Highlights of 2022 Flying Vee Exploration (TSXV: AL) (FSE: 6LLN) (OTC: ALXEF) ("ALX" or the "Company") is pleased to provide an update on 2022 exploration activities at the Flying Vee Nickel Project ("Flying Vee", or the "Project"), located in the Athabasca region of northern Saskatchewan. With 30 subjects and the relative small number of data points, you should use REML, which has better properties than full maximum likelihood with small samples.Vancouver, British Columbia-(Newsfile Corp. If it is off, then check your coding of trial to make sure that trials with the same conditions are consistent across individuals. Make sure and check the summary(mod) output where it tells you the Ns in each grouping. If you have coded your data so that trial 1 is the same condition across subjects (i.e., it represents the same combination of ambient temp and relative humidity) and similarly trial 2 represents the same combination of conditions, etc., then this model syntax will be right whether your data are indeed purely nested or cross-classified. This would mean that your syntax should shift to the following: mod <- lmer(Temp ~ Mass + Age + Fat + (1|Subject) + (1|Trial), data=data) This to me sounds like a cross-classified problem rather than a purely nested trial. So for some (but not all) subjects they had exposure to different trials. Given EdM's comment and a closer reading of your post, I want to make sure that I understand this correctly.
Without looking at your data, I am a little unclear about the trial variable. Does this mean I just didn't collect enough data to be able to fit this model? Or is there another explanation/anything I can do? I have 43 observations and 6 different trials. Here is my model, in package lme4 in R: >mod=lmer(Temp ~ Mass + Age + Fat + (1|Subject/Trial), data=data, REML=FALSE)Įrror: number of levels of each grouping factor must be < number of observations The response variable is the subjects' temperatures and the fixed effects are the subjects' mass, age, and body fat percentage. I also think I need to nest Subject within Trial due to the fact that different climatic conditions effect the subjects' temperatures.
Because of the non-independence of some of the subjects' data due to participating more than once, I decided to use a linear mixed effects model with Subject as the random effect. Each trial had a different ambient temperature, relative humidity, etc. There were 30 subjects and some participated in more than one trial. The response variable is the subjects' internal (core) temperatures. I ran an experiment where subjects exercised in different climatic conditions.
0 kommentar(er)
