to test the package.

- 
data(din32645)
+ 
data(din32645)

Format

@@ -85,10 +94,10 @@

References

DIN 32645 (equivalent to ISO 11843), Beuth Verlag, Berlin, 1994

-

Dintest. Plugin for MS Excel for evaluations of calibration data. Written +

Dintest. Plugin for MS Excel for evaluations of calibration data. Written by Georg Schmitt, University of Heidelberg. Formerly available from the Website of the University of Heidelberg.

-

Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including +

Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations 1995). Analytica Chimica Acta 391, 105 - 126.

@@ -96,9 +105,9 @@

Examples

data(din32645)
 m <- lm(y ~ x, data = din32645)
-calplot(m)

+calplot(m)

 ## Prediction of x with confidence interval
-(prediction <- inverse.predict(m, 3500, alpha = 0.01))
#> $Prediction
+(prediction <- inverse.predict(m, 3500, alpha = 0.01))
#> $Prediction
 #> [1] 0.1054792
 #> 
 #> $`Standard Error`
@@ -114,7 +123,7 @@
 # was collected from Procontrol 3.1 (isomehr GmbH) in this case
 round(prediction$Confidence,5)
#> [1] 0.07434

 ## Critical value:
-(crit <- lod(m, alpha = 0.01, beta = 0.5))
#> $x
+(crit <- lod(m, alpha = 0.01, beta = 0.5))
#> $x
 #> [1] 0.0698127
 #> 
 #> $y
@@ -129,12 +138,12 @@
 # In German, the smallest detectable value is the "Erfassungsgrenze", and we
 # should get 0.14 according to DIN, which we achieve by using the method 
 # described in it:
-lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
+lod.din <- lod(m, alpha = 0.01, beta = 0.01, method = "din")
 round(lod.din$x, 2)
#> [1] 0.14

 ## Limit of quantification
 # This accords to the test data coming with the test data from Dintest again, 
 # except for the last digits of the value cited for Procontrol 3.1 (0.2121)
-(loq <- loq(m, alpha = 0.01))
#> $x
+(loq <- loq(m, alpha = 0.01))
#> $x
 #> [1] 0.2119575
 #> 
 #> $y
@@ -143,7 +152,7 @@
 #> 
round(loq$x,4)
#> [1] 0.212

 # A similar value is obtained using the approximation 
 # LQ = 3.04 * LC (Currie 1999, p. 120)
-3.04 * lod(m,alpha = 0.01, beta = 0.5)$x
#> [1] 0.2122306
+3.04 * lod(m,alpha = 0.01, beta = 0.5)$x
#> [1] 0.2122306
-

Site built with pkgdown.

+

Site built with pkgdown.

diff --git a/docs/reference/index.html b/docs/reference/index.html index c0f2661..9b514f2 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -18,19 +18,25 @@ + + - + + + - + + + @@ -69,56 +75,78 @@
-

Function reference

-

version 0.1-37.9001

+

+ Reference + version 0.1-37.9001 +

-

All functions

-

- - -

Plot calibration graphs from univariate linear models

- - -

-

- - -

Calibration data from DIN 32645

- - -

Predict x from y for a linear calibration

- - -

Estimate a limit of detection (LOD)

- - -

Estimate a limit of quantification (LOQ)

- - -

Calibration data from Massart et al. (1997), example 1

- - -

Calibration data from Massart et al. (1997), example 3

- - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

All functions

+

+
+

calplot

+

Plot calibration graphs from univariate linear models

+

chemCal-package

+

Calibration functions for analytical chemistry

+

data

+

Calibration data from DIN 32645

+

inverse.predict

+

Predict x from y for a linear calibration

+

lod

+

Estimate a limit of detection (LOD)

+

loq

+

Estimate a limit of quantification (LOQ)

+

data

+

Calibration data from Massart et al. (1997), example 1

+

data

+

Calibration data from Massart et al. (1997), example 3

@@ -136,7 +164,7 @@
-

Site built with pkgdown.

