Displaying 3 results from an estimated 3 matches for "anal1".
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2008 Mar 14
1
Lme does not work without a random effect (UNCLASSIFIED)
...N 1
6 6.94 C Y 1
7 6.79 D N 1
8 6.93 D Y 1
9 6.23 A N 2
10 6.83 A Y 2
11 6.61 B N 2
12 6.86 B Y 2
13 6.51 C N 2
14 6.90 C Y 2
15 5.90 D N 2
16 6.97 D Y 2
A result with the random effect:
Anal1<-lme(LCU~ST1*SURF,random=~1|Block,data=data1)
> summary(Anal1)
Linear mixed-effects model fit by REML
Data: data1
AIC BIC logLik
25.38958 26.18399 -2.694789
Random effects:
Formula: ~1 | Block
(Intercept) Residual
StdDev: 0.1421141 0.218483
Fixed effects: LCU ~...
2010 Mar 31
2
interpretation of p values for highly correlated logistic analysis
...dog white
In this toy data you can see that roman:alpha and roman:beta are
pretty good predictors of colour
Let's say I perform logistic analysis directly on the raw data with
colour as a response variable:
> options(contrasts=c("contr.treatment","contr.poly"))
> anal1 <- glm(data$colour~data$roman+data$animal,family=binomial)
then I find that my P values for each individual level coefficient approach 1:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -41.65 19609.49 -0.002 0.998
data$romanbeta 42.35 19609....
2008 Mar 17
0
Summary Regard with Lme does not work without a random effect (UN CLASSIFIED)
...N 1
6 6.94 C Y 1
7 6.79 D N 1
8 6.93 D Y 1
9 6.23 A N 2
10 6.83 A Y 2
11 6.61 B N 2
12 6.86 B Y 2
13 6.51 C N 2
14 6.90 C Y 2
15 5.90 D N 2
16 6.97 D Y 2
A result with the random effect:
Anal1<-lme(LCU~ST1*SURF,random=~1|Block,data=data1)
> summary(Anal1)
Linear mixed-effects model fit by REML
Data: data1
AIC BIC logLik
25.38958 26.18399 -2.694789
Random effects:
Formula: ~1 | Block
(Intercept) Residual
StdDev: 0.1421141 0.218483
Fixed effects: LCU ~...