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Auto-generated via `{sandpaper}`
Source  : 711443a
Branch  : main
Author  : enerammer <[email protected]>
Time    : 2024-11-13 09:13:59 +0000
Message : Merge pull request KUBDatalab#176 from chrbknudsen/main

Regne eksempel på SEXRAT i datasæt
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actions-user committed Nov 13, 2024
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4 changes: 2 additions & 2 deletions clt.md
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Expand Up @@ -45,7 +45,7 @@ mean(random_numbers)
```

``` output
[1] 0.5487439
[1] 0.5193978
```
The important point of the Central Limit Theorem is, that if we take a large
number of random samples, and calculate the mean of each of these samples,
Expand All @@ -59,7 +59,7 @@ mean(runif(100))
```

``` output
[1] 0.4917325
[1] 0.5577847
```
And we can use the `replicate()` function to repeat that calculation several times, in this case 1000 times:

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38 changes: 19 additions & 19 deletions kmeans.md
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Expand Up @@ -144,31 +144,31 @@ clustering
```

``` output
K-means clustering with 3 clusters of sizes 50, 28, 100
K-means clustering with 3 clusters of sizes 100, 50, 28
Cluster means:
Alcohol Malicacid Ash Alcalinityofash Magnesium Totalphenols Flavanoids
1 13.33680 2.396800 2.3718 18.51000 108.6000 2.432400 2.214800
2 13.82214 1.773929 2.4900 16.96429 105.3571 2.923929 3.111429
3 12.60250 2.463600 2.3293 20.69600 93.7400 2.050400 1.633500
1 12.60250 2.463600 2.3293 20.69600 93.7400 2.050400 1.633500
2 13.33680 2.396800 2.3718 18.51000 108.6000 2.432400 2.214800
3 13.82214 1.773929 2.4900 16.96429 105.3571 2.923929 3.111429
Nonflavanoidphenols Proanthocyanins Colorintensity Hue
1 0.3236000 1.707200 5.143600 0.966720
2 0.2985714 1.986786 6.202857 1.103571
3 0.3987000 1.421900 4.694800 0.911900
1 0.3987000 1.421900 4.694800 0.911900
2 0.3236000 1.707200 5.143600 0.966720
3 0.2985714 1.986786 6.202857 1.103571
OD280OD315ofdilutedwines Proline
1 2.862800 894.60
2 2.984643 1301.50
3 2.381700 517.75
1 2.381700 517.75
2 2.862800 894.60
3 2.984643 1301.50
Clustering vector:
[1] 1 1 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 1 1 2 2 1 2 1 1 1
[38] 2 1 1 1 1 1 3 1 1 1 1 1 2 2 2 2 2 1 2 1 2 2 3 3 3 3 3 3 3 3 3 1 1 1 3 3 1
[75] 1 3 3 3 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3
[112] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 1 1 3 3
[149] 3 3 3 3 3 3 3 1 3 1 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 1 1 3
[1] 2 2 3 3 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 2 2 3 3 2 3 2 2 2
[38] 3 2 2 2 2 2 1 2 2 2 2 2 3 3 3 3 3 2 3 2 3 3 1 1 1 1 1 1 1 1 1 2 2 2 1 1 2
[75] 2 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 2 2 1 1
[149] 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 1
Within cluster sum of squares by cluster:
[1] 815783.7 550201.0 1263330.5
[1] 1263330.5 815783.7 550201.0
(between_SS / total_SS = 85.1 %)
Available components:
Expand All @@ -188,9 +188,9 @@ table()
``` output
true
quess 1 2 3
1 30 9 11
2 28 0 0
3 1 62 37
1 1 62 37
2 30 9 11
3 28 0 0
```

The algorithm have no idea about the numbering, the three groups are numbered
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74 changes: 37 additions & 37 deletions md5sum.txt
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2 changes: 1 addition & 1 deletion normal-distribution.md
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Expand Up @@ -196,7 +196,7 @@ rnorm(5, mean = 0, sd = 1 )
```

``` output
[1] -0.5854523 0.4889116 1.8896220 1.3496765 0.6922389
[1] 0.300443206 0.018065595 0.007747242 0.438378525 0.155126838
```
Den returnerer (her) fem tilfældige værdier fra en normalfordeling med (her)
middelværdi 0 og standardafvigelse 1.
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