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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 ~...