Warning: package 'lattice' was built under R version 4.3.3
This is vegan 2.6-4
library(FD)
Loading required package: ade4
Loading required package: ape
Attaching package: 'ape'
The following object is masked from 'package:dplyr':
where
Loading required package: geometry
library(sf)
Linking to GEOS 3.11.2, GDAL 3.6.2, PROJ 9.2.0; sf_use_s2() is TRUE
library(DHARMa)
Warning: package 'DHARMa' was built under R version 4.3.3
This is DHARMa 0.4.7. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')
library(glmm.hp)
Warning: package 'glmm.hp' was built under R version 4.3.3
Loading required package: MuMIn
Warning: package 'MuMIn' was built under R version 4.3.3
Thank you for using this package! If you use this package in your research, please cite the following references
Jiangshan Lai, Weijie Zhu, Dongfang Cui, Lingfeng Mao(2023). Extension of the glmm.hp package to Zero-Inflated generalized linear mixed models and multiple regression. Journal of Plant Ecology,16(6):rtad038
Site information
sites <-read.csv("Raw Data/sites.csv")sites <-st_as_sf(sites, coords=c("Long","Lat"), crs=4326)# plot(sites)# st_write(sites, "studysites.shp")# # #read in bioclim data from other repoenv <- terra::rast("F:/CH2_ant-plant_mutualism_synthesis/na_rasters.grd")#MAP and MAT from Worldclim climsites <- terra::extract(env, sites)sites <-cbind(climsites, sites)sites <-select(sites, 2, 5, 21:25)#clean up NDVI valuessites <-rename(sites, MAT = wc2.1_30s_bio_1, MAP = wc2.1_30s_bio_12)sites <-select(sites, 1:3, 5, 7)sites <-unique(sites)ndvi <-read.csv("Raw Data/NDVI.csv")ndvi$NDVI20231[is.na(ndvi$NDVI20231)] <-0ndvi$NDVI20241[is.na(ndvi$NDVI20241)] <-0ndvi <-mutate(ndvi, ndvi = NDVI20231 + NDVI20241)ndvi <-select(ndvi, 1:3, 8)ndvi <-left_join(ndvi, sites, by =c("Site", "Year"))
Annual abundance and richness
plts <-read.csv("Raw Data/annuals/allplants_IDed.csv")plts <- dplyr::select(plts, -X)plts <- dplyr::select(plts, -6)#not an annualplts <-filter(plts, species !="Ambrosia.salsola")str(plts)
plt_wide$uniID <-paste(plt_wide$Site, plt_wide$Year.NA, plt_wide$Microsite, plt_wide$Rep.NA)plt_wide$uniID <-gsub(" ", "", plt_wide$uniID)plt_wide[is.na(plt_wide)] <-0env <- dplyr::select(plt_wide, 1:5, uniID)comm <- dplyr::select(plt_wide, 6:90)richness <-specnumber(comm)abun <-rowSums(comm)env$rich <- richnessenv$abun <- abun#import plant metadata for zero rowsreps <-read.csv("Raw Data/reps.csv")reps <-filter(reps, season =="spring")reps$uniID <-paste(reps$Site, reps$year, reps$Microsite, reps$Rep)reps$uniID <-gsub(" ", "", reps$uniID)plt.ag <-left_join(reps, env, by ="uniID")plt.ag$rich[is.na(plt.ag$rich)] <-0plt.ag$abun[is.na(plt.ag$abun)] <-0plt.ag <-rename(plt.ag, Site = Site.x, Year = year)plt.ag$Year <-as.factor(plt.ag$Year)ndvi$Year <-as.factor(ndvi$Year)plt.ag <-left_join(plt.ag, ndvi, by =c("Site", "Year"))
Warning in left_join(plt.ag, ndvi, by = c("Site", "Year")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 1 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
precip <-read.csv("Raw Data/winterprecip_compiled.csv")precip$Year <-as.factor(precip$Year)plt.ag <-left_join(plt.ag, precip, by =c("Site", "Year"))plt.ag <-filter(plt.ag, Season =="spring")#remove doubled rowsplt.ag <-unique(plt.ag)#attach provence for invasive/native infoprov <-read.csv("Raw Data/plantprov.csv")plts <-left_join(plts, prov, by ="species")prov.ag <- plts %>%group_by(Year.NA, Rep.NA, Site, Microsite, prov) %>%summarise(prov.abun =sum(abun)) %>%ungroup()
`summarise()` has grouped output by 'Year.NA', 'Rep.NA', 'Site', 'Microsite'.
