[This email is either empty or too large to be displayed at this time]
[This email is either empty or too large to be displayed at this time]
An embedded and charset-unspecified text was scrubbed... Name: Data_and_Sampling_codes.txt URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20090421/31462ac5/attachment-0001.txt>
Dear R users, I need to do sampling without replacement (bootstraps). I have two variables (Xvar, Yvar). I have a correlation from original data set cor(Xvar, Yvar)=0.6174221. I am doing 50000 sampling, and in each sampling calculating correlations, saving, sorting and getting 95% cutt off point (0.1351877). I am getting maximum value as 0.3507219 (much smaller than correlation of my original data). I repeated the sampling a couple of time and none of them produced a correlation coefficient higher than my original data set. However, if I sort out my Xvar and Yvar and obtain correlation it is 0.9657125 which is much higher than correlation for my original data. I am doing sampling in another program and getting at least 1% higher correlation than mine. Now I am getting confused with sampling(random data) in R. My data and codes for the scenario above are in the attached file. I want to understand where I am making a mistake. Any comment is deeply appreciated. Kind Regards Seyit Ali ------------------------------------------------------------------------------------------------------------------ Dr. Seyit Ali KAYIS Selcuk University Faculty of Agriculture Kampus, Konya, TURKEY s_a_kayis at yahoo.com, s_a_kayis at hotmail.com Tell: +90 332 223 2830 Mobile: +90 535 587 1139 Fax: +90 332 241 0108 Greetings from Konya, TURKEY http://www.ziraat.selcuk.edu.tr/skayis/ ---------------------------------------------------------------------------------------------------------------------- _________________________________________________________________ No-one wants to be lonely this Autumn Find someone to snuggle up with Fchannel%2Findex%2Easpx%3Ftrackingid%3D1048628&_t=773568480&_r=nzWINDOWSliveMAILemailTAGLINES&_m=EXT -------------- next part -------------- An embedded and charset-unspecified text was scrubbed... Name: Data_and_Sampling_codes.txt URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20090421/c9a15418/attachment-0002.txt>
Dear R users, I need to do sampling without replacement (bootstraps). I have two variables (Xvar, Yvar). I have a correlation from original data set cor(Xvar, Yvar)=0.6174221. I am doing 50000 sampling, and in each sampling calculating correlations, saving, sorting and getting 95% cutt off point (0.1351877). I am getting maximum value as 0.3507219 (much smaller than correlation of my original data). I repeated the sampling a couple of time and none of them produced a correlation coefficient higher than my original data set. However, if I sort out my Xvar and Yvar and obtain correlation it is 0.9657125 which is much higher than correlation for my original data. I am doing sampling in another program and getting at least 1% higher correlation than mine. Now I am getting confused with sampling(random data) in R. My data and codes for the scenario above are below Xvar<-c(0.1818182,0.5384615,0.5535714,0.4680851,0.4545455,0.4385965,0.5185185,0.4035088,0.4901961,0.3650794,0.462963,0.4,0.56,0.3965517,0.4909091, 