political Analyses
Yrian Derreumaux
6/4/2019
- Load Packages
- Set WD
- Load in the data
- Adding a variable for dem.est and rep.est + status to in and out group estimates.
- Adding a variable for groups
- Set First Sample, Participant and Group as factors as factors
- Correlation Matrix
- Attention Checks
- Ceating long format data set for sampling behavior
- Creating long format data set for point-estimates
- Plotting Sampling behavior
- Plotting Point-estimates
- Effects and Dummy coding categorical predictors for the sampling models
- Effects and Dummy coding categorical predictors for the point-estimate models.
- Generalized mixed models for Sampling Behavior
- Histogram for DV
- Model 1[sampling]: More in group sampling than outgroup – collapsing across condition and first sample
- Model 2[sampling]: More in group sampling with positive first sample – collapsing across condition
- Model 3[sampling]: Positive first samples – holding Samp group (in/out) and condition constant?
- Model 4[sampling]: Contrast coding. Better/Worse = 0; Same = 1
- Model 5[sampling]: What is happening in the better condition?
- Linear mixed models for for point-estimates
- Power Simulations with the Simr package
- Post-hoc power with current sample size. Monte Carlo simulations with 1k simulations
- Power curve for the in group first sample coefficient
Load Packages
library(psych)
library(readr)
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## collapselibrary(pwr)Set WD
#setwd("~/Desktop/Desktop - Yrian’s Macbook Pro/School/Projects/Motivated Sampling Project/Political Study Results/")Load in the data
personality.master.data.Dem <- read.csv("2019.personality.master.data.Democrat.csv")
personality.master.data.Rep <- read_csv("2019.personality.master.data.Republican.csv")## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## .default = col_double(),
## Tot.Mturk.Studies = col_character(),
## Tot.Econ.Studies = col_character(),
## Tot.Psych.Studies = col_character(),
## Delete.This = col_character(),
## Q1945 = col_character(),
## Val = col_character()
## )## See spec(...) for full column specifications.personality.master.data.Dem <- personality.master.data.Dem[,-c(1)]
personality.master.data.Rep <- personality.master.data.Rep[,-c(1)]
personality.master.data.Rep$Condition[personality.master.data.Rep$Condition == 1] <- 1
personality.master.data.Rep$Condition[personality.master.data.Rep$Condition == -1] <- 2
personality.master.data.Rep$Condition[personality.master.data.Rep$Condition == 0] <- 3Adding a variable for dem.est and rep.est + status to in and out group estimates.
personality.master.data.Dem$In.Est <- personality.master.data.Dem$Dem.Est
personality.master.data.Dem$Out.Est <- personality.master.data.Dem$Rep.Est
personality.master.data.Rep$In.Est <- personality.master.data.Rep$Rep.Est
personality.master.data.Rep$Out.Est <- personality.master.data.Rep$Dem.Est
personality.master.data.Dem$In.status <- personality.master.data.Dem$Status.Dem
personality.master.data.Dem$Out.status <- personality.master.data.Dem$Status.Rep
personality.master.data.Rep$In.status <- personality.master.data.Rep$Status.Rep
personality.master.data.Rep$Out.status <- personality.master.data.Rep$Status.DemAdding a variable for groups
Set First Sample, Participant and Group as factors as factors
Subsetting collapsed data frame to make a correlation matrix with only the pertinent variables
personality.master.data.Dem$Group <- "Dem"
personality.master.data.Rep$Group <- "Rep"
master.personality.Both <- rbind(personality.master.data.Dem, personality.master.data.Rep)
master.personality.Both <- as.data.frame(master.personality.Both)
master.personality.Both$First.Sample <- factor(master.personality.Both$First.Sample)
master.personality.Both$Participant <- factor(master.personality.Both$Participant)Subsetting collapsed data frame to make a correlation matrix with only the pertinent variables
mydata <- master.personality.Both[, c(9, 10, 11, 12, 14, 15, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 32, 33, 34, 36, 37, 38, 39)]
mydata$Val <- as.numeric(mydata$Val)
mydata$Val[mydata$Val == "pos"] <- 1
mydata$Val[mydata$Val == "neg"] <- 0Correlation Matrix
m <- cor(mydata, use="pairwise.complete.obs")
#compute p.values for m to put in graph later.
pval <- psych::corr.test(m, adjust="none")$p
#make a correlation matrix that has p.values and titles.
corrplot(cor(m),insig="p-value")
#corrplot(cor(m), type="upper", p.mat=pval, insig="p-value",
# tl.pos="n", sig.level=0)
#corrplot(cor(m), type="lower", add=T, tl.pos="d", cl.pos="n")Attention Checks
#Count of attention checks. 0 is none, 1 is one and 2 is 2.
table(master.personality.Both$Att)##
## 0 1 2
## 267 259 14hist(master.personality.Both$Att)
Ceating long format data set for sampling behavior
sampled.in.out <- data.frame(rep(master.personality.Both$Participant,2), rep(master.personality.Both$Condition,2),
c(master.personality.Both$in_samples, master.personality.Both$out_samples), factor(rep(c(1,2), each=540), labels = c("In", "Out")),
rep(master.personality.Both$Val, 2), rep(master.personality.Both$Group, 2),
rep(master.personality.Both$SDO, 2))
names(sampled.in.out) <- c("Participant", "Condition", "n_trials", "Samp_Group", "Valence", "Group", "SDO")
##Making sampled group into character so that it can be effects coded
sampled.in.out$Samp_GroupString <- as.character(sampled.in.out$Samp_Group)
sampled.in.out$GroupString <- as.character(sampled.in.out$Group)
sampled.in.out$ValenceString <- as.character(sampled.in.out$Valence)Creating long format data set for point-estimates
#####Ceating a long format data set to look at Point-Estimates (DV) from the master data (both rep and dem)
Evaluation.in.out <- data.frame(rep(master.personality.Both$Participant,2), rep(master.personality.Both$Condition,2),
c(master.personality.Both$In.Est, master.personality.Both$Out.Est), factor(rep(c(1,2), each=540), labels = c("In", "Out")),
rep(master.personality.Both$Val, 2), rep(master.personality.Both$Group, 2), rep(master.personality.Both$Status.Dem, 2),
rep(master.personality.Both$Status.Rep, 2), rep(master.personality.Both$SE_Importance, 2), rep(master.personality.Both$SE_Mem,2),
rep(master.personality.Both$SE_Private, 2), rep(master.personality.Both$SE_Public, 2), rep(master.personality.Both$SDO, 2))
##Ranaming the variables
names(Evaluation.in.out) <- c("Participant", "Condition", "P.Estimates", "Evaluated.Group", "Valence", "Group", "Dem.Status", "Rep.Estimate",
"SE.Importance", "SE.Mem", "SE.Private", "SE.Public", "SDO")
##Making sampled group into character so that it can be effects coded
Evaluation.in.out$Evaluated.GroupString <- as.character(Evaluation.in.out$Evaluated.Group)
Evaluation.in.out$GroupString <- as.character(Evaluation.in.out$Group)
Evaluation.in.out$ValenceString <- as.character(Evaluation.in.out $Valence)Plotting Sampling behavior
SEFunctionForggplot <- function(vector) {
y <- mean(vector, na.rm = TRUE)
ymin <- y - sd(vector, na.rm = TRUE) / sqrt(length(vector))
ymax <- y + sd(vector, na.rm = TRUE) / sqrt(length(vector))
return(data.frame(y = y, ymin = ymin, ymax = ymax))
}
####For political sampling behavior
# tiff("example.tiff", more arguments)
ggplot(sampled.in.out, aes(as.factor(Condition), n_trials,
color = paste(as.factor(Valence), as.factor(Samp_Group)))) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(.4),
size = 3) +
stat_summary(fun.data = SEFunctionForggplot, geom = "errorbar",
position = position_dodge(.4), width = .3, size = 1) +
scale_x_discrete(labels = c("Worse", "Same", "Better"),
name = "Condition") +
scale_y_continuous(name = "Trials (N)") +
scale_color_manual(name = "Valence, \nSampling Group",
labels = c("\nNegative, \nIn-group\n",
"\nNegative, \nOut-Group\n",
"\nPositive, \nIn-group\n",
"\nPositive, \nOut-Group\n"),
values = c("darkgoldenrod1", "darkorange3",
"steelblue1", "steelblue4")) +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
# dev.off()Plotting Point-estimates
ggplot(Evaluation.in.out, aes(as.factor(Condition), P.Estimates,
color = paste(as.factor(Valence), as.factor(Evaluated.Group)))) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(.4),
size = 3) +
stat_summary(fun.data = SEFunctionForggplot, geom = "errorbar",
position = position_dodge(.4), width = .3, size = 1) +
scale_x_discrete(labels = c("worse", "Same", "Better"),
name = "Condition") +
scale_y_continuous(name = "Point.Estimate") +
scale_color_manual(name = "Valence, \nSampling Group",
labels = c("\nNegative, \nIn-group\n",
"\nNegative, \nOut-Group\n",
"\nPositive, \nIn-group\n",
"\nPositive, \nOut-Group\n"),
values = c("steelblue4", "darkorange3",
"steelblue1", "darkgoldenrod1")) +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
Effects and Dummy coding categorical predictors for the sampling models
#Because it gets more complex to calculate the throw away group when you effects code, I am essentially making all iterations
#Change conditions to 1 2 3 for clarity
sampled.in.out$Condition[sampled.in.out$Condition == 1] <- "Worse"
sampled.in.out$Condition[sampled.in.out$Condition == 2] <- "Same"
sampled.in.out$Condition[sampled.in.out$Condition == 3] <- "Better"
#Effects + dummy coding for. call it _a.