+

Site built with pkgdown.

diff --git a/docs/reference/inverse.predict.html b/docs/reference/inverse.predict.html index af05986..32f8f44 100644 --- a/docs/reference/inverse.predict.html +++ b/docs/reference/inverse.predict.html @@ -18,19 +18,37 @@ + + + + + + + - + + + @@ -85,43 +103,44 @@ This is elaborated in the package vignette.

- 
inverse.predict(object, newdata, …,
+    
inverse.predict(object, newdata, …,
   ws, alpha=0.05, var.s = "auto")

     
     
 Arguments

-    

-      
object

-      

-    A univariate model object of class lm or 
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
object
A univariate model object of class lm or 
     rlm 
-    with model formula y ~ x or y ~ x - 1.
-  
-      
newdata

-      
 
-    A vector of observed y values for one sample.
-  

-      
…

-      

-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  

-      
ws

-      
 
-    The weight attributed to the sample. This argument is obligatory
-    if object has weights.
-  

-      
alpha

-      

-    The error tolerance level for the confidence interval to be reported.
-  

-      
var.s

-      

-    The estimated variance of the sample measurements. The default is to take
+    with model formula y ~ x or y ~ x - 1.
newdata
A vector of observed y values for one sample.
…
Placeholder for further arguments that might be needed by 
+    future implementations.
ws
The weight attributed to the sample. This argument is obligatory
+    if object has weights.
alpha
The error tolerance level for the confidence interval to be reported.
var.s
The estimated variance of the sample measurements. The default is to take
     the residual standard error from the calibration and to adjust it
     using ws, if applicable. This means that var.s
-    overrides ws.
-  
-    
+    overrides ws.

     
     
Value

 
@@ -201,7 +220,7 @@
 
 
 

-  
Site built with pkgdown.

+  
Site built with pkgdown.

 

 
       
diff --git a/docs/reference/lod.html b/docs/reference/lod.html
index b9c30e6..13ce70a 100644
--- a/docs/reference/lod.html
+++ b/docs/reference/lod.html
@@ -18,19 +18,35 @@
 
 
 
+
+
 
 
 
 
 
+  
+  
+
 
+
+
 
-
+
 
 
+
+
   
 
   
@@ -83,45 +99,46 @@
   one-sided significance test).

     
 
-    
lod(object, …, alpha = 0.05, beta = 0.05, method = "default", tol = "default")

+    
lod(object, …, alpha = 0.05, beta = 0.05, method = "default", tol = "default")

     
     
 Arguments

-    

-      
object

-      

-    A univariate model object of class lm or 
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
object
A univariate model object of class lm or 
     rlm 
     with model formula y ~ x or y ~ x - 1, 
-    optionally from a weighted regression.
-  
-      
…

-      

-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  

-      
alpha

-      

-    The error tolerance for the decision limit (critical value).
-  

-      
beta

-      

-    The error tolerance beta for the detection limit.
-  

-      
method

-      

-    The “default” method uses a prediction interval at the LOD
+    optionally from a weighted regression.
…
Placeholder for further arguments that might be needed by 
+    future implementations.
alpha
The error tolerance for the decision limit (critical value).
beta
The error tolerance beta for the detection limit.
method
The “default” method uses a prediction interval at the LOD
     for the estimation of the LOD, which obviously requires
     iteration. This is described for example in Massart, p. 432 ff.
     The “din” method uses the prediction interval at
-    x = 0 as an approximation.
-  
-      
tol

-      

-    When the “default” method is used, the default tolerance
+    x = 0 as an approximation.
tol
When the “default” method is used, the default tolerance
     for the LOD on the x scale is the value of the smallest non-zero standard
-    divided by 1000. Can be set to a numeric value to override this.
-  
-    
+    divided by 1000. Can be set to a numeric value to override this.