You can override using the `.groups` argument.
Warning: Setting row names on a tibble is deprecated.
#comm <- dplyr::select(comm, -uniID)comm <- comm%>%filter(rowSums(across(where(is.numeric)))!=0)row.names(ptraits) <- ptraits$speciesptraits <- dplyr::select(ptraits, seed.weight)ptraits <-filter(ptraits, seed.weight >0)names.use <-names(comm)[(names(comm) %in%row.names(ptraits))]#add the uniID to the name vectornames.use <-append(names.use, "uniID")df.subset <- comm[, names.use]df.subset <- df.subset%>%filter(rowSums(across(where(is.numeric)))!=0)sampleIDs <- df.subset$uniIDdf.subset <- dplyr::select(df.subset, -uniID)func.seeds <-dbFD(ptraits, df.subset, w.abun =TRUE)
Warning in is.euclid(x.dist): Zero distance(s)
Warning in is.euclid(x.dist): Zero distance(s)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
df.subset$cwm.seedweight <- func.seeds$CWMdf.subset$uniID <- sampleIDscwm <-cbind(df.subset$uniID, df.subset$cwm.seedweight)cwm <-rename(cwm, uniID ='df.subset$uniID')#join to larger dataframeenv <-left_join(env, cwm, by ="uniID")#microsite level by provence#make a vector of each set of species by provenceprov <-filter(prov, species !="Ambrosia.salsola")inv <-filter(prov, prov =="invasive")nat <-filter(prov, prov =="native")invnames <- inv$speciesptraits.inv <-filter(ptraits, row.names(ptraits) %in% invnames)invnames <-append(invnames, "uniID")df.subset <- comm[, names.use]df.subset.inv <- df.subsetdf.inv <- df.subset.inv[, invnames]df.inv <- df.inv%>%filter(rowSums(across(where(is.numeric)))!=0)uniID <- df.inv$uniIDdf.inv <-select(df.inv, -uniID)row.names(df.inv) <- uniID
Warning: Setting row names on a tibble is deprecated.
#site levels by speciesfunc.inv <-dbFD(ptraits.inv, df.inv, w.abun =TRUE)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
Warning: Setting row names on a tibble is deprecated.
#site levels by speciesfunc.nat <-dbFD(ptraits.nat, df.nat, w.abun =TRUE)
Warning in is.euclid(x.dist): Zero distance(s)
Warning in is.euclid(x.dist): Zero distance(s)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
cwm.nat <- func.nat$CWMcwm.nat$uniID <- uniIDcwm.nat <-rename(cwm.nat, nat.cwm = seed.weight)#cwm to main datasetplt.ag <-left_join(plt.ag, cwm, by ="uniID")plt.ag <-left_join(plt.ag, cwm.inv, by ="uniID")plt.ag <-left_join(plt.ag, cwm.nat, by ="uniID")#site level by speciescomm.site <- plt_wide %>%group_by(Site, Year.NA) %>%summarise(across(where(is.numeric), sum))
`summarise()` has grouped output by 'Site'. You can override using the
`.groups` argument.
Warning: Setting row names on a tibble is deprecated.
names.use <-names(comm.site)[(names(comm.site) %in%row.names(ptraits))]#no zero rows at site leveldf.subset <- comm.site[, names.use]func.site <-dbFD(ptraits, df.subset, w.abun =TRUE)
Warning in is.euclid(x.dist): Zero distance(s)
Warning in is.euclid(x.dist): Zero distance(s)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
#Site levels by provenancenames.use <-names(comm.site)[(names(comm.site) %in%row.names(ptraits.inv))]#no zero rows at site leveldf.subset.inv <- comm.site[, names.use]func.site.inv <-dbFD(ptraits.inv, df.subset.inv, w.abun =TRUE)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
names.use <-names(comm.site)[(names(comm.site) %in%row.names(ptraits.nat))]#no zero rows at site leveldf.subset.nat <- comm.site[, names.use]func.site.nat <-dbFD(ptraits.nat, df.subset.nat, w.abun =TRUE)
Warning in is.euclid(x.dist): Zero distance(s)
Warning in is.euclid(x.dist): Zero distance(s)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: Only one continuous trait or dimension in 'x'. FRic was measured as the range, NOT as the convex hull volume.
FDiv: Cannot not be computed when 'x' contains one single continuous trait or dimension.