sampled.in.out$Condition_a[sampled.in.out$Condition == "Worse"] <- -1
sampled.in.out$Condition_a[sampled.in.out$Condition == "Same"] <- 1
sampled.in.out$Condition_a[sampled.in.out$Condition == "Better"] <- 0
sampled.in.out$Condition_a_eff <- factor(sampled.in.out$Condition_a)
sampled.in.out$Condition_a_dum <- factor(sampled.in.out$Condition,
levels = c("Worse", "Better", "Same"))
#Effects + dummy coding, let's call it _c.
sampled.in.out$Condition_c[sampled.in.out$Condition == "Worse"] <- -1
sampled.in.out$Condition_c[sampled.in.out$Condition == "Same"] <- 0
sampled.in.out$Condition_c[sampled.in.out$Condition == "Better"] <- 1
sampled.in.out$Condition_c_eff <- factor(sampled.in.out$Condition_c)
sampled.in.out$Condition_c_dum <- factor(sampled.in.out$Condition,
levels = c("Worse", "Same", "Better"))
#Effects + dummy coding, let's call it _d.
sampled.in.out$Condition_d[sampled.in.out$Condition == "Worse"] <- 1
sampled.in.out$Condition_d[sampled.in.out$Condition == "Same"] <- 0
sampled.in.out$Condition_d[sampled.in.out$Condition == "Better"] <- -1
sampled.in.out$Condition_d_eff <- factor(sampled.in.out$Condition_d)
sampled.in.out$Condition_d_dum <- factor(sampled.in.out$Condition,
levels = c("Same", "Better", "Worse"))
##contrast coding where we collapse better and worse condition and compare to same group
sampled.in.out$Condition_contr[sampled.in.out$Condition == "Worse"] <- 0
sampled.in.out$Condition_contr[sampled.in.out$Condition== "Same"] <- 1
sampled.in.out$Condition_contr[sampled.in.out$Condition == "Better"] <- 0
sampled.in.out$Condition_contr[sampled.in.out$Condition_contr == 0] <- "Other"
sampled.in.out$Condition_contr[sampled.in.out$Condition_contr == 1] <- "Same"
sampled.in.out$Condition_contr_dum <- factor(sampled.in.out$Condition_contr,
levels = c("Same","Other"))
sampled.in.out$Condition_contr_eff <- factor(sampled.in.out$Condition_contr,
levels = c("Same","Other"))
#effects + dummy coding In and Out group with out as thro-away (Samp_Group)
sampled.in.out$Samp_GroupB_eff <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_dum <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_eff <- as.factor(sampled.in.out$Samp_GroupB_eff)
sampled.in.out$Samp_GroupB_dum <- as.factor(sampled.in.out$Samp_GroupB_dum)
sampled.in.out$Samp_GroupB_eff <- factor(sampled.in.out$Samp_GroupB_eff,
levels = c("In", "Out"))
sampled.in.out$Samp_GroupB_dum <- factor(sampled.in.out$Samp_GroupB_dum,
levels = c("In", "Out"))
#effects + dummy coding In and Out group with in as throw-away (Samp_Group1)
sampled.in.out$Samp_GroupB_eff1 <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_dum1 <- sampled.in.out$Samp_GroupString
sampled.in.out$Samp_GroupB_eff1 <- as.factor(sampled.in.out$Samp_GroupB_eff1)
sampled.in.out$Samp_GroupB_dum1 <- as.factor(sampled.in.out$Samp_GroupB_dum1)
sampled.in.out$Samp_GroupB_eff1 <- factor(sampled.in.out$Samp_GroupB_eff1,
levels = c("Out", "In"))
sampled.in.out$Samp_GroupB_dum1 <- factor(sampled.in.out$Samp_GroupB_dum1,
levels = c("Out", "In"))
#effects + dummy coding group so that Rep is throw-away (Group)
sampled.in.out$Group_eff <- sampled.in.out$Group
sampled.in.out$Group_dum <- sampled.in.out$Group
sampled.in.out$Group_eff <- as.factor(sampled.in.out$Group_eff)
sampled.in.out$Group_dum <- as.factor(sampled.in.out$Group_dum)
sampled.in.out$Group_eff <- factor(sampled.in.out$Group_eff,
levels = c("Dem", "Rep"))
sampled.in.out$Group_dum <- factor(sampled.in.out$Group_dum,
levels = c("Dem", "Rep"))
#effects + dummy coding group so that Dem is reference (Group1)
sampled.in.out$Group_eff1 <- sampled.in.out$Group
sampled.in.out$Group_dum1 <- sampled.in.out$Group
sampled.in.out$Group_eff1 <- as.factor(sampled.in.out$Group_eff1)
sampled.in.out$Group_dum1 <- as.factor(sampled.in.out$Group_dum1)
sampled.in.out$Group_eff1 <- factor(sampled.in.out$Group_eff1,
levels = c("Rep", "Dem"))
sampled.in.out$Group_dum1 <- factor(sampled.in.out$Group_dum1,
levels = c("Rep", "Dem"))
#coding valence
sampled.in.out$Valence_eff <- sampled.in.out$ValenceString
sampled.in.out$Valence_dum <- sampled.in.out$ValenceString
sampled.in.out$Valence_eff <- as.factor(sampled.in.out$Valence_eff)
sampled.in.out$Valence_dum <- as.factor(sampled.in.out$Valence_dum)
sampled.in.out$Valence_eff1[sampled.in.out$ValenceString == "pos"] <- "pos"
sampled.in.out$Valence_eff1[sampled.in.out$ValenceString == "neg"] <- "shitty"
sampled.in.out$Valence_dum1 <- sampled.in.out$Valence_eff1
#sorry about the naming convention here. I needed a name with a letter lower in the alphabet.
sampled.in.out$Valence_eff1 <- as.factor(sampled.in.out$Valence_eff1)
sampled.in.out$Valence_dum1 <- as.factor(sampled.in.out$Valence_dum1) Applying the contr functions for sampling models.
##Making the contrasts with dummy alternatives
contrasts(sampled.in.out$Condition_a_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_a_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_a_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_a_eff)) = c("Worse", "Better")
colnames(contrasts(sampled.in.out$Condition_a_dum)) = c("Worse", "Better")
contrasts(sampled.in.out$Condition_c_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_c_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_c_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_c_eff)) = c("Worse", "Same")
colnames(contrasts(sampled.in.out$Condition_c_dum)) = c("Worse", "Same")
contrasts(sampled.in.out$Condition_d_eff) <-contr.sum(3)
contrasts(sampled.in.out$Condition_d_dum) <-contr.sum(3)
contrasts(sampled.in.out$Condition_d_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(sampled.in.out$Condition_d_eff)) = c("Better", "Same")
colnames(contrasts(sampled.in.out$Condition_d_dum)) = c("Same", "Better")
contrasts(sampled.in.out$Condition_contr_eff) <-contr.sum(2)
contrasts(sampled.in.out$Condition_contr_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Condition_contr_eff)) = c("Same")
colnames(contrasts(sampled.in.out$Condition_contr_dum)) = c("Same")
contrasts(sampled.in.out$Samp_GroupB_eff) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Samp_GroupB_eff)) = c("In.Group")
colnames(contrasts(sampled.in.out$Samp_GroupB_dum)) = c("In.Group")
contrasts(sampled.in.out$Samp_GroupB_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Samp_GroupB_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Samp_GroupB_eff1)) = c("Out.Group")
colnames(contrasts(sampled.in.out$Samp_GroupB_dum1)) = c("Out.Group")
contrasts(sampled.in.out$Group_eff) <-contr.sum(2)
contrasts(sampled.in.out$Group_dum) <-contr.sum(2)
contrasts(sampled.in.out$Group_eff) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Group_eff)) = c("Dem")
colnames(contrasts(sampled.in.out$Group_dum)) = c("Dem")
contrasts(sampled.in.out$Group_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Group_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Group_eff1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Group_eff1)) = c("Rep")
colnames(contrasts(sampled.in.out$Group_dum1)) = c("Rep")
contrasts(sampled.in.out$Valence_eff) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Valence_eff)) = c("Neg")
colnames(contrasts(sampled.in.out$Valence_dum)) = c("Neg")
contrasts(sampled.in.out$Valence_eff1) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum1) <-contr.sum(2)
contrasts(sampled.in.out$Valence_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(sampled.in.out$Valence_eff1)) = c("Pos")
colnames(contrasts(sampled.in.out$Valence_dum1)) = c("Pos")Effects and Dummy coding categorical predictors for the point-estimate models.
