Messwert gegenüber Schätzwert

Wie erstelle ich Grafiken, was ist zu beachten?

Moderatoren: EDi, jogo

Antworten
kvonnn

Messwert gegenüber Schätzwert

Beitrag von kvonnn »

Hallo allerseits,

wie gesagt möchte ich reales Gewicht mit einem berechneten vergleichen um eine Aussage über die Genauigkeit des Schätzwerts machen zu können, mit folgenden Daten:

Code: Alles auswählen

    treatment variety plant truss weight estimation
1        LOC        2    D      1   47.1       34.7
2        LOC        2    D      1   41.2       35.2
3        LOC        2    D      1   35.0       27.9
4        LOC        2    D      1   43.5       37.2
5        LOC        2    D      1   31.3       29.2
6        LOC        2    D      1   18.3       15.1
7        LOC        2    D      1    5.7        5.2
8        LOC        2    D      1    5.4        4.1
9        LOC        2    D      2   24.9       20.5
10       LOC        2    D      2   24.4       18.9
11       LOC        2    D      2   17.7       16.1
12       LOC        2    D      2    7.9        4.6
13       LOC        2    D      2    8.3        5.7
14       LOC        2    D      2    6.2        5.4
15       LOC        2    D      2    6.8        6.8
16       LOC        2    D      2    6.2        5.2
17       LOC        2    D      3   11.2        9.1
18       LOC        2    D      3    6.6        6.9
19       LOC        2    D      3    6.3        6.1
20       LOC        2    D      3    6.2        6.0
21       LOC        2    D      4   11.2        9.7
22       LOC        2    D      4   13.7       13.1
23       LOC        2    D      4    7.6        8.5
24       NOP        3    N      1   78.4       60.4
25       NOP        3    N      1   34.3       25.2
26       NOP        3    N      1   31.2       31.1
27       NOP        3    N      1   31.1       28.2
28       NOP        3    N      1   30.7       25.5
29       NOP        3    N      1   29.0       25.2
30       NOP        3    N      1   26.2       18.8
31       NOP        3    N      1   25.1       23.3
32       NOP        3    N      1   21.6       19.6
33       NOP        3    N      1   23.1       16.3
34       NOP        3    N      1   15.8       12.9
35       NOP        3    N      1   12.2       10.7
36       NOP        3    N      2   33.2       24.5
37       NOP        3    N      2   33.5       22.7
38       NOP        3    N      2   33.3       33.2
39       NOP        3    N      2   12.9       12.2
40       NOP        3    N      2   12.9       14.2
41       NOP        3    N      2   10.8        8.5
42       NOP        3    N      2   12.5       11.1
43       NOP        3    N      2    6.8        6.3
44       NOP        3    N      3    8.0        9.9
45       NOP        3    N      3    5.5        4.1
46       NOP        2    E      1   58.7       43.4
47       NOP        2    E      1   52.6       41.6
48       NOP        2    E      1   39.0       33.6
49       NOP        2    E      1   42.1       37.7
50       NOP        2    E      1   41.1       35.3
51       NOP        2    E      1   27.6       25.3
52       NOP        2    E      1   25.6       20.4
53       NOP        2    E      2   32.5       28.7
54       NOP        2    E      2   24.8       22.0
55       NOP        2    E      2   17.4       16.3
56       NOP        2    E      2   10.9       10.3
57       NOP        2    E      2   11.4       10.0
58       NOP        2    E      2    7.2        8.7
59       NOP        2    E      3   29.1       25.4
60       NOP        2    E      3   16.0       12.9
61       NOP        2    E      3    8.9        9.9
62       NOP        2    E      3    7.5        8.7
63       LOC        3    K      1   86.8       68.0
64       LOC        3    K      1   50.9       43.2
65       LOC        3    K      1   51.5       43.9
66       LOC        3    K      1   48.6       48.2
67       LOC        3    K      1   36.9       32.7
68       LOC        3    K      1   23.8       21.0
69       LOC        3    K      2   41.5       46.3
70       LOC        3    K      2   36.9       31.8
71       LOC        3    K      2   29.0       26.8
72       LOC        3    K      2   12.9       11.0
73       LOC        3    K      2   13.4       13.8
74       LOC        3    K      2   10.6        8.0
75       LOC        3    K      2    8.0       10.8
76       LOC        3    K      2    9.2       10.7
77       LOC        3    K      3    8.6        4.6
78       LOC        3    K      3   10.2        7.1
79       LOC        3    K      3    5.8        5.5
