Hi all subscribing to the r-sig-mixed-model list. I have questions regarding the model I use, weather it makes sense, and why I receive an error message in case 1 but not in case 2. It is a repeated measure experiment. The experiment consists of two fields, that are divided up in stripes were every 2nd stripe has been treated with hay-transfer and every 2nd is a control were no hay transfer has been conducted. In each stripe 2 permanent plots of 2 m x 2 m were placed out, and vegetation monitored for three years. Due to that there are differences in elevation between the plot, each plots elevation has been measured. The Explanatory variables I have is then Year (2009,2010,2011),Treatment (Hay/No Hay), and (Elevation). The response variables are % cover of different vegetation groups. If I take the vegetation group sedges as an example. Case 1: mydata<-lme(Sedges~as.factor(Year)*Treatment*Elevation) gives me this error message "Error in getGroups.data.frame(dataMix, groups) : Invalid formula for groups" If I however include Site Number (There are 2 sites, with identical design) I don?t get any error message at all. Including it as a random is in my opinion not wrong, but not necessary for this experiment. Case 2: mydata<-lme(Sedges~as.factor(Year)*Treatment*Elevation,random=~1|SiteNumber) Would greatly appreciate any help on this issue. Best regards, Petter Hedberg
Question regarding lme mixed model, error in case 1, not in case 2
2 messages · Petter Hedberg, Joerg Luedicke
Some thoughts: 1) In "Case 1" you don't specify a random effect and thus your model would reduce to a simple linear model. I have never tried it but I can imagine that specifying at least one random effect is required by -lme-. 2) Did you look at main effects and 2-way interactions first before including the 3-way interaction effect? 3) With only 2 fields, estimating a random effect will not be very useful. But what about stripes? I think you should have varying intercepts and/or slopes across stripes (or at least check if there is variation across stripes). If you have perfectly balanced data and no variation across stripes, I would believe you do not really need a mixed effects model here. But I might very well miss something since I am not familiar with agricultural research. 4) If your dependent variable is a percentage/ proportion, a linear model might not be suitable. How are your outcome variables measured exactly? J.
On Wed, Mar 21, 2012 at 6:26 AM, Petter Hedberg <phedberg at biol.uw.edu.pl> wrote:
Hi all subscribing to the r-sig-mixed-model list. I have questions regarding the model I use, weather it makes sense, and why I receive an error message in case 1 but not in case 2. It is a repeated measure experiment. The experiment consists of two fields, that are divided up in stripes were every 2nd stripe has been treated with hay-transfer and every 2nd is a control were no hay transfer has been conducted. In each stripe 2 permanent plots of 2 m x 2 m were placed out, and vegetation monitored for three years. Due to that there are differences in elevation between the plot, each plots elevation has been measured. The Explanatory variables I have is then Year (2009,2010,2011),Treatment (Hay/No Hay), and (Elevation). The response variables are % cover of different vegetation groups. If I take the vegetation group sedges as an example. Case 1: mydata<-lme(Sedges~as.factor(Year)*Treatment*Elevation) gives me this error message "Error in getGroups.data.frame(dataMix, groups) : ?Invalid formula for groups" If I however include Site Number (There are 2 sites, with identical design) I don?t get any error message at all. Including it as a random is in my opinion not wrong, but not necessary for this experiment. Case 2: mydata<-lme(Sedges~as.factor(Year)*Treatment*Elevation,random=~1|SiteNumber) Would greatly appreciate any help on this issue. Best regards, Petter Hedberg
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