R/stats_table.R
In dill/readdst: Convert Distance for Windows projects to R analyses
Defines functions
stats_table
Documented in
stats_table
#' Generate table of possible statistics to test
#'
#' To use \code{\link{get_stats}} we need a set of statistics to test. We also require their codes (to look up in the Distance for Windows database) and their equivalent values in \code{mrds} (or how to calculate those values). This function provides such a table.
#'
#' @param engine which engine do we need to compute stats for?
#' @section Details:
#' Data for this table (numeric code and descriptions) is from the \code{DistIni.mdb} which is shipped with Distance for Windows. See also \url{https://github.com/distancedevelopment/readdst/wiki/distance-results-codes}.
#'
#' @section Additional notes:
#' Note that the Cramer-von Mises p-value as recorded in Distance for Windows is only recorded to the nearest 0.1.
#'
#' @return a \code{data.frame} with statistics Distance for Windows collects that have equivalents in \code{mrds}. The \code{data.frame} has three columns: \code{Code}, the numeric code for the statistic (as used in the Distance for Windows database); \code{Name}, the short name for this statistic; \code{MRDS}, the operation required to obtain the equivalent statistic in \code{mrds}; \code{Description}, a short description of the statistic.
#' @author David L Miller
stats_table <- function(engine="CDS"){
# here is a lookup table
# this trick thanks to Noam Ross
stat_tab <- read.delim(textConnection(
' Code | Name | Tolerance | MRDS | Description
# ----- | --------------- | --------- | ---------------------------------------------------- | ------------
1 | NULL | | NULL | Number of sampler points
100 | NULL | | NULL | Mean coverage probability
1000 | NULL | | NULL | minimum AIC, within survey and data filter
1001 | NULL | | NULL | minimum AICc, within survey and data filter
1002 | NULL | | NULL | minimum BIC, within survey and data filter
1003 | NULL | | NULL | minimum LogL, within survey and data filter
101 | NULL | | NULL | Minimum coverage probability
1010 | n | 0 | nrow(model$data) | Number of observations
102 | NULL | | NULL | Maximum coverage probability
1020 | NULL | | NULL | Number of samples
103 | NULL | | NULL | Standard deviation of coverage probability
1030 | NULL | | NULL | Total survey effort
1040 | NULL | | NULL | Encounter rate
1041 | NULL | | NULL | Encounter rate coeff. of var.
1042 | NULL | | NULL | Encounter rate lower conf. limit
1043 | NULL | | NULL | Encounter rate upper conf. limit
1044 | NULL | | NULL | Encounter rate degrees of freedom
2 | NULL | | NULL | Number of sampler points
2010 | parameters | 0 | length(model$par) | Total number of parameters
2020 | AIC | 1e-3 | model$criterion | Akaike Information Criterion
2030 | Chi^2 p | 1e-3 | ddf.gof(model, breaks=gof$intervals, qq=FALSE)$chisquare$chi1$p | Goodness-of-fit chi-square test probability (test 1, after pooling)
2040 | NULL | | NULL | PDF (lines) or slope of PDF (points) at x=0
2050 | P_a | 1e-3 | model_sum$average.p | Probability of detection
2051 | CV(P_a) | 1e-3 | as.numeric(model_sum$average.p.se)/model_sum$average.p | Probability of detection coeff. of var.
2052 | NULL | | NULL | Probability of detection lower conf. limit
2053 | NULL | | NULL | Probability of detection upper conf. limit
2054 | NULL | | NULL | Probability of detection degrees of freedom
2060 | NULL | | NULL | Effective strip width/detection radius
2061 | NULL | | NULL | Effective strip width/detection radius coeff. of var.
2062 | NULL | | NULL | Effective strip width/detection radius lower conf. limit
2063 | NULL | | NULL | Effective strip width/detection radius upper conf. limit
2064 | NULL | | NULL | Effective strip width/detection radius degrees of freedom
2070 | NULL | | NULL | Small-sample adjusted Akaike Information Criterion value
2080 | NULL | | NULL | Bayes\' Information Criterion
2090 | log-likelihood | 1e-4 | model$lnl | Log likelihood
2100 | K-S p | 1e-5 | NULL | Goodness-of-fit Kolmogorov-Smirnov test probability
2110 | C-vM p | 1e-1 | round(ddf.gof(model, qq=FALSE)$dsgof$CvM$p, 1) | Goodness-of-fit Cramer-von Mises (uniform weighting) test probability
2120 | NULL | | NULL | Goodness-of-fit Cramer-von Mises (cosine weighting) test probability
2150 | NULL | | NULL | Number of key function parameters
2160 | NULL | | NULL | Number of adjustment term parameters
2170 | NULL | | NULL | Number of covariate parameters
3 | NULL | | NULL | Number of sampler lines
3010 | NULL | | NULL | Mean cluster size
3011 | NULL | | NULL | Mean cluster size coeff. of var.
