Description Usage Arguments Value Examples
Significant Cutoff Value for Logistic Regression
1 2 |
data |
data |
y |
name for y, must be coded as 1 and 0. The outcome must be 1 |
x |
name for x |
cut.numb |
number of cutoff points |
n.per |
the least percentage of the smaller group comprised in all patients |
y.per |
the least percentage of the smaller outcome patients comprised in each group |
p.cut |
cutoff of p value, default is 0.05 |
strict |
logical. TRUE means significant differences for each group combination were considered. FALSE means considering for any combination |
include |
direction of cutoff point. Any left letter of lower or upper |
round |
digital. Default is 2 |
adjust |
numeric value, adjust methord for p value. 1, defaulted, represents Bonferroni. 2 represent formula given by Douglas G in 1994 |
a dataframe contains cutoff points value, subject numbers in each group, dumb variable, or of regression and p value.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
1: all combination: 27
2: filter by n.per
--
==
Combination: 13
3: filter by y.per
Combination: 1
4: last combination: 1
cut1 n n.per y y.per dump or pvalue p.adjust
1 120.3 8/24 0.25/0.75 7/6 0.88/0.25 b2 0.05 0.01 0.01
1: all combination: 27
2: filter by n.per
--
==
Combination: 13
3: filter by y.per
Combination: 4
4: last combination: 4
cut1 n n.per y y.per dump or pvalue p.adjust
1 120.3 8/24 0.25/0.75 7/6 0.88/0.25 b2 0.05 0.01 0.04
2 121.0 9/23 0.28/0.72 8/5 0.89/0.22 b2 0.03 0.00 0.02
3 140.8 10/22 0.31/0.69 8/5 0.80/0.23 b2 0.07 0.01 0.02
4 146.7 12/20 0.38/0.62 9/4 0.75/0.20 b2 0.08 0.00 0.02
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