0.4716981,0.4310345,0.2,0.1509434,0.2647059,0.173913,0.1914894,0.1914894,0.1489362,0.1363636,0.2244898,0.2325581,0.1333333,0.1818182,0.1702128, 0.2173913,0.2380952,0.1632653,0.5614035,0.3396226,0.4909091,0.3770492,0.5,0.5185185,0.5,0.4666667,0.4464286,0.362069,0.4285714,0.4561404, 0.4736842,0.4545455,0.4166667,0.4181818,0.4590164,0.5166667,0.5423729,0.4833333,0.5454545,0.4393939,0.5172414,0.4098361,0.4745763,0.4754098, 0.5166667,0.5,0.4603175,0.42,0.4038462,0.4897959,0.3148148,0.3673469,0.4,0.4583333,0.3877551,0.4375,0.4117647,0.4313725,0.5333333,0.3962264, 0.3548387,0.5272727,0.4137931,0.3928571,0.4666667,0.4210526,0.4363636,0.4545455,0.4310345,0.4237288,0.4814815,0.4912281,0.4333333,0.4,0.4285714, 0.4516129,0.5090909,0.4464286,0.4642857,0.4166667,0.4098361,0.4909091,0.3809524,0.5272727,0.4814815,0.5254237,0.627451,0.5,0.5471698,0.5454545, 0.5925926,0.5769231,0.5818182,0.4444444,0.4915254,0.4727273,0.4107143,0.4285714,0.4310345,0.4237288,0.4285714,0.440678,0.4237288,0.4807692, 0.4150943,0.4615385,0.4107143,0.4814815,0.4074074,0.4210526,0.5263158,0.440678,0.4576271,0.5344828,0.5,0.5636364,0.4677419,0.5,0.5192308, 0.4642857,0.5090909,0.58,0.4482759,0.5098039,0.4035088,0.4210526,0.5098039,0.4385965,0.5283019,0.5471698,0.625,0.4310345,0.4912281,0.5283019, 0.4576271,0.5471698,0.4745763,0.4821429) Yvar<-c(0.2553191,0.4107143,0.5660377,0.3888889,0.3606557,0.2898551,0.3818182,0.4,0.4,0.3278689,0.2903226,0.4074074,0.4181818,0.3,0.2238806,0.3728814, 0.3709677,0.2307692,0.2830189,0.2244898,0.2142857,0.2131148,0.22,0.2258065,0.2321429,0.2,0.2264151,0.22,0.2115385,0.2459016,0.1166667,0.1785714, 0.2068966,0.6,0.4285714,0.3134328,0.4461538,0.3965517,0.4769231,0.6181818,0.4827586,0.3709677,0.3965517,0.4821429,0.4545455,0.359375,0.4576271, 0.4516129,0.5272727,0.4603175,0.4,0.4912281,0.5384615,0.5,0.4516129,0.4126984,0.4655172,0.5263158,0.4925373,0.358209,0.4285714,0.4920635, 0.4482759,0.3235294,0.4,0.4375,0.440678,0.3898305,0.35,0.4528302,0.58,0.4153846,0.3174603,0.5185185,0.3870968,0.2894737,0.3709677,0.369863, 0.3676471,0.3636364,0.3088235,0.328125,0.4032258,0.4084507,0.3188406,0.3636364,0.3823529,0.2816901,0.4722222,0.5,0.3521127,0.4393939,0.3787879, 0.453125,0.4324324,0.4057971,0.4545455,0.4492754,0.5,0.4098361,0.4067797,0.3666667,0.3928571,0.4285714,0.5,0.2923077,0.4561404,0.45,0.5538462, 0.4626866,0.4057971,0.3676471,0.5322581,0.5428571,0.375,0.4411765,0.4571429,0.4,0.3846154,0.3870968,0.4915254,0.530303,0.4375,0.4918033,0.4179104, 0.4032258,0.3606557,0.5178571,0.4848485,0.390625,0.375,0.4375,0.3666667,0.4,0.4477612,0.2571429,0.4032258,0.3382353,0.4814815,0.4090909,0.3548387, 0.4821429,0.5,0.557377,0.4333333,0.5454545,0.4590164,0.3943662,0.5076923,0.5,0.3283582,0.3676471,0.559322) my.cor<-cor(Xvar, Yvar) print(my.cor) nperm<-49999 Perm.Cor<-NULL for (iperm in 1:nperm) { XvarNew<-sample(Xvar, size=length(Xvar), replace=FALSE) YvarNew<-sample(Yvar, size=length(Yvar), replace=FALSE) perm.cor<-cor(XvarNew, YvarNew) Perm.Cor<-c(Perm.Cor, perm.cor) } print(max(Perm.Cor)) XvarSorted<-sort(Xvar, decreasing=TRUE) YvarSorted<-sort(Yvar, decreasing=TRUE) max.cor<-cor(XvarSorted, YvarSorted) print(max.cor) if(mat.cor>0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=TRUE) if(mat.cor<0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=FALSE) T95<-Perm.Cor.Sorted[(nperm+1)*0.05] # 