3012 | NULL | | NULL | Mean cluster size lower conf. interval
3013 | NULL | | NULL | Mean cluster size upper conf. interval
3014 | NULL | | NULL | Mean cluster size degrees of freedom
3020 | NULL | | NULL | Size bias regression correlation coefficient
3030 | NULL | | NULL | Size bias regression correlation test probability
3040 | NULL | | NULL | Expected cluster size, correcting for size bias
3041 | NULL | | NULL | Expected cluster size coeff. of var.
3042 | NULL | | NULL | Expected cluster size lower conf. limit
3043 | NULL | | NULL | Expected cluster size upper conf. limit
3044 | NULL | | NULL | Expected cluster size degrees of freedom
4 | NULL | | NULL | Maximum possible area coverage
4010 | NULL | | NULL | Density of clusters
4011 | NULL | | NULL | Density of clusters analytic coeff. of var.
4012 | NULL | | NULL | Density of clusters analytic lower conf. limit
4013 | NULL | | NULL | Density of clusters analytic upper conf. limit
4014 | NULL | | NULL | Density of clusters degrees of freedom
4020 | density | 1e-4 | as.numeric(dht$individuals$D$Estimate)[nrow(dht$individuals$D)] | Density of individuals
4021 | CV(density) | 1e-4 | as.numeric(dht$individuals$D$cv)[nrow(dht$individuals$D)] | Density of individuals analytic coeff. of var.
4022 | density lcl | 1e-4 | NULL | Density of individuals analytic lower conf. limit
4023 | density ucl | 1e-4 | NULL | Density of individuals analytic upper conf. limit
4024 | density df | 1e-1 | NULL | Density of individuals degrees of freedom
4030 | individuals | 1e-1 | as.numeric(dht$individuals$N$Estimate)[nrow(dht$individuals$N)] | Number of individuals
4031 | CV(individuals) | 1e-4 | as.numeric(dht$individuals$N$cv)[nrow(dht$individuals$N)] | Number of individuals analytic coeff. of var.
4032 | individuals lcl | 1e-1 | NULL | Number of individuals analytic lower conf. limit
4033 | individuals ucl | 1e-1 | NULL | Number of individuals analytic upper conf. limit
4034 | individuals df | 1e-1 | NULL | Number of individuals analytic degrees of freedom
4040 | NULL | | NULL | Density of clusters bootstrap
4041 | NULL | | NULL | Density of clusters bootstrap coeff. of var.
4042 | NULL | | NULL | Density of clusters bootstrap lower conf. limit
4043 | NULL | | NULL | Density of clusters bootstrap upper conf. limit
4050 | NULL | | NULL | Density of individuals bootstrap
4051 | NULL | | NULL | Density of individuals bootstrap coeff. of var.
4052 | NULL | | NULL | Density of individuals bootstrap lower conf. limit
4053 | NULL | | NULL | Density of individuals bootstrap upper conf. limit
4060 | NULL | | NULL | Number of individuals bootstrap
4061 | NULL | | NULL | Number of individuals bootstrap coeff. of var.
4062 | NULL | | NULL | Number of individuals bootstrap lower conf. limit
4063 | NULL | | NULL | Number of individuals upper confidence limit
4064 | NULL | | NULL | Number of bootstrap resamples
5 | NULL | | NULL | Realized area coverage
5010 | NULL | | NULL | Generalized cross-validation metric for DSM model
5020 | NULL | | NULL | Sample size (number of segments) for DSM model
5030 | NULL | | NULL | Proportion of deviance explained by DSM model
5040 | NULL | | NULL | The estimated scale parameter/error variance
6 | NULL | | NULL | Proportion of survey area covered
7 | NULL | | NULL | Mean realized sampler line length (mean over strata)'),
sep='|', comment.char="#", strip.white=TRUE, stringsAsFactors=FALSE)
stat_tab <- stat_tab[stat_tab$MRDS!="NULL", ]
stat_tab$Code <- as.numeric(stat_tab$Code)
# do this properly later...
# calculate D/N etc as Distance does rather than back-calculating from
# dht() estimates...
# 4020 | density | as.numeric(dht$individuals$D$Estimate)[nrow(dht$individuals$D)] | Density of individuals
# 4021 | CV(density) | as.numeric(dht$individuals$D$cv)[nrow(dht$individuals$D)] | Density of individuals analytic coeff. of var.
