Meta-analysis

We estimated all models with running frequency as the dependent behavior variable (here). To summarize the results over our clubs, we will perform a meta-analysis using a Fisher-type combination of one-tailed p-values, described by Hedges and Olkin (2014) and Snijders and Bosker (2011) (Chapter 3). We also performed this meta-analysis on models with running volume as the dependent variable (find the script here)

We perform a double test:

For detecting if there are any club-networks with a positive parameter value, the null hypothesis tested is: for all clubs, the value of this parameter is zero or less than zero; with the alternative hypothesis: for at least one club, the value of this parameter is greater than zero.
For detecting if there are any club-networks with a negative parameter value, the null hypothesis tested is: for all clubs, the value of this parameter is zero or greater than zero; with the alternative hypothesis: for at least one club, the value of this parameter is less than zero.

For each of these combined tests, the p-value is given. We use for each test the significance level of α/2, which yields an overall combined test at significance level α. Four different overall results are possible. Indicating the right-sided and left-sided p-values by p_r and p_l respectively, these results are:

p_r \(\gt\) α/2 & p_l \(\gt\) α/2:

No evidence for any non-zero parameter values.

p_r \(\leq\) α/2 & p_l \(\gt\) α/2:

Evidence that some club-networks have a positive parameter value, but no evidence for any negative parameter values.

p_r \(\gt\) α/2 & p_l \(\leq\) α/2:

Evidence that some clubs have a negative parameter value, no evidence for any positive parameter values

p_r \(\leq\) α/2 & p_l \(\leq\) α/2:

Evidence that some clubs have a negative parameter value, and some others have a positive parameter value.

getting started

clean up

# empty environment
rm(list=ls())
# free up memory
gc()

general custom functions

fpackage.check: Check if packages are installed (and install if not) in R (source)
fload.R: function to load R-objects under new names.

fpackage.check <- function(packages) {
    lapply(packages, FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
            install.packages(x, dependencies = TRUE)
            library(x, character.only = TRUE)
        }
    })
}

fload.R  <- function(fileName){
  load(fileName)
  get(ls()[ls() != "fileName"])
}

colorize <- function(x, color) {sprintf("<span style='color: %s;'>%s</span>", color, x) }

necessary packages

RSiena: for meta-analysis in siena08
knitr: generating tables
kableExtra: extend knitr’ table functionality
dplyr: data manipulation

packages = c("RSiena", "knitr", "kableExtra", "dplyr")
fpackage.check(packages)

First, we read in the sienaFit-objects of our 5 clubs. We estimated for each club 6 models, and we listed these models for each club.

We use fload.R to load the objects under new names. We list models 1-6 across clubs for subsequent meta-analysis.

# large lists, takes a lot of time to load
# in case of limited memory space, exclude club 3 for now.
club1 <-  fload.R("test/sienaFit/sienaFit_club1.RData")
club2 <-  fload.R("test/sienaFit/sienaFit_club2.RData")
club4 <-  fload.R("test/sienaFit/sienaFit_club4.RData")
club5 <-  fload.R("test/sienaFit/sienaFit_club5.RData")

# we list model 1-6
m1List <- list(club1[[1]], club2[[1]], club4[[1]], club5[[1]])
m2List <- list(club1[[2]], club2[[2]], club4[[2]], club5[[2]])
m3List <- list(club1[[3]], club2[[3]], club4[[3]], club5[[3]])
m4List <- list(club1[[4]], club2[[4]], club4[[4]], club5[[4]])
m5List <- list(club1[[5]], club2[[5]], club4[[5]], club5[[5]])
m6List <- list(club1[[6]], club2[[6]], club4[[6]], club5[[6]])

# remove the excess data, and add club 3's rsiena objects.
rm(club1, club2, club4, club5)
club3 <-  fload.R("test/sienaFit/sienaFit_club3.RData")
# when facing facing storage capacity issues, check the capacity:
#memory.limit()
# we increase the limit
#memory.limit(size=56000)

# add to the lists
m1List[[5]] <- club3[[1]]
m2List[[5]] <- club3[[2]]
m3List[[5]] <- club3[[3]]
m4List[[5]] <- club3[[4]]
m5List[[5]] <- club3[[5]]
m6List[[5]] <- club3[[6]]
rm(club3)

Models

We perform the meta-analysis seperately for 6 model specifications.

Model 1 (indegree)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m1List, function(x){x$theta})
standers <- sapply(m1List, function(x){x$se}) 

(eff.names <-
    m1List[[1]]$effects[m1List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m1List, bound=100)

efnames <- names(ans8[1:43])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:43,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=43, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m1.RData")

Model 2 (avAlt)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m2List, function(x){x$theta})
standers <- sapply(m2List, function(x){x$se}) 

(eff.names <-
    m2List[[1]]$effects[m2List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m2List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m2.RData")

Model 3 (avAttHigher)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m3List, function(x){x$theta})
standers <- sapply(m3List, function(x){x$se}) 

(eff.names <-
    m3List[[1]]$effects[m3List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m3List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m3.RData")

Model 4 (avAttLower)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m4List, function(x){x$theta})
standers <- sapply(m4List, function(x){x$se}) 

(eff.names <-
    m4List[[1]]$effects[m4List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m4List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m4.RData")

Model 5 (avAttHigher+Lower)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m5List, function(x){x$theta})
standers <- sapply(m5List, function(x){x$se}) 

(eff.names <-
    m5List[[1]]$effects[m5List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m5List, bound=100)

efnames <- names(ans8[1:45])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:45,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=45, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m5.RData")

additional check: are results driven by club 5?

we note that club 5 displayed the most significant influence effects. Following the suggestion of an anonymous reviewer, we checked the robustness of our aggregate findings by excluding this club from our summary of estimates.

Note, that the results of club 5 are in the 4th object of our list!!

# remove club 5
m5List_ <- m5List[-4]

# We extract the parameter estimates and standard errors 
parameters <- sapply(m5List_, function(x){x$theta})
standers <- sapply(m5List_, function(x){x$se}) 

ans8 <- siena08(m5List_, bound=100)

Fishers <- t(sapply(1:45,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# reorder
colnames(Fishers)
Fishers <- Fishers[, c(2,4,1,3,5)]

# so, now: we exclude from our previously made table, the columns representing club 5 results;
# and we replace the Fisher statistics with the above

df3 <- df2[-c(5:10)]
df3 <- cbind(df3, round(Fishers,3))


save(df3, file = "test/sienaFit/meta-analysis/meta_freq_m5_withoutclub5.RData")

Model 6 (avSim)

We extract the parameter estimates and standard errors for our models.

We use siena08 to perform Fisher’s method for combining independent p-values.

# We extract the parameter estimates and standard errors 
parameters <- sapply(m6List, function(x){x$theta})
standers <- sapply(m6List, function(x){x$se}) 

(eff.names <-
    m6List[[1]]$effects[m6List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m6List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m6.RData")

Results

And we present the results:

Model 1 (indegree)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.522 (0.519)	5.598 (0.54)	4.741 (0.581)	0.984 (0.762)	6.052 (0.579)	0.211	319.673	1.000	0.000	10
constant kudonet rate (period 2)	1.405 (0.525)	6.069 (0.553)	3.235 (0.397)	0.513 (0.41)	5.357 (0.533)	0.230	322.463	1.000	0.000	10
constant kudonet rate (period 3)	0.572 (0.235)	6.09 (0.552)	3.498 (0.435)	1.129 (0.702)	6.229 (0.621)	0.126	321.550	1.000	0.000	10
constant kudonet rate (period 4)	0.66 (0.261)	4.224 (0.403)	8.406 (1.214)	0.537 (0.429)	4.987 (0.5)	0.233	290.767	1.000	0.000	10
constant kudonet rate (period 5)	0.328 (0.175)	5.064 (0.457)	7.461 (0.935)	0.706 (0.465)	5.893 (0.538)	0.195	338.659	1.000	0.000	10
constant kudonet rate (period 6)	1.044 (0.347)	5.06 (0.472)	4.867 (0.592)	1.05 (0.63)	7.184 (0.666)	0.101	337.605	1.000	0.000	10
constant kudonet rate (period 7)	0.715 (0.287)	5.75 (0.532)	5.533 (0.786)	2.598 (1.432)	5.467 (0.512)	0.084	316.001	1.000	0.000	10
constant kudonet rate (period 8)	1.626 (0.515)	5.657 (0.516)	9.896 (1.233)	1.008 (0.581)	5.621 (0.506)	0.086	348.169	1.000	0.000	10
constant kudonet rate (period 9)	0.507 (0.208)	4.894 (0.436)	6.904 (0.732)	2.277 (1.415)	6.606 (0.578)	0.125	380.889	1.000	0.000	10
constant kudonet rate (period 10)	1.962 (0.596)	3.4 (0.342)	5.279 (0.705)	0.408 (0.319)	6.411 (0.563)	0.212	323.544	1.000	0.000	10
constant kudonet rate (period 11)	1.587 (0.522)	4.923 (0.433)	11.436 (2.015)	1.594 (1.021)	6.905 (0.571)	0.124	345.900	1.000	0.000	10
outdegree (density)	-7.397 (1.509)	-6.963 (0.373)	-4.818 (0.18)	3.362 (2.874)	-3.904 (0.182)	1578.454	4.223	0.000	0.937	10
reciprocity	5.651 (0.744)	4.415 (0.257)	4.385 (0.137)	2.613 (0.7)	3.797 (0.178)	0.000	1883.621	1.000	0.000	10
GWESP I -> K -> J (69)	1.237 (0.345)	0.87 (0.131)	1.235 (0.075)	2.136 (0.643)	1.712 (0.097)	0.001	677.584	1.000	0.000	10
indegree - popularity (sqrt)	0.257 (0.31)	0.173 (0.042)	0.68 (0.059)	-1.247 (0.911)	-0.093 (0.031)	18.478	165.943	0.047	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.514 (0.049)	0 (NA)	0 (NA)	118.001	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.726 (0.187)	0.771 (0.091)	0.244 (0.021)	-1.56 (0.885)	0.07 (0.019)	6.489	262.962	0.773	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.015)	0 (NA)	0 (NA)	0 (NA)	42.959	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.431 (0.35)	2.548 (0.127)	3.921 (2.016)	0.563 (0.219)	0.293	431.989	1.000	0.000	8
gender alter	0.225 (0.349)	0.166 (0.07)	-0.123 (0.061)	0 (NA)	0.159 (0.048)	8.238	27.289	0.411	0.001	8
gender ego	-0.438 (0.405)	0.086 (0.071)	-0.121 (0.066)	0 (NA)	-0.14 (0.05)	23.115	4.738	0.003	0.785	8
same gender	-0.491 (0.31)	0.193 (0.067)	0.063 (0.06)	0 (NA)	0.084 (0.046)	6.132	23.127	0.632	0.003	8
freq_run alter	-0.203 (0.334)	0.093 (0.032)	0.096 (0.039)	0.208 (0.61)	-0.015 (0.033)	5.774	26.233	0.834	0.003	10
freq_run ego	-0.597 (0.377)	-0.1 (0.031)	0.025 (0.041)	-0.181 (0.577)	0.046 (0.033)	22.980	8.728	0.011	0.558	10
freq_run similarity	-5.114 (1.702)	-0.077 (0.127)	0.371 (0.163)	-3.613 (2.368)	0.218 (0.122)	21.448	16.268	0.018	0.092	10
higher freq_run	1.225 (1.811)	0.508 (0.186)	0.25 (0.212)	-0.306 (2.682)	-0.323 (0.204)	8.157	19.897	0.613	0.030	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.684 (0.165)	-1.578 (0.113)	0 (NA)	-1.206 (0.105)	361.893	0.000	0.000	1.000	6
rate freq_run (period 1)	3.537 (1.61)	2.347 (0.559)	1.952 (0.297)	7.016 (10.666)	2.593 (0.571)	0.618	108.317	1.000	0.000	10
rate freq_run (period 2)	2.411 (0.869)	1.97 (0.48)	1.837 (0.27)	3.453 (3.419)	3.321 (0.731)	0.346	114.772	1.000	0.000	10
rate freq_run (period 3)	3.591 (1.4)	2.556 (0.598)	2.273 (0.361)	1.633 (1.04)	2.833 (0.601)	0.130	111.813	1.000	0.000	10
rate freq_run (period 4)	2.595 (1.08)	1.809 (0.432)	2.14 (0.338)	3.171 (2.881)	2.545 (0.525)	0.308	110.105	1.000	0.000	10
rate freq_run (period 5)	2.668 (1.085)	1.538 (0.34)	2.061 (0.296)	4.315 (3.916)	2.793 (0.508)	0.305	129.285	1.000	0.000	10
rate freq_run (period 6)	4.335 (1.889)	2.568 (0.567)	2.041 (0.302)	0.831 (0.588)	3.842 (0.879)	0.185	114.999	1.000	0.000	10
rate freq_run (period 7)	3.139 (1.307)	2.519 (0.542)	2.023 (0.293)	1.266 (0.897)	3.686 (0.803)	0.181	120.613	1.000	0.000	10
rate freq_run (period 8)	1.722 (0.572)	3.544 (0.844)	1.464 (0.211)	9.245 (22.933)	2.17 (0.428)	0.844	122.534	1.000	0.000	10
rate freq_run (period 9)	1.889 (0.64)	2.689 (0.629)	1.674 (0.223)	1.531 (0.94)	3.602 (0.68)	0.109	137.560	1.000	0.000	10
rate freq_run (period 10)	3.166 (1.162)	2.218 (0.527)	1.721 (0.245)	1.005 (0.61)	2.081 (0.441)	0.109	122.219	1.000	0.000	10
rate freq_run (period 11)	1.564 (0.535)	1.329 (0.316)	1.813 (0.251)	2.021 (1.4)	2.175 (0.452)	0.158	126.489	1.000	0.000	10
freq_run linear shape	-0.084 (0.072)	-0.199 (0.071)	-0.094 (0.031)	-0.422 (0.312)	-0.043 (0.044)	37.951	0.807	0.000	1.000	10
freq_run quadratic shape	-0.108 (0.024)	-0.019 (0.013)	-0.029 (0.009)	-0.211 (0.079)	0.005 (0.009)	57.245	2.672	0.000	0.988	10
freq_run indegree	0.01 (0.018)	0.013 (0.005)	0.002 (0.009)	0.157 (0.136)	0.007 (0.004)	2.177	24.109	0.995	0.007	10
freq_run: effect from gender	-0.109 (0.114)	0.026 (0.086)	-0.2 (0.057)	0 (NA)	-0.118 (0.063)	28.160	2.371	0.000	0.967	8
freq_run: effect from freq_other	-0.044 (0.047)	0.003 (0.019)	0.006 (0.014)	-0.072 (0.127)	0.044 (0.018)	7.968	14.835	0.632	0.138	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

