Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me.
Ruposh --- [EMAIL PROTECTED] wrote: > Send R-help mailing list submissions to > r-help@stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, > visit > https://stat.ethz.ch/mailman/listinfo/r-help > or, via email, send a message with subject or body > 'help' to > [EMAIL PROTECTED] > > You can reach the person managing the list at > [EMAIL PROTECTED] > > When replying, please edit your Subject line so it > is more specific > than "Re: Contents of R-help digest..." > > > Today's Topics: > > 1. Re: bivariate interpolation (Adelchi Azzalini) > 2. Coding for contrasts in unbalanced designs > (Guillaume Brutel) > 3. Re: substitute values (Mark Wardle) > 4. Odp: converting a list to a data.frame (Petr > PIKAL) > 5. Re: substitute values (Sergio Della Franca) > 6. the numimum number of fixed factors lme > package can deal with > (Fang, Yongxiang) > 7. Re: converting a list to a data.frame (Mark > Wardle) > 8. 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Info on SPATSTAT window and maps (Giuseppe > Brundu) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 3 Apr 2007 11:58:27 +0200 > From: Adelchi Azzalini <[EMAIL PROTECTED]> > Subject: Re: [R] bivariate interpolation > To: "eric lee" <[EMAIL PROTECTED]> > Cc: r-help@stat.math.ethz.ch > Message-ID: > <[EMAIL PROTECTED]> > Content-Type: text/plain; charset=ISO-8859-1 > > On Tue, 3 Apr 2007 11:43:55 +0200, Adelchi Azzalini > wrote: > > AA> On Mon, 2 Apr 2007 22:26:06 -0400, eric lee > wrote: > AA> > AA> EL> Hi. I'm trying to take a data set with two > independent and one > AA> EL> dependent variable and enter a x,y value to > predict the dependent > AA> EL> with a nonparametric technique. I've been > using interpp in the akima > AA> EL> package, (windows xp, R 2.4.1), but get > values that are orders of > AA> EL> magnitude off when the predictors are > slightly out of the range of > AA> EL> the data set. Can you recommend a function > for me? I've read that > AA> EL> predict.loess has the same problem or just > lists NA. Smooth.spline > AA> EL> looks good, but can only do one predictor > instead of bivariate. I've > AA> EL> tried help.search("predict"), but couldn't > find what I needed. > AA> EL> Thanks. > AA> EL> > AA> > AA> one option is to use sm.regression of package > sm, along these lines > AA> > AA> x <- cbind(runif(100,-2, 2), runif(100,-2, 2)) > AA> y <- x[,1]^2 + x[,2]^2 + 0.7*x[,1]*x[,2] + > rnorm(100)/3 > AA> sm.regression(x,y) > AA> ev.pt <- rbind(c(-2,-2),c(2,2), > c(2,-2),c(-2,2)) > AA> a <- sm.regression(x,y, eval.points=ev.pt, > eval.grid=FALSE) > > === message truncated === ____________________________________________________________________________________ 8:00? 8:25? 8:40? Find a flick in no time