#join all the site levels to the other plant site levelcwm.site <- func.site$CWMcwm.site <-rename(cwm.site, cwm.site = seed.weight)cwm.site.inv <- func.site.inv$CWMcwm.site.inv <-rename(cwm.site.inv, cwm.site.inv = seed.weight)cwm.site.nat <- func.site.nat$CWMcwm.site.nat <-rename(cwm.site.nat, cwm.site.nat = seed.weight)mn.plt <-cbind(mn.plt, cwm.site, cwm.site.inv, cwm.site.nat)
Seed Removal
seed2023 <-read.csv("Raw Data/seedrates2023.csv")#526 put outseed2023 <-filter(seed2023, ending.weight !="disturbed")seed2023 <-filter(seed2023, ending.weight !="broken")seed2023 <-filter(seed2023, ending.weight !="was never put out")seed2023 <- dplyr::select(seed2023, 1:7)seed2024 <-read.csv("Raw Data/seedsrates2024.csv")seed2024 <-filter(seed2024, ending.weight !="disturbed")seed2024 <-filter(seed2024, ending.weight !="not put out"& ending.weight !="never put out")seed2024$Site <-gsub("yucca", "HOM", seed2024$Site)seeds <-rbind(seed2023, seed2024)seeds$ending.weight <-as.numeric(seeds$ending.weight)unique(seeds$Size)
ants <-filter(ants, Species !="alate")ants <-filter(ants, Species !="alata")ants <-filter(ants, Species !="damaged")ants <-filter(ants, Genus !="damaged")ants <-filter(ants, Species !="alate/queen") ants$Genus <-gsub(" ", "", ants$Genus)sp <- ants %>%group_by(Genus, Species) %>%summarize(sum(count))
`summarise()` has grouped output by 'Genus'. You can override using the
`.groups` argument.
#reorder ants datatr_wide <- antstr_wide$species.site <-paste(tr_wide$Species, tr_wide$Site)tr_wide <- tr_wide %>%mutate(pres =ifelse(abun >0, 1, 0))tr_wide <- dplyr::select(tr_wide, 1:4, 7, 8)tr_wide <-pivot_wider(tr_wide, names_from = species.site, values_from = pres)tr_wide[is.na(tr_wide)] <-0tr_wide$ID <-paste(tr_wide$Year, tr_wide$Season, tr_wide$Site, tr_wide$label)tr_wide <- tr_wide %>%ungroup() %>% dplyr::select(65, 5:64)#make a vector of the row names and remove the first valuetr.names <- tr_wide$IDtr_wide <- tr_wide[,-1]names.use <-names(tr_wide)[(names(tr_wide) %in% atraits$species.site)]#add the uniID to the name vector#names.use <- append(names.use, "uniID")df.subset <- tr_wide[, names.use, drop =FALSE]df <- df.subset[,order(names.use), drop =FALSE]atraits <- atraits[order(atraits$species.site),]row.names(df) <- tr.names
Warning: Setting row names on a tibble is deprecated.
colnames(df) <-gsub(" ", "", colnames(df))#fill in missing C. depilis traits with those from other sitesatraits[4,2] <-0.4389248atraits[6,2] <-0.4389248atraits[4,3] <-0.885atraits[6,3] <-0.885atr.names <- atraits$species.siteatr.names <-gsub(" ", "", atr.names)atraits <- atraits[,2:3]row.names(atraits) <- atr.names
Warning: Setting row names on a tibble is deprecated.
setdiff(row.names(atraits), colnames(df))
character(0)
setdiff(colnames(df), row.names(atraits))
character(0)
identical(row.names(atraits), colnames(df))
[1] TRUE
ck <-cbind(row.names(atraits), colnames(df))rownames(atraits) ==colnames(df)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
FRic: No dimensionality reduction was required. The 2 PCoA axes were kept as 'traits'.
FDiv: Could not be calculated for communities with <3 functionally singular species.
cwmants <-cbind(tr.names, func.ants$CWM)cwmants$tr.names <-gsub("Cuyama 1", "Cuyama1", cwmants$tr.names)cwmants$tr.names <-gsub("Cuyama 2", "Cuyama2", cwmants$tr.names)#make the labels match and join back to trap datacwmants <-separate(cwmants, col =1, into =c("year", "season", "Site", "Label"), sep =" ")
names.use <-names(tr_wide)[(names(tr_wide) %in% atraits$species.site)]#add the uniID to the name vector#names.use <- append(names.use, "uniID")df.subset <- tr_wide[, names.use, drop =FALSE]df <- df.subset[,order(names.use), drop =FALSE]atraits <- atraits[order(atraits$species.site),]row.names(df) <- tr.names
Warning: Setting row names on a tibble is deprecated.