Model 2 (avAlt)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.532 (0.543)	5.593 (0.543)	4.747 (0.613)	0.989 (0.839)	6.057 (0.626)	0.259	294.982	1.000	0.000	10
constant kudonet rate (period 2)	1.399 (0.512)	6.073 (0.565)	3.227 (0.408)	0.519 (0.392)	5.362 (0.538)	0.201	312.629	1.000	0.000	10
constant kudonet rate (period 3)	0.574 (0.233)	6.097 (0.553)	3.489 (0.449)	1.147 (0.736)	6.211 (0.643)	0.137	309.838	1.000	0.000	10
constant kudonet rate (period 4)	0.658 (0.273)	4.217 (0.417)	8.398 (1.023)	0.54 (0.421)	4.992 (0.463)	0.226	319.436	1.000	0.000	10
constant kudonet rate (period 5)	0.328 (0.175)	5.063 (0.471)	7.494 (0.93)	0.711 (0.487)	5.888 (0.544)	0.212	329.237	1.000	0.000	10
constant kudonet rate (period 6)	1.039 (0.356)	5.065 (0.481)	4.868 (0.563)	1.055 (0.613)	7.191 (0.644)	0.091	348.558	1.000	0.000	10
constant kudonet rate (period 7)	0.711 (0.288)	5.76 (0.515)	5.522 (0.788)	2.64 (1.462)	5.472 (0.522)	0.086	319.740	1.000	0.000	10
constant kudonet rate (period 8)	1.602 (0.507)	5.65 (0.497)	9.889 (1.281)	1.009 (0.615)	5.633 (0.483)	0.105	364.844	1.000	0.000	10
constant kudonet rate (period 9)	0.507 (0.208)	4.89 (0.423)	6.911 (0.694)	2.297 (1.423)	6.626 (0.576)	0.124	400.933	1.000	0.000	10
constant kudonet rate (period 10)	1.965 (0.572)	3.396 (0.34)	5.276 (0.722)	0.407 (0.314)	6.416 (0.534)	0.206	337.633	1.000	0.000	10
constant kudonet rate (period 11)	1.586 (0.495)	4.933 (0.422)	11.421 (1.985)	1.624 (1.11)	6.9 (0.591)	0.150	345.189	1.000	0.000	10
outdegree (density)	-7.377 (1.547)	-6.95 (0.36)	-4.822 (0.18)	3.302 (2.896)	-3.91 (0.178)	1623.942	4.126	0.000	0.941	10
reciprocity	5.664 (0.765)	4.413 (0.261)	4.392 (0.138)	2.578 (0.658)	3.792 (0.178)	0.000	1847.616	1.000	0.000	10
GWESP I -> K -> J (69)	1.238 (0.348)	0.869 (0.13)	1.236 (0.075)	2.16 (0.669)	1.705 (0.098)	0.002	675.188	1.000	0.000	10
indegree - popularity (sqrt)	0.258 (0.317)	0.173 (0.042)	0.681 (0.058)	-1.255 (0.916)	-0.09 (0.032)	17.581	167.333	0.062	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.515 (0.049)	0 (NA)	0 (NA)	116.634	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.731 (0.182)	0.769 (0.088)	0.245 (0.02)	-1.573 (0.883)	0.071 (0.019)	6.569	271.228	0.765	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.014)	0 (NA)	0 (NA)	0 (NA)	44.193	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.438 (0.343)	2.548 (0.124)	3.935 (2.001)	0.568 (0.223)	0.273	453.633	1.000	0.000	8
gender alter	0.224 (0.356)	0.166 (0.07)	-0.123 (0.062)	0 (NA)	0.159 (0.048)	8.148	27.409	0.419	0.001	8
gender ego	-0.435 (0.405)	0.086 (0.071)	-0.119 (0.065)	0 (NA)	-0.139 (0.049)	23.079	4.734	0.003	0.786	8
same gender	-0.497 (0.316)	0.193 (0.067)	0.064 (0.06)	0 (NA)	0.084 (0.045)	6.075	23.418	0.639	0.003	8
freq_run alter	-0.219 (0.332)	0.093 (0.031)	0.095 (0.039)	0.24 (0.605)	-0.015 (0.033)	5.898	26.246	0.824	0.003	10
freq_run ego	-0.588 (0.373)	-0.099 (0.031)	0.026 (0.04)	-0.215 (0.58)	0.047 (0.033)	22.810	8.697	0.011	0.561	10
freq_run similarity	-5.152 (1.668)	-0.077 (0.128)	0.371 (0.161)	-3.613 (2.326)	0.225 (0.127)	22.112	16.367	0.015	0.090	10
higher freq_run	1.151 (1.84)	0.502 (0.187)	0.245 (0.212)	-0.132 (2.627)	-0.324 (0.202)	8.200	19.507	0.609	0.034	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.688 (0.169)	-1.583 (0.114)	0 (NA)	-1.203 (0.105)	358.179	0.000	0.000	1.000	6
rate freq_run (period 1)	3.557 (1.739)	2.327 (0.566)	1.948 (0.293)	8.179 (22.694)	2.71 (0.553)	0.932	110.568	1.000	0.000	10
rate freq_run (period 2)	2.409 (0.955)	1.966 (0.474)	1.84 (0.287)	3.499 (3.215)	3.324 (0.782)	0.309	105.760	1.000	0.000	10
rate freq_run (period 3)	3.576 (1.598)	2.55 (0.66)	2.27 (0.365)	1.664 (1.083)	2.896 (0.612)	0.154	105.554	1.000	0.000	10
rate freq_run (period 4)	2.593 (1.067)	1.785 (0.393)	2.144 (0.328)	3.364 (3.974)	2.611 (0.543)	0.458	115.105	1.000	0.000	10
rate freq_run (period 5)	2.636 (1.111)	1.539 (0.348)	2.057 (0.311)	5.228 (5.738)	2.899 (0.618)	0.417	113.913	1.000	0.000	10
rate freq_run (period 6)	4.335 (1.736)	2.557 (0.586)	2.04 (0.307)	0.845 (0.61)	3.843 (0.867)	0.186	113.365	1.000	0.000	10
rate freq_run (period 7)	3.2 (1.185)	2.499 (0.613)	2.015 (0.284)	1.215 (0.829)	3.83 (0.802)	0.155	122.079	1.000	0.000	10
rate freq_run (period 8)	1.711 (0.571)	3.507 (0.781)	1.467 (0.201)	8.29 (20.435)	2.213 (0.451)	0.841	128.868	1.000	0.000	10
rate freq_run (period 9)	1.868 (0.663)	2.688 (0.633)	1.677 (0.243)	1.555 (0.959)	3.688 (0.737)	0.113	124.496	1.000	0.000	10
rate freq_run (period 10)	3.16 (1.065)	2.213 (0.478)	1.719 (0.252)	0.995 (0.637)	2.103 (0.417)	0.125	127.828	1.000	0.000	10
rate freq_run (period 11)	1.578 (0.524)	1.328 (0.309)	1.811 (0.255)	2.054 (1.334)	2.184 (0.447)	0.130	127.446	1.000	0.000	10
freq_run linear shape	-0.084 (0.071)	-0.169 (0.081)	-0.095 (0.034)	-0.375 (0.319)	-0.137 (0.052)	39.366	0.561	0.000	1.000	10
freq_run quadratic shape	-0.108 (0.024)	-0.018 (0.013)	-0.029 (0.009)	-0.23 (0.084)	-0.018 (0.011)	63.030	0.276	0.000	1.000	10
freq_run indegree	0.011 (0.018)	0.013 (0.005)	0.001 (0.009)	0.157 (0.142)	0.007 (0.004)	2.155	24.993	0.995	0.005	10
freq_run average alter	-0.002 (0.095)	-0.057 (0.078)	0.009 (0.075)	-0.225 (0.229)	0.212 (0.052)	9.170	24.988	0.516	0.005	10
freq_run: effect from gender	-0.108 (0.114)	0.013 (0.09)	-0.199 (0.058)	0 (NA)	-0.059 (0.066)	24.349	2.421	0.002	0.965	8
freq_run: effect from freq_other	-0.044 (0.049)	0.002 (0.019)	0.007 (0.015)	-0.1 (0.134)	0.046 (0.019)	8.350	14.557	0.595	0.149	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

Model 3 (avAttHigher)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.534 (0.536)	5.6 (0.551)	4.75 (0.619)	1.005 (0.732)	6.059 (0.634)	0.181	289.727	1.000	0.000	10
constant kudonet rate (period 2)	1.397 (0.504)	6.074 (0.587)	3.236 (0.417)	0.519 (0.394)	5.357 (0.554)	0.203	296.106	1.000	0.000	10
constant kudonet rate (period 3)	0.578 (0.238)	6.091 (0.533)	3.495 (0.44)	1.138 (0.702)	6.222 (0.613)	0.123	331.522	1.000	0.000	10
constant kudonet rate (period 4)	0.669 (0.265)	4.221 (0.407)	8.452 (1.122)	0.532 (0.418)	4.984 (0.47)	0.226	310.578	1.000	0.000	10
constant kudonet rate (period 5)	0.33 (0.178)	5.054 (0.471)	7.489 (0.891)	0.707 (0.453)	5.886 (0.545)	0.187	334.731	1.000	0.000	10
constant kudonet rate (period 6)	1.051 (0.354)	5.071 (0.461)	4.87 (0.584)	1.058 (0.639)	7.168 (0.663)	0.103	345.547	1.000	0.000	10
constant kudonet rate (period 7)	0.708 (0.284)	5.748 (0.543)	5.517 (0.815)	2.629 (1.467)	5.462 (0.507)	0.087	309.677	1.000	0.000	10
constant kudonet rate (period 8)	1.633 (0.534)	5.662 (0.5)	9.926 (1.156)	1.008 (0.659)	5.636 (0.507)	0.132	364.549	1.000	0.000	10
constant kudonet rate (period 9)	0.504 (0.203)	4.898 (0.438)	6.923 (0.768)	2.31 (1.452)	6.609 (0.551)	0.128	385.651	1.000	0.000	10
constant kudonet rate (period 10)	1.954 (0.571)	3.399 (0.341)	5.268 (0.693)	0.403 (0.304)	6.412 (0.54)	0.196	338.589	1.000	0.000	10
constant kudonet rate (period 11)	1.589 (0.519)	4.935 (0.442)	11.491 (1.819)	1.625 (1.05)	6.911 (0.589)	0.128	340.135	1.000	0.000	10
outdegree (density)	-7.367 (1.552)	-6.935 (0.368)	-4.836 (0.18)	3.311 (2.84)	-3.908 (0.175)	1622.270	4.210	0.000	0.937	10
reciprocity	5.668 (0.759)	4.399 (0.261)	4.384 (0.138)	2.603 (0.661)	3.793 (0.178)	0.000	1860.940	1.000	0.000	10
GWESP I -> K -> J (69)	1.246 (0.346)	0.867 (0.13)	1.235 (0.075)	2.129 (0.635)	1.707 (0.095)	0.001	690.209	1.000	0.000	10
indegree - popularity (sqrt)	0.255 (0.316)	0.172 (0.042)	0.679 (0.058)	-1.238 (0.91)	-0.091 (0.031)	18.275	169.468	0.050	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.513 (0.049)	0 (NA)	0 (NA)	116.604	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.725 (0.185)	0.766 (0.091)	0.245 (0.021)	-1.566 (0.88)	0.071 (0.019)	6.565	263.660	0.766	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.089 (0.015)	0 (NA)	0 (NA)	0 (NA)	41.856	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.44 (0.347)	2.536 (0.125)	3.928 (2.007)	0.563 (0.223)	0.278	439.415	1.000	0.000	8
gender alter	0.206 (0.35)	0.165 (0.071)	-0.123 (0.061)	0 (NA)	0.158 (0.048)	8.231	26.730	0.411	0.001	8
gender ego	-0.431 (0.406)	0.084 (0.07)	-0.121 (0.065)	0 (NA)	-0.14 (0.049)	23.346	4.713	0.003	0.788	8
same gender	-0.492 (0.317)	0.192 (0.066)	0.063 (0.06)	0 (NA)	0.083 (0.046)	6.014	23.288	0.646	0.003	8
freq_run alter	-0.203 (0.349)	0.094 (0.032)	0.097 (0.04)	0.232 (0.605)	-0.015 (0.033)	5.714	26.307	0.839	0.003	10
freq_run ego	-0.59 (0.387)	-0.101 (0.032)	0.024 (0.04)	-0.198 (0.576)	0.047 (0.034)	22.539	8.683	0.013	0.562	10
freq_run similarity	-5.096 (1.701)	-0.076 (0.131)	0.375 (0.163)	-3.55 (2.344)	0.225 (0.124)	21.282	16.563	0.019	0.085	10
higher freq_run	1.198 (1.913)	0.508 (0.189)	0.261 (0.208)	-0.195 (2.665)	-0.326 (0.204)	8.149	19.828	0.614	0.031	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.682 (0.165)	-1.58 (0.116)	0 (NA)	-1.203 (0.105)	354.656	0.000	0.000	1.000	6
rate freq_run (period 1)	3.655 (1.734)	2.329 (0.524)	1.958 (0.307)	7.285 (12.663)	2.692 (0.562)	0.699	109.592	1.000	0.000	10
rate freq_run (period 2)	2.406 (0.899)	1.998 (0.458)	1.844 (0.28)	3.536 (3.326)	3.377 (0.819)	0.318	109.756	1.000	0.000	10
rate freq_run (period 3)	3.56 (1.402)	2.578 (0.589)	2.276 (0.346)	1.64 (1.074)	2.889 (0.59)	0.142	117.925	1.000	0.000	10
rate freq_run (period 4)	2.629 (1.154)	1.817 (0.407)	2.154 (0.336)	3.249 (3.778)	2.598 (0.596)	0.456	107.691	1.000	0.000	10
rate freq_run (period 5)	2.62 (1.099)	1.543 (0.339)	2.077 (0.33)	4.351 (3.696)	2.855 (0.611)	0.272	111.478	1.000	0.000	10
rate freq_run (period 6)	4.407 (1.739)	2.63 (0.621)	2.063 (0.321)	0.837 (0.59)	3.866 (0.797)	0.173	113.627	1.000	0.000	10
rate freq_run (period 7)	3.25 (1.314)	2.6 (0.609)	2.033 (0.315)	1.241 (0.867)	3.868 (0.845)	0.172	111.278	1.000	0.000	10
rate freq_run (period 8)	1.727 (0.601)	3.665 (0.93)	1.485 (0.224)	8.495 (20.166)	2.216 (0.458)	0.825	112.813	1.000	0.000	10
rate freq_run (period 9)	1.866 (0.605)	2.722 (0.604)	1.688 (0.257)	1.551 (0.988)	3.715 (0.754)	0.122	122.794	1.000	0.000	10
rate freq_run (period 10)	3.193 (1.271)	2.31 (0.532)	1.736 (0.245)	0.986 (0.608)	2.102 (0.433)	0.120	124.397	1.000	0.000	10
rate freq_run (period 11)	1.583 (0.526)	1.364 (0.309)	1.835 (0.264)	1.992 (1.406)	2.197 (0.448)	0.166	126.209	1.000	0.000	10
freq_run linear shape	-0.125 (0.084)	-0.368 (0.127)	-0.152 (0.046)	-0.361 (0.353)	-0.179 (0.078)	46.007	0.503	0.000	1.000	10
freq_run quadratic shape	-0.095 (0.027)	0.007 (0.021)	-0.017 (0.012)	-0.229 (0.11)	0.022 (0.012)	31.360	8.919	0.001	0.540	10
freq_run average attraction higher	1.561 (1.713)	2.977 (1.835)	2.688 (1.689)	-0.777 (2.734)	2.813 (1.306)	2.546	24.387	0.990	0.007	10
freq_run indegree	0 (0.022)	0.009 (0.006)	-0.015 (0.014)	0.163 (0.142)	0.001 (0.005)	6.729	12.814	0.751	0.234	10
freq_run: effect from gender	-0.096 (0.114)	0.019 (0.087)	-0.182 (0.059)	0 (NA)	-0.07 (0.068)	21.743	2.543	0.005	0.960	8
freq_run: effect from freq_other	-0.061 (0.05)	-0.001 (0.019)	0.006 (0.015)	-0.073 (0.127)	0.044 (0.018)	9.185	14.261	0.515	0.161	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

