Hello Helpers,
I have some problems with fitting the model for my data...
-->my Literatur says (crawley testbook)=
Non-normality of errors-->I get a banana shape Q-Q plot with opening
of banana downwards
Structure of data:
origin wt pes gender
1 wild 5.35 147.0 male
2 wild 5.90 148.0 male
3 wild 6.00 156.0 male
4 wild 7.50 157.0 male
5 wild 5.90 148.0 male
6 wild 5.95 148.0 male
7 wild 8.55 160.5 male
8 wild 5.90 148.0 male
9 wild 8.45 161.0 male
10 wild 4.90 147.0 male
11 wild 6.80 153.0 male
12 wild 5.75 146.0 male
13 wild 8.60 160.0 male
14 captive 6.85 159.0 male
15 captive 7.00 160.0 male
16 captive 6.80 155.0 male
..
...
283 site 4.10 130.4 female
284 site 3.55 131.1 female
285 site 4.20 135.7 female
286 site 3.45 128.0 female
287 site 3.65 125.3 female
The goal of my analysis is to work out what effect the categorial
factors(origin, gender) on the relation between
log(wt)~log(pes)(-->Condition, fett ressource), have.
Does the source(origin) of translocated animals have an affect on
performance(condition)in the new area?
I have already a best fit model and it looks quite good (or not?see below).
two slopes(gender difference)and 6 intercepts(3origin levels*2gender levels)
lm(formula = log(wt) ~ log(pes) + origin + gender + gender:log(pes))
Residuals:
Min 1Q Median 3Q Max
-0.54181 -0.07671 0.01520 0.09474 0.28818
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.39879 1.97605 -3.744 0.000219 ***
log(pes) 1.78020 0.40118 4.437 1.31e-05 ***
originsite 0.06572 0.01935 3.397 0.000781 ***
originwild 0.07655 0.03552 2.155 0.032011 *
gendermale -9.32418 2.37476 -3.926 0.000109 ***
log(pes):gendermale 1.90393 0.47933 3.972 9.06e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1433 on 281 degrees of freedom
Multiple R-Squared: 0.7227, Adjusted R-squared: 0.7177
F-statistic: 146.4 on 5 and 281 DF, p-value: < 2.2e-16
When plot this model I get a banana-shape in Normal Q-Q Plot(with open
site pointing downwards) , indicating non-normality of my data....how
to handle this?
-->Do I have unbalanced data?
captive site wild
n--> 119 149 19
My problem is that I see that my data is not as good as the
modelsummary tells.
Should I include another term in my model formular?
I think I have to differenciate more, but I don't know
how.(contrasts?, TukeyHSD?,Akaike Information Criterion? or lme())to
many different ways out there.
Cheers,
Tobi
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