Reading NCEA Data
We did complete case analysis only.
library(readxl)
NCEADataTZ <- read_excel("NCEADataTZ.xlsx",
col_types = c("text", "numeric", "text",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "text"))
## Warning: Expecting numeric in B15 / R15C2: got 'NA'
## Warning: Expecting numeric in B74 / R74C2: got 'NA'
## Warning: Expecting numeric in B78 / R78C2: got 'NA'
## New names:
## * `` -> ...18
## * `` -> ...19
## * `` -> ...20
## * `` -> ...21
## * `` -> ...22
## * ...
NCEAData <- NCEADataTZ[, 1:17]
NCEAData.CC <- NCEAData[complete.cases(NCEAData), ]
kable(NCEAData.CC) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
Entry #
|
BMI
|
BMIOrdinal
|
ConcussionCount
|
ConcussionBinary
|
ConcussionOrdinal
|
SpinalOrdinal
|
Spinalcount
|
ShoulderPathologyCategory
|
ElbPathologycategory
|
UEBinary
|
Uecount
|
kneepathologycategory
|
anklepathologycategory
|
HpImpingementCategory
|
LEBinary
|
LEcount
|
|
198
|
26.04
|
3
|
1
|
1
|
1
|
3
|
4
|
0
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
|
197
|
20.56
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
5
|
|
196
|
21.99
|
2
|
0
|
0
|
0
|
3
|
8
|
0
|
2
|
3
|
4
|
0
|
0
|
2
|
2
|
2
|
|
195
|
20.22
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
194
|
21.01
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
193
|
21.84
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
3
|
9
|
|
192
|
25.01
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
191
|
19.50
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
190
|
18.28
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
189
|
23.01
|
2
|
1
|
1
|
1
|
3
|
4
|
3
|
0
|
3
|
8
|
0
|
0
|
2
|
2
|
2
|
|
188
|
22.91
|
2
|
0
|
0
|
0
|
3
|
8
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
1
|
1
|
|
187
|
23.10
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
6
|
|
186
|
21.44
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
184
|
22.36
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
2
|
3
|
8
|
|
183
|
22.36
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
182
|
20.13
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
3
|
|
181
|
22.01
|
2
|
0
|
0
|
0
|
3
|
8
|
0
|
0
|
0
|
0
|
0
|
2
|
0
|
2
|
2
|
|
180
|
21.03
|
2
|
1
|
1
|
1
|
3
|
6
|
3
|
1
|
3
|
15
|
3
|
3
|
3
|
3
|
15
|
|
179
|
22.64
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
178
|
21.19
|
2
|
0
|
0
|
0
|
3
|
6
|
2
|
3
|
3
|
12
|
1
|
0
|
0
|
1
|
1
|
|
177
|
21.03
|
2
|
2
|
1
|
1
|
0
|
0
|
0
|
1
|
2
|
2
|
0
|
2
|
0
|
3
|
3
|
|
176
|
21.01
|
2
|
1
|
1
|
1
|
3
|
6
|
3
|
0
|
3
|
12
|
3
|
0
|
0
|
3
|
5
|
|
175
|
23.08
|
2
|
5
|
1
|
3
|
3
|
8
|
1
|
0
|
2
|
2
|
0
|
3
|
0
|
3
|
11
|
|
174
|
20.16
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
173
|
24.26
|
2
|
0
|
0
|
0
|
3
|
8
|
0
|
0
|
0
|
0
|
0
|
2
|
0
|
3
|
3
|
|
172
|
23.08
|
2
|
2
|
1
|
1
|
3
|
12
