X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=doc%2Fstatistics.texi;h=e18c6109dba73f69aed403bcd4c677980ab48690;hb=0054d7bf45fb09a455698d1a180db93055f49dba;hp=796fde91d3349fa4799aa979e2691abaac72fff6;hpb=a5d5cfde1dec0777a4d5c8f554027324be9dba57;p=pspp diff --git a/doc/statistics.texi b/doc/statistics.texi index 796fde91d3..e18c6109db 100644 --- a/doc/statistics.texi +++ b/doc/statistics.texi @@ -12,6 +12,7 @@ far. * 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. @@ -138,6 +139,7 @@ FREQUENCIES [@{FREQ,PERCENT@}] [@{NOMISSING,MISSING@}] /BARCHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})] [@{FREQ,PERCENT@}] + /ORDER=@{ANALYSIS,VARIABLE@} (These options are not currently implemented.) @@ -148,9 +150,8 @@ FREQUENCIES 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. @@ -231,6 +232,8 @@ 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 @@ -409,8 +412,9 @@ large quantity of output. @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@}] @@ -420,11 +424,20 @@ 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. +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 @@ -435,10 +448,14 @@ This example will produce a scatterplot where @var{height} is plotted versus @va 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}. @@ -447,6 +464,60 @@ 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 @@ -526,6 +597,8 @@ CROSSTABS @{BOX,NOBOX@} /CELLS=@{COUNT,ROW,COLUMN,TOTAL,EXPECTED,RESIDUAL,SRESIDUAL, ASRESIDUAL,ALL,NONE@} + /COUNT=@{ASIS,CASE,CELL@} + @{ROUND,TRUNCATE@} /STATISTICS=@{CHISQ,PHI,CC,LAMBDA,UC,BTAU,CTAU,RISK,GAMMA,D, KAPPA,ETA,CORR,ALL,NONE@} /BARCHART @@ -625,6 +698,15 @@ Suppress cells entirely. If @subcmd{CELLS} is not specified at all then only @subcmd{COUNT} will be selected. +By default, crosstabulation and statistics use raw case weights, +without rounding. Use the @subcmd{/COUNT} subcommand to perform +rounding: CASE rounds the weights of individual weights as cases are +read, CELL rounds the weights of cells within each crosstabulation +table after it has been constructed, and ASIS explicitly specifies the +default non-rounding behavior. When rounding is requested, ROUND, the +default, rounds to the nearest integer and TRUNCATE rounds toward +zero. + The @subcmd{STATISTICS} subcommand selects statistics for computation: @table @asis @@ -707,6 +789,8 @@ FACTOR VARIABLES=@var{var_list} [ /METHOD = @{CORRELATION, COVARIANCE@} ] + [ /ANALYSIS=@var{var_list} ] + [ /EXTRACTION=@{PC, PAF@}] [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}] @@ -725,7 +809,10 @@ FACTOR VARIABLES=@var{var_list} 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. @@ -813,6 +900,65 @@ If @subcmd{PAIRWISE} is set, then a case is considered missing only if either of 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, for the purposes of GLM analysis, +only system-missing values are considered +to be missing; user-missing values are not regarded as missing. +If @subcmd{EXCLUDE} is set, which is the default, then user-missing +values are considered to be missing as well as system-missing values. +A case for which any dependent variable or any factor +variable has a missing value is excluded from the analysis. + @node LOGISTIC REGRESSION @section LOGISTIC REGRESSION @@ -1435,7 +1581,7 @@ of variable preceding @subcmd{WITH} against variable following @display T-TEST /MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@} - /CRITERIA=CIN(@var{confidence}) + /CRITERIA=CI(@var{confidence}) (One Sample mode.) @@ -1640,8 +1786,9 @@ The default is 0.05. @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 @@ -1651,11 +1798,29 @@ of similar values and you already know the number of clusters. 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. -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. +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. + + +@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 @@ -1670,6 +1835,12 @@ clustering variables contain missing values. Otherwise it is clustered 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 @@ -1848,3 +2019,5 @@ exclude them. 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