# 4022 | density lcl | NULL | Density of individuals analytic lower conf. limit
# 4023 | density ucl | NULL | Density of individuals analytic upper conf. limit
# 4024 | density df | NULL | Density of individuals degrees of freedom
# 4030 | individuals | as.numeric(dht$individuals$N$Estimate)[nrow(dht$individuals$N)] | Number of individuals
# 4031 | CV(individuals) | as.numeric(dht$individuals$N$cv)[nrow(dht$individuals$N)] | Number of individuals analytic coeff. of var.
# 4032 | individuals lcl | NULL | Number of individuals analytic lower conf. limit
# 4033 | individuals ucl | NULL | Number of individuals analytic upper conf. limit
# 4034 | individuals df | NULL | Number of individuals analytic degrees of freedom
if(engine %in% c("CDS", "MCDS")){
# estimate Dhat = n/(2*w*L*P_a) or Dhat = n/(a*P_a)
stat_tab[stat_tab$Code==4020,][,4] <- "nrow(model$data)/(model_sum$average.p*dht$individuals$summary$CoveredArea[nrow(dht$individuals$summary)])"
# estimate Nhat = A*n/(2*w*L*P_a) or Nhat = A*n/(a*P_a)
stat_tab[stat_tab$Code==4030,][,4] <- "dht$individuals$summary$Area[nrow(dht$individuals$summary)]*nrow(model$data)/(model_sum$average.p*dht$individuals$summary$CoveredArea[nrow(dht$individuals$summary)])"
}
return(stat_tab)
}
dill/readdst documentation built on Sept. 23, 2021, 1:50 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions stats_table
Documented in stats_table
#' Generate table of possible statistics to test
#'
#' To use \code{\link{get_stats}} we need a set of statistics to test. We also require their codes (to look up in the Distance for Windows database) and their equivalent values in \code{mrds} (or how to calculate those values). This function provides such a table.
#'
#' @param engine which engine do we need to compute stats for?
#' @section Details:
#' Data for this table (numeric code and descriptions) is from the \code{DistIni.mdb} which is shipped with Distance for Windows. See also \url{https://github.com/distancedevelopment/readdst/wiki/distance-results-codes}.
#'
#' @section Additional notes:
#' Note that the Cramer-von Mises p-value as recorded in Distance for Windows is only recorded to the nearest 0.1.
#'
#' @return a \code{data.frame} with statistics Distance for Windows collects that have equivalents in \code{mrds}. The \code{data.frame} has three columns: \code{Code}, the numeric code for the statistic (as used in the Distance for Windows database); \code{Name}, the short name for this statistic; \code{MRDS}, the operation required to obtain the equivalent statistic in \code{mrds}; \code{Description}, a short description of the statistic.
#' @author David L Miller
stats_table <- function(engine="CDS"){
# here is a lookup table
# this trick thanks to Noam Ross
stat_tab <- read.delim(textConnection(
' Code | Name | Tolerance | MRDS | Description
# ----- | --------------- | --------- | ---------------------------------------------------- | ------------
1 | NULL | | NULL | Number of sampler points
100 | NULL | | NULL | Mean coverage probability
1000 | NULL | | NULL | minimum AIC, within survey and data filter
1001 | NULL | | NULL | minimum AICc, within survey and data filter
1002 | NULL | | NULL | minimum BIC, within survey and data filter
1003 | NULL | | NULL | minimum LogL, within survey and data filter
101 | NULL | | NULL | Minimum coverage probability
1010 | n | 0 | nrow(model$data) | Number of observations
102 | NULL | | NULL | Maximum coverage probability
1020 | NULL | | NULL | Number of samples
103 | NULL | | NULL | Standard deviation of coverage probability
1030 | NULL | | NULL | Total survey effort
1040 | NULL | | NULL | Encounter rate
1041 | NULL | | NULL | Encounter rate coeff. of var.