95% treshold value T99<-Perm.Cor.Sorted[(nperm+1)*0.01] # 99% treshold value I want to understand where I am making a mistake. Any comment is deeply appreciated. Kind Regards Seyit Ali ------------------------------------------------------------------------------------------------------------------ Dr. Seyit Ali KAYIS Selcuk University Faculty of Agriculture Kampus, Konya, TURKEY s_a_kayis@yahoo.com, s_a_kayis@hotmail.com Tell: +90 332 223 2830 Mobile: +90 535 587 1139 Fax: +90 332 241 0108 Greetings from Konya, TURKEY http://www.ziraat.selcuk.edu.tr/skayis/ ---------------------------------------------------------------------------------------------------------------------- _________________________________________________________________ Earning enough? Find out with SEEK Salary Survey %2Eco%2Enz%2F%3Ftracking%3Dsk%3Atl%3Asknzsal%3Amsnnz%3A0%3Ahottag%3Aearn%5Fenough&_t=757263783&_r=Seek_NZ_tagline&_m=EXT [[alternative HTML version deleted]]
Seyit Ali Kayis wrote:> Dear R users, > > I need to do sampling without replacement (bootstraps). I have two variables (Xvar, Yvar). > I have a correlation from original data set cor(Xvar, Yvar)=0.6174221. I am doing 50000 sampling, > and in each sampling calculating correlations, saving, sorting and getting 95% cutt off point (0.1351877). > I am getting maximum value as 0.3507219 (much smaller than correlation of my original data). > I repeated the sampling a couple of time and none of them produced a correlation > coefficient higher than my original data set. However, if I sort out my Xvar and Yvar and > obtain correlation it is 0.9657125 which is much higher than correlation for my original data. > I am doing sampling in another program and getting at least 1% higher correlation than mine. > Now I am getting confused with sampling(random data) in R. My data and codes for the scenario above are below > > > Xvar<-c(0.1818182,0.5384615,0.5535714,0.4680851,0.4545455,0.4385965,0.5185185,0.4035088,0.4901961,0.3650794,0.462963,0.4,0.56,0.3965517,0.4909091, > 0.4716981,0.4310345,0.2,0.1509434,0.2647059,0.173913,0.1914894,0.1914894,0.1489362,0.1363636,0.2244898,0.2325581,0.1333333,0.1818182,0.1702128, > 0.2173913,0.2380952,0.1632653,0.5614035,0.3396226,0.4909091,0.3770492,0.5,0.5185185,0.5,0.4666667,0.4464286,0.362069,0.4285714,0.4561404, > 0.4736842,0.4545455,0.4166667,0.4181818,0.4590164,0.5166667,0.5423729,0.4833333,0.5454545,0.4393939,0.5172414,0.4098361,0.4745763,0.4754098, > 0.5166667,0.5,0.4603175,0.42,0.4038462,0.4897959,0.3148148,0.3673469,0.4,0.4583333,0.3877551,0.4375,0.4117647,0.4313725,0.5333333,0.3962264, > 0.3548387,0.5272727,0.4137931,0.3928571,0.4666667,0.4210526,0.4363636,0.4545455,0.4310345,0.4237288,0.4814815,0.4912281,0.4333333,0.4,0.4285714, > 0.4516129,0.5090909,0.4464286,0.4642857,0.4166667,0.4098361,0.4909091,0.3809524,0.5272727,0.4814815,0.5254237,0.627451,0.5,0.5471698,0.5454545, > 0.5925926,0.5769231,0.5818182,0.4444444,0.4915254,0.4727273,0.4107143,0.4285714,0.4310345,0.4237288,0.4285714,0.440678,0.4237288,0.4807692, > 