#same deal as above but for the PE data set
#Change conditions to 1 2 3 for clarity
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 1] <- "Worse"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 2] <- "Same"
Evaluation.in.out$Condition[Evaluation.in.out$Condition == 3] <- "Better"
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Worse"] <- -1
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Same"] <- 1
Evaluation.in.out$Condition_a[Evaluation.in.out$Condition == "Better"] <- 0
Evaluation.in.out$Condition_a_eff <- factor(Evaluation.in.out$Condition_a)
Evaluation.in.out$Condition_a_dum <- factor(Evaluation.in.out$Condition,
levels = c("Worse", "Better", "Same"))
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Worse"] <- -1
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Same"] <- 0
Evaluation.in.out$Condition_c[Evaluation.in.out$Condition == "Better"] <- 1
Evaluation.in.out$Condition_c_eff <- factor(Evaluation.in.out$Condition_c)
Evaluation.in.out$Condition_c_dum <- factor(Evaluation.in.out$Condition,
levels = c("Worse", "Same", "Better"))
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Worse"] <- 1
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Same"] <- 0
Evaluation.in.out$Condition_d[Evaluation.in.out$Condition == "Better"] <- -1
Evaluation.in.out$Condition_d_eff <- factor(Evaluation.in.out$Condition_d)
Evaluation.in.out$Condition_d_dum <- factor(Evaluation.in.out$Condition,
levels = c("Same", "Better", "Worse"))
Evaluation.in.out$Evaluated.Group_eff <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_dum <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_eff <- as.factor(Evaluation.in.out$Evaluated.Group_eff)
Evaluation.in.out$Evaluated.Group_dum <- as.factor(Evaluation.in.out$Evaluated.Group_dum)
Evaluation.in.out$Evaluated.Group_eff <- factor(Evaluation.in.out$Evaluated.Group_eff,
levels = c("In", "Out"))
Evaluation.in.out$Evaluated.Group_dum <- factor(Evaluation.in.out$Evaluated.Group_dum,
levels = c("In", "Out"))
Evaluation.in.out$Evaluated.Group_eff1 <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_dum1 <- Evaluation.in.out$Evaluated.GroupString
Evaluation.in.out$Evaluated.Group_eff1 <- as.factor(Evaluation.in.out$Evaluated.Group_eff)
Evaluation.in.out$Evaluated.Group_dum1 <- as.factor(Evaluation.in.out$Evaluated.Group_dum)
Evaluation.in.out$Evaluated.Group_eff1 <- factor(Evaluation.in.out$Evaluated.Group_eff1,
levels = c("Out", "In"))
Evaluation.in.out$Evaluated.Group_dum1 <- factor(Evaluation.in.out$Evaluated.Group_dum1,
levels = c("Out", "In"))
Evaluation.in.out$Group_eff <- Evaluation.in.out$Group
Evaluation.in.out$Group_dum <- Evaluation.in.out$Group
Evaluation.in.out$Group_eff <- as.factor(Evaluation.in.out$Group_eff)
Evaluation.in.out$Group_dum <- as.factor(Evaluation.in.out$Group_dum)
Evaluation.in.out$Group_eff <- factor(Evaluation.in.out$Group_eff,
levels = c("Rep", "Dem"))
Evaluation.in.out$Group_dum <- factor(Evaluation.in.out$Group_dum,
levels = c("Rep", "Dem"))
Evaluation.in.out$Group_eff1 <- Evaluation.in.out$Group
Evaluation.in.out$Group_dum1 <- Evaluation.in.out$Group
Evaluation.in.out$Group_eff1 <- as.factor(Evaluation.in.out$Group_eff)
Evaluation.in.out$Group_dum1 <- as.factor(Evaluation.in.out$Group_dum)
Evaluation.in.out$Group_eff1 <- factor(Evaluation.in.out$Group_eff1,
levels = c("Dem", "Rep"))
Evaluation.in.out$Group_dum1 <- factor(Evaluation.in.out$Group_dum1,
levels = c("Dem", "Rep"))
Evaluation.in.out$Valence_eff <- Evaluation.in.out$ValenceString
Evaluation.in.out$Valence_dum <- Evaluation.in.out$ValenceString
Evaluation.in.out$Valence_eff <- as.factor(Evaluation.in.out$Valence_eff)
Evaluation.in.out$Valence_dum <- as.factor(Evaluation.in.out$Valence_dum)
Evaluation.in.out$Valence_eff1[Evaluation.in.out$ValenceString == "pos"] <- "pos"
Evaluation.in.out$Valence_eff1[Evaluation.in.out$ValenceString == "neg"] <- "shitty"
Evaluation.in.out$Valence_dum1 <- Evaluation.in.out$Valence_eff1
Evaluation.in.out$Valence_eff1 <- as.factor(Evaluation.in.out$Valence_eff1)
Evaluation.in.out$Valence_dum1 <- as.factor(Evaluation.in.out$Valence_dum1)Applying the contr functions for sampling models.
##Making the contrasts with dummy alternatives
contrasts(Evaluation.in.out$Condition_a_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_a_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_a_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_a_eff)) = c("Worse", "Better")
colnames(contrasts(Evaluation.in.out$Condition_a_dum)) = c("Worse", "Better")
contrasts(Evaluation.in.out$Condition_c_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_c_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_c_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_c_eff)) = c("Worse", "Same")
colnames(contrasts(Evaluation.in.out$Condition_c_dum)) = c("Worse", "Same")
contrasts(Evaluation.in.out$Condition_d_eff) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_d_dum) <-contr.sum(3)
contrasts(Evaluation.in.out$Condition_d_dum) <- contr.treatment(3, base = 3)
colnames(contrasts(Evaluation.in.out$Condition_d_eff)) = c("Better", "Same")
colnames(contrasts(Evaluation.in.out$Condition_d_dum)) = c("Same", "Better")
contrasts(Evaluation.in.out$Evaluated.Group_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Evaluated.Group_eff)) = c("In.Group")
colnames(contrasts(Evaluation.in.out$Evaluated.Group_dum)) = c("In.Group")
contrasts(Evaluation.in.out$Evaluated.Group_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Evaluated.Group_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Evaluated.Group_eff1)) = c("Out.Group")
colnames(contrasts(Evaluation.in.out$Evaluated.Group_dum1)) = c("Out.Group")
contrasts(Evaluation.in.out$Group_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Group_eff)) = c("Rep")
colnames(contrasts(Evaluation.in.out$Group_dum)) = c("Rep")
contrasts(Evaluation.in.out$Group_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Group_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Group_eff1)) = c("Dem")
colnames(contrasts(Evaluation.in.out$Group_dum1)) = c("Dem")
contrasts(Evaluation.in.out$Valence_eff) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Valence_eff)) = c("Neg")
colnames(contrasts(Evaluation.in.out$Valence_dum)) = c("Neg")
contrasts(Evaluation.in.out$Valence_eff1) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum1) <-contr.sum(2)
contrasts(Evaluation.in.out$Valence_dum1) <- contr.treatment(2, base = 2)
colnames(contrasts(Evaluation.in.out$Valence_eff1)) = c("Pos")
colnames(contrasts(Evaluation.in.out$Valence_dum1)) = c("Pos")Generalized mixed models for Sampling Behavior
Histogram for DV
###Histogram for dv
hist(sampled.in.out$n_trials)
Model 1[sampling]: More in group sampling than outgroup – collapsing across condition and first sample
#here we are dummy coding group with out group as the reference group and effects coding both condition and valence.
collapsed.sampling.1 <- glmer(n_trials~Samp_GroupB_dum*Valence_eff*Condition_c_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_dum * Valence_eff * Condition_c_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 5504.5 5569.3 -2739.3 5478.5 1067
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5430 -0.3192 -0.0143 0.2435 5.4074
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3101 0.5568
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 1.6800320
## Samp_GroupB_dumIn.Group 0.0453533
## Valence_effNeg 0.0002077
## Condition_c_effWorse -0.0572763
## Condition_c_effSame 0.0909362
## Samp_GroupB_dumIn.Group:Valence_effNeg -0.0217248
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.0494602
## Samp_GroupB_dumIn.Group:Condition_c_effSame -0.0519302
## Valence_effNeg:Condition_c_effWorse 0.0875519
## Valence_effNeg:Condition_c_effSame -0.0703728
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse -0.0277120
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -0.0188083
## Std. Error
## (Intercept) 0.0304008
## Samp_GroupB_dumIn.Group 0.0239009
## Valence_effNeg 0.0300155
## Condition_c_effWorse 0.0433094
## Condition_c_effSame 0.0425531
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.0239009
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.0347300
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.0336198
## Valence_effNeg:Condition_c_effWorse 0.0433092
## Valence_effNeg:Condition_c_effSame 0.0425532
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.0347300
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.0336198
## z value
## (Intercept) 55.263
## Samp_GroupB_dumIn.Group 1.898
## Valence_effNeg 0.007
## Condition_c_effWorse -1.322
## Condition_c_effSame 2.137
## Samp_GroupB_dumIn.Group:Valence_effNeg -0.909
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 1.424
## Samp_GroupB_dumIn.Group:Condition_c_effSame -1.545
## Valence_effNeg:Condition_c_effWorse 2.022
## Valence_effNeg:Condition_c_effSame -1.654
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse -0.798
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -0.559
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Samp_GroupB_dumIn.Group 0.0578 .
