[@{FREQ,PERCENT@}] [@{NOMISSING,MISSING@}]
/BARCHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
[@{FREQ,PERCENT@}]
+ /ORDER=@{ANALYSIS,VARIABLE@}
(These options are not currently implemented.)
The @subcmd{FREQ} and @subcmd{PERCENT} options on @subcmd{HISTOGRAM} and
@subcmd{PIECHART} are accepted but not currently honoured.
+The @subcmd{ORDER} subcommand is accepted but ignored.
+
@node EXAMINE
@section EXAMINE
@display
GRAPH
- /HISTOGRAM = @var{var}
- /SCATTERPLOT [(BIVARIATE)] = @var{var1} WITH @var{var2} [BY @var{var3}]
+ /HISTOGRAM [(NORMAL)]= @var{var}
+ /SCATTERPLOT [(BIVARIATE)] = @var{var1} WITH @var{var2} [BY @var{var3}]
+ /BAR = @{@var{summary-function}(@var{var1}) | @var{count-function}@} BY @var{var2} [BY @var{var3}]
[ /MISSING=@{LISTWISE, VARIABLE@} [@{EXCLUDE, INCLUDE@}] ]
[@{NOREPORT,REPORT@}]
@subcmd{HISTOGRAM} or @subcmd{SCATTERPLOT} can be specified, i.e. only one plot
can be produced per call of @cmd{GRAPH}. The @subcmd{MISSING} is optional.
+@menu
+* SCATTERPLOT:: Cartesian Plots
+* HISTOGRAM:: Histograms
+* BAR CHART:: Bar Charts
+@end menu
+
+@node SCATTERPLOT
+@subsection Scatterplot
@cindex scatterplot
-The subcommand @subcmd{SCATTERPLOT} produces an xy plot of the data. The different
-values of the optional third variable @var{var3} will result in different colours and/or
-markers for the plot. The following is an example for producing a scatterplot.
+The subcommand @subcmd{SCATTERPLOT} produces an xy plot of the
+data. The different values of the optional third variable @var{var3}
+will result in different colours and/or markers for the plot. The
+following is an example for producing a scatterplot.
@example
GRAPH
on the value of the @var{gender} variable, the colour of the datapoint is different. With
this plot it is possible to analyze gender differences for @var{height} vs.@: @var{weight} relation.
+@node HISTOGRAM
+@subsection Histogram
@cindex histogram
The subcommand @subcmd{HISTOGRAM} produces a histogram. Only one variable is allowed for
the histogram plot.
+The keyword @subcmd{NORMAL} may be specified in parentheses, to indicate that the ideal normal curve
+should be superimposed over the histogram.
For an alternative method to produce histograms @pxref{EXAMINE}. The
following example produces a histogram plot for the variable @var{weight}.
/HISTOGRAM = @var{weight}.
@end example
+@node BAR CHART
+@subsection Bar Chart
+@cindex bar chart
+
+The subcommand @subcmd{BAR} produces a bar chart.
+This subcommand requires that a @var{count-function} be specified (with no arguments) or a @var{summary-function} with a variable @var{var1} in parentheses.
+Following the summary or count function, the keyword @subcmd{BY} should be specified and then a catagorical variable, @var{var2}.
+The values of the variable @var{var2} determine the labels of the bars to be plotted.
+Optionally a second categorical variable @var{var3} may be specified in which case a clustered (grouped) bar chart is produced.
+
+Valid count functions are
+@table @subcmd
+@item COUNT
+The weighted counts of the cases in each category.
+@item PCT
+The weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
+@item CUFREQ
+The cumulative weighted counts of the cases in each category.
+@item CUPCT
+The cumulative weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
+@end table
+
+The summary function is applied to @var{var1} across all cases in each category.
+The recognised summary functions are:
+@table @subcmd
+@item SUM
+The sum.
+@item MEAN
+The arithmetic mean.
+@item MAXIMUM
+The maximum value.
+@item MINIMUM
+The minimum value.
+@end table
+
+The following examples assume a dataset which is the results of a survey.
+Each respondent has indicated annual income, their sex and city of residence.
+One could create a bar chart showing how the mean income varies between of residents of different cities, thus:
+@example
+GRAPH /BAR = MEAN(@var{income}) BY @var{city}.
