X-Git-Url: https://pintos-os.org/cgi-bin/gitweb.cgi?a=blobdiff_plain;f=doc%2Fexpressions.texi;h=7226116bd13e8897c29c2dbf120acf913ca17857;hb=03a7b96a587a58bda342b4eb5ce5f935880ba2b7;hp=77908e35ed5a1ba51510d7389912cc8c67a5df85;hpb=e8b26fb0d765310d4c7400c39465008f1bb8601d;p=pspp diff --git a/doc/expressions.texi b/doc/expressions.texi index 77908e35ed..7226116bd1 100644 --- a/doc/expressions.texi +++ b/doc/expressions.texi @@ -331,9 +331,13 @@ Returns the remainder when @var{number} is divided by 10. If @end deftypefn @cindex rounding -@deftypefn {Function} {} RND (@var{number}) -Takes the absolute value of @var{number} and rounds it to an integer. -Then, if @var{number} was negative originally, negates the result. +@deftypefn {Function} {} RND (@var{number} [, @var{mult}[, @var{fuzzbits}]]) +Rounds @var{number} and rounds it to a multiple of @var{mult} (by +default 1). Halves are rounded away from zero, as are values that +fall short of halves by less than @var{fuzzbits} of errors in the +least-significant bits of @var{number}. If @var{fuzzbits} is not +specified then the default is taken from SET FUZZBITS (@pxref{SET +FUZZBITS}), which is 6 unless overridden. @end deftypefn @cindex truncation @@ -617,7 +621,7 @@ format for @var{format}, system-missing is returned. @end deftypefn @cindex strings, searching backwards -@deftypefn {Function} {} RINDEX (@var{string}, @var{format}) +@deftypefn {Function} {} RINDEX (@var{haystack}, @var{needle}) Returns a positive integer indicating the position of the last occurrence of @var{needle} in @var{haystack}. Returns 0 if @var{haystack} does not contain @var{needle}. Returns system-missing if @@ -630,7 +634,7 @@ Searches @var{haystack} for the last occurrence of each part, and returns the largest value. Returns 0 if @var{haystack} does not contain any part in @var{needle}. It is an error if @var{needle_len} does not evenly divide the length of @var{needle}. Returns system-missing -if @var{needle} is an empty string. +if @var{needle} is an empty string or if needle_len is less than 1. @end deftypefn @cindex padding strings @@ -1215,12 +1219,12 @@ Cauchy distribution with location parameter @var{a} and scale parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. @end deftypefn -@deftypefn {Function} {} PDF.CHISQ (@var{x}, @var{df}) -@deftypefnx {Function} {} CDF.CHISQ (@var{x}, @var{df}) +@c @deftypefn {Function} {} PDF.CHISQ (@var{x}, @var{df}) +@deftypefn {Function} {} CDF.CHISQ (@var{x}, @var{df}) @deftypefnx {Function} {} SIG.CHISQ (@var{x}, @var{df}) @deftypefnx {Function} {} IDF.CHISQ (@var{p}, @var{df}) @deftypefnx {Function} {} RV.CHISQ (@var{df}) -@deftypefnx {Function} {} NPDF.CHISQ (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.CHISQ (@var{x}, @var{df}, @var{lambda}) @deftypefnx {Function} {} NCDF.CHISQ (@var{x}, @var{df}, @var{lambda}) Chi-squared distribution with @var{df} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. @@ -1250,8 +1254,8 @@ and nonnegative power parameter @var{b}. Constraints: @var{a} > 0, @deftypefnx {Function} {} SIG.F (@var{x}, @var{df1}, @var{df2}) @deftypefnx {Function} {} IDF.F (@var{p}, @var{df1}, @var{df2}) @deftypefnx {Function} {} RV.F (@var{df1}, @var{df2}) -@deftypefnx {Function} {} NPDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) -@deftypefnx {Function} {} NCDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) +@c @deftypefnx {Function} {} NCDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) F-distribution of two chi-squared deviates with @var{df1} and @var{df2} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. Constraints: @var{df1} > 0, @@ -1267,21 +1271,21 @@ Gamma distribution with shape parameter @var{a} and scale parameter @var{p} < 1. @end deftypefn -@deftypefn {Function} {} PDF.HALFNRM (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} CDF.HALFNRM (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.HALFNRM (@var{p}, @var{a}, @var{b}) -@deftypefnx {Function} {} RV.HALFNRM (@var{a}, @var{b}) -Half-normal