## Valence_effNeg 0.9945
## Condition_c_effWorse 0.1860
## Condition_c_effSame 0.0326 *
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.3634
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.1544
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.1224
## Valence_effNeg:Condition_c_effWorse 0.0432 *
## Valence_effNeg:Condition_c_effSame 0.0982 .
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.4249
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.5759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Vlnc_N Cnd__W Cnd__S Sm_GB_I.G:V_N
## Smp_GrB_I.G -0.402
## Valenc_ffNg -0.002 0.006
## Cndtn_c_ffW 0.055 -0.039 0.002
## Cndtn_c_ffS 0.006 0.014 0.009 -0.534
## Sm_GB_I.G:V_N 0.006 -0.002 -0.408 0.013 -0.014
## S_GB_I.G:C__W -0.038 0.077 0.013 -0.416 0.216 -0.021
## S_GB_I.G:C__S 0.014 -0.015 -0.014 0.219 -0.399 0.041
## Vlnc_N:C__W 0.000 0.013 0.057 -0.002 -0.006 -0.039
## Vlnc_N:C__S 0.010 -0.014 0.007 -0.006 0.003 0.014
## S_GB_I.G:V_N:C__W 0.013 -0.021 -0.039 0.015 -0.002 0.077
## S_GB_I.G:V_N:C__S -0.014 0.041 0.014 -0.002 -0.004 -0.015
## S_GB_I.G:C__W S_GB_I.G:C__S V_N:C__W V_N:C__S
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S -0.533
## Vlnc_N:C__W 0.015 -0.002
## Vlnc_N:C__S -0.002 -0.004 -0.534
## S_GB_I.G:V_N:C__W -0.017 -0.012 -0.416 0.216
## S_GB_I.G:V_N:C__S -0.012 0.027 0.219 -0.399
## S_GB_I.G:V_N:C__W
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S
## Vlnc_N:C__W
## Vlnc_N:C__S
## S_GB_I.G:V_N:C__W
## S_GB_I.G:V_N:C__S -0.533Model 2[sampling]: More in group sampling with positive first sample – collapsing across condition
#here we are dummy coding valence and group with negative first sample and out group as the reference group and effects coding condition.
collapsed.sampling.2 <- glmer(n_trials~Samp_GroupB_dum*Valence_dum1*Condition_c_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_dum * Valence_eff * Condition_c_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 5504.5 5569.3 -2739.3 5478.5 1067
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5430 -0.3192 -0.0143 0.2435 5.4074
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3101 0.5568
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 1.6800320
## Samp_GroupB_dumIn.Group 0.0453533
## Valence_effNeg 0.0002077
## Condition_c_effWorse -0.0572763
## Condition_c_effSame 0.0909362
## Samp_GroupB_dumIn.Group:Valence_effNeg -0.0217248
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.0494602
## Samp_GroupB_dumIn.Group:Condition_c_effSame -0.0519302
## Valence_effNeg:Condition_c_effWorse 0.0875519
## Valence_effNeg:Condition_c_effSame -0.0703728
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse -0.0277120
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -0.0188083
## Std. Error
## (Intercept) 0.0304008
## Samp_GroupB_dumIn.Group 0.0239009
## Valence_effNeg 0.0300155
## Condition_c_effWorse 0.0433094
## Condition_c_effSame 0.0425531
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.0239009
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.0347300
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.0336198
## Valence_effNeg:Condition_c_effWorse 0.0433092
## Valence_effNeg:Condition_c_effSame 0.0425532
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.0347300
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.0336198
## z value
## (Intercept) 55.263
## Samp_GroupB_dumIn.Group 1.898
## Valence_effNeg 0.007
## Condition_c_effWorse -1.322
## Condition_c_effSame 2.137
## Samp_GroupB_dumIn.Group:Valence_effNeg -0.909
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 1.424
## Samp_GroupB_dumIn.Group:Condition_c_effSame -1.545
## Valence_effNeg:Condition_c_effWorse 2.022
## Valence_effNeg:Condition_c_effSame -1.654
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse -0.798
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame -0.559
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Samp_GroupB_dumIn.Group 0.0578 .
## Valence_effNeg 0.9945
## Condition_c_effWorse 0.1860
## Condition_c_effSame 0.0326 *
## Samp_GroupB_dumIn.Group:Valence_effNeg 0.3634
## Samp_GroupB_dumIn.Group:Condition_c_effWorse 0.1544
## Samp_GroupB_dumIn.Group:Condition_c_effSame 0.1224
## Valence_effNeg:Condition_c_effWorse 0.0432 *
## Valence_effNeg:Condition_c_effSame 0.0982 .
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effWorse 0.4249
## Samp_GroupB_dumIn.Group:Valence_effNeg:Condition_c_effSame 0.5759
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Vlnc_N Cnd__W Cnd__S Sm_GB_I.G:V_N
## Smp_GrB_I.G -0.402
## Valenc_ffNg -0.002 0.006
## Cndtn_c_ffW 0.055 -0.039 0.002
## Cndtn_c_ffS 0.006 0.014 0.009 -0.534
## Sm_GB_I.G:V_N 0.006 -0.002 -0.408 0.013 -0.014
## S_GB_I.G:C__W -0.038 0.077 0.013 -0.416 0.216 -0.021
## S_GB_I.G:C__S 0.014 -0.015 -0.014 0.219 -0.399 0.041
## Vlnc_N:C__W 0.000 0.013 0.057 -0.002 -0.006 -0.039
## Vlnc_N:C__S 0.010 -0.014 0.007 -0.006 0.003 0.014
## S_GB_I.G:V_N:C__W 0.013 -0.021 -0.039 0.015 -0.002 0.077
## S_GB_I.G:V_N:C__S -0.014 0.041 0.014 -0.002 -0.004 -0.015
## S_GB_I.G:C__W S_GB_I.G:C__S V_N:C__W V_N:C__S
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S -0.533
## Vlnc_N:C__W 0.015 -0.002
## Vlnc_N:C__S -0.002 -0.004 -0.534
## S_GB_I.G:V_N:C__W -0.017 -0.012 -0.416 0.216
## S_GB_I.G:V_N:C__S -0.012 0.027 0.219 -0.399
## S_GB_I.G:V_N:C__W
## Smp_GrB_I.G
## Valenc_ffNg
## Cndtn_c_ffW
## Cndtn_c_ffS
## Sm_GB_I.G:V_N
## S_GB_I.G:C__W
## S_GB_I.G:C__S
## Vlnc_N:C__W
## Vlnc_N:C__S
## S_GB_I.G:V_N:C__W
## S_GB_I.G:V_N:C__S -0.533Model 3[sampling]: Positive first samples – holding Samp group (in/out) and condition constant?
#here we effects code group and condition and dummy code valence
collapsed.sampling.3 <- glmer(n_trials~Samp_GroupB_eff*Condition_c_eff*Valence_dum+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_eff * Condition_c_eff * Valence_dum +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 5504.5 5569.3 -2739.3 5478.5 1067
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5430 -0.3192 -0.0143 0.2435 5.4074
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3101 0.5568
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 1.71336
## Samp_GroupB_effIn.Group 0.03354
## Condition_c_effWorse -0.10624
## Condition_c_effSame 0.14475
## Valence_dumNeg -0.02131
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.03859
## Samp_GroupB_effIn.Group:Condition_c_effSame -0.01656
## Samp_GroupB_effIn.Group:Valence_dumNeg -0.02172
## Condition_c_effWorse:Valence_dumNeg 0.14739
## Condition_c_effSame:Valence_dumNeg -0.15955
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg -0.02771
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg -0.01881
## Std. Error
## (Intercept) 0.03901
## Samp_GroupB_effIn.Group 0.01692
## Condition_c_effWorse 0.05547
## Condition_c_effSame 0.05505
## Valence_dumNeg 0.05482
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.02476
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.02345
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.02390
## Condition_c_effWorse:Valence_dumNeg 0.07880
## Condition_c_effSame:Valence_dumNeg 0.07804
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.03473
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg 0.03362
## z value
## (Intercept) 43.917
## Samp_GroupB_effIn.Group 1.983
## Condition_c_effWorse -1.915
## Condition_c_effSame 2.629
## Valence_dumNeg -0.389
## Samp_GroupB_effIn.Group:Condition_c_effWorse 1.558
## Samp_GroupB_effIn.Group:Condition_c_effSame -0.706
## Samp_GroupB_effIn.Group:Valence_dumNeg -0.909
## Condition_c_effWorse:Valence_dumNeg 1.871
## Condition_c_effSame:Valence_dumNeg -2.044
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg -0.798
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg -0.559
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## Samp_GroupB_effIn.Group 0.04742 *
