When setting your obsCovs for the function pcount (package unmarked) how does R "know" which obsCov observation corresponds to each y value? - r

I'm relatively new at R particularly with this package. I am running n-mixture models assessing detection probabilities and abundance. I have abundance data, site covariates and observation covariates. There are three repeated observations(rounds)/site. The observation covariates are set up as columns (three column/covariate, one for each round). The rows are individual sites. The abundance data is formatted similarly, with each column heading representing a different round. I've copied my code below.
y.abun2<-COYE[2:4]
obsCovs.ss <- list(temp=Covariate2021[3:5], Date=Covariate2021[13:15], Cloud=Covariate2021[17:19], Wind=Covariate2021[21:23],Observ=Covariate2021[25:27])
siteCovs.ss <- Covariate2021[c(29,30,31,32)]
coyeabund<-unmarkedFramePCount(y=y.abun2, siteCovs = siteCovs.ss,
obsCovs = obsCovs.ss)
After this I scale using this code:
coyeabund#siteCovs$TreeCover <-
scale(coyeabund#siteCovs$TreeCover)
Moving on to my model I use this code:
abun.coye.full<-pcount(~TreeCover+temp+Date+Cloud+Wind+Observ ~ HHSDI+ProportionNH+Quality, coyeabund,mixture="NB", K=132,se=TRUE)
Is the model matching the observation covariates to the abundance measurements to each round? (i.e., is it able to tell that temp column 5 corresponds to the third round of abundance measurements?)
The models seem fine so far but I am so new at this I want to confirm that I haven't gone astray.

Related

Model predicted values around mean using training data

I tried to ask these questions through imputations, but I want to see if this can be done with predictive modelling instead. I am trying to use information from 2003-2004 NHANES to predict future NHANES cycles. For some context, in 2003-2004 NHANES measured blood contaminants in individual people's blood. In this cycle, they also measured things such as triglycerides, cholesterol etc. that influence the concentration of these blood contaminants.
The first step in my workflow is the impute missing blood contaminant concentrations in 2003-2004 using the measured values of triglycerides, cholesterol etc. This is an easy step and very straightforward. This will be my training dataset.
For future NHANES years (for example 2005-2006), they took individual blood samples combined them (or pooled in other words) and then measured blood contaminants. I need to figure out what the individual concentrations were in these cycles. I have individual measurements for triglycerides, cholesterol etc. and the pooled value is considered the mean. Could I use the mean, 2003-2004 data to unpool or predict the values? For example, if a pool contains 8 individuals, we know the mean, the distribution (2003-2004) and the other parameters (triglycerides) which we can use in the regression to estimate the blood contaminants in those 8 individuals. This would be my test dataset where I have the same contaminants as in the training dataset, with a column for the number of individuals in each pool and the mean value. Alternatively, I can create rows of empty values for contaminants, add mean values separately.
I can easily run MICE, but I need to make sure that the distribution of the imputed data matches 2003-2004 and that the average of the imputed 8 individuals from the pools is equal to the measured pool. So the 8 values for each pool, need to average to the measured pool value while the distribution has to be the same as 2003-2004.
Does that make sense? Happy to provide context if need be. There is an outline code below.
library(mice)
library(tidyverse)
library(VIM)
#Papers detailing these functions can be found in MICE Cran package
df <- read.csv('2003_2004_template.csv', stringsAsFactors = TRUE, na.strings = c("", NA))
#Checking out the NA's that we are working with
non_detect_summary <- as.data.frame(df %>% summarize_all(funs(sum(is.na(.)))))
#helpful representation of ND
aggr_plot <- aggr(df[, 7:42], col=c('navyblue', 'red'),
numbers=TRUE,
sortVars=TRUE,
labels=names(df[, 7:42]),
cex.axis=.7,
gap=3,
ylab=c("Histogram of Missing Data", "Pattern"))
#Mice time, m is the number of imputed datasets (you can think of this as # of cycles)
#You can check out what regression methods below in console
methods(mice)
#Pick Method based on what you think is the best method. Read up.
#Now apply the right method
imputed_data <- mice(df, m = 30)
summary(imputed_data)
#if you want to see imputed values
imputed_data$imp
#finish the dataset
finished_imputed_data <- complete(imputed_data)
#Check for any missing values
sapply(finished_imputed_data, function(x) sum(is.na(x))) #All features should have a value of zero
#Helpful plot is the density plot. The density of the imputed data for each imputed dataset is showed
#in magenta while the density of the observed data is showed in blue.
#Again, under our previous assumptions we expect the distributions to be similar.
densityplot(x = imputed_data, data = ~ LBX028LA+LBX153LA+LBX189LA)
#Print off finished dataset
write_csv(finished_imputed_data, "finished_imputed_data.csv")
#This is where I need to use the finished_imputed_data to impute the values in the future years.