80       LOC        3    K      3    6.8        6.9
81       LOC        3    K      3    5.8        5.3
82       LOC        3    K      4    6.0        5.5
83       CLE        2    R      1   50.9       40.6
84       CLE        2    R      1   39.1       36.4
85       CLE        2    R      1   30.8       26.2
86       CLE        2    R      1   27.3       22.4
87       CLE        2    R      1   26.1       22.3
88       CLE        2    R      1   18.9       15.2
89       CLE        2    R      1   19.0       14.8
90       CLE        2    R      1   17.3       14.7
91       CLE        2    R      1   14.8       16.9
92       CLE        2    R      1   10.5        8.4
93       CLE        2    R      1    9.9        6.6
94       CLE        2    R      1    8.6       12.4
95       CLE        2    R      1    9.1       11.9
96       CLE        2    R      1    8.9       13.2
97       CLE        2    R      1    9.4       11.5
98       CLE        2    R      1    7.2        6.6
99       CLE        2    R      1    6.8        6.6
100      CLE        2    R      1    6.7        7.1
101      CLE        2    R      1    6.2        6.0
102      CLE        2    R      2   39.2       34.4
103      CLE        2    R      2   12.0        9.0
104      CLE        2    R      2    9.9        7.4
105      CLE        2    R      2    7.8        9.9
106      CLE        2    R      2    9.5       14.1
107      CLE        2    R      2    6.6        6.6
108      CLE        2    R      3   14.6       16.2
109      CLE        2    R      3   12.0       10.2
110      CLE        2    R      3    8.7       10.6
111      CLE        2    R      3    8.1        9.8
112      CLE        2    R      3    8.2       10.4
113      CLE        2    R      3    7.1        7.9
114      NOP        1    M      1   43.9       34.2
115      NOP        1    M      1   33.7       35.3
116      NOP        1    M      1   40.6       40.9
117      NOP        1    M      1   37.9       32.1
118      NOP        1    M      1   39.9       41.3
119      NOP        1    M      1   25.6       22.2
120      NOP        1    M      2   45.2       38.8
121      NOP        1    M      2   30.9       28.5
122      NOP        1    M      2   22.9       23.5
123      NOP        1    M      2   23.3       23.3
124      NOP        1    M      2   22.9       23.5
125      NOP        1    M      2   18.3       14.0
126      NOP        1    M      2   18.2       18.2
127      NOP        1    M      2   12.4       22.7
128      NOP        1    M      3   21.0       16.9
129      NOP        1    M      3   16.6       14.1
130      NOP        1    M      3   10.5        7.9
131      NOP        1    M      3   13.1       12.0
132      NOP        1    M      3    6.9        6.6
133      NOP        1    M      3    7.8        8.6
134      NOP        1    M      3    6.3        6.3
135      NOP        1    M      4   21.3       16.1
136      NOP        1    M      4   19.4       20.8
137      DIF        2    Q      1   48.2       37.4
138      DIF        2    Q      1   50.1       43.3
139      DIF        2    Q      1   51.7       44.0
140      DIF        2    Q      1   47.0       39.4
141      DIF        2    Q      1   31.9       27.3
142      DIF        2    Q      1   25.4       26.9
143      DIF        2    Q      1   24.6       23.1
144      DIF        2    Q      1    8.9       13.2
145      DIF        2    Q      2   49.7       38.3
146      DIF        2    Q      2   37.5       32.4
147      DIF        2    Q      2   30.3       26.0
148      DIF        2    Q      2   26.4       23.7
149      DIF        2    Q      2   16.6       14.6
150      DIF        2    Q      2   14.7       12.0
151      DIF        2    Q      3   58.0       48.8
152      DIF        2    Q      3   28.4       22.7
153      DIF        2    Q      3   26.5       19.9
154      DIF        2    Q      3   10.7        8.1
155      DIF        2    Q      3    7.2        6.0
156      DIF        2    Q      4   15.7       12.0
157      CLE        1    R      1   75.6       63.3
158      CLE        1    R      1   43.6       39.8
159      CLE        1    R      1   43.4       38.1
160      CLE        1    R      1   45.2       37.9
161      CLE        1    R      1   41.6       34.8
162      CLE        1    R      1   36.5       28.9
163      CLE        1    R      1   26.7       21.9
164      CLE        1    R      1   20.1       14.5
165      CLE        1    R      2   26.5       22.3
166      CLE        1    R      2   26.9       20.8