## Condition_c_effWorse 0.05547 .
## Condition_c_effSame 0.00856 **
## Valence_dumNeg 0.69752
## Samp_GroupB_effIn.Group:Condition_c_effWorse 0.11915
## Samp_GroupB_effIn.Group:Condition_c_effSame 0.48008
## Samp_GroupB_effIn.Group:Valence_dumNeg 0.36338
## Condition_c_effWorse:Valence_dumNeg 0.06140 .
## Condition_c_effSame:Valence_dumNeg 0.04091 *
## Samp_GroupB_effIn.Group:Condition_c_effWorse:Valence_dumNeg 0.42490
## Samp_GroupB_effIn.Group:Condition_c_effSame:Valence_dumNeg 0.57587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__W Cnd__S Vlnc_N Sm_GB_I.G:C__W
## Smp_GrB_I.G -0.016
## Cndtn_c_ffW 0.035 -0.014
## Cndtn_c_ffS 0.012 0.006 -0.526
## Valenc_dmNg -0.701 0.011 -0.025 -0.010
## Sm_GB_I.G:C__W -0.014 0.097 -0.025 0.013 0.010
## Sm_GB_I.G:C__S 0.006 -0.056 0.014 -0.011 -0.005 -0.524
## S_GB_I.G:V_ 0.011 -0.708 0.010 -0.004 -0.010 -0.069
## Cndt__W:V_N -0.026 0.010 -0.704 0.370 0.046 0.017
## Cndt__S:V_N -0.009 -0.004 0.371 -0.705 0.019 -0.009
## S_GB_I.G:C__W: 0.010 -0.069 0.018 -0.009 -0.009 -0.713
## S_GB_I.G:C__S: -0.004 0.039 -0.009 0.008 0.009 0.366
## Sm_GB_I.G:C__S S_GB_I.G:V C__W:V C__S:V S_GB_I.G:C__W:
## Smp_GrB_I.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valenc_dmNg
## Sm_GB_I.G:C__W
## Sm_GB_I.G:C__S
## S_GB_I.G:V_ 0.040
## Cndt__W:V_N -0.010 -0.009
## Cndt__S:V_N 0.008 0.009 -0.534
## S_GB_I.G:C__W: 0.374 0.077 -0.016 0.005
## S_GB_I.G:C__S: -0.698 -0.015 0.006 -0.004 -0.533Model 4[sampling]: Contrast coding. Better/Worse = 0; Same = 1
collapsed.sampling.4 <- glmer(n_trials~Samp_GroupB_dum*Condition_contr_dum+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.4)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula:
## n_trials ~ Samp_GroupB_dum * Condition_contr_dum + (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 5496.8 5521.7 -2743.4 5486.8 1075
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6144 -0.3092 -0.0194 0.2335 5.5153
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3137 0.5601
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 1.63483 0.03699
## Samp_GroupB_dumIn.Group 0.06875 0.02926
## Condition_contr_dumSame 0.13512 0.06398
## Samp_GroupB_dumIn.Group:Condition_contr_dumSame -0.07302 0.05027
## z value Pr(>|z|)
## (Intercept) 44.192 <2e-16 ***
## Samp_GroupB_dumIn.Group 2.350 0.0188 *
## Condition_contr_dumSame 2.112 0.0347 *
## Samp_GroupB_dumIn.Group:Condition_contr_dumSame -1.453 0.1463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__S
## Smp_GrB_I.G -0.409
## Cndtn_cnt_S -0.569 0.237
## S_GB_I.G:C_ 0.238 -0.582 -0.397Model 5[sampling]: What is happening in the better condition?
collapsed.sampling.5 <- glmer(n_trials~Samp_GroupB_eff*Condition_d_dum*Valence_eff+ (1|Participant), data = sampled.in.out, family = 'poisson',
control=glmerControl(optimizer="bobyqa"))
summary(collapsed.sampling.5)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: poisson ( log )
## Formula: n_trials ~ Samp_GroupB_eff * Condition_d_dum * Valence_eff +
## (1 | Participant)
## Data: sampled.in.out
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 5504.5 5569.3 -2739.3 5478.5 1067
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5430 -0.3192 -0.0143 0.2435 5.4074
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.3101 0.5568
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 1.6701613
## Samp_GroupB_effIn.Group 0.0474067
## Condition_d_dumSame 0.0975190
## Condition_d_dumBetter 0.0001231
## Valence_effNeg 0.0630417
## Samp_GroupB_effIn.Group:Condition_d_dumSame -0.0506953
## Samp_GroupB_effIn.Group:Condition_d_dumBetter -0.0234951
## Samp_GroupB_effIn.Group:Valence_effNeg -0.0247179
## Condition_d_dumSame:Valence_effNeg -0.1534728
## Condition_d_dumBetter:Valence_effNeg -0.0676161
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.0044511
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 0.0371157
## Std. Error
## (Intercept) 0.0492085
## Samp_GroupB_effIn.Group 0.0218217
## Condition_d_dumSame 0.0686716
## Condition_d_dumBetter 0.0667061
## Valence_effNeg 0.0490103
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.0299243
## Samp_GroupB_effIn.Group:Condition_d_dumBetter 0.0294287
## Samp_GroupB_effIn.Group:Valence_effNeg 0.0218217
## Condition_d_dumSame:Valence_effNeg 0.0686753
## Condition_d_dumBetter:Valence_effNeg 0.0666979
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.0299243
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 0.0294287
## z value
## (Intercept) 33.941
## Samp_GroupB_effIn.Group 2.172
## Condition_d_dumSame 1.420
## Condition_d_dumBetter 0.002
## Valence_effNeg 1.286
## Samp_GroupB_effIn.Group:Condition_d_dumSame -1.694
## Samp_GroupB_effIn.Group:Condition_d_dumBetter -0.798
## Samp_GroupB_effIn.Group:Valence_effNeg -1.133
## Condition_d_dumSame:Valence_effNeg -2.235
## Condition_d_dumBetter:Valence_effNeg -1.014
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.149
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 1.261
## Pr(>|z|)
## (Intercept) <2e-16 ***
## Samp_GroupB_effIn.Group 0.0298 *
## Condition_d_dumSame 0.1556
## Condition_d_dumBetter 0.9985
## Valence_effNeg 0.1983
## Samp_GroupB_effIn.Group:Condition_d_dumSame 0.0902 .
## Samp_GroupB_effIn.Group:Condition_d_dumBetter 0.4247
## Samp_GroupB_effIn.Group:Valence_effNeg 0.2573
## Condition_d_dumSame:Valence_effNeg 0.0254 *
## Condition_d_dumBetter:Valence_effNeg 0.3107
## Samp_GroupB_effIn.Group:Condition_d_dumSame:Valence_effNeg 0.8818
## Samp_GroupB_effIn.Group:Condition_d_dumBetter:Valence_effNeg 0.2072
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sm_GB_I.G Cnd__S Cnd__B Vlnc_N Sm_GB_I.G:C__S
## Smp_GrB_I.G -0.021
## Cndtn_d_dmS -0.711 0.015
## Cndtn_d_dmB -0.730 0.016 0.524
## Valenc_ffNg 0.015 0.012 -0.011 -0.011
## Sm_GB_I.G:C__S 0.016 -0.729 -0.010 -0.011 -0.008
## Sm_GB_I.G:C__B 0.016 -0.742 -0.011 -0.016 -0.009 0.541
## S_GB_I.G:V_ 0.012 -0.030 -0.008 -0.009 -0.021 0.022
## Cndt__S:V_N -0.010 -0.008 0.012 0.008 -0.714 0.010
## Cndt__B:V_N -0.011 -0.009 0.008 -0.002 -0.735 0.006
## S_GB_I.G:C__S: -0.008 0.022 0.010 0.006 0.016 0.011
## S_GB_I.G:C__B: -0.009 0.022 0.006 0.004 0.016 -0.016
## Sm_GB_I.G:C__B S_GB_I.G:V C__S:V C__B:V S_GB_I.G:C__S:
## Smp_GrB_I.G
## Cndtn_d_dmS
## Cndtn_d_dmB
## Valenc_ffNg
## Sm_GB_I.G:C__S
## Sm_GB_I.G:C__B
## S_GB_I.G:V_ 0.022
## Cndt__S:V_N 0.006 0.015
## Cndt__B:V_N 0.004 0.016 0.524
## S_GB_I.G:C__S: -0.016 -0.729 -0.010 -0.011
## S_GB_I.G:C__B: -0.030 -0.742 -0.011 -0.016 0.541Linear mixed models for for point-estimates
Histogram for DV
hist(Evaluation.in.out$P.Estimates)
Model 1[point-estimates]: In group biases holding valence and condition constant.
