Beginners should definitely read the Posting Guide mentioned in the footer of this and every message from this mailing list. One key point is that this mailing list is for plain text format only, and your message gets damaged to varying degrees when you fail to set the plain text format option in your email program. (there are too many email programs for us to be able to help you figure out how to do that... that is up to you.)
Another point from the PG worth mentioning is that contributed packages are not strictly speaking on topic here. There should be a hint as to where to ask questions about it in the description [1].
You should learn about the base R function str. Try typing
?str
at the R console (command line)
[1] https://cran.r-project.org/web/packages/prospectr/index.html see URL
On September 9, 2019 1:21:04 AM PDT, rhotuser using yahoo.co.jp wrote:
>Dear Sir,
>
>After installing prospectr,
>> data(NIRsoil)> mode(NIRsoil)[1] "list">
>I am a beginner and the structure of “NIRsoil” is complicated.
>
>Judging from the explanation of NIRsoil (see below), there are six
>elements: NT, Ciso, CEC, train, validation, spc.
>
>Please tell me how to know the structure of such “NIRsoil”.
>When using mode (NIRsoil) , The answer is just a “list”.
>Is there a command or function "Detailstructure (NIRsoil)"?
>> NIRsoil
>.
>. spc.2494 spc.2496 spc.24981 0.3724227 0.3724588 0.37256772
> 0.3159643 0.3164802 0.31681803 0.3416802 0.3422180 0.34263164
> 0.3650448 0.3654987 0.36580555 0.3050233 0.3058226 0.30640076
> 0.3599009 0.3603650 0.36070207 0.3365467 0.3369826 0.33725788
> 0.3922267 0.3926329 0.39288209 0.3149452 0.3153307 0.315540410
>0.4951153 0.4954152 0.495766411 0.3414548 0.3418657 0.342114012
>0.3314737 0.3319690 0.332336513 0.4153854 0.4156993 0.416079614
>0.3311567 0.3316245 0.331926815 0.3289777 0.3294281 0.329720616
>0.4268982 0.4274236 0.427819517 0.3421552 0.3426531 0.342981518
>0.3452011 0.3457459 0.346121719 0.3497368 0.3497175 0.349840320
>0.3896840 0.3901652 0.390537521 0.4062244 0.4066894 0.407021322
>0.3792031 0.3796499 0.379960123 0.3252305 0.3256037 0.325836624
>0.3427994 0.3431511 0.343363125 0.3410443 0.3414203 0.341648926
>0.3947710 0.3952165 0.395562027 0.2930943 0.2934901 0.293697728
>0.4138924 0.4144398 0.414861929 0.2906964 0.2910948 0.291324130
>0.4000607 0.4004497 0.400749131 0.3275807 0.3278961 0.328067632
>0.3343273 0.3347059 0.334947033 0.2923162 0.2927208 0.292952534
>0.3181143 0.3184221 0.318574835 0.3959161 0.3963806 0.396695436
>0.3412020 0.3415608 0.341737737 0.3109980 0.3119904 0.312796538
>0.3452408 0.3457109 0.346040139 0.2352630 0.2357922 0.236104440
>0.3731434 0.3735985 0.373948341 0.3776968 0.3780733 0.378314142
>0.4279117 0.4278319 0.427811343 0.3499106 0.3502492 0.350448044
>0.4060152 0.4064416 0.406781645 0.4434104 0.4437352 0.444117646
>0.3106347 0.3111292 0.311479047 0.3048005 0.3052686 0.305555748
>0.3074032 0.3078929 0.308188549 0.3099467 0.3103620 0.310607950
>0.4140719 0.4140101 0.414070551 0.3047789 0.3051992 0.305453152
>0.4372076 0.4371921 0.437301153 0.3784582 0.3788292 0.379046054
>0.3070685 0.3074959 0.307739255 0.3929690 0.3930495 0.393248956
>0.3104950 0.3109303 0.311175657 0.3110389 0.3114884 0.311783658
>0.3561086 0.3565895 0.356935659 0.2946499 0.2950254 0.295230760
>0.3396696 0.3402275 0.340604561 0.4174463 0.4174852 0.417609462
>0.3838490 0.3845536 0.385175463 0.4214520 0.4217800 0.422008564
>0.3136719 0.3141062 0.314410265 0.3137282 0.3141668 0.314444466
>0.4341556 0.4341235 0.434172067 0.3028214 0.3032053 0.303438768
>0.2929183 0.2933525 0.293613269 0.2901444 0.2905667 0.290814470
>0.2926482 0.2930853 0.293328871 0.3964101 0.3968658 0.397207272
>0.3205706 0.3209653 0.321186973 0.2965653 0.2970253 0.297328574
>0.3271259 0.3274269 0.327623075 0.2910520 0.2914549 0.291697476
>0.3266725 0.3271569 0.327475777 0.3405427 0.3410150 0.341319778
>0.3164929 0.3169560 0.317269479 0.3489597 0.3495394 0.349918280
>0.3369682 0.3374025 0.337660681 0.2608885 0.2613245 0.261581182