Wie folgt habe ich versucht es darzustellen:

Code: Alles auswählen

ggplot(d, aes(x=d$estimation, y=d$weight))+
  geom_point(shape=19, alpha=1/4)+
geom_smooth(method=lm,se=FALSE)
Nun ist mir nicht ganz klar was die Grafik sagt, könnt ihr mir da Aufschluss geben?
Beziehungsweise habt ihr eine Idee welcher plot der geeignete sein könnte?
bigben
Beiträge: 2771
Registriert: Mi Okt 12, 2016 9:09 am

Re: Messwert gegenüber Schätzwert

Beitrag von bigben »

Die Genauigkeit kann man sinnvoll darstellen als die Differenz zwischen Gewicht und Schätzung oder als Differenz zwischen Gewicht und Schätzung in Prozent des richtigen Wertes. Dementsprechend würde ich das "richtige" Gewicht auf der x-Achse und die Differenz zwischen beiden auf der y-Achse (absolut oder in Prozent) darstellen.

poste mal das Ergebnis von

Code: Alles auswählen

dput(d)
wenn Du von uns Code bekommen möchtest, der an Deinen Daten getestet wurde.

LG,
Bernhard
---
Programmiere stets so, dass die Maxime Deines Programmierstils Grundlage allgemeiner Gesetzgebung sein könnte
kvonnn

Re: Messwert gegenüber Schätzwert

Beitrag von kvonnn »

Hallo Bernhard,

vielen Dank wieder mal für die Hilfe!!

Das Ergebnis von dput:

Code: Alles auswählen

structure(list(treatment = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c(" CLE ", 
" DIF ", " LOC ", " NOP ", " OUT "), class = "factor"), variety = c(2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 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, 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, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3), plant = structure(c(3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L), .Label = c(" B ", " C ", " D ", 
" E ", " H ", " K ", " M ", " N ", " O ", " Q ", " R "), class = "factor"), 
    truss = c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 
    3, 3, 3, 3, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 
    2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 
    2, 2, 2, 3, 3, 3, 3, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 
    3, 3, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 
    3, 3, 3, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 
    2, 3, 3, 3, 3, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 
    2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 1, 1, 
    1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4, 1, 1, 1, 1, 1, 
    1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 
    4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 
    1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 
    1, 1, 1, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
    2, 2, 4, 4), weight = c(47.1, 41.2, 35, 43.5, 31.3, 18.3, 
    5.7, 5.4, 24.9, 24.4, 17.7, 7.9, 8.3, 6.2, 6.8, 6.2, 11.2, 
    6.6, 6.3, 6.2, 11.2, 13.7, 7.6, 78.4, 34.3, 31.2, 31.1, 30.7, 
    29, 26.2, 25.1, 21.6, 23.1, 15.8, 12.2, 33.2, 33.5, 33.3, 
    12.9, 12.9, 10.8, 12.5, 6.8, 8, 5.5, 58.7, 52.6, 39, 42.1, 
    41.1, 27.6, 25.6, 32.5, 24.8, 17.4, 10.9, 11.4, 7.2, 29.1, 
    16, 8.9, 7.5, 86.8, 50.9, 51.5, 48.6, 36.9, 23.8, 41.5, 36.9, 
    29, 12.9, 13.4, 10.6, 8, 9.2, 8.6, 10.2, 5.8, 6.8, 5.8, 6, 
    50.9, 39.1, 30.8, 27.3, 26.1, 18.9, 19, 17.3, 14.8, 10.5, 
    9.9, 8.6, 9.1, 8.9, 9.4, 7.2, 6.8, 6.7, 6.2, 39.2, 12, 9.9, 
    7.8, 9.5, 6.6, 14.6, 12, 8.7, 8.1, 8.2, 7.1, 43.9, 33.7, 
    40.6, 37.9, 39.9, 25.6, 45.2, 30.9, 22.9, 23.3, 22.9, 18.3, 
    18.2, 12.4, 21, 16.6, 10.5, 13.1, 6.9, 7.8, 6.3, 21.3, 19.4, 
    48.2, 50.1, 51.7, 47, 31.9, 25.4, 24.6, 8.9, 49.7, 37.5, 
    30.3, 26.4, 16.6, 14.7, 58, 28.4, 26.5, 10.7, 7.2, 15.7, 
    75.6, 43.6, 43.4, 45.2, 41.6, 36.5, 26.7, 20.1, 26.5, 26.9, 
    16.7, 11.2, 11.6, 10.8, 10.2, 10.1, 8.6, 8.7, 42.4, 41.7, 
    35.8, 32.9, 31.8, 29, 32.9, 30.9, 25.4, 22.9, 26.9, 22.9, 
    6.7, 5.4, 75.3, 31.4, 20, 13.5, 6.5, 5.6, 6.1, 26.2, 15.3, 
