I have an issue with a linear mixed-effect model trying to estimates the effects of color cues on individuals' perception of parties ideological position and the way they interact with statements providing information about party policy preferences.
m <- lmer(ideo ~ colorDimensionlr_position + (1 + color + Dimension + lr_position | ResponseId) + (1 + color + Dimension + lr_position | Issues), data=data_long3 )
The dependent variable "ideo" represent participants perception of the party ideological position. "color" represents the color displayed in the card. "Dimension" indicates whether the issue adressed by the statement displayed in the card was sociocultural or economic. "lr_position" indicates whether the issue adressed by the statement was a left-wing statement or a right-wing statement.
When I run the model i obtain this warning message:
Warning message: Model failed to converge with 2 negative eigenvalues: -2.2e+01 -4.4e+01
Is it the model too complex? When I do not include random slopes but only random intercepts I do not have this problem.
Thank you so much for your help.
Is it the model too complex? When I do not include random slopes but only random intercepts I do not have this problem.
Thank you so much for your help.
