Linear mixed models in lmt - Revision history

127.0.0.1: /* Column rank of $$X$$ */

2022-04-20T05:51:06Z

Column rank of $$X$$

← Older revision		Revision as of 05:51, 20 April 2022
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	===Column rank of $$X$$===		===Column rank of $$X$$===
	lmt will try to find and remove linear dependencies in $$X$$. However, the respective computations are applied within trait and include only fixed classification variables. That means fitting classification variables implicitly via applying polynomials of power zero to a continuous co-varibales, or switching off the centering of continuous co-variables, can lead to linear dependencies in $$X$$ which are not accounted for. Then subsequent operations may fail or results maybe incorrect.		lmt will try to find and remove linear dependencies in $$X$$. However, the respective computations are applied within trait and include only fixed classification variables. That means fitting classification variables implicitly via applying polynomials of power zero to a continuous co-varibales, or switching off the centering of continuous co-variables, or fitting genetic groups as fixed can lead to linear dependencies in $$X$$ which are not accounted for. Then subsequent operations may fail or results maybe incorrect.

Boerner: /* Getting $$X$$ to full column rank */

2022-03-04T02:04:09Z

Getting $$X$$ to full column rank

← Older revision		Revision as of 02:04, 4 March 2022
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	{{lmt}} will center fixed continuous variables. That is, the polynomial $$x+x^2$$ becomes $$x-\overline{x}+(x-\overline{x})^2$$. Nested variables will be centered within their nesting. Centering implies that one should not fit an intercept via $$x^0$$ as it will results in an error message. However, centering can be explicitly switched off in the specifier of each variable.		{{lmt}} will center fixed continuous variables. That is, the polynomial $$x+x^2$$ becomes $$x-\overline{x}+(x-\overline{x})^2$$. Nested variables will be centered within their nesting. Centering implies that one should not fit an intercept via $$x^0$$ as it will results in an error message. However, centering can be explicitly switched off in the specifier of each variable.

	===~~Getting~~ $$X$$ ~~to full column rank~~===		===Column rank of $$X$$===
	lmt will try to find and remove linear dependencies in $$X$$. However, the respective ~~algorithm is~~ applied within trait and ~~includes~~ only fixed classification variables. That means ~~that~~ fitting classification variables implicitly via applying ~~a polynomial~~ of power zero to a continuous co-~~varibale~~, or switching off the centering of continuous co-variables can lead to linear ~~dependecies~~ in $$X$$ which are not accounted for. Then subsequent operations may fail or results maybe incorrect.		lmt will try to find and remove linear dependencies in $$X$$. However, the respective computations are applied within trait and include only fixed classification variables. That means fitting classification variables implicitly via applying polynomials of power zero to a continuous co-varibales, or switching off the centering of continuous co-variables, can lead to linear dependencies in $$X$$ which are not accounted for. Then subsequent operations may fail or results maybe incorrect.

Boerner: /* Getting $$X$$ to full column rank */

2022-03-04T02:02:19Z

Getting $$X$$ to full column rank

← Older revision		Revision as of 02:02, 4 March 2022
Line 387:		Line 387:

	===Getting $$X$$ to full column rank===		===Getting $$X$$ to full column rank===
			lmt will try to find and remove linear dependencies in $$X$$. However, the respective algorithm is applied within trait and includes only fixed classification variables. That means that fitting classification variables implicitly via applying a polynomial of power zero to a continuous co-varibale, or switching off the centering of continuous co-variables can lead to linear dependecies in $$X$$ which are not accounted for. Then subsequent operations may fail or results maybe incorrect.

Boerner: /* {{lmt}}'s factor naming convention */

2021-10-25T11:08:50Z

{{lmt}}'s factor naming convention

← Older revision		Revision as of 11:08, 25 October 2021
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	{{lmt}} models as many factors as there are independent co-variance structures in the model, a factor for all fixed classification variables, and a factor for all fixed continuous co-variables. These factors are internally named, where the internal names are used in the output files, in output file names, and to override defaults in the parameter file. The convention used to name the factors is:		{{lmt}} models as many factors as there are independent co-variance structures in the model, a factor for all fixed classification variables, and a factor for all fixed continuous co-variables. These factors are internally named, where the internal names are used in the output files, in output file names, and to override defaults in the parameter file. The convention used to name the factors is:

	*"~~fxcl~~" is the default name for the factor modelling the fixed classification variables		*"fxco" is the default name for the factor modelling the fixed classification variables and fixed continuous variables
	*"fxco" is the default name for the factor modelling the fixed continuous variables
	*the names of random factors are user defined where the actual name is that of the variance		*the names of random factors are user defined where the actual name is that of the variance

127.0.0.1: /* Model syntax */

2021-10-22T00:06:27Z

Model syntax

← Older revision		Revision as of 00:06, 22 October 2021
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	=== Model syntax ===		=== Model syntax ===

	~~The~~ syntax for communicating the model to {{lmt}} is effectively: "'''just write the model'''", where the model is written [[Parameter_file_elements#.3Ceqn.3E\|into the space of a particular xml element of the parameter file]].		Thee syntax for communicating the model to {{lmt}} is effectively: "'''just write the model'''", where the model is written [[Parameter_file_elements#.3Ceqn.3E\|into the space of a particular xml element of the parameter file]].


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	A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.		A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.

	==={{lmt}}'s factor naming convention===		==={{lmt}}'s factor naming convention===

127.0.0.1: /* Hard-coded polynomials */

2021-07-21T11:43:24Z

Hard-coded polynomials

← Older revision		Revision as of 11:43, 21 July 2021
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	!polynomial		!polynomial
	!abbreviation		!abbreviation
			\|-
			\|Legendre order 0
			\|l0
	\|-		\|-
	\|Legendre order 1		\|Legendre order 1

Boerner: /* Treatment of fixed co-variables */

2021-03-13T04:41:51Z

Treatment of fixed co-variables

← Older revision		Revision as of 04:41, 13 March 2021
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	*the names of random factors are user defined where the actual name is that of the variance		*the names of random factors are user defined where the actual name is that of the variance

	===Treatment of fixed ~~co-~~variables===		===Treatment of fixed continuous variables===
			{{lmt}} will center fixed continuous variables. That is, the polynomial $$x+x^2$$ becomes $$x-\overline{x}+(x-\overline{x})^2$$. Nested variables will be centered within their nesting. Centering implies that one should not fit an intercept via $$x^0$$ as it will results in an error message. However, centering can be explicitly switched off in the specifier of each variable.

	===Getting $$X$$ to full column rank===		===Getting $$X$$ to full column rank===

Boerner: /* {{lmt}}'s factor naming convention */

2021-03-13T04:35:38Z

{{lmt}}'s factor naming convention

← Older revision		Revision as of 04:35, 13 March 2021
Line 374:		Line 374:
	==={{lmt}}'s factor naming convention===		==={{lmt}}'s factor naming convention===

	{{lmt}} models as many factors as there are independent co-variance structures in the model, a factor for all fixed classification variables and a factor for all fixed continuous co-variables. These factors are named, where the names are used in the output files, in output file names and to override defaults in the parameter file. The convention used to name the factors is:		{{lmt}} models as many factors as there are independent co-variance structures in the model, a factor for all fixed classification variables, and a factor for all fixed continuous co-variables. These factors are internally named, where the internal names are used in the output files, in output file names, and to override defaults in the parameter file. The convention used to name the factors is:

	*"fxcl" is the default name for the factor modelling the fixed classification variables		*"fxcl" is the default name for the factor modelling the fixed classification variables

Boerner at 04:34, 13 March 2021

2021-03-13T04:34:09Z

← Older revision		Revision as of 04:34, 13 March 2021
Line 372:		Line 372:

	A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.		A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.
	===~~Factor~~ naming convention===		==={{lmt}}'s factor naming convention===

			{{lmt}} models as many factors as there are independent co-variance structures in the model, a factor for all fixed classification variables and a factor for all fixed continuous co-variables. These factors are named, where the names are used in the output files, in output file names and to override defaults in the parameter file. The convention used to name the factors is:

			*"fxcl" is the default name for the factor modelling the fixed classification variables
			*"fxco" is the default name for the factor modelling the fixed continuous variables
			*the names of random factors are user defined where the actual name is that of the variance

	===Treatment of fixed co-variables===		===Treatment of fixed co-variables===
	===Getting $$X$$ to full column rank===		===Getting $$X$$ to full column rank===

Boerner at 04:24, 13 March 2021

2021-03-13T04:24:18Z

← Older revision		Revision as of 04:24, 13 March 2021
Line 372:		Line 372:

	A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.		A miss-fit between the number of referenced polynomials and the number of referenced $$\Sigma$$ diagonal elements will cause an error stop.
			===Factor naming convention===
			===Treatment of fixed co-variables===
			===Getting $$X$$ to full column rank===