Renata Diaz 2021-03-11
To show the 95% interval, we need to load the distribution of shape metric values from the samples from the feasible set for a few communities. See rov_metric.md.
## Joining, by = c("sim", "source", "dat", "site", "singletons", "s0", "n0", "nparts")
## Joining, by = c("dat", "site", "s0", "n0", "nparts")
## Joining, by = c("sim", "source", "dat", "site", "singletons", "s0", "n0", "nparts")
## Joining, by = c("dat", "site", "s0", "n0", "nparts")
## Joining, by = c("sim", "source", "dat", "site", "singletons", "s0", "n0", "nparts")
## Joining, by = c("dat", "site", "s0", "n0", "nparts")
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 10 rows containing missing values (geom_bar).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 10 rows containing missing values (geom_bar).
## Warning in ks.test(simpson_ks$simpson_95_ratio_1t_FIA,
## simpson_ks$`simpson_95_ratio_1t_Other datasets`): p-value will be approximate in
## the presence of ties
## Warning in ks.test(simpson_ks$simpson_percentile_FIA,
## simpson_ks$`simpson_percentile_Other datasets`): p-value will be approximate in
## the presence of ties
##
## Two-sample Kolmogorov-Smirnov test
##
## data: simpson_ks$simpson_95_ratio_1t_FIA and simpson_ks$`simpson_95_ratio_1t_Other datasets`
## D = 0.045455, p-value = 0.8849
## alternative hypothesis: two-sided
##
## Two-sample Kolmogorov-Smirnov test
##
## data: simpson_ks$simpson_percentile_FIA and simpson_ks$`simpson_percentile_Other datasets`
## D = 0.048485, p-value = 0.8327
## alternative hypothesis: two-sided
## Warning in ks.test(skewness_ks$skew_95_ratio_1t_FIA,
## skewness_ks$`skew_95_ratio_1t_Other datasets`): p-value will be approximate in
## the presence of ties
## Warning in ks.test(skewness_ks$skew_percentile_FIA,
## skewness_ks$`skew_percentile_Other datasets`): p-value will be approximate in
## the presence of ties
##
## Two-sample Kolmogorov-Smirnov test
##
## data: skewness_ks$skew_95_ratio_1t_FIA and skewness_ks$`skew_95_ratio_1t_Other datasets`
## D = 0.033333, p-value = 0.993
## alternative hypothesis: two-sided
##
## Two-sample Kolmogorov-Smirnov test
##
## data: skewness_ks$skew_percentile_FIA and skewness_ks$`skew_percentile_Other datasets`
## D = 0.090909, p-value = 0.1308
## alternative hypothesis: two-sided
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## Warning: Removed 1 row(s) containing missing values (geom_path).
## Warning: Removed 1 rows containing non-finite values (stat_bin).
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 10 rows containing missing values (geom_bar).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 10 rows containing missing values (geom_bar).
## quartz_off_screen
## 2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## quartz_off_screen
## 2
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