Generalized Mixed Model

Model Info
Info	Value	Comment
Model Type	Logistic	Model for binary y
Call	glm	Hall ~ 1 + T30 + position + (1 \| subject)
Link function	Logit	Log of the odd of y=1 over y=0
Direction	P(y=1)/P(y=0)	P( Hall = false ) / P( Hall = true )
Distribution	Binomial	Dichotomous event distribution of y
LogLikel.	-674.59	Less is better
R-squared	0.40	Marginal
R-squared	0.49	Conditional
AIC	1357.17	Less is better
BIC	1377.64	Less is better
Deviance	1217.05	Conditional
Residual DF	1228.00

Model Results

Fixed Effect Omnibus tests
	X²	df	p
T30	163.72	1.00	< .001
position	0.24	1.00	0.627

Fixed Effects Parameter Estimates
					95% Exp(B) Confidence Interval
Names	Effect	Estimate	SE	exp(B)	Lower	Upper	z	p
(Intercept)	(Intercept)	-0.67	0.12	0.51	0.41	0.64	-5.79	< .001
T30	T30	-1.09	0.08	0.34	0.29	0.40	-12.80	< .001
position1	back right - middle center	-0.07	0.14	0.93	0.71	1.23	-0.49	0.627

Random Components
Groups	Name	SD	Variance
subject	(Intercept)	0.78	0.61
Residuals		1.00	1.00
Note. Number of Obs: 1232 , groups: subject , 88

Effects Plots

Generalized Mixed Model

contrasts apply only to factors

Debug

Error in `contrasts<-`(`*tmp*`, ncol(ca), value = ca): contrasts apply only to factors

private$.run()
private$.preparePlots(private$.model)
predict(model, type = type, newdata = newdata)
predict.merMod(model, type = type, newdata = newdata)
model.matrix(RHS, data = mfnew, contrasts.arg = attr(X, "contrasts"))
model.matrix.default(RHS, data = mfnew, contrasts.arg = attr(X, "contrasts"))
`contrasts<-`(`*tmp*`, ncol(ca), value = ca)
stop("contrasts apply only to factors")

Model Info
Info	Value	Comment
Model Type	Logistic	Model for binary y
Call	glm	Hall ~ 1 + position + T30 + (1 \| subject)
Link function	Logit	Log of the odd of y=1 over y=0
Direction	P(y=1)/P(y=0)	P( Hall = false ) / P( Hall = true )
Distribution	Binomial	Dichotomous event distribution of y
LogLikel.	-674.59	Less is better
R-squared	0.40	Marginal
R-squared	0.49	Conditional
AIC	1357.17	Less is better
BIC	1377.64	Less is better
Deviance	1217.05	Conditional
Residual DF	1228.00

Model Results

Fixed Effect Omnibus tests
	X²	df	p
position	0.24	1.00	0.627
T30	163.72	1.00	< .001

Fixed Effects Parameter Estimates
					95% Exp(B) Confidence Interval
Names	Effect	Estimate	SE	exp(B)	Lower	Upper	z	p
(Intercept)	(Intercept)	-0.67	0.12	0.51	0.41	0.64	-5.79	< .001
position1	back right - middle center	-0.07	0.14	0.93	0.71	1.23	-0.49	0.627
T30	T30	-1.09	0.08	0.34	0.29	0.40	-12.80	< .001

Random Components
Groups	Name	SD	Variance
subject	(Intercept)	0.78	0.61
Residuals		1.00	1.00

Effects Plots

Mixed Model

Model Info
Info
Estimate		Linear mixed model fit by REML
Call		Halligkeit ~ 1 + T30 + position+( 1 \| subject )
AIC		11693.98
BIC		11714.09
R-squared Marginal		0.26
R-squared Conditional		0.33

Model Results

Fixed Effect Omnibus tests
	F	Num df	Den df	p
T30	461.01	1	1187.96	< .001
position	1.29	1	1205.33	0.256

Fixed Effects Parameter Estimates
				95% Confidence Interval
Names	Effect	Estimate	SE	Lower	Upper	df	t	p
(Intercept)	(Intercept)	51.83	1.19	49.50	54.17	86.61	43.48	< .001
T30	T30	11.30	0.53	10.26	12.33	1187.96	21.47	< .001
position1	back right - middle center	1.79	1.57	-1.30	4.87	1205.33	1.14	0.256

Random Components
Groups	Name	SD	Variance	ICC
subject	(Intercept)	8.56	73.24	0.09
Residual		26.91	724.39

Effects Plots

Note: Random effects are plotted by subject

Generalized Mixed Model

Model Info
Info	Value	Comment
Model Type	Logistic	Model for binary y
Call	glm	Hall ~ 1 + T30 + (1 \| subject)
Link function	Logit	Log of the odd of y=1 over y=0
Direction	P(y=1)/P(y=0)	P( Hall = false ) / P( Hall = true )
Distribution	Binomial	Dichotomous event distribution of y
LogLikel.	-674.70	Less is better
R-squared	0.40	Marginal
R-squared	0.49	Conditional
AIC	1355.41	Less is better
BIC	1370.76	Less is better
Deviance	1216.90	Conditional
Residual DF	1229.00

Model Results

Fixed Effect Omnibus tests
	X²	df	p
T30	163.60	1.00	< .001

Fixed Effects Parameter Estimates
				95% Exp(B) Confidence Interval
Names	Estimate	SE	exp(B)	Lower	Upper	z	p
(Intercept)	-0.67	0.12	0.51	0.41	0.64	-5.78	< .001
T30	-1.08	0.08	0.34	0.29	0.40	-12.79	< .001

Random Components
Groups	Name	SD	Variance
subject	(Intercept)	0.78	0.61
Residuals		1.00	1.00

Effects Plots

Note: Random effects are plotted by subject Results