|
1
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
171
|
22.72
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
170
|
22.86
|
2
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
169
|
18.60
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
168
|
24.26
|
2
|
0
|
0
|
0
|
3
|
4
|
2
|
0
|
3
|
4
|
2
|
0
|
2
|
3
|
4
|
|
167
|
24.26
|
2
|
1
|
1
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
1
|
1
|
|
166
|
21.08
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
165
|
25.45
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
3
|
3
|
9
|
|
164
|
18.04
|
1
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
163
|
19.24
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
2
|
0
|
2
|
3
|
4
|
|
162
|
25.90
|
3
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
|
161
|
23.01
|
2
|
0
|
0
|
0
|
2
|
2
|
1
|
1
|
3
|
3
|
0
|
1
|
0
|
1
|
1
|
|
160
|
22.36
|
2
|
1
|
1
|
1
|
3
|
12
|
0
|
0
|
0
|
0
|
3
|
3
|
0
|
3
|
16
|
|
159
|
25.51
|
3
|
1
|
1
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
3
|
3
|
|
158
|
21.40
|
2
|
1
|
1
|
1
|
0
|
0
|
3
|
0
|
3
|
8
|
0
|
0
|
0
|
0
|
0
|
|
157
|
24.35
|
2
|
1
|
1
|
1
|
0
|
0
|
2
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
|
156
|
19.41
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
155
|
23.83
|
2
|
2
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
154
|
21.50
|
2
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
4
|
|
153
|
21.46
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
152
|
21.19
|
2
|
3
|
1
|
2
|
3
|
4
|
1
|
2
|
3
|
8
|
0
|
1
|
0
|
3
|
4
|
|
151
|
21.57
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
150
|
33.54
|
3
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
3
|
3
|
|
149
|
18.83
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
3
|
4
|
0
|
0
|
0
|
1
|
1
|
|
148
|
24.08
|
2
|
2
|
1
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
147
|
26.22
|
3
|
2
|
1
|
1
|
0
|
0
|
1
|
0
|
2
|
2
|
3
|
1
|
3
|
3
|
12
|
|
146
|
20.01
|
2
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
145
|
21.08
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
144
|
22.64
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
143
|
21.67
|
2
|
0
|
0
|
0
|
3
|
6
|
1
|
0
|
2
|
2
|
2
|
0
|
2
|
3
|
6
|
|
142
|
21.33
|
2
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
2
|
0
|
0
|
0
|
3
|
3
|
|
141
|
23.76
|
2
|
3
|
1
|
2
|
3
|
10
|
2
|
0
|
3
|
4
|
0
|
3
|
2
|
3
|
13
|
|
140
|
21.68
|
2
|
0
|
0
|
0
|
3
|
6
|
2
|
0
|
3
|
4
|
2
|
2
|
2
|
3
|
9
|
|
139
|
21.19
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
8
|
|
138
|
23.85
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
2
|
1
|
3
|
0
|
3
|
12
|
|
137
|
21.68
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
136
|
29.04
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
3
|
2
|
3
|
7
|
|
135
