* DESCRIPTIVES:: Descriptive statistics.
* FREQUENCIES:: Frequency tables.
* EXAMINE:: Testing data for normality.
+* GRAPH:: Plot data.
* CORRELATIONS:: Correlation tables.
* CROSSTABS:: Crosstabulation tables.
* FACTOR:: Factor analysis and Principal Components analysis.
[@{FREQ[(@var{y_max})],PERCENT[(@var{y_max})]@}] [@{NONORMAL,NORMAL@}]
/PIECHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
[@{FREQ,PERCENT@}] [@{NOMISSING,MISSING@}]
+ /BARCHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
+ [@{FREQ,PERCENT@}]
+ /ORDER=@{ANALYSIS,VARIABLE@}
+
(These options are not currently implemented.)
- /BARCHART=@dots{}
/HBAR=@dots{}
/GROUPED=@dots{}
@end display
The @cmd{FREQUENCIES} procedure outputs frequency tables for specified
variables.
@cmd{FREQUENCIES} can also calculate and display descriptive statistics
-(including median and mode) and percentiles,
-@cmd{FREQUENCIES} can also output
-histograms and pie charts.
+(including median and mode) and percentiles, and various graphical representations
+of the frequency distribution.
The @subcmd{VARIABLES} subcommand is the only required subcommand. Specify the
variables to be analyzed.
The @subcmd{HISTOGRAM} subcommand causes the output to include a histogram for
each specified numeric variable. The X axis by default ranges from
the minimum to the maximum value observed in the data, but the @subcmd{MINIMUM}
-and @subcmd{MAXIMUM} keywords can set an explicit range. Specify @subcmd{NORMAL} to
-superimpose a normal curve on the histogram. Histograms are not
-created for string variables.
+and @subcmd{MAXIMUM} keywords can set an explicit range.
+@footnote{The number of
+bins is chosen according to the Freedman-Diaconis rule:
+@math{2 \times IQR(x)n^{-1/3}}, where @math{IQR(x)} is the interquartile range of @math{x}
+and @math{n} is the number of samples. Note that
+@cmd{EXAMINE} uses a different algorithm to determine bin sizes.}
+Histograms are not created for string variables.
+
+Specify @subcmd{NORMAL} to superimpose a normal curve on the
+histogram.
@cindex piechart
The @subcmd{PIECHART} subcommand adds a pie chart for each variable to the data. Each
slice represents one value, with the size of the slice proportional to
the value's frequency. By default, all non-missing values are given
-slices. The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to limit the
-displayed slices to a given range of values. The @subcmd{MISSING} keyword adds
-slices for missing values.
-
-The @subcmd{FREQ} and @subcmd{PERCENT} options on @subcmd{HISTOGRAM} and @subcmd{PIECHART} are accepted
-but not currently honoured.
+slices.
+The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to limit the
+displayed slices to a given range of values.
+The keyword @subcmd{NOMISSING} causes missing values to be omitted from the
+piechart. This is the default.
+If instead, @subcmd{MISSING} is specified, then a single slice
+will be included representing all system missing and user-missing cases.
+
+@cindex bar chart
+The @subcmd{BARCHART} subcommand produces a bar chart for each variable.
+The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to omit
+categories whose counts which lie outside the specified limits.
+The @subcmd{FREQ} option (default) causes the ordinate to display the frequency
+of each category, whereas the @subcmd{PERCENT} option will display relative
+percentages.
+
+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
dependent variable.
Following the dependent variables, factors may be specified.
-The factors (if desired) should be preceeded by a single @subcmd{BY} keyword.
+The factors (if desired) should be preceded by a single @subcmd{BY} keyword.
The format for each factor is
@display
@var{factorvar} [BY @var{subfactorvar}].
normal distribution, whilst the spread vs.@: level plot can be useful to visualise
how the variance of differs between factors.
Boxplots will also show you the outliers and extreme values.
+@footnote{@subcmd{HISTOGRAM} uses Sturges' rule to determine the number of
+bins, as approximately @math{1 + \log2(n)}, where @math{n} is the number of samples.
+Note that @cmd{FREQUENCIES} uses a different algorithm to find the bin size.}
The @subcmd{SPREADLEVEL} plot displays the interquartile range versus the
median. It takes an optional parameter @var{t}, which specifies how the data
The @subcmd{ID} subcommand is relevant only if @subcmd{/PLOT=BOXPLOT} or
@subcmd{/STATISTICS=EXTREME} has been given.
-If given, it shoule provide the name of a variable which is to be used
+If given, it should provide the name of a variable which is to be used
to labels extreme values and outliers.
Numeric or string variables are permissible.
-If the @subcmd{ID} subcommand is not given, then the casenumber will be used for
+If the @subcmd{ID} subcommand is not given, then the case number will be used for
labelling.
The @subcmd{CINTERVAL} subcommand specifies the confidence interval to use in
there are many distinct values, then @cmd{EXAMINE} will produce a very
large quantity of output.