Model 4 (avAttLower)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.531 (0.536)	5.613 (0.528)	4.744 (0.569)	1.003 (0.778)	6.068 (0.615)	0.212	316.159	1.000	0.000	10
constant kudonet rate (period 2)	1.386 (0.51)	6.078 (0.565)	3.229 (0.384)	0.515 (0.41)	5.362 (0.542)	0.227	319.390	1.000	0.000	10
constant kudonet rate (period 3)	0.566 (0.231)	6.091 (0.552)	3.478 (0.446)	1.136 (0.743)	6.22 (0.641)	0.145	311.134	1.000	0.000	10
constant kudonet rate (period 4)	0.666 (0.251)	4.227 (0.4)	8.39 (1.024)	0.536 (0.436)	5 (0.479)	0.241	322.086	1.000	0.000	10
constant kudonet rate (period 5)	0.333 (0.171)	5.064 (0.472)	7.472 (0.83)	0.698 (0.466)	5.888 (0.537)	0.191	348.595	1.000	0.000	10
constant kudonet rate (period 6)	1.031 (0.351)	5.072 (0.48)	4.859 (0.599)	1.056 (0.632)	7.193 (0.667)	0.100	331.673	1.000	0.000	10
constant kudonet rate (period 7)	0.707 (0.28)	5.758 (0.52)	5.512 (0.766)	2.573 (1.476)	5.469 (0.505)	0.095	327.378	1.000	0.000	10
constant kudonet rate (period 8)	1.604 (0.495)	5.671 (0.493)	9.865 (1.181)	1.026 (0.618)	5.631 (0.496)	0.101	371.254	1.000	0.000	10
constant kudonet rate (period 9)	0.511 (0.213)	4.894 (0.439)	6.889 (0.751)	2.276 (1.396)	6.619 (0.551)	0.122	388.026	1.000	0.000	10
constant kudonet rate (period 10)	1.953 (0.539)	3.4 (0.331)	5.274 (0.694)	0.405 (0.319)	6.402 (0.537)	0.215	347.057	1.000	0.000	10
constant kudonet rate (period 11)	1.575 (0.493)	4.933 (0.438)	11.42 (2.137)	1.603 (1.043)	6.914 (0.608)	0.130	323.595	1.000	0.000	10
outdegree (density)	-7.472 (1.55)	-6.943 (0.37)	-4.819 (0.182)	3.383 (2.867)	-3.902 (0.177)	1597.647	4.258	0.000	0.935	10
reciprocity	5.661 (0.749)	4.409 (0.261)	4.395 (0.14)	2.638 (0.732)	3.796 (0.172)	0.000	1859.751	1.000	0.000	10
GWESP I -> K -> J (69)	1.231 (0.34)	0.867 (0.129)	1.238 (0.074)	2.107 (0.622)	1.704 (0.097)	0.001	687.966	1.000	0.000	10
indegree - popularity (sqrt)	0.294 (0.314)	0.173 (0.042)	0.679 (0.059)	-1.237 (0.915)	-0.092 (0.031)	18.104	162.873	0.053	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.516 (0.051)	0 (NA)	0 (NA)	110.140	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.732 (0.185)	0.768 (0.091)	0.244 (0.021)	-1.578 (0.875)	0.071 (0.019)	6.668	263.459	0.756	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.015)	0 (NA)	0 (NA)	0 (NA)	42.115	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.44 (0.342)	2.548 (0.124)	3.996 (2.017)	0.577 (0.225)	0.268	455.019	1.000	0.000	8
gender alter	0.22 (0.355)	0.166 (0.072)	-0.123 (0.062)	0 (NA)	0.159 (0.048)	8.186	26.942	0.416	0.001	8
gender ego	-0.456 (0.404)	0.086 (0.071)	-0.121 (0.065)	0 (NA)	-0.14 (0.049)	23.538	4.717	0.003	0.787	8
same gender	-0.485 (0.318)	0.194 (0.067)	0.063 (0.06)	0 (NA)	0.084 (0.045)	5.903	23.282	0.658	0.003	8
freq_run alter	-0.201 (0.327)	0.092 (0.032)	0.095 (0.04)	0.224 (0.614)	-0.015 (0.034)	5.772	25.622	0.834	0.004	10
freq_run ego	-0.6 (0.375)	-0.098 (0.032)	0.027 (0.041)	-0.188 (0.58)	0.047 (0.034)	22.406	8.721	0.013	0.559	10
freq_run similarity	-5.16 (1.615)	-0.072 (0.13)	0.379 (0.157)	-3.534 (2.25)	0.225 (0.126)	22.797	17.077	0.012	0.073	10
higher freq_run	1.244 (1.817)	0.495 (0.188)	0.243 (0.215)	-0.241 (2.731)	-0.323 (0.206)	8.061	19.186	0.623	0.038	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.687 (0.167)	-1.585 (0.114)	0 (NA)	-1.205 (0.101)	371.049	0.000	0.000	1.000	6
rate freq_run (period 1)	3.684 (1.718)	2.302 (0.53)	1.96 (0.296)	6.111 (7.241)	2.672 (0.591)	0.477	110.210	1.000	0.000	10
rate freq_run (period 2)	2.376 (0.898)	1.965 (0.44)	1.831 (0.293)	3.261 (2.765)	3.318 (0.737)	0.262	109.762	1.000	0.000	10
rate freq_run (period 3)	3.679 (1.629)	2.501 (0.596)	2.262 (0.338)	1.603 (1.081)	2.852 (0.572)	0.167	117.010	1.000	0.000	10
rate freq_run (period 4)	2.467 (0.86)	1.778 (0.399)	2.142 (0.347)	3.104 (3.44)	2.55 (0.546)	0.409	111.087	1.000	0.000	10
rate freq_run (period 5)	2.545 (1.016)	1.535 (0.333)	2.056 (0.304)	3.756 (2.675)	2.882 (0.632)	0.179	118.568	1.000	0.000	10
rate freq_run (period 6)	4.336 (2.069)	2.543 (0.557)	2.037 (0.312)	0.826 (0.586)	3.77 (0.774)	0.202	115.906	1.000	0.000	10
rate freq_run (period 7)	3.234 (1.295)	2.519 (0.571)	1.995 (0.293)	1.329 (0.914)	3.782 (0.834)	0.164	117.312	1.000	0.000	10
rate freq_run (period 8)	1.709 (0.569)	3.451 (0.854)	1.472 (0.212)	8.685 (35.241)	2.277 (0.484)	1.033	117.126	1.000	0.000	10
rate freq_run (period 9)	1.853 (0.63)	2.676 (0.592)	1.681 (0.234)	1.584 (0.945)	3.695 (0.789)	0.099	128.637	1.000	0.000	10
rate freq_run (period 10)	3.163 (1.231)	2.26 (0.492)	1.723 (0.226)	1.006 (0.656)	2.068 (0.414)	0.139	136.208	1.000	0.000	10
rate freq_run (period 11)	1.592 (0.552)	1.34 (0.303)	1.821 (0.243)	1.931 (1.315)	2.193 (0.449)	0.151	133.111	1.000	0.000	10
freq_run linear shape	-0.006 (0.078)	0.063 (0.154)	-0.041 (0.043)	-0.311 (0.339)	0.234 (0.076)	9.369	17.918	0.497	0.056	10
freq_run quadratic shape	-0.071 (0.028)	0.043 (0.035)	-0.012 (0.013)	-0.153 (0.099)	0.063 (0.016)	19.528	25.395	0.034	0.005	10
freq_run average attraction lower	4.835 (2.263)	5.879 (3.167)	3.165 (1.93)	2.585 (3.155)	8.928 (2.06)	0.663	47.916	1.000	0.000	10
freq_run indegree	0.048 (0.026)	0.017 (0.006)	0.023 (0.016)	0.193 (0.153)	0.024 (0.006)	0.461	47.650	1.000	0.000	10
freq_run: effect from gender	-0.185 (0.121)	0.034 (0.091)	-0.205 (0.059)	0 (NA)	-0.087 (0.067)	27.726	2.402	0.001	0.966	8
freq_run: effect from freq_other	-0.048 (0.048)	0.008 (0.02)	0.008 (0.015)	-0.047 (0.131)	0.031 (0.02)	7.376	11.352	0.690	0.331	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