|
25.01
|
3
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
134
|
22.86
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
3
|
7
|
|
133
|
23.62
|
2
|
1
|
1
|
1
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
132
|
24.26
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
131
|
21.90
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
130
|
27.52
|
3
|
3
|
1
|
2
|
3
|
8
|
0
|
2
|
2
|
2
|
3
|
2
|
3
|
3
|
10
|
|
129
|
19.30
|
2
|
2
|
1
|
1
|
3
|
20
|
3
|
1
|
3
|
18
|
3
|
3
|
3
|
3
|
40
|
|
128
|
21.12
|
2
|
1
|
1
|
1
|
3
|
8
|
0
|
0
|
2
|
2
|
3
|
0
|
0
|
3
|
8
|
|
127
|
27.16
|
3
|
0
|
0
|
0
|
3
|
6
|
0
|
0
|
0
|
0
|
2
|
3
|
3
|
3
|
12
|
|
125
|
20.57
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
124
|
23.76
|
2
|
2
|
1
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
123
|
23.54
|
2
|
5
|
1
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
121
|
22.36
|
2
|
3
|
1
|
2
|
3
|
10
|
1
|
3
|
3
|
23
|
2
|
3
|
0
|
3
|
20
|
|
120
|
23.35
|
2
|
2
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
119
|
20.41
|
2
|
0
|
0
|
0
|
3
|
18
|
0
|
3
|
3
|
5
|
0
|
0
|
0
|
1
|
1
|
|
118
|
25.16
|
2
|
4
|
1
|
2
|
3
|
12
|
0
|
0
|
0
|
0
|
1
|
3
|
0
|
3
|
4
|
|
117
|
23.08
|
2
|
1
|
1
|
1
|
0
|
0
|
1
|
3
|
3
|
8
|
0
|
1
|
0
|
2
|
2
|
|
116
|
23.54
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
115
|
19.81
|
2
|
5
|
1
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
3
|
4
|
|
114
|
25.07
|
3
|
0
|
0
|
0
|
3
|
12
|
0
|
0
|
0
|
0
|
3
|
0
|
2
|
3
|
5
|
|
113
|
24.08
|
2
|
2
|
1
|
1
|
0
|
0
|
2
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
|
112
|
21.19
|
2
|
5
|
1
|
3
|
3
|
20
|
1
|
0
|
2
|
2
|
2
|
3
|
2
|
3
|
11
|
|
111
|
21.08
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
1
|
1
|
|
110
|
19.53
|
2
|
1
|
1
|
1
|
3
|
10
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
2
|
|
109
|
21.97
|
2
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
1
|
1
|
|
108
|
24.44
|
2
|
1
|
1
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
1
|
0
|
3
|
3
|
13
|
|
107
|
22.86
|
2
|
1
|
1
|
1
|
2
|
2
|
3
|
0
|
3
|
6
|
0
|
0
|
1
|
1
|
1
|
|
106
|
22.64
|
2
|
1
|
1
|
1
|
3
|
6
|
0
|
0
|
0
|
0
|
3
|
0
|
3
|
3
|
27
|
|
105
|
30.08
|
3
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
3
|
1
|
3
|
7
|
|
104
|
21.33
|
2
|
0
|
0
|
0
|
3
|
8
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
3
|
16
|
|
103
|
21.33
|
2
|
0
|
0
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
3
|
1
|
0
|
3
|
6
|
|
102
|
22.19
|
2
|
0
|
0
|
0
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
101
|
23.35
|
2
|
1
|
1
|
1
|
3
|
8
|
0
|
1
|
1
|
1
|
0
|
2
|
3
|
3
|
6
|
|
100
|
23.78
|
2
|
2
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
99
|
21.68
|
2
|
1
|
1
|
1
|
0
|
0
|
1
|
0
|
2
|
2
|
0
|
0
|
0
|
0
|
0
|
|
98
|
23.83
|
2
