Dear all,
I am encountering some issues with my data and need some help.
I am trying to run glm analysis with a presence/absence variable as
response variable and several explanatory variable (time, location,
presence/absence data, abundance data).
First I tried to use the glm() function, however I was having 2 warnings
concerning glm.fit () :
# 1: glm.fit: algorithm did not converge
# 2: glm.fit: fitted probabilities numerically 0 or 1 occurred
After some investigation I found out that the problem was most probably
quasi complete separation and therefor decide to use brglm and/or logistf.
* logistf : analysis does not run
When running logistf() I get a error message saying :
# error in chol.default(x) :
# leading minor 39 is not positive definite
I looked into logistf package manual, on Internet, in the theoretical
and technical paper of Heinze and Ploner and cannot find where this
function is used and if the error can be fixed by some settings.
* brglm : analysis run
However I get a warning message saying :
# In fit.proc(x = X, y = Y, weights = weights, start = start, etastart #
= etastart, :
# Iteration limit reached
Like before i cannot find where and why this function is used while
running the package and if it can be fixed by adjusting some settings.
In a more general way, I was wondering what are the fundamental
differences of these packages.
I hope this make sense enough and I am sorry if this is kind of
statistical evidence that I'm not aware of.
It is my first time asking a question so I apologize if it's not like it
should be and kindly ask you to not hesitate to let me know about it.
Thank you for your help
Xochitl C.
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Here an extract of my table and the different formula I run :
> head (CPUE_table)
Year Quarter Subarea Latitude Longitude Presence.S CPUE.S Presence.H
CPUE.H Presence.NP CPUE.NP Presence.BW CPUE.BW Presence.C CPUE.C
Presence.P CPUE.P Presence.W CPUE.W
1 2000 1 31F1 51.25 1.5 0 0 0
0 0 0 0 0 1 76.002
0 0 1 3358.667
2 2000 1 31F2 51.25 2.5 0 0 0
0 0 0 0 0 1 12.500
0 0 1 3028.500
3 2000 1 32F1 51.75 1.5 0 0 0
0 0 0 0 0 1 5.500
0 0 1 2256.750
4 2000 1 32F2 51.75 2.5 0 0 0
0 0 0 0 0 1 10.000
0 0 1 808.000
5 2000 1 32F3 51.75 3.5 0 0 0
0 0 0 0 0 1 19.000
0 0 1 277.000
6 2000 1 33F1 52.25 1.5 0 0 0
0 0 0 0 0 0 0.000
0 0 1 2.000
> tail (CPUE_table)
Year Quarter Subarea Latitude Longitude Presence.S CPUE.S
Presence.H CPUE.H Presence.NP CPUE.NP Presence.BW CPUE.BW Presence.C
CPUE.C Presence.P CPUE.P Presence.W CPUE.W
4435 2012 3 50F3 60.75 3.5 1 103.000
1 110.000 1 2379.00 1 20.000 1 6.000
0 0 1 22.000
4436 2012 3 51E8 61.25 -1.5 1 1311.600
1 12.000 1 4194.78 0 0.000 1 18.000
0 0 0 0.000
4437 2012 3 51E9 61.25 -0.5 1 34.336
1 46.671 1 11031.56 1 2.668 1 3.335
0 0 1 3.333
4438 2012 3 51F0 61.25 0.5 1 430.500
1 148.000 1 1212.22 1 3279.200 1 2.000
0 0 1 2.000
4439 2012 3 51F1 61.25 1.5 1 115.000
1 85.000 1 2089.50 1 1.000 1 22.000
1 2 1 40.000
4440 2012 3 51F2 61.25 2.5 1 72.500
1 35.500 1 270.48 1 516.300 1 11.500
1 1 1 16.000
logistf_binomPres <- logistf (Presence.S ~ (Presence.BW + Presence.W +
Presence.C + Presence.NP +Presence.P + Presence.H +CPUE.BW + CPUE.H +
CPUE.P + CPUE.NP + CPUE.W + CPUE.C + Year + Quarter + Latitude +
Longitude)^2, data = CPUE_table)
Brglm_binomPres <- brglm (Presence.S ~ (Presence.BW + Presence.W +
Presence.C + Presence.NP +Presence.P + Presence.H +CPUE.BW + CPUE.H +
CPUE.P + CPUE.NP + CPUE.W + CPUE.C + Year + Quarter + Latitude +
Longitude)^2, family = binomial, data = CPUE_table)
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--
<>< <>< <>< <><
Xochitl CORMON
+33 (0)3 21 99 56 84
Doctorante en sciences halieutiques
PhD student in fishery sciences
<>< <>< <>< <><
IFREMER
Centre Manche Mer du Nord
150 quai Gambetta
62200 Boulogne-sur-Mer
<>< <>< <>< <><
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