1042 | NULL | | NULL | Encounter rate lower conf. limit
1043 | NULL | | NULL | Encounter rate upper conf. limit
1044 | NULL | | NULL | Encounter rate degrees of freedom
2 | NULL | | NULL | Number of sampler points
2010 | parameters | 0 | length(model$par) | Total number of parameters
2020 | AIC | 1e-3 | model$criterion | Akaike Information Criterion
2030 | Chi^2 p | 1e-3 | ddf.gof(model, breaks=gof$intervals, qq=FALSE)$chisquare$chi1$p | Goodness-of-fit chi-square test probability (test 1, after pooling)
2040 | NULL | | NULL | PDF (lines) or slope of PDF (points) at x=0
2050 | P_a | 1e-3 | model_sum$average.p | Probability of detection
2051 | CV(P_a) | 1e-3 | as.numeric(model_sum$average.p.se)/model_sum$average.p | Probability of detection coeff. of var.
2052 | NULL | | NULL | Probability of detection lower conf. limit
2053 | NULL | | NULL | Probability of detection upper conf. limit
2054 | NULL | | NULL | Probability of detection degrees of freedom
2060 | NULL | | NULL | Effective strip width/detection radius
2061 | NULL | | NULL | Effective strip width/detection radius coeff. of var.
2062 | NULL | | NULL | Effective strip width/detection radius lower conf. limit
2063 | NULL | | NULL | Effective strip width/detection radius upper conf. limit
2064 | NULL | | NULL | Effective strip width/detection radius degrees of freedom
2070 | NULL | | NULL | Small-sample adjusted Akaike Information Criterion value
2080 | NULL | | NULL | Bayes\' Information Criterion
2090 | log-likelihood | 1e-4 | model$lnl | Log likelihood
2100 | K-S p | 1e-5 | NULL | Goodness-of-fit Kolmogorov-Smirnov test probability
2110 | C-vM p | 1e-1 | round(ddf.gof(model, qq=FALSE)$dsgof$CvM$p, 1) | Goodness-of-fit Cramer-von Mises (uniform weighting) test probability
2120 | NULL | | NULL | Goodness-of-fit Cramer-von Mises (cosine weighting) test probability
2150 | NULL | | NULL | Number of key function parameters
2160 | NULL | | NULL | Number of adjustment term parameters
2170 | NULL | | NULL | Number of covariate parameters
3 | NULL | | NULL | Number of sampler lines
3010 | NULL | | NULL | Mean cluster size
3011 | NULL | | NULL | Mean cluster size coeff. of var.
3012 | NULL | | NULL | Mean cluster size lower conf. interval
3013 | NULL | | NULL | Mean cluster size upper conf. interval
3014 | NULL | | NULL | Mean cluster size degrees of freedom
3020 | NULL | | NULL | Size bias regression correlation coefficient
3030 | NULL | | NULL | Size bias regression correlation test probability
3040 | NULL | | NULL | Expected cluster size, correcting for size bias
3041 | NULL | | NULL | Expected cluster size coeff. of var.
3042 | NULL | | NULL | Expected cluster size lower conf. limit
3043 | NULL | | NULL | Expected cluster size upper conf. limit
3044 | NULL | | NULL | Expected cluster size degrees of freedom
4 | NULL | | NULL | Maximum possible area coverage
4010 | NULL | | NULL | Density of clusters
4011 | NULL | | NULL | Density of clusters analytic coeff. of var.
4012 | NULL | | NULL | Density of clusters analytic lower conf. limit
4013 | NULL | | NULL | Density of clusters analytic upper conf. limit
4014 | NULL | | NULL | Density of clusters degrees of freedom
4020 | density | 1e-4 | as.numeric(dht$individuals$D$Estimate)[nrow(dht$individuals$D)] | Density of individuals
4021 | CV(density) | 1e-4 | as.numeric(dht$individuals$D$cv)[nrow(dht$individuals$D)] | Density of individuals analytic coeff. of var.
