I have read that one ,I want to this method to be used to my data.but I donot know how to put my data into R.
James W. MacDonald wrote: > > > > bbslover wrote: >> >> >> Steve Lianoglou-6 wrote: >>> Hi, >>> >>> On Oct 22, 2009, at 2:35 PM, bbslover wrote: >>> >>>> Usage >>>> data(gasoline) >>>> Format >>>> A data frame with 60 observations on the following 2 variables. >>>> octane >>>> a numeric vector. The octane number. >>>> NIR >>>> a matrix with 401 columns. The NIR spectrum >>>> >>>> and I see the gasoline data to see below >>>> NIR.1686 nm NIR.1688 nm NIR.1690 nm NIR.1692 nm NIR.1694 nm NIR.1696 >>>> nm >>>> NIR.1698 nm NIR.1700 nm >>>> 1 1.242645 1.250789 1.246626 1.250985 1.264189 1.244678 1.245913 >>>> 1.221135 >>>> 2 1.189116 1.223242 1.253306 1.282889 1.215065 1.225211 1.227985 >>>> 1.198851 >>>> 3 1.198287 1.237383 1.260979 1.276677 1.218871 1.223132 1.230321 >>>> 1.208742 >>>> 4 1.201066 1.233299 1.262966 1.272709 1.211068 1.215044 1.232655 >>>> 1.206696 >>>> 5 1.259616 1.273713 1.296524 1.299507 1.226448 1.230718 1.232864 >>>> 1.202926 >>>> 6 1.24109 1.262138 1.288401 1.291118 1.229769 1.227615 1.22763 >>>> 1.207576 >>>> 7 1.245143 1.265648 1.274731 1.292441 1.218317 1.218147 1.222273 >>>> 1.200446 >>>> 8 1.222581 1.245782 1.26002 1.290305 1.221264 1.220265 1.227947 >>>> 1.188174 >>>> 9 1.234969 1.251559 1.272416 1.287405 1.211995 1.213263 1.215883 >>>> 1.196102 >>>> >>>> look at this NIR.1686 nm NIR.1688 nm NIR.1690 nm NIR.1692 nm NIR. >>>> 1694 nm >>>> NIR.1696 nm NIR.1698 nm NIR.1700 nm >>>> >>>> how can I add letters NIR to my variable, because my 600 >>>> independents never >>>> have NIR as the prefix. however, it is needed to model the plsr. for >>>> example aa=plsr(y~NIR, data=data ,....), the prefix NIR is >>>> necessary, how >>>> can I do with it? >>> I'm not really sue that I'm getting you, but if your problem is that >>> the column names of your data.frame don't match the variable names >>> you'd like to use in your formula, just change the colnames of your >>> data.frame to match your formula. >>> >>> BTW - I have no idea where to get this gasoline data set, so I'm just >>> imagining: >>> >>> eg. >>> colnames(gasoline) <- c('put', 'the', 'variable', 'names', 'that', >>> 'you', 'want', 'here') >>> >>> -steve >>> >>> -- >>> Steve Lianoglou >>> Graduate Student: Computational Systems Biology >>> | Memorial Sloan-Kettering Cancer Center >>> | Weill Medical College of Cornell University >>> Contact Info: http://cbio.mskcc.org/~lianos/contact >>> >>> ______________________________________________ >>> R-help@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. >>> >>> >> >> thanks for you. but the numbers of indenpendence are so many, it is not >> easy >> to identify them one by one, is there some better way? > > You don't need to identify anything. What you need to do is read the > help page for the function you want to use, so you (at the very least) > know how to use the function. > > > library(pls) > > data(gasoline) > > fit <- plsr(octane~NIR, data=gasoline, validation = "CV") > > summary(fit) > Data: X dimension: 60 401 > Y dimension: 60 1 > Fit method: kernelpls > Number of components considered: 53 > > VALIDATION: RMSEP > Cross-validated using 10 random segments. > (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps > CV 1.543 1.372 0.3827 0.2522 0.2347 0.2455 0.2281 > adjCV 1.543 1.367 0.3740 0.2497 0.2360 0.2407 0.2243 > 7 comps 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps > CV 0.2311 0.2352 0.2455 0.2534 0.2737 0.2814 0.2832 > adjCV 0.2257 0.2303 0.2395 0.2473 0.2646 0.2705 0.2726 > 14 comps 15 comps 16 comps 17 comps 18 comps 19 comps 20 > comps > CV 0.2913 0.2932 0.2985 0.3137 0.3289 0.3323 > 0.3391 > adjCV 0.2808 0.2821 0.2863 0.3008 0.3141 0.3172 > 0.3228 > 21 comps 22 comps 23 comps 24 comps 25 comps 26 comps 27 > comps > CV 0.3476 0.3384 0.3316 0.3213 0.3155 0.3118 > 0.3062 > adjCV 0.3307 0.3217 0.3154 0.3057 0.3002 0.2964 > 0.2908 > 28 comps 29 comps 30 comps 31 comps 32 comps 33 comps 34 > comps > CV 0.3033 0.3034 0.3074 0.3083 0.3094 0.3087 > 0.3105 > adjCV 0.2881 0.2881 0.2917 0.2926 0.2936 0.2929 > 0.2946 > 35 comps 36 comps 37 comps 38 comps 39 comps 40 comps 41 > comps > CV 0.3108 0.3106 0.3105 0.3104 0.3104 0.3105 > 0.3105 > adjCV 0.2949 0.2947 0.2946 0.2945 0.2945 0.2945 > 0.2946 > 42 comps 43 comps 44 comps 45 comps 46 comps 47 comps 48 > comps > CV 0.3105 0.3105 0.3105 0.3105 0.3105 0.3105 > 0.3105 > adjCV 0.2946 0.2946 0.2946 0.2946 0.2946 0.2946 > 0.2946 > 49 comps 50 comps 51 comps 52 comps 53 comps > CV 0.3105 0.3105 0.3105 0.3105 0.3105 > adjCV 0.2946 0.2946 0.2946 0.2946 0.2946 > > TRAINING: % variance explained > 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps 7 comps > 8 comps > X 70.97 78.56 86.15 95.4 96.12 96.97 97.32 > 98.1 > octane 31.90 94.66 97.71 98.0 98.68 98.93 99.06 > 99.1 > 9 comps 10 comps 11 comps 12 comps 13 comps 14 comps 15 > comps > X 98.32 98.71 98.84 99.00 99.21 99.46 > 99.52 > octane 99.20 99.24 99.36 99.44 99.49 99.51 > 99.58 > 16 comps 17 comps 18 comps 19 comps 20 comps 21 comps 22 > comps > X 99.57 99.64 99.68 99.76 99.78 99.82 > 99.84 > octane 99.65 99.69 99.78 99.81 99.86 99.89 > 99.92 > 23 comps 24 comps 25 comps 26 comps 27 comps 28 comps 29 > comps > X 99.88 99.91 99.92 99.93 99.94 99.95 > 99.96 > octane 99.93 99.94 99.95 99.97 99.98 99.99 > 99.99 > 30 comps 31 comps 32 comps 33 comps 34 comps 35 comps 36 > comps > X 99.96 99.97 99.97 99.98 99.98 99.98 > 99.98 > octane 99.99 100.00 100.00 100.00 100.00 100.00 > 100.00 > 37 comps 38 comps 39 comps 40 comps 41 comps 42 comps 43 > comps > X 99.99 99.99 99.99 99.99 100 100 > 100 > octane 100.00 100.00 100.00 100.00 100 100 > 100 > 44 comps 45 comps 46 comps 47 comps 48 comps 49 comps 50 > comps > X 100 100 100 100 100 100 > 100 > octane 100 100 100 100 100 100 > 100 > 51 comps 52 comps 53 comps > X 100 100 100 > octane 100 100 100 > > >> >> > > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > > ______________________________________________ > R-help@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. > > -- View this message in context: http://www.nabble.com/data-frame-is-killing-me%21-help-tp26015079p26029667.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@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.