How to include plots / rows with zero values in the presence / absence community matrix in a CCA using R Vegan package

I am trying to do CCA using a presence / absence matrix of plant quadrat data and continuous environmental data for the same quadrats, using the Vegan package in R. Some of the quadrats have no plant species present (the row for the quadrat is full of 0's) but do have corresponding environmental data in another dataframe. The context of the study is that the environmental data is metal concentrations in soil, which are typically high where there are no plant species, so the quadrats with zero species do contribute to the data, and are not errors or NA's. When running the CCA with the R Vegan Package so far I have had to delete these rows to get it to work, otherwise it returns the error
'Error in cca.default(d$X, d$Y, d$Z) :
all row sums must be >0 in the community data matrix' .
Is there a way to include the data from quadrats that have no plant species in the CCA? I have read in this paper, which also uses the Vegan package,: https://www.researchgate.net/publication/229087061_Relationships_between_the_presence_of_odonate_species_and_environmental_characteristics_in_lowland_ponds_of_central_Italy and that has a similar research design, that they have included plots with zero species by adding a 'zero species' variable but do not elaborate on how this is done.
I am new to coding so any help is very much appreciated,
Thanks in advance

Here is how to do it. Assume your data set is called comm and it has some rows (sampling units) that have no species:
comm$ZERO <- as.numeric(rowSums(comm) == 0)
This will add a new column ZERO which is 1 for rows that had no species, and 0 for others.
Personally, I would be worried about doing this. Correspondence Analysis is a compositional analysis, and adding a column (species) that never occurs with any other species (by definition) creates a data set with two disjunct blocks. In unconstrained CA this disjunct block manifests in first eigenvalue 1 – which is the theoretical maximum in CA. This first eigenvector will separate the blocks: ZERO species and the sampling units with ZERO species in one extreme, and all other species and sampling units in another extreme of the first axis. The second axis of this ZERO ordination will be identical to the first axis without ZERO, so in effect you just add this disjunction axis to the ordination.
Things are slightly different with CCA which actually looks at the fitted values of your species, and these fitted values may not be disjunct. So technically you can do it. However, it is not quite clear to me what you do if you do so. Even if the data set is not completely disjunct with CCA, the zero sampling units will probably be far separated from other points, and all plotted in the same point.

How can I get the spatial correlation between two datsets in r?

I have two arrays:
data1=array(-10:30, c(2160,1080,12))
data2=array(-20:30, c(2160,1080,12))
#Add in some NAs
ind <- which(data1 %in% sample(data1, 1500))
data1[ind] <- NA
One is modelled global gridded data (lon,lat,month) and the other, global gridded observations (lon,lat,month).
I want to assess how 'skillful' the modelled data is at recreating the obs. I think the best way to do this is with a spatial correlation between the datasets. How can I do that?
I tried a straightforward x<-cor(data1,data2) but that just returned x<-NA_real_.
Then I was thinking that I probably have to break it up by month or season. So, just looking at one month x<-cor(data1[,,1],data2[,,1]) it returned a matrix of size 1080*1080 (most of which are NAs).
How can I get a spatial correlation between these two datasets? i.e. I want to see where the modelled data performs 'well' i.e. has high correlation with observations, or where it does badly (low correlation with observations).

How to predict new variables for a new randomly generate dataset using multiple regression in R?

