From 50674f361dd72e552f90d7ff136c15d56a0b8a53 Mon Sep 17 00:00:00 2001 From: alexkowa Date: Wed, 17 Jul 2024 09:18:30 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20statisti?= =?UTF-8?q?kat/VIM@23454f108dc34ced9ebec65517693112b5dca620=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/VisualImp.html | 226 +++++++++++++++++- .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 33063 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 0 -> 210408 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 0 -> 205558 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 0 -> 21653 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 21724 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 0 -> 55451 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 0 -> 55581 bytes .../figure-html/unnamed-chunk-17-1.png | Bin 0 -> 307117 bytes .../figure-html/unnamed-chunk-18-1.png | Bin 0 -> 342438 bytes .../figure-html/unnamed-chunk-19-1.png | Bin 0 -> 52385 bytes .../figure-html/unnamed-chunk-20-1.png | Bin 0 -> 61030 bytes .../figure-html/unnamed-chunk-21-1.png | Bin 0 -> 228686 bytes .../figure-html/unnamed-chunk-24-1.png | Bin 0 -> 27493 bytes .../figure-html/unnamed-chunk-25-1.png | Bin 0 -> 29570 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 0 -> 31455 bytes pkgdown.yml | 2 +- 17 files changed, 226 insertions(+), 2 deletions(-) create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-10-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-11-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-12-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-13-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-14-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-15-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-16-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-17-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-18-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-19-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-20-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-21-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-24-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-25-1.png create mode 100644 articles/VisualImp_files/figure-html/unnamed-chunk-9-1.png diff --git a/articles/VisualImp.html b/articles/VisualImp.html index 18b5ddf..e855065 100644 --- a/articles/VisualImp.html +++ b/articles/VisualImp.html @@ -92,7 +92,7 @@

+ +
+

Function pbox() +

+

The function pbox(), can be used to create parallel +boxplots of one variable of interest with information about missing/ +imputed values in other variables.

+
+dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
+
+# for missing values
+dataset$BodyWgt <- log(dataset$BodyWgt)
+dataset$Span <- log(dataset$Span)
+
+pbox(dataset)
+

+
+# for imputed values
+pbox(kNN(dataset), delimiter = "_imp")
+

+

This plot consists of several boxplots. The first, white plot, is a +standard boxplot of the variable of interest, in this case of the +variable Dream. Second, boxplots grouped by observed and missing/imputed +values according to selection are produced for the other +variables, NonD and Span.

+

Additionally, the frequencies of the missing/imputed values can be +represented by numbers. If so, the first line corresponds to the +observed values of the variable of interest and their distribution in +the different groups, the second line to the missing/imputed values.

+

If interactive=TRUE, clicking in the left margin of the +plot results in switching to the previous variable and clicking in the +right margin results in switching to the next variable. Clicking +anywhere else on the graphics device quits the interactive session.

+
+
+

Function parcoordMiss +

+

The function parcoordMiss(), can be used to create a +parallel coordinate plot with adjustments for missing/imputed +values.

+
+dataset <- sleep[, c("Dream", "NonD", "BodyWgt", "Span")] # dataset with missings
+
+## for missing values
+parcoordMiss(dataset, plotvars=2:4, interactive = FALSE)
+legend("top", col = c("skyblue", "red"), lwd = c(1,1),
+       legend = c("observed in Dream", "missing in Dream"))
+

+
+## for imputed values
+parcoordMiss(kNN(dataset), plotvars=2:4, delimiter = "_imp" , 
+             interactive = FALSE)
+legend("top", col = c("skyblue", "orange"), lwd = c(1,1),
+       legend = c("observed in Dream", "imputed in Dream"))
+

+

Missing values in the plotted variables may be represented by a point +above the corresponding coordinate axis to prevent disconnected lines. +In addition, observations with missing/imputed values in selected +variables may be highlighted.

+
+
+

Function spineMiss +

+

The function spineMiss(), can be used to create a +spineplot or spinogramm and highlights missing/imputed values in other +variables by splitting each cell into two parts. Additional information +about missing/imputed values in the variable of interest is shown on the +right hand side.

+
+data(sleep, package = "VIM") # dataset with missings
+table(sleep$Exp) # categorical variable of interest
+#> 
+#>  1  2  3  4  5 
+#> 27 13  4  5 13
+
+## for missing values
+spineMiss(sleep[, c("Exp", "Sleep")])
+

+
+## for imputed values
+spineMiss(kNN(sleep[, c("Exp", "Sleep")]), delimiter = "_imp")
+

+

The variable of interest (Exp) is a categorical +variable, because of this the function creates a spineplot. Thus the +proportion of highlighted observations in each category/class is +displayed on the vertical axis. This fact allows to compare the +proportions of missing/imputed values among the different +categories/classes.

