. ، datum و vegetation…

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نوع زبان: انگلیسی حجم: 7.27 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 81 صفحه
سطح مطلب: نامشخص پسوند فایل: pptx
گروه موضوعی: زمان استخراج مطلب: 2019/06/05 11:05:11

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

., model, datum, variable, vegetation, residual, sp۱۹, tree, parametric, regression, log, predictor,

توجه: این مطلب در تاریخ 2019/06/05 11:05:11 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

https://www.fs.fed.us/rm/ogden/research/Rworkshop/Rworkshop_files/SP2_VegModeling.pptx

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: ., model, datum, variable, vegetation, residual, sp۱۹, tree, parametric, regression, log, predictor,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

۱ vegetation modeling outline ۲ model types predictive models predictor data predictive model types parametric nonparametric model example tree based random forests modeling dataset response predictor data discussion – scale of predictors predictor data extraction data exploration summary statistics na values attaching data frame predictor variables response variables binary continuous model generation tree based models random forests classification regression trees variable importance proximity model prediction polygon data predictor data clipping stacking apply model and display map modelmap build model model diagnostics make map ۳ in general there are ۳ reasons we model vegetation ۱. explanatory to explain why something is happening… find a pattern or a cause. ۲. descriptive to see an association between variables not aimed at prediction. ۳. predictive to predict an occurrence based on known attributes. examples is the height of a tree related to its diameter did the last ۲ years of drought affect tree mortality rates are forest disturbances changing the carbon cycle can we predict the distribution of vegetation ۱ years from now based on climate models can we predict the current distribution of vegetation across the landscape from the spectral signature of remotely sensed data vegetation modeling an article on different reasons for modeling. shmueli g. ۲ ۱ . to explain or to predict statistical science ۲۵ ۳ ۲۸۹ ۳۱ . ۴ predicting the distribution of vegetation across the landscape from available maps of environmental variables such as geology topography and climate and spectral data from remotely sensed imagery or products. statistical models are built by finding relationships between vegetation data and the available digital data and then applied across given digital landscapes. predictive vegetation models . .using flexible statistical models and gis tools . . . .and generate maps of forest attributes. integrate forest inventory data . . . .with available digital data . . y f x۱ x۲ xn satellite imagery dems soils ۵ digital environmental data and remotely sensed products are becoming increasingly more available at many different scales. remotely sensed data and derived products snapshot of what is on the ground caution represents current vegetation distributions based on reflectance patterns but does not explain potential occurrences of vegetation. topography variables elevation aspect slope no direct physiological influence on vegetation caution these variables are local to the model domain be careful when extrapolating over space or time. climate variables temperature precipitation directly related to physiological responses of vegetation surrogates for topographical variables caution these variables are better for extrapolating over space and time but recognize other limitations such as current location and dispersal range and species competition may also change through time.. other surrogates geology soils soil moisture availability solar radiation resources directly used by plants for growth caution similar to climate variables predictor data ۶ model objective find a relationship between vegetation data and predictor data for prediction purposes. predictive model types the model in its simplest form looks like the following y ~ f x۱ x۲ x۳ … ε where y is a response variable the x s are predictor variables and ε is the associated error. there are many different types of models parametric models – make assumptions about the underlying distribution of the data. maximum likelihood classification discriminant analysis general linear models ex. linear regression . . . nonparametric models – make no assumptions about the underlying data distributions. generalized additive models machine learning models artificial neural networks . . . ۷ predictive model types parametric models parametric models – make assumptions about the underlying distribution of the data assumptions the shape of the distribution of the underlying population is bell shaped normal . errors are independent. errors are normally distributed with mean of and a constant variance. advantages easy to interpret high power with low sample sizes disadvantages if sample data are not from a normally distributed population may lead to incorrect conclusions. examples maximum likelihood classification discriminant analysis linear regression multiple linear regression generalized linear models parametric nonparametric ۸ predictive model types parametric models – regression example previous example using regression to fill in missing values. import data and subset for sp۱۹ only path c peru rworkshop where workshop material is setwd path tree read.csv plotdata tree.csv stringsasfactors false sp۱۹ tree tree spcd ۱۹ is.na tree dia start off with a scatter plot par mfrow c ۱ ۲ plot sp۱۹ dia sp۱۹ ht xlab diameter ylab height main subalpine fir abline lm sp۱۹ ht ~ sp۱۹ dia we saw some heteroscedasticity unequal variance and transformed data to log scale. plot log sp۱۹ dia log sp۱۹ ht xlab diameter ylab height main subalpine fir log scale abline lm log sp۱۹ ht ~ log sp۱۹ dia we looked at summary of models and saw lower residual error and higher r۲ values. r.mod lm ht~dia data sp۱۹ summary r.mod r.mod.log.ht.dia lm log ht ~log dia data sp۱۹ summary r.mod.log.ht.dia ۹ previous example cont.. then we looked at the residuals versus the fitted values and normal qq plots. par mfrow c ۲ ۲ plot r.mod fitted r.mod.log.ht.dia residuals xlab fitted ylab residuals main fitted versus residuals abline h qqnorm r.mod residuals main normal q q plot qqline r.mod residuals plot r.mod.log.ht.dia fitted r.mod.log.ht.dia residuals xlab fitted ylab residuals main fitted versus residuals log scale abline h qqnorm r.mod.log.ht.dia residuals main normal q q plot log scale qqline r.mod.log.ht.dia residuals note using transformations we were able to work with data that was nonlinear with unequal variance structure using a parametric model. predictive model types parametric models – regression example ۱ predictive model types nonparametric models nonparametric models – make no assumptions about the underlying distribution of the data. note vegetation data typically are not normally distributed across the landscape therefore it is most often better to use a nonparametric model. advantages if sample data are not from a normally distributed population using a parametric may lead to incorrect conclusions. disadvantages need larger sample sizes to have the same power as parametric statistics harder to interpret can overfit data examples generalized additive models classification and regression trees i.e. cart artificial neural networks ann multivariate adaptive regression splines mars ensemble modeling i.e. random forests random forests breiman ۲ ۱ generates a series of classification and regression tree models.. .. sampling with replacement from training data bootstrap .. selecting predictor variables at random for each node .. outputting the class that most frequently results .. and calculating an out of bag error estimate .. and measuring variable importance through permutation randomforest – liaw wiener modelmap – freeman frescino random forests ۱۲ modeling example extract data from each layer at each sample plot location. prediction …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: ., model, datum, variable, vegetation, residual, sp۱۹, tree, parametric, regression, log, predictor,

این فایل پاورپوینت شامل 81 اسلاید و به زبان انگلیسی و حجم آن 7.27 مگا بایت است. نوع قالب فایل pptx بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/06/05 11:05:11 استخراج شده است.

https://www.fs.fed.us/rm/ogden/research/Rworkshop/Rworkshop_files/SP2_VegModeling.pptx

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