    8.2, 12.8, 10.8, 6.9, 50.3, 49.8, 41.1, 35.4, 24.2, 21.6, 
    20.8, 88.5, 46.4, 31.2, 25.9, 11, 7.5, 12.9, 15.2, 9.9, 10.8, 
    11.4, 8.7, 87.8, 77.6, 57.5, 55.8, 56.2, 40.6, 42, 39.4, 
    16.1, 19.7, 7.4, 5.9, 61.7, 33.7, 25.7, 13.4, 12.8, 10.1, 
    9.9, 9.7, 20.3, 12, 9.5, 7.2, 14.6, 38.5, 30.8, 29.5, 29.8, 
    29.7, 27.2, 23, 24.2, 71.9, 69.9, 43.6, 15, 7.9, 49.7, 82.3, 
    39.3, 38.2, 25.7, 20.5, 67.9, 53.1, 29.6, 40.7, 19.3, 19, 
    18, 11.6, 34.5, 35, 26.3, 15.2, 22.9, 15.3, 6.3, 49, 34.8, 
    35.4, 23.4, 24.8, 18.1, 58.2, 51.4, 51.6, 34.4, 45.2, 23.8, 
    52.9, 52.3, 36.2, 19.3, 12.3, 11.3, 9.5, 36.8, 12.6, 9.3, 
    8.7, 6.8, 6.5, 5.4), estimation = c(34.7, 35.2, 27.9, 37.2, 
    29.2, 15.1, 5.2, 4.1, 20.5, 18.9, 16.1, 4.6, 5.7, 5.4, 6.8, 
    5.2, 9.1, 6.9, 6.1, 6, 9.7, 13.1, 8.5, 60.4, 25.2, 31.1, 
    28.2, 25.5, 25.2, 18.8, 23.3, 19.6, 16.3, 12.9, 10.7, 24.5, 
    22.7, 33.2, 12.2, 14.2, 8.5, 11.1, 6.3, 9.9, 4.1, 43.4, 41.6, 
    33.6, 37.7, 35.3, 25.3, 20.4, 28.7, 22, 16.3, 10.3, 10, 8.7, 
    25.4, 12.9, 9.9, 8.7, 68, 43.2, 43.9, 48.2, 32.7, 21, 46.3, 
    31.8, 26.8, 11, 13.8, 8, 10.8, 10.7, 4.6, 7.1, 5.5, 6.9, 
    5.3, 5.5, 40.6, 36.4, 26.2, 22.4, 22.3, 15.2, 14.8, 14.7, 
    16.9, 8.4, 6.6, 12.4, 11.9, 13.2, 11.5, 6.6, 6.6, 7.1, 6, 
    34.4, 9, 7.4, 9.9, 14.1, 6.6, 16.2, 10.2, 10.6, 9.8, 10.4, 
    7.9, 34.2, 35.3, 40.9, 32.1, 41.3, 22.2, 38.8, 28.5, 23.5, 
    23.3, 23.5, 14, 18.2, 22.7, 16.9, 14.1, 7.9, 12, 6.6, 8.6, 
    6.3, 16.1, 20.8, 37.4, 43.3, 44, 39.4, 27.3, 26.9, 23.1, 
    13.2, 38.3, 32.4, 26, 23.7, 14.6, 12, 48.8, 22.7, 19.9, 8.1, 
    6, 12, 63.3, 39.8, 38.1, 37.9, 34.8, 28.9, 21.9, 14.5, 22.3, 
    20.8, 13.2, 10.7, 8.6, 8.7, 13.4, 15, 10.4, 8.8, 30, 37.3, 
    29.3, 29.7, 25.1, 26.6, 31.2, 27.1, 20.2, 17, 24.7, 14.7, 
    6.7, 4.1, 44.1, 25.3, 16.5, 13.9, 5.9, 4.7, 4.6, 19.1, 10.9, 
    10.9, 7.3, 12.2, 6.7, 41.6, 39.1, 29.5, 29, 25.5, 20.5, 20.5, 
    93.5, 39.7, 27.9, 21.3, 8.6, 7.6, 11.4, 15.6, 14.9, 9.8, 
    8.3, 5.1, 68.9, 73.7, 53.4, 47.2, 55.9, 41.6, 38.2, 37.4, 
    10.5, 14.9, 3.8, 5.1, 52.2, 22.3, 16.5, 7.9, 7.2, 8.1, 7.4, 
    7.9, 15.2, 6.5, 6.5, 4, 9.7, 35.1, 28.9, 29.1, 24.8, 25.4, 
    25, 22.6, 17.7, 55.8, 54.3, 36.5, 13.1, 8.4, 39.5, 68.1, 
    28.2, 33, 21.6, 13.9, 56.8, 41.1, 22.1, 30, 14.1, 14.8, 15.8, 
    10.3, 24.7, 27.1, 21.6, 10.1, 16.2, 11.3, 4.8, 35.1, 28.6, 
    29, 20.1, 18.6, 17.8, 49.2, 39.6, 41.8, 31.8, 34.5, 22.3, 
    46.9, 41.4, 29.1, 14.1, 10, 8.1, 12.1, 28.9, 11.9, 5.4, 5, 
    5.9, 5.4, 4.1)), .Names = c("treatment", "variety", "plant", 
"truss", "weight", "estimation"), class = "data.frame", row.names = c(NA, 
-305L))
Eigentlich müsste ja weight und estimation reichen, falls es das erleichtert:

Code: Alles auswählen

weight = c(47.1, 41.2, 35, 43.5, 31.3, 18.3, 
    5.7, 5.4, 24.9, 24.4, 17.7, 7.9, 8.3, 6.2, 6.8, 6.2, 11.2, 
    6.6, 6.3, 6.2, 11.2, 13.7, 7.6, 78.4, 34.3, 31.2, 31.1, 30.7, 
    29, 26.2, 25.1, 21.6, 23.1, 15.8, 12.2, 33.2, 33.5, 33.3, 
    12.9, 12.9, 10.8, 12.5, 6.8, 8, 5.5, 58.7, 52.6, 39, 42.1, 
    41.1, 27.6, 25.6, 32.5, 24.8, 17.4, 10.9, 11.4, 7.2, 29.1, 
    16, 8.9, 7.5, 86.8, 50.9, 51.5, 48.6, 36.9, 23.8, 41.5, 36.9, 
    29, 12.9, 13.4, 10.6, 8, 9.2, 8.6, 10.2, 5.8, 6.8, 5.8, 6, 
    50.9, 39.1, 30.8, 27.3, 26.1, 18.9, 19, 17.3, 14.8, 10.5, 
    9.9, 8.6, 9.1, 8.9, 9.4, 7.2, 6.8, 6.7, 6.2, 39.2, 12, 9.9, 
    7.8, 9.5, 6.6, 14.6, 12, 8.7, 8.1, 8.2, 7.1, 43.9, 33.7, 
    40.6, 37.9, 39.9, 25.6, 45.2, 30.9, 22.9, 23.3, 22.9, 18.3, 
    18.2, 12.4, 21, 16.6, 10.5, 13.1, 6.9, 7.8, 6.3, 21.3, 19.4, 
    48.2, 50.1, 51.7, 47, 31.9, 25.4, 24.6, 8.9, 49.7, 37.5, 
    30.3, 26.4, 16.6, 14.7, 58, 28.4, 26.5, 10.7, 7.2, 15.7, 
    75.6, 43.6, 43.4, 45.2, 41.6, 36.5, 26.7, 20.1, 26.5, 26.9, 
    16.7, 11.2, 11.6, 10.8, 10.2, 10.1, 8.6, 8.7, 42.4, 41.7, 
    35.8, 32.9, 31.8, 29, 32.9, 30.9, 25.4, 22.9, 26.9, 22.9, 
    6.7, 5.4, 75.3, 31.4, 20, 13.5, 6.5, 5.6, 6.1, 26.2, 15.3, 
    8.2, 12.8, 10.8, 6.9, 50.3, 49.8, 41.1, 35.4, 24.2, 21.6, 
    20.8, 88.5, 46.4, 31.2, 25.9, 11, 7.5, 12.9, 15.2, 9.9, 10.8, 
    11.4, 8.7, 87.8, 77.6, 57.5, 55.8, 56.2, 40.6, 42, 39.4, 
    16.1, 19.7, 7.4, 5.9, 61.7, 33.7, 25.7, 13.4, 12.8, 10.1, 
    9.9, 9.7, 20.3, 12, 9.5, 7.2, 14.6, 38.5, 30.8, 29.5, 29.8, 
    29.7, 27.2, 23, 24.2, 71.9, 69.9, 43.6, 15, 7.9, 49.7, 82.3, 
    39.3, 38.2, 25.7, 20.5, 67.9, 53.1, 29.6, 40.7, 19.3, 19, 
    18, 11.6, 34.5, 35, 26.3, 15.2, 22.9, 15.3, 6.3, 49, 34.8, 
    35.4, 23.4, 24.8, 18.1, 58.2, 51.4, 51.6, 34.4, 45.2, 23.8, 
    52.9, 52.3, 36.2, 19.3, 12.3, 11.3, 9.5, 36.8, 12.6, 9.3, 
    8.7, 6.8, 6.5, 5.4), estimation = c(34.7, 35.2, 27.9, 37.2, 
    29.2, 15.1, 5.2, 4.1, 20.5, 18.9, 16.1, 4.6, 5.7, 5.4, 6.8, 
    5.2, 9.1, 6.9, 6.1, 6, 9.7, 13.1, 8.5, 60.4, 25.2, 31.1, 
    28.2, 25.5, 25.2, 18.8, 23.3, 19.6, 16.3, 12.9, 10.7, 24.5, 
    22.7, 33.2, 12.2, 14.2, 8.5, 11.1, 6.3, 9.9, 4.1, 43.4, 41.6, 
    33.6, 37.7, 35.3, 25.3, 20.4, 28.7, 22, 16.3, 10.3, 10, 8.7, 
    25.4, 12.9, 9.9, 8.7, 68, 43.2, 43.9, 48.2, 32.7, 21, 46.3, 