Model 5 (avAttHigher+Lower)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.523 (0.524)	5.603 (0.539)	4.749 (0.551)	1.02 (0.75)	6.061 (0.611)	0.185	317.493	1.000	0.000	10
constant kudonet rate (period 2)	1.389 (0.52)	6.076 (0.58)	3.237 (0.433)	0.525 (0.403)	5.368 (0.535)	0.210	301.211	1.000	0.000	10
constant kudonet rate (period 3)	0.568 (0.23)	6.096 (0.548)	3.483 (0.457)	1.14 (0.717)	6.221 (0.639)	0.129	311.358	1.000	0.000	10
constant kudonet rate (period 4)	0.66 (0.261)	4.216 (0.399)	8.424 (1.048)	0.531 (0.437)	4.997 (0.485)	0.249	316.411	1.000	0.000	10
constant kudonet rate (period 5)	0.327 (0.173)	5.056 (0.469)	7.493 (0.904)	0.713 (0.48)	5.886 (0.549)	0.202	331.660	1.000	0.000	10
constant kudonet rate (period 6)	1.029 (0.349)	5.07 (0.46)	4.871 (0.602)	1.061 (0.616)	7.181 (0.659)	0.090	344.074	1.000	0.000	10
constant kudonet rate (period 7)	0.704 (0.284)	5.751 (0.497)	5.528 (0.804)	2.574 (1.432)	5.467 (0.52)	0.087	327.540	1.000	0.000	10
constant kudonet rate (period 8)	1.605 (0.521)	5.657 (0.491)	9.913 (1.264)	1.022 (0.635)	5.631 (0.501)	0.113	359.512	1.000	0.000	10
constant kudonet rate (period 9)	0.504 (0.21)	4.895 (0.432)	6.904 (0.817)	2.313 (1.319)	6.614 (0.574)	0.098	367.989	1.000	0.000	10
constant kudonet rate (period 10)	1.958 (0.55)	3.4 (0.34)	5.292 (0.702)	0.405 (0.315)	6.418 (0.568)	0.210	325.101	1.000	0.000	10
constant kudonet rate (period 11)	1.567 (0.504)	4.938 (0.424)	11.422 (2.565)	1.646 (1.016)	6.908 (0.591)	0.110	330.342	1.000	0.000	10
outdegree (density)	-7.464 (1.573)	-6.962 (0.368)	-4.826 (0.181)	3.161 (2.745)	-3.9 (0.175)	1622.091	4.163	0.000	0.940	10
reciprocity	5.665 (0.754)	4.424 (0.256)	4.388 (0.139)	2.627 (0.646)	3.793 (0.176)	0.000	1864.632	1.000	0.000	10
GWESP I -> K -> J (69)	1.225 (0.342)	0.871 (0.132)	1.236 (0.076)	2.107 (0.619)	1.708 (0.095)	0.001	684.135	1.000	0.000	10
indegree - popularity (sqrt)	0.294 (0.317)	0.173 (0.042)	0.68 (0.059)	-1.183 (0.867)	-0.093 (0.031)	18.802	163.191	0.043	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.515 (0.05)	0 (NA)	0 (NA)	113.691	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.735 (0.187)	0.771 (0.091)	0.244 (0.021)	-1.542 (0.842)	0.071 (0.019)	6.792	264.233	0.745	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.015)	0 (NA)	0 (NA)	0 (NA)	42.566	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.433 (0.345)	2.544 (0.127)	3.918 (1.9)	0.567 (0.227)	0.273	429.915	1.000	0.000	8
gender alter	0.22 (0.356)	0.167 (0.071)	-0.123 (0.062)	0 (NA)	0.159 (0.048)	8.063	27.650	0.427	0.001	8
gender ego	-0.456 (0.404)	0.086 (0.07)	-0.121 (0.065)	0 (NA)	-0.141 (0.049)	23.740	4.740	0.003	0.785	8
same gender	-0.486 (0.317)	0.194 (0.067)	0.062 (0.06)	0 (NA)	0.084 (0.046)	5.957	23.253	0.652	0.003	8
freq_run alter	-0.205 (0.332)	0.093 (0.032)	0.097 (0.039)	0.216 (0.576)	-0.016 (0.033)	5.810	26.108	0.831	0.004	10
freq_run ego	-0.594 (0.389)	-0.099 (0.032)	0.025 (0.04)	-0.186 (0.547)	0.047 (0.034)	22.285	8.769	0.014	0.554	10
freq_run similarity	-5.16 (1.7)	-0.077 (0.128)	0.38 (0.168)	-3.453 (2.12)	0.22 (0.125)	22.063	16.129	0.015	0.096	10
higher freq_run	1.22 (1.843)	0.501 (0.191)	0.252 (0.21)	-0.182 (2.494)	-0.328 (0.204)	8.187	19.362	0.611	0.036	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.691 (0.166)	-1.58 (0.117)	0 (NA)	-1.204 (0.103)	356.498	0.000	0.000	1.000	6
rate freq_run (period 1)	3.662 (1.668)	2.326 (0.536)	1.963 (0.312)	4.752 (4.503)	2.683 (0.55)	0.343	110.212	1.000	0.000	10
rate freq_run (period 2)	2.383 (0.917)	1.987 (0.483)	1.847 (0.287)	3.236 (3.274)	3.318 (0.719)	0.362	109.374	1.000	0.000	10
rate freq_run (period 3)	3.689 (1.571)	2.537 (0.597)	2.284 (0.37)	1.48 (0.895)	2.837 (0.626)	0.119	107.463	1.000	0.000	10
rate freq_run (period 4)	2.452 (1)	1.801 (0.408)	2.155 (0.334)	3.189 (3.98)	2.545 (0.517)	0.490	113.870	1.000	0.000	10
rate freq_run (period 5)	2.543 (1.037)	1.547 (0.35)	2.073 (0.315)	3.765 (3.189)	2.876 (0.619)	0.267	114.146	1.000	0.000	10
rate freq_run (period 6)	4.38 (1.742)	2.6 (0.589)	2.062 (0.323)	0.8 (0.57)	3.765 (0.882)	0.179	109.124	1.000	0.000	10
rate freq_run (period 7)	3.223 (1.42)	2.558 (0.553)	2.011 (0.294)	1.295 (0.905)	3.759 (0.823)	0.182	118.946	1.000	0.000	10
rate freq_run (period 8)	1.71 (0.598)	3.542 (0.844)	1.487 (0.22)	4.847 (6.024)	2.256 (0.5)	0.477	114.450	1.000	0.000	10
rate freq_run (period 9)	1.864 (0.685)	2.707 (0.661)	1.691 (0.231)	1.516 (0.953)	3.678 (0.788)	0.121	125.211	1.000	0.000	10
rate freq_run (period 10)	3.212 (1.153)	2.295 (0.536)	1.738 (0.245)	0.904 (0.533)	2.061 (0.399)	0.098	129.312	1.000	0.000	10
rate freq_run (period 11)	1.607 (0.566)	1.355 (0.308)	1.834 (0.28)	1.709 (0.996)	2.186 (0.484)	0.093	116.494	1.000	0.000	10
freq_run linear shape	-0.007 (0.1)	-0.079 (0.31)	-0.087 (0.078)	0.149 (0.668)	0.243 (0.154)	8.549	10.092	0.575	0.432	10
freq_run quadratic shape	-0.071 (0.03)	0.041 (0.037)	-0.009 (0.013)	-0.203 (0.128)	0.062 (0.016)	18.223	25.183	0.051	0.005	10
freq_run average attraction higher	-0.034 (1.836)	1.437 (2.406)	1.559 (2.086)	-4.544 (5.303)	-0.136 (1.703)	7.356	8.597	0.691	0.571	10
freq_run average attraction lower	4.776 (2.43)	4.44 (4.337)	2.398 (2.286)	6.187 (5.882)	9.015 (2.642)	1.017	34.915	1.000	0.000	10
freq_run indegree	0.047 (0.032)	0.014 (0.008)	0.008 (0.028)	0.306 (0.238)	0.025 (0.009)	1.432	29.042	0.999	0.001	10
freq_run: effect from gender	-0.184 (0.123)	0.028 (0.093)	-0.192 (0.06)	0 (NA)	-0.089 (0.071)	25.486	2.289	0.001	0.971	8
freq_run: effect from freq_other	-0.047 (0.051)	0.005 (0.02)	0.007 (0.015)	-0.026 (0.147)	0.031 (0.02)	7.030	11.327	0.723	0.333	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