     
     
Value

 
@@ -143,16 +160,16 @@
     
Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., 
   Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A,
   Chapter 13.7.8

-    
J. Inczedy, T. Lengyel, and A.M. Ure (2002) International Union of Pure and
+
J. Inczedy, T. Lengyel, and A.M. Ure (2002) International Union of Pure and
   Applied Chemistry Compendium of Analytical Nomenclature: Definitive Rules.
   Web edition.

-    
Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
+
Currie, L. A. (1997) Nomenclature in evaluation of analytical methods including
   detection and quantification capabilities (IUPAC Recommendations 1995). 
   Analytica Chimica Acta 391, 105 - 126.

     
     
See also

 
-    
Examples for din32645

+    
Examples for din32645

     
 
     
Examples

@@ -200,7 +217,7 @@
 
 
 

-  
Site built with pkgdown.

+  
Site built with pkgdown.

 

 
       
diff --git a/docs/reference/loq.html b/docs/reference/loq.html
index ad7fa86..2fb01a8 100644
--- a/docs/reference/loq.html
+++ b/docs/reference/loq.html
@@ -18,19 +18,34 @@
 
 
 
+
+
 
 
 
 
 
+  
+  
+
 
+
+
 
-
+
 
 
+
+
   
 
   
@@ -79,62 +94,63 @@
     $$L = k c(L)$$
   where c(L) is half of the length of the confidence interval at the limit L
   (DIN 32645, equivalent to ISO 11843). c(L) is internally estimated by
-  inverse.predict, and L is obtained by iteration.

+  inverse.predict, and L is obtained by iteration.

     
 
-    
loq(object, …, alpha = 0.05, k = 3, n = 1, w.loq = "auto",
+    
loq(object, …, alpha = 0.05, k = 3, n = 1, w.loq = "auto",
     var.loq = "auto", tol = "default")

     
     
 Arguments

-    

-      
object

-      

-    A univariate model object of class lm or 
+    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
object
A univariate model object of class lm or 
     rlm 
     with model formula y ~ x or y ~ x - 1, 
     optionally from a weighted regression. If weights are specified
     in the model, either w.loq or var.loq have to 
-    be specified.
-  
-      
alpha

-      

-    The error tolerance for the prediction of x values in the calculation.
-  

-      
…

-      

-    Placeholder for further arguments that might be needed by 
-    future implementations.
-  

-      
k

-      
 
-    The inverse of the maximum relative error tolerated at the
-    desired LOQ.
-  

-      
n

-      

-    The number of replicate measurements for which the LOQ should be
-    specified.
-  

-      
w.loq

-      

-    The weight that should be attributed to the LOQ. Defaults
+    be specified.
alpha
The error tolerance for the prediction of x values in the calculation.
…
Placeholder for further arguments that might be needed by 
+    future implementations.
k
The inverse of the maximum relative error tolerated at the
+    desired LOQ.
n
The number of replicate measurements for which the LOQ should be
+    specified.
w.loq
The weight that should be attributed to the LOQ. Defaults
     to one for unweighted regression, and to the mean of the weights
-    for weighted regression. See massart97ex3 for 
+    for weighted regression. See massart97ex3 for 
     an example how to take advantage of knowledge about the 
-    variance function.
-  
-      
var.loq

-      

-    The approximate variance at the LOQ. The default value is 
-    calculated from the model.
-  

-      
tol

-      

-    The default tolerance for the LOQ on the x scale is the value of the
+    variance function.
var.loq
The approximate variance at the LOQ. The default value is 
+    calculated from the model.
tol
The default tolerance for the LOQ on the x scale is the value of the
     smallest non-zero standard divided by 1000. Can be set to a 
-    numeric value to override this.
-  
-    
+    numeric value to override this.

     
     
Value

 
@@ -149,7 +165,7 @@
     
     
See also

 
-    
Examples for din32645

+    
Examples for din32645

     
 
     
Examples

@@ -195,7 +211,7 @@
 
 
 

-  
Site built with pkgdown.