I have generate a MARS regression model using known soil property data collected from field samples across the great plains region. I reduced all variables down to 5 predictor variables (elevation, tpi, k_factor, precipitation and temperature) and a single dependent variable (soil organic content:SOC). I split the original data set to a training class and a test class. I was able to utilize my model to predict values on the test dataset after the model was created just fine.
I want to predict on a newly generated dataset with data derived from geospatial rasters across teh great plains region. I generated random samples based on the study are size and created a point shapefile over the area. The rasters were written into the points where they intersected to give me a table full of the 5 perdictor variables for each point. I do not have a SOC raster, so my new table is missing that column.
My intention was to predict the SOC values based on the 5 predictor variables in the new table. However, I keep getting an error " variable lengths differ" for each of my columns. I would like to export the predictions back to the new table to be able to visualize the distribution of SOC within GIS. Below is example of my code:
setwd("E:\\Fall19\\stats\\FinalProject\\Excel_tables")
table=read.csv("sel_el_train.csv")
attach(table)
my_data=table[,c(8,9,15,16,18,19)]
mars1 <- earth(
SOC ~ ., data=my_data)
print(mars1)
summary(mars1)
plot(mars1)
predict(mars1, newdata=test.data)
Below are screen shots of the bottom of the record. You can see a difference of the number of records i built the model out of and the dataset I'm trying to predict on.

I figured it out. The method I was using was very particular about heading spelling. My K_factor variable was not spelled correctly. Once all column names matched up, everything worked well.

Time series forecasting, dealing with known big orders

I have many data sets with known outliers (big orders)
data <- matrix(c("08Q1","08Q2","08Q3","08Q4","09Q1","09Q2","09Q3","09Q4","10Q1","10Q2","10Q3","10Q4","11Q1","11Q2","11Q3","11Q4","12Q1","12Q2","12Q3","12Q4","13Q1","13Q2","13Q3","13Q4","14Q1","14Q2","14Q3","14Q4","15Q1", 155782698, 159463653.4, 172741125.6, 204547180, 126049319.8, 138648461.5, 135678842.1, 242568446.1, 177019289.3, 200397120.6, 182516217.1, 306143365.6, 222890269.2, 239062450.2, 229124263.2, 370575384.7, 257757410.5, 256125841.6, 231879306.6, 419580274, 268211059, 276378232.1, 261739468.7, 429127062.8, 254776725.6, 329429882.8, 264012891.6, 496745973.9, 284484362.55),ncol=2,byrow=FALSE)
The top 11 outliers of this specific series are:
outliers <- matrix(c("14Q4","14Q2","12Q1","13Q1","14Q2","11Q1","11Q4","14Q2","13Q4","14Q4","13Q1",20193525.68, 18319234.7, 12896323.62, 12718744.01, 12353002.09, 11936190.13, 11356476.28, 11351192.31, 10101527.85, 9723641.25, 9643214.018),ncol=2,byrow=FALSE)
What methods are there that i can forecast the time series taking these outliers into consideration?
I have already tried replacing the next biggest outlier (so running the data set 10 times replacing the outliers with the next biggest until the 10th data set has all the outliers replaced).
I have also tried simply removing the outliers (so again running the data set 10 times removing an outlier each time until all 10 are removed in the 10th data set)
I just want to point out that removing these big orders does not delete the data point completely as there are other deals that happen in that quarter
My code tests the data through multiple forecasting models (ARIMA weighted on the out sample, ARIMA weighted on the in sample, ARIMA weighted, ARIMA, Additive Holt-winters weighted and Multiplcative Holt-winters weighted) so it needs to be something that can be adapted to these multiple models.
Here are a couple more data sets that i used, i do not have the outliers for these series yet though
data <- matrix(c("08Q1","08Q2","08Q3","08Q4","09Q1","09Q2","09Q3","09Q4","10Q1","10Q2","10Q3","10Q4","11Q1","11Q2","11Q3","11Q4","12Q1","12Q2","12Q3","12Q4","13Q1","13Q2","13Q3","13Q4","14Q1","14Q2","14Q3", 26393.99306, 13820.5037, 23115.82432, 25894.41036, 14926.12574, 15855.8857, 21565.19002, 49373.89675, 27629.10141, 43248.9778, 34231.73851, 83379.26027, 54883.33752, 62863.47728, 47215.92508, 107819.9903, 53239.10602, 71853.5, 59912.7624, 168416.2995, 64565.6211, 94698.38748, 80229.9716, 169205.0023, 70485.55409, 133196.032, 78106.02227), ncol=2,byrow=FALSE)
data <- matrix(c("08Q1","08Q2","08Q3","08Q4","09Q1","09Q2","09Q3","09Q4","10Q1","10Q2","10Q3","10Q4","11Q1","11Q2","11Q3","11Q4","12Q1","12Q2","12Q3","12Q4","13Q1","13Q2","13Q3","13Q4","14Q1","14Q2","14Q3",3311.5124, 3459.15634, 2721.486863, 3286.51708, 3087.234059, 2873.810071, 2803.969394, 4336.4792, 4722.894582, 4382.349583, 3668.105825, 4410.45429, 4249.507839, 3861.148928, 3842.57616, 5223.671347, 5969.066896, 4814.551389, 3907.677816, 4944.283864, 4750.734617, 4440.221993, 3580.866991, 3942.253996, 3409.597269, 3615.729974, 3174.395507),ncol=2,byrow=FALSE)
If this is too complicated then an explanation of how, in R, once outliers are detected using certain commands, the data is dealt with to forecast. e.g smoothing etc and how i can approach that writing a code myself (not using the commands that detect outliers)