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
FRic: No dimensionality reduction was required. The 2 PCoA axes were kept as 'traits'.
FDiv: Could not be calculated for communities with <3 functionally singular species.
cwmgran <-cbind(tr.names, func.gran$CWM)cwmgran$tr.names <-gsub("Cuyama 1", "Cuyama1", cwmgran$tr.names)cwmgran$tr.names <-gsub("Cuyama 2", "Cuyama2", cwmgran$tr.names)#make the labels match and join back to trap datacwmgran <-separate(cwmgran, col =1, into =c("year", "season", "Site", "Label"), sep =" ")
`summarise()` has grouped output by 'site'. You can override using the
`.groups` argument.
atraits$species.site <-paste(atraits$ant.species, atraits$site)atraits <-ungroup(atraits) %>% dplyr::select(5, 3, 4)#need the number of good pitfalls, per site, per visitrps <- ptsreps %>%group_by(year, Site, season) %>%summarise(pit.count =n())
`summarise()` has grouped output by 'year', 'Site'. You can override using the
`.groups` argument.
#fill in missing C. depilis traits with those from other sitesatraits[4,2] <-0.4389248atraits[6,2] <-0.4389248atraits[4,3] <-0.885atraits[6,3] <-0.885atr.names <- atraits$species.siteatr.names <-gsub(" ", "", atr.names)atraits <- atraits[,2:3]row.names(atraits) <- atr.names
Warning: Setting row names on a tibble is deprecated.
#set them to numbersfunc.ants.site <-dbFD(atraits, tr_wide, w.abun =TRUE)
Warning in is.euclid(x.dist): Zero distance(s)
Warning in is.euclid(x.dist): Zero distance(s)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
FRic: No dimensionality reduction was required. The 2 PCoA axes were kept as 'traits'.
FDiv: Could not be calculated for communities with <3 functionally singular species.
#need to filter out species to just the granivoresgtraits <-read.csv("Raw Data/anttraits.csv")gtraits <-mutate(gtraits, r.mand = mandible/webers)gtraits <- gtraits %>%group_by(site, ant.species) %>%summarise(r.mand =mean(r.mand), webers =mean(webers))
`summarise()` has grouped output by 'site'. You can override using the
`.groups` argument.
gtraits$species.site <-paste(gtraits$ant.species, gtraits$site)gtraits <-filter(gtraits, ant.species %in% sp)gtraits <-ungroup(gtraits) %>% dplyr::select(5, 3, 4)#need the number of good pitfalls, per site, per visitrps <- ptsreps %>%group_by(year, Site, season) %>%summarise(pit.count =n())
`summarise()` has grouped output by 'year', 'Site'. You can override using the
`.groups` argument.
Warning: Setting row names on a tibble is deprecated.
names(gr_wide) <- names#set them to numbersfunc.gran.site <-dbFD(gtraits, gr_wide, w.abun =TRUE)
FEVe: Could not be calculated for communities with <3 functionally singular species.
FDis: Equals 0 in communities with only one functionally singular species.
FRic: To respect s > t, FRic could not be calculated for communities with <3 functionally singular species.
FRic: No dimensionality reduction was required. The 2 PCoA axes were kept as 'traits'.
FDiv: Could not be calculated for communities with <3 functionally singular species.
#adding a random effect for year doesn't improvem1 <-glmmTMB(rich ~ arid + Microsite.x + (1|Site) + (1|Year), family =poisson(), plt.ag)#adding ndvi does not improvem2 <-glmmTMB(rich ~ arid * Microsite.x + ndvi + (1|Site), family =poisson(), plt.ag)
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
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AIC(m2.int, m2.int3)
df AIC
m2.int 7 8688.874
m2.int3 8 8587.392
BIC(m2.int, m2.int3)
df BIC
m2.int 7 8723.087
m2.int3 8 8626.493
g1 <-glmm.hp(m2.int.hp, commonality =TRUE)
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
plot(g1)
glmm.hp(m2.int.hp)
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
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to those used in fitting the original model.