#
collapsed.evaluation.1 <- lmer(P.Estimates~Evaluated.Group_dum*Condition_c_eff*Valence_eff*+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_dum * Condition_c_eff * Valence_eff *
## +(1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 7650.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3484 -0.4843 0.0722 0.4964 3.9430
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.8512 0.9226
## Residual 69.8962 8.3604
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 62.7440
## Evaluated.Group_dumIn.Group 3.0476
## Condition_c_effWorse 1.2830
## Condition_c_effSame 0.3925
## Valence_effNeg -0.8837
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -3.0052
## Evaluated.Group_dumIn.Group:Condition_c_effSame -0.6501
## Evaluated.Group_dumIn.Group:Valence_effNeg -1.5783
## Condition_c_effWorse:Valence_effNeg -1.2042
## Condition_c_effSame:Valence_effNeg 0.6438
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 1.0115
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg -0.6124
## Std. Error
## (Intercept) 0.3630
## Evaluated.Group_dumIn.Group 0.5103
## Condition_c_effWorse 0.5210
## Condition_c_effSame 0.5186
## Valence_effNeg 0.3630
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 0.7324
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.7289
## Evaluated.Group_dumIn.Group:Valence_effNeg 0.5103
## Condition_c_effWorse:Valence_effNeg 0.5210
## Condition_c_effSame:Valence_effNeg 0.5186
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 0.7324
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.7289
## df
## (Intercept) 1067.8454
## Evaluated.Group_dumIn.Group 534.0398
## Condition_c_effWorse 1067.8454
## Condition_c_effSame 1067.8454
## Valence_effNeg 1067.8454
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 534.0398
## Evaluated.Group_dumIn.Group:Condition_c_effSame 534.0398
## Evaluated.Group_dumIn.Group:Valence_effNeg 534.0398
## Condition_c_effWorse:Valence_effNeg 1067.8454
## Condition_c_effSame:Valence_effNeg 1067.8454
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 534.0398
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 534.0398
## t value
## (Intercept) 172.835
## Evaluated.Group_dumIn.Group 5.972
## Condition_c_effWorse 2.463
## Condition_c_effSame 0.757
## Valence_effNeg -2.434
## Evaluated.Group_dumIn.Group:Condition_c_effWorse -4.103
## Evaluated.Group_dumIn.Group:Condition_c_effSame -0.892
## Evaluated.Group_dumIn.Group:Valence_effNeg -3.093
## Condition_c_effWorse:Valence_effNeg -2.311
## Condition_c_effSame:Valence_effNeg 1.241
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 1.381
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg -0.840
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_dumIn.Group 4.27e-09
## Condition_c_effWorse 0.01395
## Condition_c_effSame 0.44924
## Valence_effNeg 0.01508
## Evaluated.Group_dumIn.Group:Condition_c_effWorse 4.71e-05
## Evaluated.Group_dumIn.Group:Condition_c_effSame 0.37290
## Evaluated.Group_dumIn.Group:Valence_effNeg 0.00209
## Condition_c_effWorse:Valence_effNeg 0.02101
## Condition_c_effSame:Valence_effNeg 0.21471
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg 0.16781
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg 0.40125
##
## (Intercept) ***
## Evaluated.Group_dumIn.Group ***
## Condition_c_effWorse *
## Condition_c_effSame
## Valence_effNeg *
## Evaluated.Group_dumIn.Group:Condition_c_effWorse ***
## Evaluated.Group_dumIn.Group:Condition_c_effSame
## Evaluated.Group_dumIn.Group:Valence_effNeg **
## Condition_c_effWorse:Valence_effNeg *
## Condition_c_effSame:Valence_effNeg
## Evaluated.Group_dumIn.Group:Condition_c_effWorse:Valence_effNeg
## Evaluated.Group_dumIn.Group:Condition_c_effSame:Valence_effNeg
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Cnd__W Cnd__S Vlnc_N Ev.G_I.G:C__W
## Evltd.G_I.G -0.703
## Cndtn_c_ffW 0.042 -0.029
## Cndtn_c_ffS 0.028 -0.020 -0.537
## Valenc_ffNg -0.001 0.001 0.023 -0.008
## Ev.G_I.G:C__W -0.029 0.042 -0.703 0.377 -0.016
## Ev.G_I.G:C__S -0.020 0.028 0.377 -0.703 0.005 -0.537
## E.G_I.G:V_N 0.001 -0.001 -0.016 0.005 -0.703 0.023
## Cndt__W:V_N 0.023 -0.016 0.015 -0.010 0.042 -0.010
## Cndt__S:V_N -0.008 0.005 -0.010 -0.006 0.028 0.007
## E.G_I.G:C__W: -0.016 0.023 -0.010 0.007 -0.029 0.015
## E.G_I.G:C__S: 0.005 -0.008 0.007 0.005 -0.020 -0.010
## Ev.G_I.G:C__S E.G_I.G:V C__W:V C__S:V E.G_I.G:C__W:
## Evltd.G_I.G
## Cndtn_c_ffW
## Cndtn_c_ffS
## Valenc_ffNg
## Ev.G_I.G:C__W
## Ev.G_I.G:C__S
## E.G_I.G:V_N -0.008
## Cndt__W:V_N 0.007 -0.029
## Cndt__S:V_N 0.005 -0.020 -0.537
## E.G_I.G:C__W: -0.010 0.042 -0.703 0.377
## E.G_I.G:C__S: -0.006 0.028 0.377 -0.703 -0.537Model 2[point-estimates]: Effect if furst sample valence holding group and condition constant
collapsed.evaluation.2 <- lmer(P.Estimates~Valence_dum1*Condition_c_eff*Evaluated.Group_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Valence_dum1 * Condition_c_eff * Evaluated.Group_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 7650.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3484 -0.4843 0.0722 0.4964 3.9430