year month rainfall 1910 7 153.5 1912 7 89.4 1912 7 165.5 1912 8 80.4 1915 7 122.1 1915 7 202 1915 8 94.1 1915 8 254.7 1916 6 121.8 1916 7 98.7 1916 9 175.3 1917 9 87.6 1918 8 88.2 1918 8 90.1 1918 8 150.6 1919 6 103.9 1919 7 89.1 1919 7 147.9 1919 8 104 1920 7 104 1920 7 110.4 1920 8 157.1 1920 8 354.7 1921 7 128.5 1922 7 96.4 1922 8 85.3 1922 8 127.3 1922 8 150.4 1923 6 101.1 1923 8 119.2 1925 7 85.6 1925 7 166.2 1925 7 183.3 1925 7 185.1 1925 8 89.2 1926 7 107.1 1926 7 150.8 1926 7 153.4 1926 8 93 1926 8 126.8 1927 7 121.7 1927 8 96.8 1928 8 99.3 1929 6 84.5 1929 7 126.9 1929 8 81.8 1930 7 100.2 1930 7 109.8 1930 7 133.9 1930 7 150.1 1931 7 122.3 1931 8 149.1 1932 8 143 1933 7 98.1 1933 7 125.2 1933 9 88.1 1934 9 97.2 1935 7 176.9 1935 7 179.3 1936 8 86.8 1936 8 87.4 1936 8 125.8 1937 9 89.1 1938 9 89.8 1940 7 88.7 1940 7 102.6 1940 7 106.7 1940 7 148.9 1940 7 180.8 1940 7 283.9 1940 9 221.1 1941 8 94.3 1942 8 106.2 1942 8 165.4 1945 6 105 1945 7 147.1 1945 8 88.6 1946 6 96.5 1946 6 106.8 1946 6 107.2 1946 6 117.2 1946 8 97.4 1947 7 159.4 1947 8 101.1 1948 6 106.5 1948 9 114.9 1954 7 126 1955 6 122.2 1956 6 219.9 1956 7 103 1956 7 115.4 1956 9 110.1 1956 9 176.4 1957 7 153.2 1958 7 145.3 1958 9 102.8 1959 7 101.9 1959 8 84.9 1959 8 100.7 1960 6 135.3 1960 7 88.9 1961 9 103.1 1963 6 91.9 1963 6 169.2 1963 7 174 1964 7 86.5 1964 7 89.4 1964 9 116.6 1964 9 126 1965 7 91.9 1965 7 108.8 1965 7 144.9 1965 8 90.8 1966 7 112.8 1966 7 113 1966 7 140 1966 7 226.3 1966 9 96.6 1967 7 96.2 1967 8 86.2 1968 7 91.9 1968 8 149.3 1968 9 100 1969 7 84.8 1969 8 89.4 1969 8 95.2 1969 9 119.8 1970 6 98.6 1970 7 82.3 1970 9 164.8 1971 7 188.6 1972 8 82.7 1972 8 84.7 1972 8 84.9 1972 8 179.2 1972 8 273.2 1974 8 84.3 1975 7 130.5 1976 8 96.2 1977 7 81.4 1977 7 155.8 1978 6 194.6 1979 8 92 1980 7 88.5 1980 8 105.9 1980 8 131 1981 7 82.5 1981 7 137 1982 7 106.8 1982 8 97.6 1983 9 131.6 1984 7 98.6 1984 8 97 1984 9 268.2 1985 7 84.3 1985 8 124.5 1986 7 147.8 1986 8 82.7 1986 8 84.4 1987 7 86.8 1987 7 294.6 1987 8 87.7 1988 7 101.6 1989 7 103.1 1990 6 81.7 1990 6 97.8 1990 6 100.7 1990 7 111 1990 8 88.5 1990 9 118.7 1990 9 120 1990 9 247.5 1991 7 140.2 1992 5 95.4 1992 8 130.3 1992 8 153.3 1992 9 87 1993 7 84.2 1993 7 91.8 1995 7 83.5 1995 7 88.2 1995 7 107.1 1995 8 82.3 1995 8 88.7 1995 8 110 1995 8 118.8 1995 8 126.1 1995 8 149.2 1996 6 83.2 1996 7 168.6 1997 5 90.4 1997 7 127.7 1998 6 85.7 1998 7 83 1998 8 108.9 1998 8 122.9 1998 8 211.4 1998 8 332.8 1999 8 163.5 1999 8 261.6 1999 9 101.4 1999 9 113.6 2000 8 82.3 2000 8 89.3 2000 8 122.9 2001 7 147.6 2001 7 273.4 2001 8 84.5 2002 8 120 2002 8 157.5 2002 8 178 2003 5 89.5 2003 7 173.5 2003 8 146.5 2003 8 177 2003 9 101 2004 6 84 2004 7 108.5 2005 6 95.5 2005 7 115 2005 9 90.5 2005 9 104.5 2006 5 93 2006 7 88 2006 7 195 2006 7 226.5 2006 7 241
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