0.4150943,0.4615385,0.4107143,0.4814815,0.4074074,0.4210526,0.5263158,0.440678,0.4576271,0.5344828,0.5,0.5636364,0.4677419,0.5,0.5192308, > 0.4642857,0.5090909,0.58,0.4482759,0.5098039,0.4035088,0.4210526,0.5098039,0.4385965,0.5283019,0.5471698,0.625,0.4310345,0.4912281,0.5283019, > 0.4576271,0.5471698,0.4745763,0.4821429) > > Yvar<-c(0.2553191,0.4107143,0.5660377,0.3888889,0.3606557,0.2898551,0.3818182,0.4,0.4,0.3278689,0.2903226,0.4074074,0.4181818,0.3,0.2238806,0.3728814, > 0.3709677,0.2307692,0.2830189,0.2244898,0.2142857,0.2131148,0.22,0.2258065,0.2321429,0.2,0.2264151,0.22,0.2115385,0.2459016,0.1166667,0.1785714, > 0.2068966,0.6,0.4285714,0.3134328,0.4461538,0.3965517,0.4769231,0.6181818,0.4827586,0.3709677,0.3965517,0.4821429,0.4545455,0.359375,0.4576271, > 0.4516129,0.5272727,0.4603175,0.4,0.4912281,0.5384615,0.5,0.4516129,0.4126984,0.4655172,0.5263158,0.4925373,0.358209,0.4285714,0.4920635, > 0.4482759,0.3235294,0.4,0.4375,0.440678,0.3898305,0.35,0.4528302,0.58,0.4153846,0.3174603,0.5185185,0.3870968,0.2894737,0.3709677,0.369863, > 0.3676471,0.3636364,0.3088235,0.328125,0.4032258,0.4084507,0.3188406,0.3636364,0.3823529,0.2816901,0.4722222,0.5,0.3521127,0.4393939,0.3787879, > 0.453125,0.4324324,0.4057971,0.4545455,0.4492754,0.5,0.4098361,0.4067797,0.3666667,0.3928571,0.4285714,0.5,0.2923077,0.4561404,0.45,0.5538462, > 0.4626866,0.4057971,0.3676471,0.5322581,0.5428571,0.375,0.4411765,0.4571429,0.4,0.3846154,0.3870968,0.4915254,0.530303,0.4375,0.4918033,0.4179104, > 0.4032258,0.3606557,0.5178571,0.4848485,0.390625,0.375,0.4375,0.3666667,0.4,0.4477612,0.2571429,0.4032258,0.3382353,0.4814815,0.4090909,0.3548387, > 0.4821429,0.5,0.557377,0.4333333,0.5454545,0.4590164,0.3943662,0.5076923,0.5,0.3283582,0.3676471,0.559322) > > my.cor<-cor(Xvar, Yvar) > print(my.cor) > > nperm<-49999 > Perm.Cor<-NULL > > for (iperm in 1:nperm) { > XvarNew<-sample(Xvar, size=length(Xvar), replace=FALSE) > YvarNew<-sample(Yvar, size=length(Yvar), replace=FALSE) > perm.cor<-cor(XvarNew, YvarNew) > Perm.Cor<-c(Perm.Cor, perm.cor) > } > print(max(Perm.Cor)) > XvarSorted<-sort(Xvar, decreasing=TRUE) > YvarSorted<-sort(Yvar, decreasing=TRUE) > max.cor<-cor(XvarSorted, YvarSorted) > print(max.cor) > if(mat.cor>0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=TRUE) > if(mat.cor<0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=FALSE) > T95<-Perm.Cor.Sorted[(nperm+1)*0.05] # 95% treshold value > T99<-Perm.Cor.Sorted[(nperm+1)*0.01] # 99% treshold value > > > > I want to understand where I am making a mistake. Any comment is deeply appreciated.Well, if you are permuting Xvar and Yvar separately or sorting them (separately), then you cannot expect to get the same correlation again. Look at the formula and make an example for yourself with just, say, 3 data points ... Uwe Ligges> Kind Regards > > Seyit Ali > > > ------------------------------------------------------------------------------------------------------------------ > Dr. Seyit Ali KAYIS > Selcuk University > Faculty of Agriculture > Kampus, Konya, TURKEY > > s_a_kayis at yahoo.com, s_a_kayis at hotmail.com > Tell: +90 332 223 2830 Mobile: +90 535 587 1139 Fax: +90 332 241 0108 > > Greetings from Konya, TURKEY > http://www.ziraat.selcuk.edu.tr/skayis/ > ---------------------------------------------------------------------------------------------------------------------- > > > > > > > > _________________________________________________________________ > Earning enough? Find out with SEEK Salary Survey > > %2Eco%2Enz%2F%3Ftracking%3Dsk%3Atl%3Asknzsal%3Amsnnz%3A0%3Ahottag%3Aearn%5Fenough&_t=757263783&_r=Seek_NZ_tagline&_m=EXT > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Hi r-help-bounces at r-project.org napsal dne 21.04.2009 12:25:01:> > Dear R users, > > I need to do sampling without replacement (bootstraps). I have twovariables> (Xvar, Yvar). > I have a correlation from original data set cor(Xvar, Yvar)=0.6174221. Iam> doing 50000 sampling, > and in each sampling calculating correlations, saving, sorting andgetting> 95% cutt off point (0.1351877). > I am getting maximum value as 0.3507219 (much smaller than correlationof my> original data). > I repeated the sampling a couple of time and none of them produced acorrelation> coefficient higher than my original data set. However, if I sort out myXvar> and Yvar and > obtain correlation it is 0.9657125 which is much higher than correlationfor> my original data. > I am doing sampling in another program and getting at least 1% higher > correlation than mine. > Now I am getting confused with sampling(random data) in R. My data andcodes> for the scenario above are below > > >Xvar<-c(0.1818182,0.5384615,0.5535714,0.4680851,0.4545455,0.4385965,0.5185185,> 0.4035088,0.4901961,0.3650794,0.462963,0.4,0.56,0.3965517,0.4909091, > 0.4716981,0.4310345,0.2,0.1509434,0.2647059,0.173913,0.1914894,0. >1914894,0.1489362,0.1363636,0.2244898,0.2325581,0.1333333,0.1818182,0.1702128,> 0.2173913,0.2380952,0.1632653,0.5614035,0.3396226,0.4909091,0.3770492, > 0.5,0.5185185,0.5,0.4666667,0.4464286,0.362069,0.4285714,0.4561404, > 0.4736842,0.4545455,0.4166667,0.4181818,0.4590164,0.5166667,0.5423729, > 0.4833333,0.5454545,0.4393939,0.5172414,0.4098361,0.4745763,0.4754098, > 0.5166667,0.5,0.4603175,0.42,0.4038462,0.4897959,0.3148148,0.3673469, > 0.4,0.4583333,0.3877551,0.4375,0.4117647,0.4313725,0.5333333,0.3962264, > 0.3548387,0.5272727,0.4137931,0.3928571,0.4666667,0.4210526,0.4363636, >0.4545455,0.4310345,0.4237288,0.4814815,0.4912281,0.4333333,0.4,0.4285714,> 0.4516129,0.5090909,0.4464286,0.4642857,0.4166667,0.4098361,0.4909091, >0.3809524,0.5272727,0.4814815,0.5254237,0.627451,0.5,0.5471698,0.5454545,> 0.5925926,0.5769231,0.5818182,0.4444444,0.4915254,0.4727273,0.4107143, > 0.4285714,0.4310345,0.4237288,0.4285714,0.440678,0.4237288,0.4807692, > 0.4150943,0.4615385,0.4107143,0.4814815,0.4074074,0.4210526,0.5263158, > 0.440678,0.4576271,0.5344828,0.5,0.5636364,0.4677419,0.5,0.5192308, > 0.4642857,0.5090909,0.58,0.4482759,0.5098039,0.4035088,0.4210526,0. >5098039,0.4385965,0.5283019,0.5471698,0.625,0.4310345,0.4912281,0.5283019,> 0.4576271,0.5471698,0.4745763,0.4821429) > >Yvar<-c(0.2553191,0.4107143,0.5660377,0.3888889,0.3606557,0.2898551,0.3818182,> 0.4,0.4,0.3278689,0.2903226,0.4074074,0.4181818,0.3,0.2238806,0.3728814, > 0.3709677,0.2307692,0.2830189,0.2244898,0.2142857,0.2131148,0.22,0. >2258065,0.2321429,0.2,0.2264151,0.22,0.2115385,0.2459016,0.1166667,0.1785714,> 0.2068966,0.6,0.4285714,0.3134328,0.4461538,0.3965517,0.4769231,0. >6181818,0.4827586,0.3709677,0.3965517,0.4821429,0.4545455,0.359375,0.4576271,> 