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
3
|
6
|
0
|
3
|
0
|
3
|
6
|
|
97
|
19.62
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
3
|
4
|
0
|
0
|
0
|
1
|
1
|
|
96
|
18.83
|
2
|
1
|
1
|
1
|
2
|
2
|
0
|
1
|
2
|
2
|
0
|
3
|
0
|
3
|
4
|
|
95
|
20.47
|
2
|
1
|
1
|
1
|
3
|
4
|
0
|
1
|
2
|
2
|
2
|
0
|
0
|
1
|
2
|
|
94
|
25.01
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
|
93
|
22.20
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
92
|
21.99
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
91
|
18.50
|
1
|
1
|
1
|
1
|
3
|
30
|
0
|
2
|
3
|
6
|
3
|
3
|
3
|
3
|
22
|
|
90
|
20.40
|
2
|
3
|
1
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
0
|
2
|
2
|
|
89
|
22.34
|
2
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
88
|
20.22
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
3
|
4
|
0
|
0
|
0
|
0
|
0
|
|
87
|
25.56
|
3
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
2
|
1
|
0
|
0
|
2
|
2
|
|
86
|
22.09
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
85
|
21.52
|
2
|
2
|
1
|
1
|
3
|
18
|
3
|
1
|
3
|
10
|
3
|
3
|
3
|
3
|
35
|
|
84
|
24.94
|
2
|
1
|
1
|
1
|
3
|
8
|
0
|
0
|
0
|
0
|
3
|
0
|
3
|
3
|
8
|
|
83
|
21.30
|
2
|
1
|
1
|
1
|
3
|
6
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
82
|
25.74
|
3
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
81
|
22.36
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
2
|
0
|
3
|
17
|
|
80
|
25.72
|
3
|
4
|
1
|
2
|
3
|
6
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
3
|
6
|
|
79
|
19.81
|
2
|
5
|
1
|
3
|
0
|
0
|
0
|
1
|
1
|
1
|
0
|
0
|
0
|
0
|
0
|
|
78
|
23.68
|
2
|
1
|
1
|
1
|
3
|
8
|
3
|
2
|
3
|
10
|
3
|
3
|
0
|
3
|
13
|
|
77
|
18.05
|
1
|
2
|
1
|
1
|
2
|
2
|
1
|
0
|
2
|
2
|
1
|
2
|
2
|
3
|
5
|
|
76
|
20.72
|
2
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
0
|
0
|
3
|
3
|
UE Binary vs three categories
I think Ue injury is 0-5,more than 5 but need theroetical backing
for categorizing it into 0, 1 and 2+
Data Descriptives
#Summary of BMI
BMISummary<-favstats(NCEAData.CC$BMI)
kable(BMISummary) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
min
|
Q1
|
median
|
Q3
|
max
|
mean
|
sd
|
n
|
missing
|
|
|
18.04
|
21.0675
|
22.195
|
23.7925
|
33.54
|
22.48067
|
2.451677
|
120
|
0
|
Visualising and tabulating Data
|
ConcussionCount
|
Freq
|
|
0
|
62
|
|
1
|
34
|
|
2
|
12
|
|
3
|
5
|
|
4
|
2
|
|
5
|
5
|
Subject Distribution by Concussions and UE injuries categorized into
0, 1 and 2+
|
|
No Conc
|
1 Conc
|
2+Conc
|
|
0
|
18
|
10
|
41
|
|
1
|
2
|
1
|
0
|
|
2+
|
14
|
13
|
21
|
Subject Distribution by Concussions and LE injuries
##
## 1 2+ No Conc
## 0 13 9 28
## 1 2 0 8
## 2+ 19 15 26
|
|
No Conc
|
1 Conc
|
2+Conc
|
|
0
|
13
|
9
|
28
|
|
1
|
2
|
0
|
8
|
|
2+
|
19
|
15
|
26
|
Subject Distribution by Concussions and Spinal
The sample has no observed spinal injury count of 1.
