MANOVA Analysis of Time Series Data - Revision history

Chris: /* Run the MANOVA */

2018-10-02T16:28:59Z

Run the MANOVA

← Older revision		Revision as of 12:28, 2 October 2018
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	=Run the MANOVA=		=Run the MANOVA=
	The first thing to point out is that you need to have sufficient samples for the number of columns. If you are comparing 64 time points for two groups, you will need 128 (? I should know this maybe) samples, or else R will complain.		The first thing to point out is that you need to have sufficient samples for the number of columns. If you are comparing 64 time points for two groups, you will need 128 (? I should know this maybe) samples, or else R will complain.
	Assuming you have sufficient data, the MANOVA is easy. ~~The~~ first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:		Assuming you have sufficient data, the MANOVA is easy. In the above example, the first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:
	> fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)		> fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)
	Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 dependent variables are in columns 2 to 65.		Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 dependent variables are in columns 2 to 65.

Chris: /* Run the MANOVA */

2018-09-28T18:39:09Z

Run the MANOVA

← Older revision		Revision as of 14:39, 28 September 2018
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	Assuming you have sufficient data, the MANOVA is easy. The first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:		Assuming you have sufficient data, the MANOVA is easy. The first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:
	> fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)		> fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)
	Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 variables are in columns 2 to 65.		Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 dependent variables are in columns 2 to 65.
	It's trivially easy to analyze a smaller window:		It's trivially easy to analyze a smaller window:
	> fit=manova(as.matrix(dat[,24:26]) ~ X, data=dat)		> fit=manova(as.matrix(dat[,24:26]) ~ X, data=dat)

Chris: /* Read in Table */

2018-09-28T18:38:09Z

Read in Table

← Older revision		Revision as of 14:38, 28 September 2018
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	Read the data into a data frame:		Read the data into a data frame:
	dat=read.table('training_accuracy.txt', header=TRUE, sep='\t')		> dat=read.table('training_accuracy.txt', header=TRUE, sep='\t')

	=Run the MANOVA=		=Run the MANOVA=

Chris: /* Run the MANOVA */

2018-09-28T18:37:56Z

Run the MANOVA

← Older revision		Revision as of 14:37, 28 September 2018
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	The first thing to point out is that you need to have sufficient samples for the number of columns. If you are comparing 64 time points for two groups, you will need 128 (? I should know this maybe) samples, or else R will complain.		The first thing to point out is that you need to have sufficient samples for the number of columns. If you are comparing 64 time points for two groups, you will need 128 (? I should know this maybe) samples, or else R will complain.
	Assuming you have sufficient data, the MANOVA is easy. The first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:		Assuming you have sufficient data, the MANOVA is easy. The first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:
	fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)		> fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)
	Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 variables are in columns 2 to 65.		Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 variables are in columns 2 to 65.
	It's trivially easy to analyze a smaller window:		It's trivially easy to analyze a smaller window:
	fit=manova(as.matrix(dat[,24:26]) ~ X, data=dat)		> fit=manova(as.matrix(dat[,24:26]) ~ X, data=dat)

	=Get the MANOVA Results=		=Get the MANOVA Results=

Chris: Created page with "Analyses of multiple (rather than repeated) measures for an individual should be carried out using MANOVA. It turns out it's fairly simple to do in R. =Read in Table= Assume..."

2018-09-28T18:36:36Z

Created page with "Analyses of multiple (rather than repeated) measures for an individual should be carried out using MANOVA. It turns out it's fairly simple to do in R. =Read in Table= Assume..."

New page

Analyses of multiple (rather than repeated) measures for an individual should be carried out using MANOVA. It turns out it's fairly simple to do in R.

=Read in Table=
Assume you have your data structured as follows:
X T01 T02 T03 ... TNN
a .33 .38 .43 .78
a .32 .39 .47 .80
b .36 .41 .44 .77
b .31 .35 .40 .81

Read the data into a data frame:
dat=read.table('training_accuracy.txt', header=TRUE, sep='\t')

=Run the MANOVA=
The first thing to point out is that you need to have sufficient samples for the number of columns. If you are comparing 64 time points for two groups, you will need 128 (? I should know this maybe) samples, or else R will complain.
Assuming you have sufficient data, the MANOVA is easy. The first column is your grouping variable. The rest of the columns, T01 to TNN are your time points. Rather than type them all out when calling the manova function, you can just refer to them by index:
fit=manova(as.matrix(dat[,2:65]) ~ X, data=dat)
Here, I am running the MANOVA on all 64 time points. Since column 1 is my grouping variable, X, the 64 variables are in columns 2 to 65.
It's trivially easy to analyze a smaller window:
fit=manova(as.matrix(dat[,24:26]) ~ X, data=dat)

=Get the MANOVA Results=
> summary(fit)
Df Pillai approx F num Df den Df Pr(>F)
X 1 0.56045 1.2551 64 63 0.1841