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Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: Removed 406 rows containing non-finite outside the scale range
(`stat_smooth()`).
cor.test(plt.ag$seed.weight, plt.ag$nat.cwm)
Pearson's product-moment correlation
data: plt.ag$seed.weight and plt.ag$nat.cwm
t = 6.8026, df = 692, p-value = 2.226e-11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.1792766 0.3188408
sample estimates:
cor
0.2503589
cor.test(plt.ag$seed.weight, plt.ag$cwm.inv)
Pearson's product-moment correlation
data: plt.ag$seed.weight and plt.ag$cwm.inv
t = 57.306, df = 770, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8857233 0.9126362
sample estimates:
cor
0.9000343
t.test(plt.ag$native, plt.ag$invasive)
Welch Two Sample t-test
data: plt.ag$native and plt.ag$invasive
t = -0.9123, df = 1427.9, p-value = 0.3618
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.908244 2.157224
sample estimates:
mean of x mean of y
24.57959 26.45510
#native seed weightsggplot(plt.ag, aes(arid, log(nat.cwm), fill = Microsite.x)) +geom_smooth(method ="lm")
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 286 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
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to those used in fitting the original model.
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to those used in fitting the original model.
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NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
$r.squaredGLMM
R2m R2c
delta 0.03832367 0.06949671
lognormal 0.06870145 0.12458421
trigamma 0.01496137 0.02713118
$delta
Fractions % Total
Unique to ndvi 0.0339 88.35
Unique to Microsite 0.0000 -0.01
Unique to arid -0.0082 -21.48
Common to ndvi, and Microsite 0.0000 0.04
Common to ndvi, and arid 0.0127 33.09
Common to Microsite, and arid 0.0000 -0.02
Common to ndvi, Microsite, and arid 0.0000 0.03
Total 0.0383 100.00
$lognormal
Fractions % Total
Unique to ndvi 0.0609 88.63
Unique to Microsite 0.0000 -0.01
Unique to arid -0.0115 -16.73
Common to ndvi, and Microsite 0.0000 0.04
Common to ndvi, and arid 0.0193 28.06
Common to Microsite, and arid 0.0000 -0.02
Common to ndvi, Microsite, and arid 0.0000 0.02
Total 0.0687 100.00
$trigamma
Fractions % Total
Unique to ndvi 0.0132 88.13
Unique to Microsite 0.0000 -0.01
Unique to arid -0.0038 -25.40
Common to ndvi, and Microsite 0.0000 0.04
Common to ndvi, and arid 0.0056 37.25
Common to Microsite, and arid 0.0000 -0.02
Common to ndvi, Microsite, and arid 0.0000 0.03
Total 0.0150 100.00
$variables
[1] "ndvi" "Microsite" "arid"
$type
[1] "commonality.analysis"
attr(,"class")
[1] "glmmhp"
em <-emtrends(m4.int, ~Microsite, var ="arid")test(em)
Microsite arid.trend SE df z.ratio p.value
open -0.0796 0.0373 Inf -2.134 0.0328
shrub -0.0826 0.0375 Inf -2.204 0.0275
pairs(em)
contrast estimate SE df z.ratio p.value
open - shrub 0.00298 0.0216 Inf 0.138 0.8901
glmm.hp(m4.nb1)
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
NA/NaN function evaluation
Warning: the null model is only correct if all the variables it uses are identical
to those used in fitting the original model.