+@end example
+
+This can be extended to also indicate how income in each city differs between the sexes.
+@example
+GRAPH /BAR = MEAN(@var{income}) BY @var{city} BY @var{sex}.
+@end example
+
+One might also want to see how many respondents there are from each city. This can be achieved as follows:
+@example
+GRAPH /BAR = COUNT BY @var{city}.
+@end example
+
+Bar charts can also be produced using the @ref{FREQUENCIES} and @ref{CROSSTABS} commands.
+
@node CORRELATIONS
@section CORRELATIONS
[ /METHOD = @{CORRELATION, COVARIANCE@} ]
+ [ /ANALYSIS=@var{var_list} ]
+
[ /EXTRACTION=@{PC, PAF@}]
[ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}]
The @cmd{FACTOR} command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find
common factors in the data or for data reduction purposes.
-The @subcmd{VARIABLES} subcommand is required. It lists the variables which are to partake in the analysis.
+The @subcmd{VARIABLES} subcommand is required. It lists the variables
+which are to partake in the analysis. (The @subcmd{ANALYSIS}
+subcommand may optionally further limit the variables that
+participate; it is not useful and implemented only for compatibility.)
The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors (components) are extracted from the data.
If @subcmd{PC} is specified, then Principal Components Analysis is used.
@display
T-TEST
/MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
- /CRITERIA=CIN(@var{confidence})
+ /CRITERIA=CI(@var{confidence})
(One Sample mode.)
@display
QUICK CLUSTER @var{var_list}
- [/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})]]
+ [/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})] CONVERGE(@var{epsilon}) [NOINITIAL]]
[/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
+ [/PRINT=@{INITIAL@} @{CLUSTER@}]
@end display
The @cmd{QUICK CLUSTER} command performs k-means clustering on the
The minimum specification is @samp{QUICK CLUSTER} followed by the names
of the variables which contain the cluster data. Normally you will also
want to specify @subcmd{/CRITERIA=CLUSTERS(@var{k})} where @var{k} is the
-number of clusters. If this is not given, then @var{k} defaults to 2.
+number of clusters. If this is not specified, then @var{k} defaults to 2.
+
+If you use @subcmd{/CRITERIA=NOINITIAL} then a naive algorithm to select
+the initial clusters is used. This will provide for faster execution but
+less well separated initial clusters and hence possibly an inferior final
+result.
+
-The command uses an iterative algorithm to determine the clusters for
-each case. It will continue iterating until convergence, or until @var{max_iter}
-iterations have been done. The default value of @var{max_iter} is 2.
+@cmd{QUICK CLUSTER} uses an iterative algorithm to select the clusters centers.
+The subcommand @subcmd{/CRITERIA=MXITER(@var{max_iter})} sets the maximum number of iterations.
+During classification, @pspp{} will continue iterating until until @var{max_iter}
+iterations have been done or the convergence criterion (see below) is fulfilled.
+The default value of @var{max_iter} is 2.
+
+If however, you specify @subcmd{/CRITERIA=NOUPDATE} then after selecting the initial centers,
+no further update to the cluster centers is done. In this case, @var{max_iter}, if specified.
+is ignored.
+
+The subcommand @subcmd{/CRITERIA=CONVERGE(@var{epsilon})} is used
+to set the convergence criterion. The value of convergence criterion is @var{epsilon}
+times the minimum distance between the @emph{initial} cluster centers. Iteration stops when
+the mean cluster distance between one iteration and the next
+is less than the convergence criterion. The default value of @var{epsilon} is zero.
The @subcmd{MISSING} subcommand determines the handling of missing variables.
If @subcmd{INCLUDE} is set, then user-missing values are considered at their face
on the basis of the non-missing values.
The default is @subcmd{LISTWISE}.
+The @subcmd{PRINT} subcommand requests additional output to be printed.
+If @subcmd{INITIAL} is set, then the initial cluster memberships will
+be printed.
+If @subcmd{CLUSTER} is set, the cluster memberships of the individual
+cases will be displayed (potentially generating lengthy output).
+
@node RANK
@section RANK
projects / pspp / blobdiff