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.8512 0.9226
## Residual 69.8962 8.3604
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 62.5949
## Valence_dum1Pos 3.3458
## Condition_c_effWorse -0.9181
## Condition_c_effSame 0.4051
## Evaluated.Group_effIn.Group 0.7347
## Valence_dum1Pos:Condition_c_effWorse 1.3969
## Valence_dum1Pos:Condition_c_effSame -0.6752
## Valence_dum1Pos:Evaluated.Group_effIn.Group 1.5783
## Condition_c_effWorse:Evaluated.Group_effIn.Group -0.9969
## Condition_c_effSame:Evaluated.Group_effIn.Group -0.6312
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group -1.0115
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.6124
## Std. Error
## (Intercept) 0.3650
## Valence_dum1Pos 0.5165
## Condition_c_effWorse 0.5280
## Condition_c_effSame 0.5200
## Evaluated.Group_effIn.Group 0.3606
## Valence_dum1Pos:Condition_c_effWorse 0.7412
## Valence_dum1Pos:Condition_c_effSame 0.7378
## Valence_dum1Pos:Evaluated.Group_effIn.Group 0.5103
## Condition_c_effWorse:Evaluated.Group_effIn.Group 0.5217
## Condition_c_effSame:Evaluated.Group_effIn.Group 0.5138
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.7324
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.7289
## df
## (Intercept) 534.0360
## Valence_dum1Pos 534.0360
## Condition_c_effWorse 534.0360
## Condition_c_effSame 534.0360
## Evaluated.Group_effIn.Group 534.0398
## Valence_dum1Pos:Condition_c_effWorse 534.0360
## Valence_dum1Pos:Condition_c_effSame 534.0360
## Valence_dum1Pos:Evaluated.Group_effIn.Group 534.0398
## Condition_c_effWorse:Evaluated.Group_effIn.Group 534.0398
## Condition_c_effSame:Evaluated.Group_effIn.Group 534.0398
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 534.0398
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 534.0398
## t value
## (Intercept) 171.492
## Valence_dum1Pos 6.478
## Condition_c_effWorse -1.739
## Condition_c_effSame 0.779
## Evaluated.Group_effIn.Group 2.037
## Valence_dum1Pos:Condition_c_effWorse 1.885
## Valence_dum1Pos:Condition_c_effSame -0.915
## Valence_dum1Pos:Evaluated.Group_effIn.Group 3.093
## Condition_c_effWorse:Evaluated.Group_effIn.Group -1.911
## Condition_c_effSame:Evaluated.Group_effIn.Group -1.229
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group -1.381
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.840
## Pr(>|t|)
## (Intercept) < 2e-16
## Valence_dum1Pos 2.11e-10
## Condition_c_effWorse 0.08264
## Condition_c_effSame 0.43628
## Evaluated.Group_effIn.Group 0.04213
## Valence_dum1Pos:Condition_c_effWorse 0.06003
## Valence_dum1Pos:Condition_c_effSame 0.36049
## Valence_dum1Pos:Evaluated.Group_effIn.Group 0.00209
## Condition_c_effWorse:Evaluated.Group_effIn.Group 0.05655
## Condition_c_effSame:Evaluated.Group_effIn.Group 0.21976
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group 0.16781
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group 0.40125
##
## (Intercept) ***
## Valence_dum1Pos ***
## Condition_c_effWorse .
## Condition_c_effSame
## Evaluated.Group_effIn.Group *
## Valence_dum1Pos:Condition_c_effWorse .
## Valence_dum1Pos:Condition_c_effSame
## Valence_dum1Pos:Evaluated.Group_effIn.Group **
## Condition_c_effWorse:Evaluated.Group_effIn.Group .
## Condition_c_effSame:Evaluated.Group_effIn.Group
## Valence_dum1Pos:Condition_c_effWorse:Evaluated.Group_effIn.Group
## Valence_dum1Pos:Condition_c_effSame:Evaluated.Group_effIn.Group
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Vln_1P Cnd__W Cnd__S E.G_I. Vl_1P:C__W Vl_1P:C__S
## Valnc_dm1Ps -0.707
## Cndtn_c_ffW 0.064 -0.045
## Cndtn_c_ffS 0.021 -0.015 -0.544
## Evltd.G_I.G 0.000 0.000 0.000 0.000
## Vln_1P:C__W -0.045 0.042 -0.712 0.388 0.000
## Vln_1P:C__S -0.015 0.028 0.384 -0.705 0.000 -0.537
## V_1P:E.G_I. 0.000 0.000 0.000 0.000 -0.707 0.000 0.000
## C__W:E.G_I. 0.000 0.000 0.000 0.000 0.064 0.000 0.000
## C__S:E.G_I. 0.000 0.000 0.000 0.000 0.021 0.000 0.000
## V_1P:C__W:E 0.000 0.000 0.000 0.000 -0.045 0.000 0.000
## V_1P:C__S:E 0.000 0.000 0.000 0.000 -0.015 0.000 0.000
## V_1P:E C__W:E C__S:E V_1P:C__W:
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Evltd.G_I.G
## Vln_1P:C__W
## Vln_1P:C__S
## V_1P:E.G_I.
## C__W:E.G_I. -0.045
## C__S:E.G_I. -0.015 -0.544
## V_1P:C__W:E 0.042 -0.712 0.388
## V_1P:C__S:E 0.028 0.384 -0.705 -0.537Model 3[point-estimates]
collapsed.evaluation.3 <- lmer(P.Estimates~Evaluated.Group_eff*Valence_dum1*Condition_c_eff+ (1|Participant), data = Evaluation.in.out)
summary(collapsed.evaluation.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## P.Estimates ~ Evaluated.Group_eff * Valence_dum1 * Condition_c_eff +
## (1 | Participant)
## Data: Evaluation.in.out
##
## REML criterion at convergence: 7650.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.3484 -0.4843 0.0722 0.4964 3.9430
##
## Random effects:
## Groups Name Variance Std.Dev.
## Participant (Intercept) 0.8512 0.9226
## Residual 69.8962 8.3604
## Number of obs: 1080, groups: Participant, 540
##
## Fixed effects:
## Estimate
## (Intercept) 62.5949
## Evaluated.Group_effIn.Group 0.7347
## Valence_dum1Pos 3.3458
## Condition_c_effWorse -0.9181
## Condition_c_effSame 0.4051
## Evaluated.Group_effIn.Group:Valence_dum1Pos 1.5783
## Evaluated.Group_effIn.Group:Condition_c_effWorse -0.9969
## Evaluated.Group_effIn.Group:Condition_c_effSame -0.6312
## Valence_dum1Pos:Condition_c_effWorse 1.3969
## Valence_dum1Pos:Condition_c_effSame -0.6752
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse -1.0115
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame 0.6124
## Std. Error
## (Intercept) 0.3650
## Evaluated.Group_effIn.Group 0.3606
## Valence_dum1Pos 0.5165
## Condition_c_effWorse 0.5280
## Condition_c_effSame 0.5200
## Evaluated.Group_effIn.Group:Valence_dum1Pos 0.5103
## Evaluated.Group_effIn.Group:Condition_c_effWorse 0.5217
## Evaluated.Group_effIn.Group:Condition_c_effSame 0.5138
## Valence_dum1Pos:Condition_c_effWorse 0.7412
## Valence_dum1Pos:Condition_c_effSame 0.7378
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.7324
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame 0.7289
## df
## (Intercept) 534.0360
## Evaluated.Group_effIn.Group 534.0398
## Valence_dum1Pos 534.0360
## Condition_c_effWorse 534.0360
## Condition_c_effSame 534.0360
## Evaluated.Group_effIn.Group:Valence_dum1Pos 534.0398
## Evaluated.Group_effIn.Group:Condition_c_effWorse 534.0398
## Evaluated.Group_effIn.Group:Condition_c_effSame 534.0398
## Valence_dum1Pos:Condition_c_effWorse 534.0360
## Valence_dum1Pos:Condition_c_effSame 534.0360
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse 534.0398
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame 534.0398
## t value
## (Intercept) 171.492
## Evaluated.Group_effIn.Group 2.037
## Valence_dum1Pos 6.478
## Condition_c_effWorse -1.739
## Condition_c_effSame 0.779
## Evaluated.Group_effIn.Group:Valence_dum1Pos 3.093
## Evaluated.Group_effIn.Group:Condition_c_effWorse -1.911
## Evaluated.Group_effIn.Group:Condition_c_effSame -1.229
## Valence_dum1Pos:Condition_c_effWorse 1.885
## Valence_dum1Pos:Condition_c_effSame -0.915
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse -1.381
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame 0.840
## Pr(>|t|)
## (Intercept) < 2e-16
## Evaluated.Group_effIn.Group 0.04213
## Valence_dum1Pos 2.11e-10
## Condition_c_effWorse 0.08264
## Condition_c_effSame 0.43628
## Evaluated.Group_effIn.Group:Valence_dum1Pos 0.00209
## Evaluated.Group_effIn.Group:Condition_c_effWorse 0.05655
## Evaluated.Group_effIn.Group:Condition_c_effSame 0.21976
## Valence_dum1Pos:Condition_c_effWorse 0.06003
## Valence_dum1Pos:Condition_c_effSame 0.36049
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse 0.16781
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame 0.40125
##
## (Intercept) ***
## Evaluated.Group_effIn.Group *
## Valence_dum1Pos ***
## Condition_c_effWorse .
## Condition_c_effSame
## Evaluated.Group_effIn.Group:Valence_dum1Pos **
## Evaluated.Group_effIn.Group:Condition_c_effWorse .
## Evaluated.Group_effIn.Group:Condition_c_effSame
## Valence_dum1Pos:Condition_c_effWorse .
## Valence_dum1Pos:Condition_c_effSame
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effWorse
## Evaluated.Group_effIn.Group:Valence_dum1Pos:Condition_c_effSame
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Ev.G_I.G Vln_1P Cnd__W Cnd__S Ev.G_I.G:V_1P
## Evltd.G_I.G 0.000
## Valnc_dm1Ps -0.707 0.000
## Cndtn_c_ffW 0.064 0.000 -0.045
## Cndtn_c_ffS 0.021 0.000 -0.015 -0.544
## Ev.G_I.G:V_1P 0.000 -0.707 0.000 0.000 0.000
## E.G_I.G:C__W 0.000 0.064 0.000 0.000 0.000 -0.045
## E.G_I.G:C__S 0.000 0.021 0.000 0.000 0.000 -0.015
## Vln_1P:C__W -0.045 0.000 0.042 -0.712 0.388 0.000
## Vln_1P:C__S -0.015 0.000 0.028 0.384 -0.705 0.000
## E.G_I.G:V_1P:C__W 0.000 -0.045 0.000 0.000 0.000 0.042
## E.G_I.G:V_1P:C__S 0.000 -0.015 0.000 0.000 0.000 0.028
## E.G_I.G:C__W E.G_I.G:C__S V_1P:C__W V_1P:C__S
## Evltd.G_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_1P
## E.G_I.G:C__W
## E.G_I.G:C__S -0.544
## Vln_1P:C__W 0.000 0.000
## Vln_1P:C__S 0.000 0.000 -0.537
## E.G_I.G:V_1P:C__W -0.712 0.388 0.000 0.000
## E.G_I.G:V_1P:C__S 0.384 -0.705 0.000 0.000
## E.G_I.G:V_1P:C__W
## Evltd.G_I.G
## Valnc_dm1Ps
## Cndtn_c_ffW
## Cndtn_c_ffS
## Ev.G_I.G:V_1P
## E.G_I.G:C__W
## E.G_I.G:C__S
## Vln_1P:C__W
## Vln_1P:C__S
## E.G_I.G:V_1P:C__W
## E.G_I.G:V_1P:C__S -0.537Power Simulations with the Simr package
Post-hoc power with current sample size. Monte Carlo simulations with 1k simulations
Sample first from in group – collapsed across conditiona and first sample valence coefficient
#We set a seed here so that we can replicate this simulation exactly.