|
|
No Conc
|
1 Conc
|
2+Conc
|
|
0
|
14
|
10
|
36
|
|
2+
|
20
|
14
|
26
|
Initial Exploratory Data Analysis Chisquare and Fisher’s Exact
Tests
##
## Pearson's Chi-squared test
##
## data: countsSpinal
## X-squared = 3.3384, df = 2, p-value = 0.1884
##
## Fisher's Exact Test for Count Data
##
## data: countsSpinal
## p-value = 0.2215
## alternative hypothesis: two.sided
##
## Pearson's Chi-squared test
##
## data: countsLE
## X-squared = 5.9684, df = 4, p-value = 0.2015
##
## Fisher's Exact Test for Count Data
##
## data: countsLE
## p-value = 0.2343
## alternative hypothesis: two.sided
##
## Pearson's Chi-squared test
##
## data: countsSpinal
## X-squared = 3.3384, df = 2, p-value = 0.1884
##
## Fisher's Exact Test for Count Data
##
## data: countsSpinal
## p-value = 0.2215
## alternative hypothesis: two.sided
Fit ordered logit model
Table of Parameter Estimates
kable(Estimates) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
Value
|
Std. Error
|
t value
|
p value
|
|
BMI
|
-0.057
|
0.074
|
-0.777
|
0.437
|
|
Spinalcount
|
-0.045
|
0.042
|
-1.064
|
0.287
|
|
Uecount
|
-0.058
|
0.052
|
-1.112
|
0.266
|
|
LEcount
|
-0.003
|
0.033
|
-0.078
|
0.938
|
|
1|2+
|
-2.564
|
1.704
|
-1.505
|
0.132
|
|
2+|No Conc
|
-1.665
|
1.694
|
-0.982
|
0.326
|
Odds Ratio for a unit change
kable(Tables) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
OR
|
2.5 %
|
97.5 %
|
|
BMI
|
0.944
|
0.816
|
1.093
|
|
Spinalcount
|
0.956
|
0.879
|
1.038
|
|
Uecount
|
0.943
|
0.847
|
1.044
|
|
LEcount
|
0.997
|
0.934
|
1.066
|
Odds Ratio for five Unit change
kable(TB.5) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
|
2.5 %
|
97.5 %
|
|
BMI
|
0.751
|
0.362
|
1.560
|
|
Spinalcount
|
0.800
|
0.524
|
1.208
|
|
Uecount
|
0.748
|
0.437
|
1.238
|
|
LEcount
|
0.987
|
0.711
|
1.377
|
Odds Ratio for 10 Unit change
Please note the CI is very large indicating its not at all reliable
and has large SE
kable(TB.10) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
|
2.5 %
|
97.5 %
|
|
BMI
|
0.563
|
0.131
|
2.433
|
|
Spinalcount
|
0.640
|
0.275
|
1.458
|
|
Uecount
|
0.559
|
0.191
|
1.532
|
|
LEcount
|
0.974
|
0.505
|
1.897
|
Looking at the distribution of Injuries etc., since the CI is very
wide
|
LE
|
Freq
|
|
0
|
50
|
|
1
|
11
|
|
2
|
7
|
|
3
|
52
|
Trying Logistic Regression
two-way contingency table of categorical outcome and predictors we
want to make sure there are no 0 or small cells
|
|
0
|
1
|
2+
|
|
0
|
28
|
8
|
26
|
|
1
|
22
|
2
|
34
|
|
|
0
|
1
|
2+
|
|
0
|
41
|
0
|
21
|
|
1
|
28
|
3
|
27
|
Fitting Logistic Regression
Since having three categories for each of the independent variable (
LE, UE and Spinal Injury) stretches the data too far resulting in some
extreme small cells with freq <= 0, we will stick with two categories
of indep variables.
Make sure to convert categorical indep variables to a factor to
indicate they are categorical variable.
##
## Call:
## glm(formula = ConcussionBinary ~ BMI + UE.bin + LE.bin + SP.bin,
## family = "binomial", data = NCEAData.CC)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6086 -1.1173 -0.8302 1.1461 1.5908
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.85693 1.78390 -1.041 0.2979
