* CORRELATIONS:: Correlation tables.
* CROSSTABS:: Crosstabulation tables.
* FACTOR:: Factor analysis and Principal Components analysis.
+* GLM:: Univariate Linear Models.
* LOGISTIC REGRESSION:: Bivariate Logistic Regression.
* MEANS:: Average values and other statistics.
* NPAR TESTS:: Nonparametric tests.
@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
/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}.
values for the particular coefficient are missing.
The default is @subcmd{LISTWISE}.
+@node GLM
+@section GLM
+
+@vindex GLM
+@cindex univariate analysis of variance
+@cindex fixed effects
+@cindex factorial anova
+@cindex analysis of variance
+@cindex ANOVA
+
+
+@display
+GLM @var{dependent_vars} BY @var{fixed_factors}
+ [/METHOD = SSTYPE(@var{type})]
+ [/DESIGN = @var{interaction_0} [@var{interaction_1} [... @var{interaction_n}]]]
+ [/INTERCEPT = @{INCLUDE|EXCLUDE@}]
+ [/MISSING = @{INCLUDE|EXCLUDE@}]
+@end display
+
+The @cmd{GLM} procedure can be used for fixed effects factorial Anova.
+
+The @var{dependent_vars} are the variables to be analysed.
+You may analyse several variables in the same command in which case they should all
+appear before the @code{BY} keyword.
+
+The @var{fixed_factors} list must be one or more categorical variables. Normally it
+will not make sense to enter a scalar variable in the @var{fixed_factors} and doing
+so may cause @pspp{} to do a lot of unnecessary processing.
+
+The @subcmd{METHOD} subcommand is used to change the method for producing the sums of
+squares. Available values of @var{type} are 1, 2 and 3. The default is type 3.
+
+You may specify a custom design using the @subcmd{DESIGN} subcommand.
+The design comprises a list of interactions where each interaction is a
+list of variables separated by a @samp{*}. For example the command
+@display
+GLM subject BY sex age_group race
+ /DESIGN = age_group sex group age_group*sex age_group*race
+@end display
+@noindent specifies the model @math{subject = age_group + sex + race + age_group*sex + age_group*race}.
+If no @subcmd{DESIGN} subcommand is specified, then the default is all possible combinations
+of the fixed factors. That is to say
+@display
+GLM subject BY sex age_group race
+@end display
+implies the model
+@math{subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race}.
+
+
+The @subcmd{MISSING} subcommand determines the handling of missing
+variables.
+If @subcmd{INCLUDE} is set, then user-missing values are included in the
+calculations, but system-missing values are not.
+If @subcmd{EXCLUDE} is set, which is the default, user-missing
+values are excluded as well as system-missing values.
+This is the default.
+
+
@node LOGISTIC REGRESSION
@section LOGISTIC REGRESSION
QUICK CLUSTER @var{var_list}
[/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})] CONVERGE(@var{epsilon}) [NOINITIAL]]
[/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
- [/PRINT=@{INITIAL@} @{CLUSTERS@}]
+ [/PRINT=@{INITIAL@} @{CLUSTER@}]
@end display
The @cmd{QUICK CLUSTER} command performs k-means clustering on the
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{CLUSTERS} is set, the cluster memberships of the individual
+If @subcmd{CLUSTER} is set, the cluster memberships of the individual
cases will be displayed (potentially generating lengthy output).
Cases are excluded on a listwise basis; if any of the variables in @var{var_list}
or if the variable @var{state_var} is missing, then the entire case will be
excluded.
+
+@c LocalWords: subcmd subcommand
projects / pspp / blobdiff