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MixedEffectsANOVA

Linear mixed models extend standard linear models by allowing 'random' effects in addition to the standard 'fixed' effects.

Linear mixed models are used to analyze data that are non independent (e.g., repetitions from each subject), multi-level / hierarchical (e.g., different datasets collected at different institutions and 'institution' is not a controlled variable), or longitudinal. See below URL for more information. More Info... Version 0.1.0

Ports/Properties

data

A packet containing at least one signal chunk with an instance axis. The chunk may have gone through DescribeStatisticalDesign previously to help design the linear model. The output is a packet containing univariate statistics.

verbose name
Data
default value
None
port type
DataPort
value type
Packet (can be None)
data direction
INOUT

result

The ANOVA model. Unused.

verbose name
Result
default value
None
port type
DataPort
value type
object (can be None)
data direction
OUT

random_intercept_fields

Instance axis data field names specified in this list are 'random effects' and are used to group instances together. A random effect is generally something that can be expected to have a non-systematic, idiosyncratic, unpredictable, or "random" influence on your data. These are sources of non-independence. In the linear model, each unique item grouped by this field(s) is allowed to have its own intercept. For example, if your dataset has multiple subjects and you have multiple responses from each subject then the different responses from each subject are not independent and you need to include 'Subject' in this list. Any items in this list will be excluded from the list of fixed-effect factors. Contrary to random effects factors, Fixed effects are expected to have a systematic and predictable influence on your data, and the experiment should exhaust all possible levels of this factor.

verbose name
Random Intercept Fields
default value
None
port type
ListPort
value type
list (can be None)

random_slope_fields

List of instance axis data field names specified in this list must be part of the predictors in the linear model, and are expected to have different effect-sizes for each of the unique items as grouped by the random_intercept_fields argument. For example, if each subject performed two difficulty levels of a task (e.g. an easy and a hard version), but you have reason to think that difficulty may affect subjects differently based on their video game experience (which is not measured), then Difficulty should be included in this list.

verbose name
Random Slope Fields
default value
None
port type
ListPort
value type
list (can be None)

par_cores

Number of cores to use in parallel processing. 1: Do not do parallel processing. -1: Auto-detect the number of cores. >1: Do parallel processing with this many cores.

verbose name
Par Cores
default value
1
port type
IntPort
value type
int (can be None)

use_caching

Enable caching.

verbose name
Use Caching
default value
False
port type
BoolPort
value type
bool (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

verbose name
Set Breakpoint (Debug Only)
default value
False
port type
BoolPort
value type
bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

verbose name
Metadata
default value
{}
port type
DictPort
value type
dict (can be None)