$r.squaredGLMM
R2m R2c
delta 0.022388652 0.032411982
lognormal 0.057293680 0.082943883
trigamma 0.004629545 0.006702178
$delta
Fractions % Total
Unique to arid 0.0010 4.26
Unique to Microsite 0.0004 1.89
Unique to ndvi 0.0194 86.53
Common to arid, and Microsite 0.0000 0.08
Common to arid, and ndvi 0.0013 5.87
Common to Microsite, and ndvi 0.0003 1.28
Common to arid, Microsite, and ndvi 0.0000 0.08
Total 0.0224 100.00
$lognormal
Fractions % Total
Unique to arid 0.0044 7.63
Unique to Microsite 0.0011 1.86
Unique to ndvi 0.0498 86.85
Common to arid, and Microsite 0.0000 0.01
Common to arid, and ndvi 0.0013 2.26
Common to Microsite, and ndvi 0.0007 1.23
Common to arid, Microsite, and ndvi 0.0001 0.16
Total 0.0573 100.00
$trigamma
Fractions % Total
Unique to arid 0.0001 2.29
Unique to Microsite 0.0001 1.84
Unique to ndvi 0.0040 86.37
Common to arid, and Microsite 0.0000 0.12
Common to arid, and ndvi 0.0004 7.97
Common to Microsite, and ndvi 0.0001 1.36
Common to arid, Microsite, and ndvi 0.0000 0.04
Total 0.0046 100.00
$variables
[1] "arid" "Microsite" "ndvi"
$type
[1] "commonality.analysis"
attr(,"class")
[1] "glmmhp"
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
summary(m11.1)
Family: gaussian ( identity )
Formula:
percent ~ ndvi + Microsite + Size * gr.rmand + mn.gr.abun + arid +
(1 | Year) + (1 | Site)
Data: seeds
AIC BIC logLik deviance df.resid
5795.0 5851.8 -2884.5 5769.0 569
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Year (Intercept) 1.661e-15 4.075e-08
Site (Intercept) 2.658e+02 1.630e+01
Residual 1.140e+03 3.377e+01
Number of obs: 582, groups: Year, 2; Site, 7
Dispersion estimate for gaussian family (sigma^2): 1.14e+03
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.82504 NaN NaN NaN
ndvi -0.01594 NaN NaN NaN
Micrositeshrub -1.44919 NaN NaN NaN
Sizemed -15.28604 NaN NaN NaN
Sizesmall -25.71801 NaN NaN NaN
gr.rmand 115.21577 NaN NaN NaN
mn.gr.abun 6.97166 NaN NaN NaN
arid 2.07619 NaN NaN NaN
Sizemed:gr.rmand 52.90179 NaN NaN NaN
Sizesmall:gr.rmand 88.08547 NaN NaN NaN
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
summary(m11.2)
Family: gaussian ( identity )
Formula: percent ~ ndvi + Microsite + Size * gr.webers + mn.gr.abun +
arid + (1 | Year) + (1 | Site)
Data: seeds
AIC BIC logLik deviance df.resid
5796.0 5852.8 -2885.0 5770.0 569
Random effects:
Conditional model:
Groups Name Variance Std.Dev.
Year (Intercept) 1.342e-30 1.158e-15
Site (Intercept) 2.280e+02 1.510e+01
Residual 1.144e+03 3.383e+01
Number of obs: 582, groups: Year, 2; Site, 7
Dispersion estimate for gaussian family (sigma^2): 1.14e+03
Conditional model:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 17.73174 NaN NaN NaN
ndvi -0.01744 NaN NaN NaN
Micrositeshrub -1.27994 NaN NaN NaN
Sizemed -2.82118 NaN NaN NaN
Sizesmall 13.60998 NaN NaN NaN
gr.webers 22.18964 NaN NaN NaN
mn.gr.abun 4.67908 NaN NaN NaN
arid 3.22514 NaN NaN NaN
Sizemed:gr.webers 8.04723 NaN NaN NaN
Sizesmall:gr.webers -1.58343 NaN NaN NaN
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
em <-emtrends(m11.9, ~"Size", var="mn.gr.abun")pairs(em)
contrast estimate SE df t.ratio p.value
large - med -2.191 2.34 584 -0.936 0.6177
large - small -1.365 2.26 584 -0.605 0.8177
med - small 0.827 2.35 584 0.351 0.9344
Results are averaged over the levels of: Microsite
P value adjustment: tukey method for comparing a family of 3 estimates
summary(em)
Size mn.gr.abun.trend SE df lower.CL upper.CL
large 4.98 1.92 584 1.22 8.75
med 7.18 2.05 584 3.14 11.21
small 6.35 1.93 584 2.56 10.14
Results are averaged over the levels of: Microsite
Confidence level used: 0.95
test(em)
Size mn.gr.abun.trend SE df t.ratio p.value
large 4.98 1.92 584 2.601 0.0095
med 7.18 2.05 584 3.495 0.0005
small 6.35 1.93 584 3.291 0.0011
Results are averaged over the levels of: Microsite
plot(em)
ggplot(seeds, aes(mn.gr.abun, percent, fill = Size)) +geom_smooth(method ="lm")
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 356 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 356 rows containing missing values or values outside the scale range
(`geom_point()`).
p <-plot(glmm.hp(m7), plot.perc =TRUE)
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
mod3 <- m7plot(glmm.hp(mod3,type="R2"))
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
p <-glmm.hp(mod3,commonality=TRUE)
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; extreme or very small
eigenvalues detected. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
Warning in fitTMB(TMBStruc): Model convergence problem; non-positive-definite
Hessian matrix. See vignette('troubleshooting')
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'