>0.3132109 0.3136024 0.313788183 0.3155325 0.3159175 0.316144884
>0.3156847 0.3160608 0.316250785 0.2751068 0.2754884 0.275712986
>0.2722970 0.2726988 0.272900087 0.3421156 0.3424138 0.342577688
>0.4408897 0.4413919 0.441767389 0.4094418 0.4098032 0.410052590
>0.2883307 0.2887366 0.288972191 0.3368085 0.3373375 0.337674492
>0.2914632 0.2918586 0.292056993 0.3701615 0.3703609 0.370657894
>0.3168159 0.3172900 0.317585795 0.3141627 0.3145356 0.314785296
>0.2717998 0.2721882 0.272409297 0.2994829 0.2998522 0.300074998
>0.4238313 0.4238643 0.423967099 0.3173946 0.3178398 0.3181364100
>0.3065352 0.3070051 0.3073053101 0.4119025 0.4122178 0.4126194102
>0.2997539 0.3001756 0.3004323103 0.3195902 0.3200457 0.3203547104
>0.3770273 0.3775299 0.3779288105 0.3119653 0.3123196 0.3125118106
>0.3007703 0.3012067 0.3014555107 0.3583141 0.3586335 0.3588411108
>0.3613160 0.3616626 0.3619002109 0.3012548 0.3016973 0.3019971110
>0.3040114 0.3044745 0.3047490111 0.3886676 0.3889934 0.3894869112
>0.3381325 0.3386472 0.3389995113 0.3228837 0.3232841 0.3235284114
>0.3175641 0.3179575 0.3182012115 0.3255604 0.3259808 0.3262322116
>0.3038366 0.3043306 0.3046326117 0.3040762 0.3045244 0.3048216118
>0.3101427 0.3105674 0.3108223119 0.3186323 0.3190890 0.3194055120
>0.3183660 0.3188377 0.3191515121 0.3716398 0.3723766 0.3729757122
>0.3336347 0.3341000 0.3344247123 0.3013631 0.3017261 0.3018954124
>0.3559751 0.3563222 0.3565449125 0.3467284 0.3475789 0.3482055126
>0.2983477 0.2992734 0.3000016127 0.2966113 0.2970161 0.2972673128
>0.3244559 0.3249162 0.3252265129 0.3687044 0.3692860 0.3697534130
>0.2949226 0.2953484 0.2956104131 0.3449411 0.3454331 0.3457713132
>0.3458079 0.3462659 0.3465872133 0.3879239 0.3882694 0.3884915134
>0.3998409 0.3997933 0.3999189135 0.2591648 0.2596039 0.2598299136
>0.3145797 0.3149986 0.3152637137 0.3184437 0.3188738 0.3191448138
>0.3101936 0.3105727 0.3108114139 0.3263323 0.3267737 0.3270491140
>0.3234212 0.3238232 0.3240880141 0.5669109 0.5681474 0.5693322142
>0.4268930 0.4277861 0.4285390 [ reached getOption("max.print") -- 683
>Finally, another question: Why 142 lines ?
> 825 observed data and 5 variables, the reference said.
>
>Reference-----------
>| NIRsoil {prospectr} | R Documentation |
>
>
>NIRSoil
>
>Description
>Soil spectral library of the ‘Chimiometrie 2006’challenge. The database
>contains absorbance spectra ofdried and sieved soil samples measured
>between 1100 nm and2498 nm at 2 nm interval. The soil samples come
>fromagricultural fields collected from all over the Walloonregion in
>Belgium. Three parameters are associated with thespectral library: Nt
>(Total Nitrogen in g/Kg of dry soil),CEC (Cation Exchange Capacity in
>meq/100 g of dry soil) andCiso (Carbon in g/100 g of dry soil). Carbon
>content hasbeen measured following the ISO14235 method.
>Usage
>data(NIRsoil)
>
>Format
>A data frame of 825 observations and 5 variables
>Details
>The dataset includes 618 training and 207 test samples with5 variables:
>Nt (Total Nitrogen), Ciso (Carbon), CEC(Cation Exchange Capacity),
>train (vector of 0,1indicating training (1) and validation (0) samples)
>and spc(a matrix with absorbance NIR data and bandpositions as
>colnames). Nt, Ciso and CEC haverespectively 22 %, 11 % and 46 % of the
>observationswith missing values.
>Source
>Pierre Dardenne from Walloon Agricultural Research Centre,Belgium.
>References
>Fernandez Pierna, J.A., and Dardenne, P., 2008. Soilparameter
>quantification by NIRS as a Chemometric challengeat 'Chimiometrie
>2006'. Chemometrics and IntelligentLaboratory Systems 91,
>94-98.Minasny, B., and McBratney, A.B., 2008. Regression rules asa tool
>for predicting soil properties from infraredreflectance spectroscopy.
>Chemometrics and IntelligentLaboratory Systems 94, 72-79.
> Package prospectr version 0.1.3 Index
>
>
>
> [[alternative HTML version deleted]]
>
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--
Sent from my phone. Please excuse my brevity.