Your outliers appear to be seasonal variations with the largest orders appearing in the 4-th quarter. Many of the forecasting models you mentioned include the capability for seasonal adjustments. As an example, the simplest model could have a linear dependence on year with corrections for all seasons. Code would look like:
df <- data.frame(period= c("08Q1","08Q2","08Q3","08Q4","09Q1","09Q2","09Q3","09Q4","10Q1","10Q2","10Q3",
"10Q4","11Q1","11Q2","11Q3","11Q4","12Q1","12Q2","12Q3","12Q4","13Q1","13Q2",
"13Q3","13Q4","14Q1","14Q2","14Q3","14Q4","15Q1"),
order= c(155782698, 159463653.4, 172741125.6, 204547180, 126049319.8, 138648461.5,
135678842.1, 242568446.1, 177019289.3, 200397120.6, 182516217.1, 306143365.6,
222890269.2, 239062450.2, 229124263.2, 370575384.7, 257757410.5, 256125841.6,
231879306.6, 419580274, 268211059, 276378232.1, 261739468.7, 429127062.8, 254776725.6,
329429882.8, 264012891.6, 496745973.9, 42748656.73))
seasonal <- data.frame(year=as.numeric(substr(df$period, 1,2)), qtr=substr(df$period, 3,4), data=df$order)
ord_model <- lm(data ~ year + qtr, data=seasonal)
seasonal <- cbind(seasonal, fitted=ord_model$fitted)
library(reshape2)
library(ggplot2)
plot_fit <- melt(seasonal,id.vars=c("year", "qtr"), variable.name = "Source", value.name="Order" )
ggplot(plot_fit, aes(x=year, y = Order, colour = qtr, shape=Source)) + geom_point(size=3)
which gives the results shown in the chart below:
Models with a seasonal adjustment but nonlinear dependence upon year may give better fits.

You already said you tried different Arima-models, but as mentioned by WaltS, your series don't seem to contain big outliers, but a seasonal-component, which is nicely captured by auto.arima() in the forecast package:
myTs <- ts(as.numeric(data[,2]), start=c(2008, 1), frequency=4)
myArima <- auto.arima(myTs, lambda=0)
myForecast <- forecast(myArima)
plot(myForecast)
where the lambda=0 argument to auto.arima() forces a transformation (or you could take log) of the data by boxcox to take the increasing amplitude of the seasonal-component into account.

The approach you are trying to use to cleanse your data of outliers is not going to be robust enough to identify them. I should add that there is a free outlier package in R called tsoutliers, but it won't do the things I am about to show you....
You have an interesting time series here. The trend changes over time with the upward trend weakening a bit. If you bring in two time trend variables with the first beginning at 1 and another beginning at period 14 and forward you will capture this change. As for seasonality, you can capture the high 4th quarter with a dummy variable. The model is parsimonios as the other 3 quarters are not different from the average plus no need for an AR12, seasonal differencing or 3 seasonal dummies. You can also capture the impact of the last two observations being outliers with two dummy variables. Ignore the 49 above the word trend as that is just the name of the series being modeled.

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