#nsim and alpha are the default in this function but I made them explicit so that we could see it.
sim <- powerSim(collapsed.sampling.1, fixed("Samp_GroupB_dumIn.Group", "z"), seed = 5, nsim = 800, alpha = .05)## Simulating: | |Simulating: |= |Simulating: |== |Simulating: |=== |Simulating: |==== |Simulating: |===== |Simulating: |====== |Simulating: |======= |Simulating: |======== |Simulating: |========= |Simulating: |========== |Simulating: |=========== |Simulating: |============ |Simulating: |============= |Simulating: |============== |Simulating: |=============== |Simulating: |================ |Simulating: |================= |Simulating: |================== |Simulating: |=================== |Simulating: |==================== |Simulating: |===================== |Simulating: |====================== |Simulating: |======================= |Simulating: |======================== |Simulating: |========================= |Simulating: |========================== |Simulating: |=========================== |Simulating: |============================ |Simulating: |============================= |Simulating: |============================== |Simulating: |=============================== |Simulating: |================================ |Simulating: |================================= |Simulating: |================================== |Simulating: |=================================== |Simulating: |==================================== |Simulating: |===================================== |Simulating: |====================================== |Simulating: |======================================= |Simulating: |======================================== |Simulating: |========================================= |Simulating: |========================================== |Simulating: |=========================================== |Simulating: |============================================ |Simulating: |============================================= |Simulating: |============================================== |Simulating: |=============================================== |Simulating: |================================================ |Simulating: |================================================= |Simulating: |================================================== |Simulating: |=================================================== |Simulating: |==================================================== |Simulating: |===================================================== |Simulating: |====================================================== |Simulating: |======================================================= |Simulating: |======================================================== |Simulating: |========================================================= |Simulating: |========================================================== |Simulating: |=========================================================== |Simulating: |============================================================ |Simulating: |=============================================================|## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationsim## Power for predictor 'Samp_GroupB_dumIn.Group', (95% confidence interval):
## 47.75% (44.24, 51.28)
##
## Test: z-test
## Effect size for Samp_GroupB_dumIn.Group is 0.045
##
## Based on 800 simulations, (0 warnings, 0 errors)
## alpha = 0.05, nrow = 1080
##
## Time elapsed: 0 h 11 m 29 s
##
## nb: result might be an observed power calculationSample more in same condition coefficient
sim2 <- powerSim(collapsed.sampling.1, fixed("Condition_c_effSame", "z"), seed = 2, nsim = 800, alpha = .05)## Simulating: | |Simulating: |= |Simulating: |== |Simulating: |=== |Simulating: |==== |Simulating: |===== |Simulating: |====== |Simulating: |======= |Simulating: |======== |Simulating: |========= |Simulating: |========== |Simulating: |=========== |Simulating: |============ |Simulating: |============= |Simulating: |============== |Simulating: |=============== |Simulating: |================ |Simulating: |================= |Simulating: |================== |Simulating: |=================== |Simulating: |==================== |Simulating: |===================== |Simulating: |====================== |Simulating: |======================= |Simulating: |======================== |Simulating: |========================= |Simulating: |========================== |Simulating: |=========================== |Simulating: |============================ |Simulating: |============================= |Simulating: |============================== |Simulating: |=============================== |Simulating: |================================ |Simulating: |================================= |Simulating: |================================== |Simulating: |=================================== |Simulating: |==================================== |Simulating: |===================================== |Simulating: |====================================== |Simulating: |======================================= |Simulating: |======================================== |Simulating: |========================================= |Simulating: |========================================== |Simulating: |=========================================== |Simulating: |============================================ |Simulating: |============================================= |Simulating: |============================================== |Simulating: |=============================================== |Simulating: |================================================ |Simulating: |================================================= |Simulating: |================================================== |Simulating: |=================================================== |Simulating: |==================================================== |Simulating: |===================================================== |Simulating: |====================================================== |Simulating: |======================================================= |Simulating: |======================================================== |Simulating: |========================================================= |Simulating: |========================================================== |Simulating: |=========================================================== |Simulating: |============================================================ |Simulating: |=============================================================|## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationsim2## Power for predictor 'Condition_c_effSame', (95% confidence interval):
## 56.25% (52.73, 59.72)
##
## Test: z-test
## Effect size for Condition_c_effSame is 0.091
##
## Based on 800 simulations, (0 warnings, 0 errors)
## alpha = 0.05, nrow = 1080
##
## Time elapsed: 0 h 11 m 23 s
##
## nb: result might be an observed power calculation``` ##Power simulation with N = 1200. Monte Carlo simulations with 1k simulations
#First we extend the participant column so that is contains 1200 participants -- essentially doubling our sample size
sim2 <- extend(collapsed.sampling.1, along="Participant", n=1200)Sampling more from the in group with n = 1200
#Next we run power analysis on the coefficient of interest. let's start with first sample holding valence and condition constant
sim2.pow <- powerSim(sim2, fixed("Samp_GroupB_dumIn.Group", "z"), seed = 2, nsim = 800, alpha = .05)## Simulating: | |Simulating: |= |Simulating: |== |Simulating: |=== |Simulating: |==== |Simulating: |===== |Simulating: |====== |Simulating: |======= |Simulating: |======== |Simulating: |========= |Simulating: |========== |Simulating: |=========== |Simulating: |============ |Simulating: |============= |Simulating: |============== |Simulating: |=============== |Simulating: |================ |Simulating: |================= |Simulating: |================== |Simulating: |=================== |Simulating: |==================== |Simulating: |===================== |Simulating: |====================== |Simulating: |======================= |Simulating: |======================== |Simulating: |========================= |Simulating: |========================== |Simulating: |=========================== |Simulating: |============================ |Simulating: |============================= |Simulating: |============================== |Simulating: |=============================== |Simulating: |================================ |Simulating: |================================= |Simulating: |================================== |Simulating: |=================================== |Simulating: |==================================== |Simulating: |===================================== |Simulating: |====================================== |Simulating: |======================================= |Simulating: |======================================== |Simulating: |========================================= |Simulating: |========================================== |Simulating: |=========================================== |Simulating: |============================================ |Simulating: |============================================= |Simulating: |============================================== |Simulating: |=============================================== |Simulating: |================================================ |Simulating: |================================================= |Simulating: |================================================== |Simulating: |=================================================== |Simulating: |==================================================== |Simulating: |===================================================== |Simulating: |====================================================== |Simulating: |======================================================= |Simulating: |======================================================== |Simulating: |========================================================= |Simulating: |========================================================== |Simulating: |=========================================================== |Simulating: |============================================================ |Simulating: |=============================================================|## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationsim2.pow## Power for predictor 'Samp_GroupB_dumIn.Group', (95% confidence interval):
## 77.00% (73.92, 79.87)
##
## Test: z-test
## Effect size for Samp_GroupB_dumIn.Group is 0.045
##
## Based on 800 simulations, (0 warnings, 0 errors)
## alpha = 0.05, nrow = 2400
##
## Time elapsed: 0 h 22 m 34 s
##
## nb: result might be an observed power calculationSample more in the same condition with n = 1200
#Next, let's look at the sampling more in the same coefficient with 1200 participants.
sim3.pow <- powerSim(sim2, fixed("Condition_c_effSame", "z"), seed = 2, nsim = 800, alpha = .05 )## Simulating: | |Simulating: |= |Simulating: |== |Simulating: |=== |Simulating: |==== |Simulating: |===== |Simulating: |====== |Simulating: |======= |Simulating: |======== |Simulating: |========= |Simulating: |========== |Simulating: |=========== |Simulating: |============ |Simulating: |============= |Simulating: |============== |Simulating: |=============== |Simulating: |================ |Simulating: |================= |Simulating: |================== |Simulating: |=================== |Simulating: |==================== |Simulating: |===================== |Simulating: |====================== |Simulating: |======================= |Simulating: |======================== |Simulating: |========================= |Simulating: |========================== |Simulating: |=========================== |Simulating: |============================ |Simulating: |============================= |Simulating: |============================== |Simulating: |=============================== |Simulating: |================================ |Simulating: |================================= |Simulating: |================================== |Simulating: |=================================== |Simulating: |==================================== |Simulating: |===================================== |Simulating: |====================================== |Simulating: |======================================= |Simulating: |======================================== |Simulating: |========================================= |Simulating: |========================================== |Simulating: |=========================================== |Simulating: |============================================ |Simulating: |============================================= |Simulating: |============================================== |Simulating: |=============================================== |Simulating: |================================================ |Simulating: |================================================= |Simulating: |================================================== |Simulating: |=================================================== |Simulating: |==================================================== |Simulating: |===================================================== |Simulating: |====================================================== |Simulating: |======================================================= |Simulating: |======================================================== |Simulating: |========================================================= |Simulating: |========================================================== |Simulating: |=========================================================== |Simulating: |============================================================ |Simulating: |=============================================================|## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationsim3.pow## Power for predictor 'Condition_c_effSame', (95% confidence interval):
## 89.62% (87.30, 91.65)
##
## Test: z-test
## Effect size for Condition_c_effSame is 0.091
##
## Based on 800 simulations, (0 warnings, 0 errors)
## alpha = 0.05, nrow = 2400
##
## Time elapsed: 0 h 57 m 32 s
##
## nb: result might be an observed power calculationPower curve for the in group first sample coefficient
Simulating 10X1000
sim2.power.curve <- powerCurve(sim2, test = fixed("Samp_GroupB_dumIn.Group", "z"), along ="Participant" , nsim=800)## Calculating power at 10 sample sizes along Participant## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationsim3.power.curve <- powerCurve(sim2, test = fixed("Condition_c_effSame", "z"), along ="Participant" , nsim=800)## Calculating power at 10 sample sizes along Participant
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients## Warning in observedPowerWarning(sim): This appears to be an "observed
## power" calculationplotting the power curves for different coefficients
plot(sim2.power.curve)
plot(sim3.power.curve)