- Forest plays a special role in carbon sequestration and thus mitigating climate change. However, the large uncertainty in biomass estimation is unable to meet the requirement of the accurate carbon accounting. The use of a suitable and rigor method to accurately estimate forest biomass is significant. Moreover, the world is increasingly facing the conflicting pressures of economic growth.
- Step 3) Feature engineering Recast education. From the graph above, you can see that the variable education has 16 levels. This is substantial, and some levels have a relatively low number of observations.
- Dummies has always stood for taking on complex concepts and making them easy to understand. Dummies helps everyone be more knowledgeable and confident in applying what they know.
- Arguments object. Either a number indicating the label to extract or a character string with the variable name for which the label should be extracted. One can also use a vector of numerics or character strings to extract mutiple labels.
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cluster.plot {psych} | R Documentation |
Plot factor/cluster loadings and assign items to clusters by their highest loading.
Description
Cluster analysis and factor analysis are procedures for grouping items in terms of a smaller number of (latent) factors or (observed) clusters. Graphical presentations of clusters typically show tree structures, although they can be represented in terms of item by cluster correlations. 8 rototiller.
Cluster.plot plots items by their cluster loadings (taken, e.g., from
ICLUST
) or factor loadings (taken, eg., from fa
). Cluster membership may be assigned apriori or may be determined in terms of the highest (absolute) cluster loading for each item. If the input is an object of class 'kmeans', then the cluster centers are plotted.
Usage
![Plot Plot](/uploads/1/3/9/1/139192993/664647848.png)
Arguments
ic.results | A factor analysis or cluster analysis output including the loadings, or a matrix of item by cluster correlations. Or the output from a kmeans cluster analysis. |
cluster | A vector of cluster membership |
cut | Assign items to clusters if the absolute loadings are > cut |
labels | If row.names exist they will be added to the plot, or, if they don't, labels can be specified. If labels =NULL, and there are no row names, then variables are labeled by row number.) |
title | Any title |
jiggle | When plotting with factor loadings that are almost identical, it is sometimes useful to 'jiggle' the points by jittering them. The default is to not jiggle. |
amount | if jiggle=TRUE, then how much should the points be jittered? |
pch | factor and clusters are shown with different pch values, starting at pch+1 |
pos | Position of the text for labels for two dimensional plots. 1=below, 2 = left, 3 = above, 4= right |
show.points | When adding labels to the points, should we show the points as well as the labels. For many points, better to not show them, just the labels. |
choose | Specify the factor/clusters to plot |
.. | Further options to plot |
![Review Review](/uploads/1/3/9/1/139192993/449472461.png)
Details
Plot With Factors Ratio
Results of either a factor analysis or cluster analysis are plotted. Each item is assigned to its highest loading factor, and then identified by variable name as well as cluster (by color). The cluster assignments can be specified to override the automatic clustering by loading.Both of these functions may be called directly or by calling the generic plot function. (see example).
Value
Plot With Factors Ratios
Graphical output is presented.
Plot With Factors In R
Author(s)
William Revelle
See Also
ICLUST
, ICLUST.graph
, fa.graph
, plot.psych