+
+
+

Function mosaicMiss +

+

The function mosaicMiss(), can be used to create a +mosaic plot with information about missing/imputed values.

+

Mosaic plots are graphical representations of multi-way contingency +tables. The frequencies of the different cells are visualized by +area-proportional rectangles (tiles). Additional tiles are be used to +display the frequencies of missing/imputed values. Furthermore, +missing/imputed values in a certain variable or combination of variables +can be highlighted in order to explore their structure.

+
+## for missing values
+# using the three categorical variables Pred, Exp and Danger
+mosaicMiss(sleep, highlight = 4, plotvars = 8:10, miss.labels = FALSE) 
+

+
+## for imputed values
+mosaicMiss(kNN(sleep), highlight = 4, plotvars = 8:10, delimiter = "_imp",
+           miss.labels = FALSE)
+

+
+
+

Function scattmatrixMiss +

+

The function scattmatrixMiss(), can be used to create +scatterplot matrix in which observations with missing/imputed values in +certain variables are highlighted.

+
+## for missing values
+x <- sleep[, 1:5]
+x[,c(1,2,4)] <- log10(x[,c(1,2,4)])
+scattmatrixMiss(x, highlight = "Dream")
+

+
+## for imputed values
+x_imp <- kNN(sleep[, 1:5])
+x_imp[,c(1,2,4)] <- log10(x_imp[,c(1,2,4)])
+scattmatrixMiss(x_imp, delimiter = "_imp", highlight = "Dream")
+

+
+
+

Function scattJitt +

+

The function scattJitt(), can be used to create a +bivariate jitter plot. The amount of observed and missing/imputed values +is visualized by jittered points. Thereby the plot region is divided +into up to four regions according to the existence of missing/imputed +values in one or both variables. In addition, the amount of observed and +missing/imputed values can be represented by a number.

+
+## for missing values
+scattJitt(sleep[, c("Dream", "Span")])
+

+
+## for imputed values
+scattJitt(kNN(sleep[, c("Dream", "Span")]), delimiter = "_imp")
+

+
+
+

Additional functions +

+

These functions are not intended for direct use, but are used by the +other plotting functions.

+
+

Function pairsVIM +

+

The function scattJitt(), can be used to create a +scatterplot matrix. This function is also used by +scattmatrixMiss().

+
+x <- sleep[, -(8:10)]
+x[,c(1,2,4,6,7)] <- log10(x[,c(1,2,4,6,7)])
+pairsVIM(x)
+

+
+
+

Function colSequence +

+

The function colSequence(), can be used to compute color +sequences by linear interpolation based on a continuous color scheme +between certain start and end colors. Color sequences may thereby be +computed in the HCL or RGB color space.

+
+p <- c(0, 0.3, 0.55, 0.8, 1)
+
+## HCL colors
+colSequence(p, c(0, 0, 100), c(0, 100, 50))
+#> [1] "#FFFFFF" "#FAC8D1" "#F09AAB" "#E16A86" "#D33F6A"
+colSequence(p, polarLUV(L=90, C=30, H=90), c(0, 100, 50))
+#> [1] "#E2E6BD" "#DBBD80" "#DB975E" "#D86B5B" "#D33F6A"
+
+## RGB colors
+colSequence(p, c(1, 1, 1), c(1, 0, 0), space="rgb")
+#> [1] "#FFFFFF" "#FFDADA" "#FFB3B3" "#FF7C7C" "#FF0000"
+colSequence(p, RGB(1, 1, 0), "red")
+#> [1] "#FFFF00" "#FFDA00" "#FFB300" "#FF7C00" "#FF0000"
+
+
+

Function rugNA +

+

The function rugNA(), can be used to add a rug +representation of missing/imputed values in only one of the variables to +scatterplots.

+

If side is 1 or 3, the rug representation consists of values +available in x but missing/imputed in y. Else if side is 2 or 4, it +consists of values available in y but missing/imputed in x.

+
+## for missing values
+x <- sleep[, "Dream"]
+y <- sleep[, "Span"]
+plot(x, y)
+rugNA(x, y, side = 1)
+rugNA(x, y, side = 2)
+

+
+## for imputed values
+x_imp <- kNN(sleep[, c("Dream","Span")])
+x <- x_imp[, "Dream"]
+y <- x_imp[, "Span"]
+miss <- x_imp[, c("Dream_imp","Span_imp")]
+plot(x, y)
+rugNA(x, y, side = 1, col = "orange", miss = miss)
+rugNA(x, y, side = 2, col = "orange", miss = miss)
+

+
+
+

Function alphablend +

+

The function alphablend(), can be used to convert colors +to semitransparent colors.

+
+alphablend("red", 0.6)
+#> [1] "#FF000099"
+
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