+@node GRAPH
+@section GRAPH
+
+@vindex GRAPH
+@cindex Exploratory data analysis
+@cindex normality, testing
+
+@display
+GRAPH
+ /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@}]
+
+@end display
+
+The @cmd{GRAPH} produces graphical plots of data. Only one of the subcommands
+@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.
+
+@example
+GRAPH
+ /SCATTERPLOT = @var{height} WITH @var{weight} BY @var{gender}.
+@end example
+
+This example will produce a scatterplot where @var{height} is plotted versus @var{weight}. Depending
+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}.
+
+@example
+GRAPH
+ /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
ASRESIDUAL,ALL,NONE@}
/STATISTICS=@{CHISQ,PHI,CC,LAMBDA,UC,BTAU,CTAU,RISK,GAMMA,D,
KAPPA,ETA,CORR,ALL,NONE@}
+ /BARCHART
(Integer mode.)
/VARIABLES=@var{var_list} (@var{low},@var{high})@dots{}
crosstabulation tables to be displayed. It has a number of possible
settings:
-@itemize @asis
+@itemize @w{}
@item
@subcmd{TABLES}, the default, causes crosstabulation tables to be output.
@subcmd{NOTABLES} suppresses them.
@samp{/STATISTICS} without any settings selects CHISQ. If the
@subcmd{STATISTICS} subcommand is not given, no statistics are calculated.
+@cindex bar chart
+The @samp{/BARCHART} subcommand produces a clustered bar chart for the first two
+variables on each table.
+If a table has more than two variables, the counts for the third and subsequent levels
+will be aggregated and the chart will be produces as if there were only two variables.
+
+
@strong{Please note:} Currently the implementation of @cmd{CROSSTABS} has the
-followings bugs:
+following limitations:
@itemize @bullet
@item
-Significance of symmetric and directional measures is not calculated.
-@item
-Asymmetric ASEs and T values for lambda are wrong.
+Significance of some symmetric and directional measures is not calculated.
@item
-ASE of Goodman and Kruskal's tau is not calculated.
+Asymptotic standard error is not calculated for
+Goodman and Kruskal's tau or symmetric Somers' d.
@item
-ASE of symmetric somers' d is wrong.
-@item
-Approximate T of uncertainty coefficient is wrong.
+Approximate T is not calculated for symmetric uncertainty coefficient.
@end itemize
Fixes for any of these deficiencies would be welcomed.
[ /METHOD = @{CORRELATION, COVARIANCE@} ]
+ [ /ANALYSIS=@var{var_list} ]
+
[ /EXTRACTION=@{PC, PAF@}]
- [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, NOROTATE@}]
+ [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}]
[ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [SIG] [ALL] [DEFAULT] ]
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.
used. By default Principal Components Analysis will be used.
The @subcmd{/ROTATION} subcommand is used to specify the method by which the extracted solution will be rotated.
-Three methods are available: @subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
-If don't want any rotation to be performed, the word @subcmd{NOROTATE} will prevent the command from performing any
-rotation on the data. Oblique rotations are not supported.
+Three orthogonal rotation methods are available:
+@subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
+There is one oblique rotation method, @i{viz}: @subcmd{PROMAX}.
+Optionally you may enter the power of the promax rotation @var{k}, which must be enclosed in parentheses.
+The default value of @var{k} is 5.
+If you don't want any rotation to be performed, the word @subcmd{NOROTATE} will prevent the command from performing any
+rotation on the data.
The @subcmd{/METHOD} subcommand should be used to determine whether the covariance matrix or the correlation matrix of the data is
to be analysed. By default, the correlation matrix is analysed.
The @subcmd{/EXPECTED} subcommand specifies the expected values of each
category.
There must be exactly one non-zero expected value, for each observed
-category, or the @subcmd{EQUAL} keywork must be specified.
+category, or the @subcmd{EQUAL} keyword must be specified.
You may use the notation @subcmd{@var{n}*@var{f}} to specify @var{n}
consecutive expected categories all taking a frequency of @var{f}.
The frequencies given are proportions, not absolute frequencies. The
The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
variables listed.
The test does not make any assumptions about the variances of the samples.
-It does however assume that the distribution is symetrical.
+It does however assume that the distribution is symmetrical.
If the @subcmd{WITH} keyword is omitted, then tests for all
combinations of the listed variables are performed.
@display
T-TEST
/MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
- /CRITERIA=CIN(@var{confidence})
+ /CRITERIA=CI(@var{confidence})
(One Sample mode.)
the name of the independent (or factor) variable.
You can use the @subcmd{STATISTICS} subcommand to tell @pspp{} to display
-ancilliary information. The options accepted are:
+ancillary information. The options accepted are:
@itemize
@item DESCRIPTIVES
Displays descriptive statistics about the groups factored by the independent
@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
@end display
@cindex Cronbach's Alpha
-The @cmd{RELIABILTY} command performs reliability analysis on the data.
+The @cmd{RELIABILITY} command performs reliability analysis on the data.
The @subcmd{VARIABLES} subcommand is required. It determines the set of variables
upon which analysis is to be performed.
projects / pspp / blobdiff