fixed estimate can be elicited by the command, after the model has run. ranef(myresult), Note that we used an identity matrix as our covariance structure (pdIdent), but other options may be more suitable for different situations (see , for more details). The argument starting values for each start = c(g = 0.1,r = 1,0,0,0,….)specifies predictor for each condition or grouping variable. The first two statements, and g = 0.1 r = 1, specify the starting values of each predictor for the first group, whereas the numbers in the subsequent list specify variation around the initial numbers for the other groups, these if predictors are allowed to vary across groups. Frankly, this can be the trickiest and most counterintuitive part of the analysis. If, as in the example above, the model allows both predictors to vary across three groups (fixed then there are six starting = c(g + r ~ group)), values in this list because there are three estimates (one for each group) and three estimates g r (one for each group). Therefore, the full argument is If start = c(g = 0.1,r = 1,0,0,0,0). the model allows only the g predictor to vary across the three groups (fixed = c(g ~ group, then there are four starting values in this list because there are three estimates (one r ~ 1)), g for each group) and one estimate (the same for each group). If the model fails to converge, r then different starting values can be used (e.g., a can be replaced with small positive or “0” negative values). Finally, the argument provides information for each iteration of the verbose=TRUE algorithm and can be deleted in order to speed up the computation. The detailed description of this and other commands in the nlme library is available at Once the model has converged on a solution, the following command can be used to display the results: summary(myresult)
fixed estimate can be elicited by the command, ranef(myresult), after the model has run. Note that we used an identity matrix as our covariance structure (pdIdent), but other options may be more suitable for different situations (see , for more details). The argument start = c(g = 0.1,r = 1,0,0,0,….)specifies starting values for each predictor for each condition or grouping variable. The first two statements, g = 0.1 and r = 1, specify the starting values of each predictor for the first group, whereas the numbers in the subsequent list specify variation around the initial numbers for the other groups, if these predictors are allowed to vary across groups. Frankly, this can be the trickiest and most counterintuitive part of the analysis. If, as in the example above, the model allows both predictors to vary across three groups (fixed = c(g + r ~ group)), then there are six starting values in this list because there are three g estimates (one for each group) and three r estimates (one for each group). Therefore, the full argument is start = c(g = 0.1,r = 1,0,0,0,0). If the model allows only the g predictor to vary across the three groups (fixed = c(g ~ group, r ~ 1)), then there are four starting values in this list because there are three g estimates (one for each group) and one r estimate (the same for each group). If the model fails to converge, then different starting values can be used (e.g., a “0” can be replaced with small positive or negative values). Finally, the argument verbose=TRUE provides information for each iteration of the algorithm and can be deleted in order to speed up the computation. The detailed description of this and other commands in the nlme library is available at Once the model has converged on a solution, the following command can be used to display the results: summary(myresult)