and the Fisher results excluding club 5:

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics, excluding club 5
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Fminus	pminus	Fplus	pplus	df
constant kudonet rate (period 1)	1.523 (0.524)	5.603 (0.539)	4.749 (0.551)	1.02 (0.75)	0.185	1.000	212.793	0.000	8
constant kudonet rate (period 2)	1.389 (0.52)	6.076 (0.58)	3.237 (0.433)	0.525 (0.403)	0.210	1.000	193.910	0.000	8
constant kudonet rate (period 3)	0.568 (0.23)	6.096 (0.548)	3.483 (0.457)	1.14 (0.717)	0.129	1.000	210.306	0.000	8
constant kudonet rate (period 4)	0.66 (0.261)	4.216 (0.399)	8.424 (1.048)	0.531 (0.437)	0.249	1.000	203.798	0.000	8
constant kudonet rate (period 5)	0.327 (0.173)	5.056 (0.469)	7.493 (0.904)	0.713 (0.48)	0.202	1.000	210.018	0.000	8
constant kudonet rate (period 6)	1.029 (0.349)	5.07 (0.46)	4.871 (0.602)	1.061 (0.616)	0.090	1.000	218.742	0.000	8
constant kudonet rate (period 7)	0.704 (0.284)	5.751 (0.497)	5.528 (0.804)	2.574 (1.432)	0.087	1.000	210.257	0.000	8
constant kudonet rate (period 8)	1.605 (0.521)	5.657 (0.491)	9.913 (1.264)	1.022 (0.635)	0.113	1.000	226.389	0.000	8
constant kudonet rate (period 9)	0.504 (0.21)	4.895 (0.432)	6.904 (0.817)	2.313 (1.319)	0.098	1.000	228.515	0.000	8
constant kudonet rate (period 10)	1.958 (0.55)	3.4 (0.34)	5.292 (0.702)	0.405 (0.315)	0.210	1.000	190.854	0.000	8
constant kudonet rate (period 11)	1.567 (0.504)	4.938 (0.424)	11.422 (2.565)	1.646 (1.016)	0.110	1.000	186.866	0.000	8
outdegree (density)	-7.464 (1.573)	-6.962 (0.368)	-4.826 (0.181)	3.161 (2.745)	1115.847	0.000	4.163	0.842	8
reciprocity	5.665 (0.754)	4.424 (0.256)	4.388 (0.139)	2.627 (0.646)	0.000	1.000	1392.126	0.000	8
GWESP I -> K -> J (69)	1.225 (0.342)	0.871 (0.132)	1.236 (0.076)	2.107 (0.619)	0.001	1.000	355.699	0.000	8
indegree - popularity (sqrt)	0.294 (0.317)	0.173 (0.042)	0.68 (0.059)	-1.183 (0.867)	5.290	0.726	163.189	0.000	8
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.515 (0.05)	0 (NA)	113.691	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.735 (0.187)	0.771 (0.091)	0.244 (0.021)	-1.542 (0.842)	6.792	0.559	245.507	0.000	8
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.015)	0 (NA)	0 (NA)	42.566	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.433 (0.345)	2.544 (0.127)	3.918 (1.9)	0.261	1.000	419.790	0.000	6
gender alter	0.22 (0.356)	0.167 (0.071)	-0.123 (0.062)	0 (NA)	8.062	0.234	12.088	0.060	6
gender ego	-0.456 (0.404)	0.086 (0.07)	-0.121 (0.065)	0 (NA)	11.300	0.080	4.736	0.578	6
same gender	-0.486 (0.317)	0.194 (0.067)	0.062 (0.06)	0 (NA)	5.888	0.436	16.496	0.011	6
freq_run alter	-0.205 (0.332)	0.093 (0.032)	0.097 (0.039)	0.216 (0.576)	3.518	0.898	25.343	0.001	8
freq_run ego	-0.594 (0.389)	-0.099 (0.032)	0.025 (0.04)	-0.186 (0.547)	22.120	0.005	3.698	0.883	8
freq_run similarity	-5.16 (1.7)	-0.077 (0.128)	0.38 (0.168)	-3.453 (2.12)	21.984	0.005	9.622	0.293	8
higher freq_run	1.22 (1.843)	0.501 (0.191)	0.252 (0.21)	-0.182 (2.494)	2.344	0.969	19.251	0.014	8
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.691 (0.166)	-1.58 (0.117)	0 (NA)	212.320	0.000	0.000	1.000	4
rate freq_run (period 1)	3.662 (1.668)	2.326 (0.536)	1.963 (0.312)	4.752 (4.503)	0.343	1.000	81.362	0.000	8
rate freq_run (period 2)	2.383 (0.917)	1.987 (0.483)	1.847 (0.287)	3.236 (3.274)	0.362	1.000	83.079	0.000	8
rate freq_run (period 3)	3.689 (1.571)	2.537 (0.597)	2.284 (0.37)	1.48 (0.895)	0.119	1.000	81.983	0.000	8
rate freq_run (period 4)	2.452 (1)	1.801 (0.408)	2.155 (0.334)	3.189 (3.98)	0.490	1.000	84.573	0.000	8
rate freq_run (period 5)	2.543 (1.037)	1.547 (0.35)	2.073 (0.315)	3.765 (3.189)	0.267	1.000	87.556	0.000	8
rate freq_run (period 6)	4.38 (1.742)	2.6 (0.589)	2.062 (0.323)	0.8 (0.57)	0.179	1.000	86.069	0.000	8
rate freq_run (period 7)	3.223 (1.42)	2.558 (0.553)	2.011 (0.294)	1.295 (0.905)	0.182	1.000	93.141	0.000	8
rate freq_run (period 8)	1.71 (0.598)	3.542 (0.844)	1.487 (0.22)	4.847 (6.024)	0.477	1.000	89.190	0.000	8
rate freq_run (period 9)	1.864 (0.685)	2.707 (0.661)	1.691 (0.231)	1.516 (0.953)	0.121	1.000	98.407	0.000	8
rate freq_run (period 10)	3.212 (1.153)	2.295 (0.536)	1.738 (0.245)	0.904 (0.533)	0.098	1.000	97.473	0.000	8
rate freq_run (period 11)	1.607 (0.566)	1.355 (0.308)	1.834 (0.28)	1.709 (0.996)	0.093	1.000	91.180	0.000	8
freq_run linear shape	-0.007 (0.1)	-0.079 (0.31)	-0.087 (0.078)	0.149 (0.668)	8.432	0.392	4.363	0.823	8
freq_run quadratic shape	-0.071 (0.03)	0.041 (0.037)	-0.009 (0.013)	-0.203 (0.128)	18.223	0.020	4.738	0.785	8
freq_run average attraction higher	-0.034 (1.836)	1.437 (2.406)	1.559 (2.086)	-4.544 (5.303)	5.838	0.665	7.335	0.501	8
freq_run average attraction lower	4.776 (2.43)	4.44 (4.337)	2.398 (2.286)	6.187 (5.882)	1.017	0.998	18.837	0.016	8
freq_run indegree	0.047 (0.032)	0.014 (0.008)	0.008 (0.028)	0.306 (0.238)	1.424	0.994	18.038	0.021	8
freq_run: effect from gender	-0.184 (0.123)	0.028 (0.093)	-0.192 (0.06)	0 (NA)	20.966	0.002	2.069	0.913	6
freq_run: effect from freq_other	-0.047 (0.051)	0.005 (0.02)	0.007 (0.015)	-0.026 (0.147)	6.908	0.547	5.677	0.683	8
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative.