    31.8, 26.8, 11, 13.8, 8, 10.8, 10.7, 4.6, 7.1, 5.5, 6.9, 
    5.3, 5.5, 40.6, 36.4, 26.2, 22.4, 22.3, 15.2, 14.8, 14.7, 
    16.9, 8.4, 6.6, 12.4, 11.9, 13.2, 11.5, 6.6, 6.6, 7.1, 6, 
    34.4, 9, 7.4, 9.9, 14.1, 6.6, 16.2, 10.2, 10.6, 9.8, 10.4, 
    7.9, 34.2, 35.3, 40.9, 32.1, 41.3, 22.2, 38.8, 28.5, 23.5, 
    23.3, 23.5, 14, 18.2, 22.7, 16.9, 14.1, 7.9, 12, 6.6, 8.6, 
    6.3, 16.1, 20.8, 37.4, 43.3, 44, 39.4, 27.3, 26.9, 23.1, 
    13.2, 38.3, 32.4, 26, 23.7, 14.6, 12, 48.8, 22.7, 19.9, 8.1, 
    6, 12, 63.3, 39.8, 38.1, 37.9, 34.8, 28.9, 21.9, 14.5, 22.3, 
    20.8, 13.2, 10.7, 8.6, 8.7, 13.4, 15, 10.4, 8.8, 30, 37.3, 
    29.3, 29.7, 25.1, 26.6, 31.2, 27.1, 20.2, 17, 24.7, 14.7, 
    6.7, 4.1, 44.1, 25.3, 16.5, 13.9, 5.9, 4.7, 4.6, 19.1, 10.9, 
    10.9, 7.3, 12.2, 6.7, 41.6, 39.1, 29.5, 29, 25.5, 20.5, 20.5, 
    93.5, 39.7, 27.9, 21.3, 8.6, 7.6, 11.4, 15.6, 14.9, 9.8, 
    8.3, 5.1, 68.9, 73.7, 53.4, 47.2, 55.9, 41.6, 38.2, 37.4, 
    10.5, 14.9, 3.8, 5.1, 52.2, 22.3, 16.5, 7.9, 7.2, 8.1, 7.4, 
    7.9, 15.2, 6.5, 6.5, 4, 9.7, 35.1, 28.9, 29.1, 24.8, 25.4, 
    25, 22.6, 17.7, 55.8, 54.3, 36.5, 13.1, 8.4, 39.5, 68.1, 
    28.2, 33, 21.6, 13.9, 56.8, 41.1, 22.1, 30, 14.1, 14.8, 15.8, 
    10.3, 24.7, 27.1, 21.6, 10.1, 16.2, 11.3, 4.8, 35.1, 28.6, 
    29, 20.1, 18.6, 17.8, 49.2, 39.6, 41.8, 31.8, 34.5, 22.3, 
    46.9, 41.4, 29.1, 14.1, 10, 8.1, 12.1, 28.9, 11.9, 5.4, 5, 
    5.9, 5.4, 4.1)
bigben
Beiträge: 2771
Registriert: Mi Okt 12, 2016 9:09 am

Re: Messwert gegenüber Schätzwert

Beitrag von bigben »

kvonnn hat geschrieben: So Jul 23, 2017 9:36 amBeziehungsweise habt ihr eine Idee welcher plot der geeignete sein könnte?
Ich würde mir folgendes anschauen:

Code: Alles auswählen

d$difference <- d$estimation - d$weight

p <- ggplot(d) + 
        geom_count(aes(x=weight, y=difference)) +
        xlab("Gemessenes Gewicht") +
        ylab("Abweichung der Schätzung\n (positive Werte zeigen zu hohen Schätzwert)")
print(p)
Darin erkennt man, dass die Schätzung das wahre Gewicht umso mehr unterschätzt, je größer das Gewicht wird. Ein eindeutiger Trend, der aus Deiner ursprünglichen Grafik nicht annäherungsweise so gut zu erkennen war.

LG,
Bernhard
---
Programmiere stets so, dass die Maxime Deines Programmierstils Grundlage allgemeiner Gesetzgebung sein könnte
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