# eofcor_pcmsg

Calculates empirical orthogonal functions via a correlation matrix (missing values allowed) (deprecated version).

## Prototype

function eofcor_pcmsg ( data : numeric, neval : integer, pcrit : float ) return_val : numeric

## Arguments

*data*

A multi-dimensioned array in which the rightmost dimension is the number of observations. Generally, this is the time dimension.

*neval*

A Scalar integer that specifies the number of eigenvalues and eigenvectors to be returned. This is usually less than or equal to the minimum number of observations or number of variables. 3 to 5 typically.

*pcrit*

A scalar value that indicates the percentage of non-missing points that must exist at any single point in order to be calculated. The default is 50%. Points that contain all missing values will automatically be set to missing.

## Return value

A multi-dimensional array of the same size as *data* with the rightmost
dimension removed and an additional leftmost dimension of the same size as
*neval* added. Double if *data* is double, float otherwise.

Will contain the following
attributes:

- trace: A scalar value equal to the trace of the covariance/correlation matrix
- eval: A one-dimensional array the same size as
*neval*containing the eigenvalues in descending order. - pcvar: A one-dimensional array the same size as
*neval*containing the percent variance associated with each eigenvalue - eof_function: A scalar integer:
- 0 =
**eofcov**was used to compute the EOFs - 1 =
**eofcor**was used to compute the EOFs - 2 =
**eofcov_pcmsg**was used to compute the EOFs - 3 =
**eofcor_pcmsg**was used to compute the EOFs

- 0 =

print(return_val@trace) print(return_val@eval)

## Description

This function is deprecated and has been replaced by the faster
**eofunc**.

Calculates empirical orthogonal functions via a correlation matrix. Though not
required, it is recommended that the values be anomalies from the temporal means at
each station/grid-point. **dim_rmvmean** may be used to accomplish
this. The eigenvectors are calculated using
LAPACK's
"dspevx" routine.

Points with all
missing values are ignored.

**Note on weighting observations**

Generally, when performing and EOF analysis on observations over the globe or a portion of the globe, the values are weighted prior to calculating. This is usually required to account for the convergence of the meridions (area weighting) which lessens the impact of high-latitude grid points that represent a small area of the globe. Most frequently, the square root of the cosine of the latitude is used to compute the area weight. The square root is used to create a covariance matrix that reflects the area of each matrix element. If weighted in this manner, the resulting covariance values will include quantities calculated via:

[x*Note that the covariance of a grid point with itself yields standard cosine weighting:sqrt(cos(lat(x)))]*[y*sqrt(cos(lat(y)))] = x*y*sqrt(cos(lat(x))*sqrt(cos(lat(y))

[x*sqrt(cos(lat(x)))]*[x*sqrt(cos(lat(x)))] = x^2 *cos(lat(x)).

**Note on standard EOF analysis**

Conventional EOF analysis yields patterns and time series which are both orthogonal. The derived patterns are a function of the domain. These patterns may produce patterns they are similar to physical modes of the system. However, the procedure is strictly mathematical (not statistical) and is not based upon physics.

## See Also

This function is deprecated, please use **eofunc**

## Examples

In the following, the attribute pcvar can be output via:

print(ev@pcvar) ; 1D vector of length "neval"

This attribute could also be used in graphics. For example, it is it could be used in a title.

title = "%=" + ev@pcvar(1)

**sprintf** can be used to format the title more precisely:

title = "%=" +sprintf("%5.2f", ev@pcvar(1) )

**Example 1**

Let x be two dimensional with dimensions variables (size = nvar) and time:

neval = 3 ; calculate 3 EOFs out of 7 ev =eofcor_pcmsg(x,neval,60) ; ev(neval,nvar)

**Example 2**

Let x be three-dimensional with dimensions of time, lat, lon. Reorder x so that time is the rightmost dimension:

y!0 = "time" ; name dimensions if not already done y!1 = "lat" ; must be named to reorder y!2 = "lon" neval = nvar ; calculate all EOFs ev =eofcor_pcmsg(y(lat|:,lon|:,time|:),neval,75) ; ev(neval,nlat,nlon)

**Example 3**

Let z be four-dimensional with dimensions lev, lat, lon, and time:

neval = 3 ; calculate 3 EOFs out of klev*nlat*mlon ev =eofcor_pcmsg(z,neval,45) ; ev will be dimensioned neval, level, lat, lon

**Example 4**

Calculate the EOFs at every other point rather. Use of a temporary array is NOT necessary but it avoids having to reorder the array twice in this example:

neval = 5 ; calculate 5 EOFs out of nlat*mlon zTemp = z(lat|::2,lon|::2,time|:) ; reorder and use temporary array ev =eofcor_pcmsg(zTemp,neval,95) ; ev(neval,nlat/2,mlon/2)

**Example 5**

Let z be four-dimensional with dimensions level, lat, lon, time. Calculate the EOFs at one specified level:

kl = 3 ; specify level neval = 8 ; calculate 8 EOFs out of nlat*mlon ev =eofcor_pcmsg(z(kl,:,:,:),neval,88) ; ev will be dimensioned neval, lat, lon

**Example 6**

Let z be four-dimensional with dimensions time, lev, lat, lon. Reorder x so that time is the rightmost dimension and calculate on one specified level:

kl = 3 ; specify level neval = 8 ; calculate 8 EOFs out of nlat*mlon zTemp = z(lev|kl,lat|:,lon|:,time|:) ev =eofcor_pcmsg(zTemp,neval,90) ; ev will be dimensioned neval, lat, lon

**Example 7**

Area weight the data prior to calculation. Let p be four-dimensional with dimensions lat, lon, and time. The array lat contains the latitudes.

; calculate the weights using the square root of the cosine of the latitude and ; also convert degrees to radians wgt =sqrt(cos(lat*0.01745329)) ; reorder data so time is fastest varying pt = p(lat|:,lon|:,time|:) ; (lat,lon,time) ptw = pt ; create an array with metadata ; weight each point prior to calculation. ;conformis used to make wgt the same size as pt ptw = pt*conform(pt,wgt,0) evec= eofcor_pcmsg(ptw,neval,93)