Model 6 (avSim)

Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs
	Club 1 (N=27)	Club 2 (N=58)	Club 3 (N=159)	Club 4 (N=9)	Club 5 (N=76)	Fminus	Fplus	pminus	pplus	df
constant kudonet rate (period 1)	1.533 (0.577)	5.602 (0.539)	4.751 (0.574)	0.986 (0.782)	6.056 (0.595)	0.227	315.075	1.000	0.000	10
constant kudonet rate (period 2)	1.387 (0.504)	6.067 (0.585)	3.235 (0.394)	0.522 (0.408)	5.349 (0.548)	0.218	305.645	1.000	0.000	10
constant kudonet rate (period 3)	0.576 (0.229)	6.087 (0.525)	3.496 (0.448)	1.12 (0.724)	6.208 (0.656)	0.138	319.626	1.000	0.000	10
constant kudonet rate (period 4)	0.66 (0.253)	4.214 (0.402)	8.427 (1.06)	0.531 (0.418)	4.988 (0.468)	0.225	321.046	1.000	0.000	10
constant kudonet rate (period 5)	0.33 (0.168)	5.063 (0.473)	7.483 (0.905)	0.699 (0.481)	5.896 (0.539)	0.201	334.877	1.000	0.000	10
constant kudonet rate (period 6)	1.04 (0.349)	5.066 (0.47)	4.88 (0.578)	1.052 (0.634)	7.187 (0.644)	0.103	350.154	1.000	0.000	10
constant kudonet rate (period 7)	0.702 (0.288)	5.757 (0.532)	5.532 (0.786)	2.613 (1.427)	5.472 (0.537)	0.083	305.903	1.000	0.000	10
constant kudonet rate (period 8)	1.621 (0.501)	5.657 (0.489)	9.884 (1.232)	1.007 (0.797)	5.647 (0.517)	0.219	356.129	1.000	0.000	10
constant kudonet rate (period 9)	0.509 (0.206)	4.889 (0.459)	6.921 (0.728)	2.247 (1.429)	6.615 (0.57)	0.133	374.034	1.000	0.000	10
constant kudonet rate (period 10)	1.965 (0.555)	3.403 (0.349)	5.296 (0.705)	0.405 (0.301)	6.406 (0.529)	0.186	338.979	1.000	0.000	10
constant kudonet rate (period 11)	1.585 (0.529)	4.929 (0.434)	11.449 (1.972)	1.602 (1.02)	6.905 (0.578)	0.123	343.038	1.000	0.000	10
outdegree (density)	-7.363 (1.503)	-6.965 (0.356)	-4.826 (0.184)	3.403 (3.801)	-3.905 (0.182)	1581.306	3.371	0.000	0.971	10
reciprocity	5.679 (0.77)	4.425 (0.256)	4.391 (0.135)	2.629 (0.872)	3.789 (0.175)	0.003	1917.155	1.000	0.000	10
GWESP I -> K -> J (69)	1.249 (0.35)	0.873 (0.13)	1.239 (0.074)	2.124 (0.639)	1.704 (0.096)	0.001	696.052	1.000	0.000	10
indegree - popularity (sqrt)	0.266 (0.305)	0.172 (0.041)	0.678 (0.059)	-1.241 (0.906)	-0.091 (0.031)	17.813	164.012	0.058	0.000	10
outdegree - popularity (sqrt)	0 (NA)	0 (NA)	-0.515 (0.049)	0 (NA)	0 (NA)	118.854	0.000	0.000	1.000	2
outdegree - activity (sqrt)	0.728 (0.186)	0.771 (0.089)	0.245 (0.02)	-1.576 (1.139)	0.071 (0.019)	4.971	269.803	0.893	0.000	10
rec.degree^(1/1) - activity	0 (NA)	-0.09 (0.015)	0 (NA)	0 (NA)	0 (NA)	43.823	0.000	0.000	1.000	2
out-isolate	0 (NA)	0.434 (0.343)	2.539 (0.126)	3.971 (2.045)	0.564 (0.224)	0.281	436.689	1.000	0.000	8
gender alter	0.207 (0.357)	0.166 (0.07)	-0.123 (0.061)	0 (NA)	0.158 (0.049)	8.325	26.684	0.402	0.001	8
gender ego	-0.439 (0.404)	0.085 (0.07)	-0.122 (0.064)	0 (NA)	-0.14 (0.049)	23.414	4.709	0.003	0.788	8
same gender	-0.484 (0.321)	0.193 (0.067)	0.062 (0.06)	0 (NA)	0.084 (0.046)	5.853	23.223	0.664	0.003	8
freq_run alter	-0.21 (0.331)	0.094 (0.032)	0.096 (0.039)	0.204 (0.721)	-0.016 (0.034)	5.971	26.107	0.818	0.004	10
freq_run ego	-0.588 (0.376)	-0.101 (0.032)	0.025 (0.041)	-0.18 (0.56)	0.048 (0.035)	22.543	8.695	0.013	0.561	10
freq_run similarity	-5.109 (1.746)	-0.079 (0.128)	0.381 (0.165)	-3.614 (3.817)	0.225 (0.126)	18.980	16.684	0.041	0.082	10
higher freq_run	1.153 (1.811)	0.509 (0.188)	0.251 (0.212)	-0.314 (3.046)	-0.33 (0.21)	8.118	19.686	0.617	0.032	10
int. reciprocity x GWESP I -> K -> J (69)	0 (NA)	-0.69 (0.168)	-1.581 (0.112)	0 (NA)	-1.201 (0.103)	368.974	0.000	0.000	1.000	6
rate freq_run (period 1)	3.755 (2.051)	2.329 (0.536)	1.961 (0.308)	6.804 (12.893)	2.735 (0.661)	0.778	100.932	1.000	0.000	10
rate freq_run (period 2)	2.413 (0.854)	1.989 (0.479)	1.85 (0.284)	3.352 (2.717)	3.437 (0.847)	0.235	107.894	1.000	0.000	10
rate freq_run (period 3)	3.656 (1.46)	2.566 (0.636)	2.28 (0.351)	1.671 (1.108)	2.934 (0.64)	0.149	110.494	1.000	0.000	10
rate freq_run (period 4)	2.573 (1.015)	1.812 (0.412)	2.163 (0.336)	3.087 (3.243)	2.627 (0.554)	0.385	112.679	1.000	0.000	10
rate freq_run (period 5)	2.598 (1.061)	1.557 (0.358)	2.068 (0.295)	4.034 (3.527)	2.919 (0.648)	0.285	117.947	1.000	0.000	10
rate freq_run (period 6)	4.476 (1.779)	2.622 (0.633)	2.059 (0.306)	0.833 (0.604)	3.923 (0.831)	0.187	115.348	1.000	0.000	10
rate freq_run (period 7)	3.336 (1.319)	2.588 (0.61)	2.019 (0.304)	1.267 (0.854)	3.944 (0.963)	0.154	109.923	1.000	0.000	10
rate freq_run (period 8)	1.709 (0.559)	3.616 (0.837)	1.485 (0.216)	9.927 (347.433)	2.296 (0.508)	0.002	115.485	1.000	0.000	8
rate freq_run (period 9)	1.857 (0.648)	2.723 (0.608)	1.688 (0.243)	1.553 (0.903)	3.819 (0.85)	0.092	122.636	1.000	0.000	10
rate freq_run (period 10)	3.248 (1.255)	2.301 (0.505)	1.731 (0.24)	1.026 (0.631)	2.112 (0.403)	0.116	132.625	1.000	0.000	10
rate freq_run (period 11)	1.606 (0.556)	1.36 (0.309)	1.838 (0.26)	2.023 (1.339)	2.228 (0.464)	0.139	125.922	1.000	0.000	10
freq_run linear shape	-0.102 (0.072)	-0.225 (0.074)	-0.103 (0.032)	-0.43 (0.395)	-0.095 (0.047)	44.415	0.506	0.000	1.000	10
freq_run quadratic shape	-0.073 (0.03)	0.029 (0.027)	-0.01 (0.013)	-0.19 (0.147)	0.046 (0.014)	17.903	20.221	0.057	0.027	10
freq_run average similarity	2.086 (1.192)	2.441 (1.231)	1.92 (0.898)	0.418 (1.495)	3.282 (0.836)	1.151	44.126	1.000	0.000	10
freq_run indegree	0.011 (0.018)	0.011 (0.006)	0.003 (0.009)	0.156 (0.206)	0.006 (0.004)	2.268	20.713	0.994	0.023	10
freq_run: effect from gender	-0.124 (0.113)	0.022 (0.087)	-0.189 (0.058)	0 (NA)	-0.047 (0.067)	22.809	2.684	0.004	0.953	8
freq_run: effect from freq_other	-0.068 (0.049)	0.002 (0.019)	0.007 (0.015)	-0.065 (0.344)	0.039 (0.018)	8.722	13.291	0.559	0.208	10
Note:
Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.
^a Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).
^b We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.
^c The Fisher Test combines independent p-values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative

References

Hedges, L. V., and I. Olkin. 2014. Statistical Methods for Meta-Analysis. Academic press.

