the following is the command I used and the results I got for my question, whether the visitation frequency of my bee is different in different experiment types in different locations. I used the lme function of R. I used experiment type as the fixed effect and location as the random effect. I used ANOVA after this to get the F value. Is this right? WIthout ANOVA how I can interpret the results? I had 5 experiment types, but in results it is showing only 4 experiment types. The top most one (expt.antless) is missing.

`> names(acera.freq1) [1] "expt.type" "visit.freq" "location" > model<-lme(visit.freq~expt.type,random=~1|location,method="ML") > summary(model) Linear mixed-effects model fit by maximum likelihood Data: NULL AIC BIC logLik 1065.928 1087.919 -525.9638 Random effects: Formula: ~1 | location (Intercept) Residual StdDev: 4.617241 4.682169 Fixed effects: visit.freq ~ expt.type Value Std.Error DF t-value p-value (Intercept) 10.192564 1.216852 148 8.376177 0.0000 expt.typeblack.ant -3.579023 1.074261 148 -3.331615 0.0011 expt.typecrazy.ant -5.804671 1.740132 148 -3.335765 0.0011 expt.typeother.ants -5.352438 1.936756 148 -2.763610 0.0064 expt.typered.biting.ant -2.680195 2.048081 148 -1.308637 0.1927 Correlation: (Intr) expt.typb. expt.typc. expt.typt. expt.typeblack.ant -0.173 expt.typecrazy.ant -0.128 0.126 expt.typeother.ants -0.081 0.146 0.027 expt.typered.biting.ant -0.132 0.117 0.143 0.046 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.65727163 -0.70522770 -0.02959964 0.53356588 3.28792891 Number of Observations: 171 Number of Groups: 19 > anova(model) numDF denDF F-value p-value (Intercept) 1 148 56.51744 <.0001 expt.type 4 148 6.28705 1e-04 `

**Contents**hide

#### Best Answer

In short, "expt.antless" condition was used as a reference.

For example, based on the summary table, you can expect that (1) visiting frequency (visit.freq) to decrease by an average of 3.58 from "expt.antless" condition to "expt.typeblack.ant" condition, (2) visiting frequency to decrease by an average of 5.8 from "expt.antless" condition to "expt.typecrazy.ant" condition, and so on.

`Fixed effects: visit.freq ~ expt.type Value Std.Error DF t-value p-value (Intercept) 10.192564 1.216852 148 8.376177 0.0000 expt.typeblack.ant -3.579023 1.074261 148 -3.331615 0.0011 expt.typecrazy.ant -5.804671 1.740132 148 -3.335765 0.0011 expt.typeother.ants -5.352438 1.936756 148 -2.763610 0.0064 expt.typered.biting.ant -2.680195 2.048081 148 -1.308637 0.1927 `

### Similar Posts:

- Solved – Collapsing data for repeated measures ANOVA
- Solved – How to determine random effects in mixed model
- Solved – ezAnova vs. lme for factorial repeated-measures design: results differ, why
- Solved – Different variance-covariance matrices of random effects per fixed-effect group in lme4
- Solved – How to obtain the p-value (check significance) of an effect in a lme4 mixed model