## BMI 0.05511 0.07801 0.706 0.4799
## UE.bin1+ 0.74023 0.39105 1.893 0.0584 .
## LE.bin1+ -0.16868 0.44191 -0.382 0.7027
## SP.bin1+ 0.66270 0.42910 1.544 0.1225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 166.22 on 119 degrees of freedom
## Residual deviance: 159.02 on 115 degrees of freedom
## AIC: 169.02
##
## Number of Fisher Scoring iterations: 4
## 2.5 % 97.5 %
## (Intercept) -5.42528036 1.6559812
## BMI -0.09944676 0.2107545
## UE.bin1+ -0.01950118 1.5194562
## LE.bin1+ -1.05446191 0.6892319
## SP.bin1+ -0.17190402 1.5207751
|
|
OR
|
2.5 %
|
97.5 %
|
|
(Intercept)
|
0.16
|
0.00
|
5.24
|
|
BMI
|
1.06
|
0.91
|
1.23
|
|
UE.bin1+
|
2.10
|
0.98
|
4.57
|
|
LE.bin1+
|
0.84
|
0.35
|
1.99
|
|
SP.bin1+
|
1.94
|
0.84
|
4.58
|
Since there can be association between the UE, LE and Spinal
Injuries we will look at UE and BMI only
|
|
OR
|
2.5 %
|
97.5 %
|
|
(Intercept)
|
0.16
|
0.00
|
4.96
|
|
BMI
|
1.07
|
0.92
|
1.25
|
|
UE.bin1+
|
2.18
|
1.04
|
4.65
|
Since there can be association between the UE, LE and Spinal
Injuries we will look at LE and BMI only
|
|
OR
|
2.5 %
|
97.5 %
|
|
(Intercept)
|
0.32
|
0.01
|
8.98
|
|
BMI
|
1.04
|
0.90
|
1.21
|
|
LE.bin1+
|
1.33
|
0.64
|
2.78
|
Since there can be association between the UE, LE and Spinal
Injuries we will look at Spinal and BMI only
|
|
OR
|
2.5 %
|
97.5 %
|
|
(Intercept)
|
0.32
|
0.01
|
9.38
|
|
BMI
|
1.03
|
0.89
|
1.20
|
|
SP.bin1+
|
1.93
|
0.94
|
4.05
|
Chisquared Test for binary outcome (Concussions) and binary
predictor (UE)
## UE.bin
## ConcussionBinary 0 1+
## 0 41 21
## 1 28 30
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: UE.2cats.tab
## X-squared = 3.2121, df = 1, p-value = 0.0731
Chisquared Test for binary outcome (Concussions) and binary
predictor (LE)
LE.2cat.Chi<-chisq.test(LE.2cats.tab)
LE.2cats.tab
## LE.bin
## ConcussionBinary 0 1+
## 0 28 34
## 1 22 36
LE.2cat.Chi
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: LE.2cats.tab
## X-squared = 0.38138, df = 1, p-value = 0.5369
Chisquared Test for binary outcome (Concussions) and binary predicto
(Spinal)
Spinal.2cat.Chi<-chisq.test(Spinal.2cats.tab)
Spinal.2cats.tab
## SP.bin
## ConcussionBinary 0 1+
## 0 36 26
## 1 24 34
Spinal.2cat.Chi
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: Spinal.2cats.tab
## X-squared = 2.703, df = 1, p-value = 0.1002
Association between indepndent count variables UE and LE
## LE.bin
## UE.bin 0 1+
## 0 34 35
## 1+ 16 35
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: UE.LE.tab
## X-squared = 3.1655, df = 1, p-value = 0.07521
Association between indepnednet count variables UE and Spinal
## UE.bin
## SP.bin 0 1+
## 0 38 22
## 1+ 31 29
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: UE.Spinal.tab
## X-squared = 1.2276, df = 1, p-value = 0.2679
Association between indepnednet count variables LE and Spinal
## LE.bin
## SP.bin 0 1+
## 0 39 21
## 1+ 11 49
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: LE.Spinal.tab
## X-squared = 24.994, df = 1, p-value = 5.75e-07
##
## Fisher's Exact Test for Count Data
##
## data: LE.Spinal.tab
## p-value = 3.36e-07
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 3.323046 21.177363