distribution with location parameter @var{a} and shape -parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} PDF.HALFNRM (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} CDF.HALFNRM (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.HALFNRM (@var{p}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} RV.HALFNRM (@var{a}, @var{b}) +@c Half-normal distribution with location parameter @var{a} and shape +@c parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. +@c @end deftypefn -@deftypefn {Function} {} PDF.IGAUSS (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} CDF.IGAUSS (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.IGAUSS (@var{p}, @var{a}, @var{b}) -@deftypefnx {Function} {} RV.IGAUSS (@var{a}, @var{b}) -Inverse Gaussian distribution with parameters @var{a} and @var{b}. -Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= @var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} PDF.IGAUSS (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} CDF.IGAUSS (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.IGAUSS (@var{p}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} RV.IGAUSS (@var{a}, @var{b}) +@c Inverse Gaussian distribution with parameters @var{a} and @var{b}. +@c Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= @var{p} < 1. +@c @end deftypefn @deftypefn {Function} {} PDF.LANDAU (@var{x}) @deftypefnx {Function} {} RV.LANDAU () @@ -1376,26 +1380,26 @@ parameter @var{sigma}. This distribution is a @pspp{} extension. Constraints: @var{a} > 0, @var{sigma} > 0, @var{x} > @var{a}. @end deftypefn -@deftypefn {Function} {} CDF.SMOD (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.SMOD (@var{p}, @var{a}, @var{b}) -Studentized maximum modulus distribution with parameters @var{a} and -@var{b}. Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= -@var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} CDF.SMOD (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.SMOD (@var{p}, @var{a}, @var{b}) +@c Studentized maximum modulus distribution with parameters @var{a} and +@c @var{b}. Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= +@c @var{p} < 1. +@c @end deftypefn -@deftypefn {Function} {} CDF.SRANGE (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.SRANGE (@var{p}, @var{a}, @var{b}) -Studentized range distribution with parameters @var{a} and @var{b}. -Constraints: @var{a} >= 1, @var{b} >= 1, @var{x} > 0, 0 <= @var{p} < -1. -@end deftypefn +@c @deftypefn {Function} {} CDF.SRANGE (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.SRANGE (@var{p}, @var{a}, @var{b}) +@c Studentized range distribution with parameters @var{a} and @var{b}. +@c Constraints: @var{a} >= 1, @var{b} >= 1, @var{x} > 0, 0 <= @var{p} < +@c 1. +@c @end deftypefn @deftypefn {Function} {} PDF.T (@var{x}, @var{df}) @deftypefnx {Function} {} CDF.T (@var{x}, @var{df}) @deftypefnx {Function} {} IDF.T (@var{p}, @var{df}) @deftypefnx {Function} {} RV.T (@var{df}) -@deftypefnx {Function} {} NPDF.T (@var{x}, @var{df}, @var{lambda}) -@deftypefnx {Function} {} NCDF.T (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.T (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NCDF.T (@var{x}, @var{df}, @var{lambda}) T-distribution with @var{df} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. Constraints: @var{df} > 0, 0 < @var{p} < 1. @@ -1449,9 +1453,9 @@ Bernoulli distribution with probability of success @var{p}. Constraints: @var{x} = 0 or 1, 0 <= @var{p} <= 1. @end deftypefn -@deftypefn {Function} {} PDF.BINOMIAL (@var{x}, @var{n}, @var{p}) -@deftypefnx {Function} {} CDF.BINOMIAL (@var{x}, @var{n}, @var{p}) -@deftypefnx {Function} {} RV.BINOMIAL (@var{n}, @var{p}) +@deftypefn {Function} {} PDF.BINOM (@var{x}, @var{n}, @var{p}) +@deftypefnx {Function} {} CDF.BINOM (@var{x}, @var{n}, @var{p}) +@deftypefnx {Function} {} RV.BINOM (@var{n}, @var{p}) Binomial distribution with @var{n} trials and probability of success @var{p}. Constraints: integer @var{n} > 0, 0 <= @var{p} <= 1, integer @var{x} <= @var{n}.