+  
Site built with pkgdown.

 

 
       
diff --git a/docs/reference/massart97ex1.html b/docs/reference/massart97ex1.html
index e84c01b..637d524 100644
--- a/docs/reference/massart97ex1.html
+++ b/docs/reference/massart97ex1.html
@@ -18,19 +18,28 @@
 
 
 
+
+
 
 
 
 
 
+  
+  
+
 
+
+
 
-
+
 
 
+
+
   
 
   
@@ -76,7 +85,7 @@
     
Sample dataset from p. 175 to test the package.

     
 
-    
data(massart97ex1)

+    
data(massart97ex1)

         
     
Format

 
@@ -108,7 +117,7 @@
 
 
 

-  
Site built with pkgdown.

+  
Site built with pkgdown.

 

 
       
diff --git a/docs/reference/massart97ex3-1.png b/docs/reference/massart97ex3-1.png
new file mode 100644
index 0000000..88668fa
Binary files /dev/null and b/docs/reference/massart97ex3-1.png differ
diff --git a/docs/reference/massart97ex3.html b/docs/reference/massart97ex3.html
index 2946139..d737fc5 100644
--- a/docs/reference/massart97ex3.html
+++ b/docs/reference/massart97ex3.html
@@ -18,19 +18,28 @@
 
 
 
+
+
 
 
 
 
 
+  
+  
+
 
+
+
 
-
+
 
 
+
+
   
 
   
@@ -76,7 +85,7 @@
     
Sample dataset from p. 188 to test the package.

     
 
-    
data(massart97ex3)

+    
data(massart97ex3)

         
     
Format

 
@@ -100,9 +109,9 @@
 w <- round(1 / (s^2), digits = 3)
 weights <- w[factor(x)]
 m <- lm(y ~ x, w = weights)
-calplot(m)
#> Warning: Assuming constant prediction variance even though model fit is weighted

+calplot(m)
#> Warning: Assuming constant prediction variance even though model fit is weighted

 # The following concords with the book p. 200
-inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
#> $Prediction
+inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
#> $Prediction
 #> [1] 5.865367
 #> 
 #> $`Standard Error`
@@ -113,7 +122,7 @@
 #> 
 #> $`Confidence Limits`
 #> [1] 3.387082 8.343652
-#> 
inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
#> $Prediction
+#> 
inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
#> $Prediction
 #> [1] 44.06025
 #> 
 #> $`Standard Error`
@@ -128,7 +137,7 @@
 # The LOD is only calculated for models from unweighted regression
 # with this version of chemCal
 m0 <- lm(y ~ x)
-lod(m0)
#> $x
+lod(m0)
#> $x
 #> [1] 5.407085
 #> 
 #> $y
@@ -136,7 +145,7 @@
 #> 13.63911 
 #> 

 # Limit of quantification from unweighted regression
-loq(m0)
#> $x
+loq(m0)
#> $x
 #> [1] 13.97764
 #> 
 #> $y
@@ -148,15 +157,16 @@
 # If we are not using a variance function, we can use the weight from
 # the above example as a first approximation (x = 15 is close to our
 # loq approx 14 from above).
-loq(m, w.loq = 1.67)
#> $x
+loq(m, w.loq = 1.67)
#> $x
 #> [1] 7.346195
 #> 
 #> $y
 #>        1 
 #> 17.90777 
-#> 
# The weight for the loq should therefore be derived at x = 7.3 instead
-# of 15, but the graphical procedure of Massart (p. 201) to derive the 
-# variances on which the weights are based is quite inaccurate anyway. 

+#> 
# The weight for the loq should therefore be derived at x = 7.3 instead
+# of 15, but the graphical procedure of Massart (p. 201) to derive the 
+# variances on which the weights are based is quite inaccurate anyway. 
+
-

Site built with pkgdown.

+

Site built with pkgdown.

-- cgit v1.2.1