## sample estimates:
## odds ratio
## 8.10374
Looking at association between outcome/Concussions (categorical)
adjusting for BMI
BMI.Conc.tab<-xtabs(~ConcussionBinary +BMI.bin , data = NCEAData.CC)
BMI.Conc.tab
## BMI.bin
## ConcussionBinary 0 1+
## 0 1 61
## 1 2 56
BMI.Conc.Chi<-chisq.test(BMI.Conc.tab)
## Warning in chisq.test(BMI.Conc.tab): Chi-squared approximation may be incorrect
BMI.Conc.Chi
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: BMI.Conc.tab
## X-squared = 0.0034226, df = 1, p-value = 0.9533
Looking at association between predictor/UE (categorical) and BMI
(covariate being adjusted for)
BMI.UE.tab<-xtabs(~UE.bin+ BMI.bin, data = NCEAData.CC)
BMI.UE.tab
## BMI.bin
## UE.bin 0 1+
## 0 0 69
## 1+ 3 48
BMI.UE.Chi<-chisq.test(BMI.UE.tab)
## Warning in chisq.test(BMI.UE.tab): Chi-squared approximation may be incorrect
BMI.UE.Chi
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: BMI.UE.tab
## X-squared = 2.0994, df = 1, p-value = 0.1474
Looking at association between predictor/UE (3 categories) and BMI
(covariate being adjusted for)
BMI.UEB.tab<-xtabs(~UE.cat + BMI.bin, data = NCEAData.CC)
BMI.UEB.tab
## BMI.bin
## UE.cat 0 1+
## 0 0 69
## 1 0 3
## 2+ 3 45
BMI.UEB.Chi<-chisq.test(BMI.UEB.tab)
## Warning in chisq.test(BMI.UEB.tab): Chi-squared approximation may be incorrect
BMI.UEB.Chi
##
## Pearson's Chi-squared test
##
## data: BMI.UEB.tab
## X-squared = 4.6154, df = 2, p-value = 0.09949
looking at associations between independent variables with 3
categories for each
countsLEUE <- table(NCEAData.CC$LE.cat, NCEAData.CC$UE.cat)
#
countsLEUE
##
## 0 1 2+
## 0 34 2 14
## 1 4 0 6
## 2+ 31 1 28
kable(countsLEUE,col.names=c("No UE", "1 UE","2+UE")) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
No UE
|
1 UE
|
2+UE
|
|
0
|
34
|
2
|
14
|
|
1
|
4
|
0
|
6
|
|
2+
|
31
|
1
|
28
|
LE.UE.Chi<-chisq.test(countsLEUE)
## Warning in chisq.test(countsLEUE): Chi-squared approximation may be incorrect
LE.UE.Chi
##
## Pearson's Chi-squared test
##
## data: countsLEUE
## X-squared = 6.1797, df = 4, p-value = 0.1861
countsLESpinal <- table(NCEAData.CC$LE.cat, NCEAData.CC$SP.cat)
#
countsLESpinal
##
## 0 2+
## 0 39 11
## 1 3 7
## 2+ 18 42
kable(countsLESpinal,col.names=c("No SP", "1 +SP")) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
No SP
|
1 +SP
|
|
0
|
39
|
11
|
|
1
|
3
|
7
|
|
2+
|
18
|
42
|
LE.SE.Chi<-chisq.test(countsLESpinal)
LE.SE.Chi
##
## Pearson's Chi-squared test
##
## data: countsLESpinal
## X-squared = 26.88, df = 2, p-value = 1.456e-06
countsSEUE <- table(NCEAData.CC$SP.cat, NCEAData.CC$UE.cat)
#
countsSEUE
##
## 0 1 2+
## 0 38 1 21
## 2+ 31 2 27
kable(countsSEUE,col.names=c("No UE", "1 UE","2+UE")) %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left",font_size = 14)
|
|
No UE
|
1 UE
|
2+UE
|
|
0
|
38
|
1
|
21
|
|
2+
|
31
|
2
|
27
|
SE.UE.Chi<-chisq.test(countsLEUE)
## Warning in chisq.test(countsLEUE): Chi-squared approximation may be incorrect
SE.UE.Chi
##
## Pearson's Chi-squared test
##
## data: countsLEUE
## X-squared = 6.1797, df = 4, p-value = 0.1861