0.4516129,0.5272727,0.4603175,0.4,0.4912281,0.5384615,0.5,0.4516129,0. > 4126984,0.4655172,0.5263158,0.4925373,0.358209,0.4285714,0.4920635, > 0.4482759,0.3235294,0.4,0.4375,0.440678,0.3898305,0.35,0.4528302,0.58, > 0.4153846,0.3174603,0.5185185,0.3870968,0.2894737,0.3709677,0.369863, > 0.3676471,0.3636364,0.3088235,0.328125,0.4032258,0.4084507,0.3188406, >0.3636364,0.3823529,0.2816901,0.4722222,0.5,0.3521127,0.4393939,0.3787879,> 0.453125,0.4324324,0.4057971,0.4545455,0.4492754,0.5,0.4098361,0. >4067797,0.3666667,0.3928571,0.4285714,0.5,0.2923077,0.4561404,0.45,0.5538462,> 0.4626866,0.4057971,0.3676471,0.5322581,0.5428571,0.375,0.4411765,0. >4571429,0.4,0.3846154,0.3870968,0.4915254,0.530303,0.4375,0.4918033,0.4179104,> 0.4032258,0.3606557,0.5178571,0.4848485,0.390625,0.375,0.4375,0. >3666667,0.4,0.4477612,0.2571429,0.4032258,0.3382353,0.4814815,0.4090909,0.3548387,> 0.4821429,0.5,0.557377,0.4333333,0.5454545,0.4590164,0.3943662,0. > 5076923,0.5,0.3283582,0.3676471,0.559322) > > my.cor<-cor(Xvar, Yvar) > print(my.cor) > > nperm<-49999 > Perm.Cor<-NULL > > for (iperm in 1:nperm) { > XvarNew<-sample(Xvar, size=length(Xvar), replace=FALSE) > YvarNew<-sample(Yvar, size=length(Yvar), replace=FALSE) > perm.cor<-cor(XvarNew, YvarNew) > Perm.Cor<-c(Perm.Cor, perm.cor) > }AFAICU you do not sample your data you shuffle them. Then you compute cor with shuffled data (X and Y are shuffled independently) which results in low correlation (it is like shuffling cards). Maybe you could use smaller size and sample not original data but a vector of indices perm.cor<-rep(NA, 49999) for (iperm in 1:nperm) { ind <- sample(1:length(Xvar), size = 100, replace=FALSE) perm.cor[iperm] <- cor(Xvar[ind], Yvar[ind]) perm.cor } max(perm.cor) hist(perm.cor) The result seems to be quite reasonable. Regards Petr> print(max(Perm.Cor)) > XvarSorted<-sort(Xvar, decreasing=TRUE) > YvarSorted<-sort(Yvar, decreasing=TRUE) > max.cor<-cor(XvarSorted, YvarSorted) > print(max.cor) > if(mat.cor>0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=TRUE) > if(mat.cor<0) Perm.Cor.Sorted<-sort(Perm.Cor, decreasing=FALSE) > T95<-Perm.Cor.Sorted[(nperm+1)*0.05] # 95% treshold value > T99<-Perm.Cor.Sorted[(nperm+1)*0.01] # 99% treshold value > > > > I want to understand where I am making a mistake. Any comment is deeplyappreciated.> > Kind Regards > > Seyit Ali > > >------------------------------------------------------------------------------------------------------------------> Dr. Seyit Ali KAYIS > Selcuk University > Faculty of Agriculture > Kampus, Konya, TURKEY > > s_a_kayis at yahoo.com, s_a_kayis at hotmail.com > Tell: +90 332 223 2830 Mobile: +90 535 587 1139 Fax: +90 332 241 0108 > > Greetings from Konya, TURKEY > http://www.ziraat.selcuk.edu.tr/skayis/ >----------------------------------------------------------------------------------------------------------------------> > > > > > > > _________________________________________________________________ > Earning enough? Find out with SEEK Salary Survey > >%2Eco%2Enz%2F%3Ftracking%3Dsk%3Atl%3Asknzsal%3Amsnnz%3A0%3Ahottag%3Aearn%> 5Fenough&_t=757263783&_r=Seek_NZ_tagline&_m=EXT > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code.