4022 | density lcl | 1e-4 | NULL | Density of individuals analytic lower conf. limit
4023 | density ucl | 1e-4 | NULL | Density of individuals analytic upper conf. limit
4024 | density df | 1e-1 | NULL | Density of individuals degrees of freedom
4030 | individuals | 1e-1 | as.numeric(dht$individuals$N$Estimate)[nrow(dht$individuals$N)] | Number of individuals
4031 | CV(individuals) | 1e-4 | as.numeric(dht$individuals$N$cv)[nrow(dht$individuals$N)] | Number of individuals analytic coeff. of var.
4032 | individuals lcl | 1e-1 | NULL | Number of individuals analytic lower conf. limit
4033 | individuals ucl | 1e-1 | NULL | Number of individuals analytic upper conf. limit
4034 | individuals df | 1e-1 | NULL | Number of individuals analytic degrees of freedom
4040 | NULL | | NULL | Density of clusters bootstrap
4041 | NULL | | NULL | Density of clusters bootstrap coeff. of var.
4042 | NULL | | NULL | Density of clusters bootstrap lower conf. limit
4043 | NULL | | NULL | Density of clusters bootstrap upper conf. limit
4050 | NULL | | NULL | Density of individuals bootstrap
4051 | NULL | | NULL | Density of individuals bootstrap coeff. of var.
4052 | NULL | | NULL | Density of individuals bootstrap lower conf. limit
4053 | NULL | | NULL | Density of individuals bootstrap upper conf. limit
4060 | NULL | | NULL | Number of individuals bootstrap
4061 | NULL | | NULL | Number of individuals bootstrap coeff. of var.
4062 | NULL | | NULL | Number of individuals bootstrap lower conf. limit
4063 | NULL | | NULL | Number of individuals upper confidence limit
4064 | NULL | | NULL | Number of bootstrap resamples
5 | NULL | | NULL | Realized area coverage
5010 | NULL | | NULL | Generalized cross-validation metric for DSM model
5020 | NULL | | NULL | Sample size (number of segments) for DSM model
5030 | NULL | | NULL | Proportion of deviance explained by DSM model
5040 | NULL | | NULL | The estimated scale parameter/error variance
6 | NULL | | NULL | Proportion of survey area covered
7 | NULL | | NULL | Mean realized sampler line length (mean over strata)'),
sep='|', comment.char="#", strip.white=TRUE, stringsAsFactors=FALSE)
stat_tab <- stat_tab[stat_tab$MRDS!="NULL", ]
stat_tab$Code <- as.numeric(stat_tab$Code)
# do this properly later...
# calculate D/N etc as Distance does rather than back-calculating from
# dht() estimates...
# 4020 | density | as.numeric(dht$individuals$D$Estimate)[nrow(dht$individuals$D)] | Density of individuals
# 4021 | CV(density) | as.numeric(dht$individuals$D$cv)[nrow(dht$individuals$D)] | Density of individuals analytic coeff. of var.
# 4022 | density lcl | NULL | Density of individuals analytic lower conf. limit
# 4023 | density ucl | NULL | Density of individuals analytic upper conf. limit
# 4024 | density df | NULL | Density of individuals degrees of freedom
# 4030 | individuals | as.numeric(dht$individuals$N$Estimate)[nrow(dht$individuals$N)] | Number of individuals
# 4031 | CV(individuals) | as.numeric(dht$individuals$N$cv)[nrow(dht$individuals$N)] | Number of individuals analytic coeff. of var.
# 4032 | individuals lcl | NULL | Number of individuals analytic lower conf. limit
# 4033 | individuals ucl | NULL | Number of individuals analytic upper conf. limit
# 4034 | individuals df | NULL | Number of individuals analytic degrees of freedom
if(engine %in% c("CDS", "MCDS")){
# estimate Dhat = n/(2*w*L*P_a) or Dhat = n/(a*P_a)
stat_tab[stat_tab$Code==4020,][,4] <- "nrow(model$data)/(model_sum$average.p*dht$individuals$summary$CoveredArea[nrow(dht$individuals$summary)])"
# estimate Nhat = A*n/(2*w*L*P_a) or Nhat = A*n/(a*P_a)
stat_tab[stat_tab$Code==4030,][,4] <- "dht$individuals$summary$Area[nrow(dht$individuals$summary)]*nrow(model$data)/(model_sum$average.p*dht$individuals$summary$CoveredArea[nrow(dht$individuals$summary)])"
}
return(stat_tab)
}
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