Snijders, T. A. B., and R. J. Bosker. 2011. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. Sage.

---
title: "Meta-analysis"
date: "Last compiled on `r format(Sys.time(), '%B, %Y')`"
bibliography: references.bib
output:
  html_document:
    css: tweaks.css
    toc: no
    toc_float: no
    collapsed: false
    number_sections: false
    toc_depth: 1
    code_folding: show
    code_download: yes
---

```{r, globalsettings, echo=FALSE, warning=FALSE, message=FALSE, results='hide'}

knitr::opts_chunk$set(echo = TRUE)
opts_chunk$set(tidy.opts=list(width.cutoff=100),tidy=TRUE, warning = FALSE, message = FALSE,comment = "#>", cache=TRUE, class.source=c("test"), class.output=c("test2"))
options(width = 100)
rgl::setupKnitr()



```

```{r klippy, echo=FALSE, include=TRUE}
klippy::klippy(position = c('top', 'right'))
#klippy::klippy(color = 'darkred')
#klippy::klippy(tooltip_message = 'Click to copy', tooltip_success = 'Done')
```


----

# Meta-analysis 

We estimated all models with running frequency as the dependent behavior variable ([here](https://robfranken.github.io/Strava/other)). To summarize the results over our clubs, we will perform a meta-analysis using a Fisher-type combination of one-tailed p-values, described by @hedges2014 and @bosker2011 (Chapter 3). We also performed this meta-analysis on models with running volume as the dependent variable (find the script [here](https://robfranken.github.io/Strava/vol_meta.html)) 

We perform a double test:

1. For detecting if there are any club-networks with a positive parameter value, the null hypothesis tested is: for all clubs, the value of this parameter is zero or less than zero; with the alternative hypothesis: for at least one club, the value of this parameter is greater than zero.
2. For detecting if there are any club-networks with a negative parameter value, the null hypothesis tested is: for all clubs, the value of this parameter is zero or greater than zero; with the alternative hypothesis: for at least one club, the value of this parameter is less than zero.

For each of these combined tests, the *p*-value is given. We use for each test the significance level of *α*/2, which yields an overall combined test at significance level *α*. Four different overall results are possible. Indicating the right-sided and left-sided *p*-values by *p<sub>r</sub>* and *p<sub>l</sub>* respectively, these results are:

- *p<sub>r</sub>* $\gt$ *α*/2 & *p<sub>l</sub>* $\gt$ *α*/2:

No evidence for any non-zero parameter values.

- *p<sub>r</sub>* $\leq$ *α*/2 & *p<sub>l</sub>* $\gt$ *α*/2:

Evidence that some club-networks have a positive parameter value, but no evidence for any negative parameter values.

- *p<sub>r</sub>* $\gt$ *α*/2 & *p<sub>l</sub>* $\leq$ *α*/2:

Evidence that some clubs have a negative parameter value, no evidence for any positive parameter values

- *p<sub>r</sub>* $\leq$ *α*/2 & *p<sub>l</sub>* $\leq$ *α*/2:

Evidence that some clubs have a negative parameter value, and some others have a positive parameter value.

<br>

----

# getting started

## clean up
```{r, results='hide'}
# empty environment
rm(list=ls())
# free up memory
gc()
```


## general custom functions

- `fpackage.check`: Check if packages are installed (and install if not) in R ([source](https://vbaliga.github.io/verify-that-r-packages-are-installed-and-loaded/))
- `fload.R`: function to load R-objects under new names.

```{r, results='hide'}

fpackage.check <- function(packages) {
    lapply(packages, FUN = function(x) {
        if (!require(x, character.only = TRUE)) {
            install.packages(x, dependencies = TRUE)
            library(x, character.only = TRUE)
        }
    })
}

fload.R  <- function(fileName){
  load(fileName)
  get(ls()[ls() != "fileName"])
}

colorize <- function(x, color) {sprintf("<span style='color: %s;'>%s</span>", color, x) }

```

<br>

## necessary packages

- `RSiena`: for meta-analysis in siena08
- `knitr`: generating tables
- `kableExtra`: extend `knitr`' table functionality
- `dplyr`: data manipulation


```{r, results='hide'}
packages = c("RSiena", "knitr", "kableExtra", "dplyr")
fpackage.check(packages)
```


First, we read in the sienaFit-objects of our 5 clubs. We estimated for each club 6 models, and we listed these models for each club. 

We use `fload.R` to load the objects under new names. We list models 1-6 across clubs for subsequent meta-analysis.

```{r get data, eval=F}

# large lists, takes a lot of time to load
# in case of limited memory space, exclude club 3 for now.
club1 <-  fload.R("test/sienaFit/sienaFit_club1.RData")
club2 <-  fload.R("test/sienaFit/sienaFit_club2.RData")
club4 <-  fload.R("test/sienaFit/sienaFit_club4.RData")
club5 <-  fload.R("test/sienaFit/sienaFit_club5.RData")

# we list model 1-6
m1List <- list(club1[[1]], club2[[1]], club4[[1]], club5[[1]])
m2List <- list(club1[[2]], club2[[2]], club4[[2]], club5[[2]])
m3List <- list(club1[[3]], club2[[3]], club4[[3]], club5[[3]])
m4List <- list(club1[[4]], club2[[4]], club4[[4]], club5[[4]])
m5List <- list(club1[[5]], club2[[5]], club4[[5]], club5[[5]])
m6List <- list(club1[[6]], club2[[6]], club4[[6]], club5[[6]])

# remove the excess data, and add club 3's rsiena objects.
rm(club1, club2, club4, club5)
club3 <-  fload.R("test/sienaFit/sienaFit_club3.RData")
# when facing facing storage capacity issues, check the capacity:
#memory.limit()
# we increase the limit
#memory.limit(size=56000)

# add to the lists
m1List[[5]] <- club3[[1]]
m2List[[5]] <- club3[[2]]
m3List[[5]] <- club3[[3]]
m4List[[5]] <- club3[[4]]
m5List[[5]] <- club3[[5]]
m6List[[5]] <- club3[[6]]
rm(club3)

```


<br>

# Models {.tabset .tabset-fade} 

We perform the meta-analysis seperately for 6 model specifications.

<!--

- cjplus: test statistic for combination of right one-sided Fisher combination test
- cjplusp: p-value for cjplus
- cjminus: test statistic for combination of left one-sided Fisher combination test
- cjminusp: p-value for cjminus
- df: degrees of freedom

https://search.r-project.org/CRAN/refmans/RSiena/html/siena08.html 


--> 

## Model 1 (indegree)

We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.

```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m1List, function(x){x$theta})
standers <- sapply(m1List, function(x){x$se}) 

(eff.names <-
    m1List[[1]]$effects[m1List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m1List, bound=100)

efnames <- names(ans8[1:43])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:43,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=43, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m1.RData")
```

## Model 2 (avAlt)
We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.

```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m2List, function(x){x$theta})
standers <- sapply(m2List, function(x){x$se}) 

(eff.names <-
    m2List[[1]]$effects[m2List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m2List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m2.RData")
```

## Model 3 (avAttHigher)
We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.
```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m3List, function(x){x$theta})
standers <- sapply(m3List, function(x){x$se}) 

(eff.names <-
    m3List[[1]]$effects[m3List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m3List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m3.RData")
```


## Model 4 (avAttLower)
We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.

```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m4List, function(x){x$theta})
standers <- sapply(m4List, function(x){x$se}) 

(eff.names <-
    m4List[[1]]$effects[m4List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m4List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m4.RData")
```

## Model 5 (avAttHigher+Lower)

We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.

```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m5List, function(x){x$theta})
standers <- sapply(m5List, function(x){x$se}) 

(eff.names <-
    m5List[[1]]$effects[m5List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m5List, bound=100)

efnames <- names(ans8[1:45])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:45,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=45, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m5.RData")
```

<br>

### additional check: are results driven by club 5?
we note that club 5 displayed the most significant influence effects. Following the suggestion of an anonymous reviewer, we checked the robustness of our aggregate findings by excluding this club from our summary of estimates.

`r colorize("Note, that the results of club 5 are in the **4th** object of our list!!", "red")`


```{r eval = FALSE,  results='hide'}
# remove club 5
m5List_ <- m5List[-4]

# We extract the parameter estimates and standard errors 
parameters <- sapply(m5List_, function(x){x$theta})
standers <- sapply(m5List_, function(x){x$se}) 

ans8 <- siena08(m5List_, bound=100)

Fishers <- t(sapply(1:45,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# reorder
colnames(Fishers)
Fishers <- Fishers[, c(2,4,1,3,5)]

# so, now: we exclude from our previously made table, the columns representing club 5 results;
# and we replace the Fisher statistics with the above

df3 <- df2[-c(5:10)]
df3 <- cbind(df3, round(Fishers,3))


save(df3, file = "test/sienaFit/meta-analysis/meta_freq_m5_withoutclub5.RData")
```


## Model 6 (avSim)

We extract the parameter estimates and standard errors for our models.

We use *siena08* to perform Fisher's method for combining independent *p*-values.

```{r eval = FALSE,  results='hide'}
# We extract the parameter estimates and standard errors 
parameters <- sapply(m6List, function(x){x$theta})
standers <- sapply(m6List, function(x){x$se}) 

(eff.names <-
    m6List[[1]]$effects[m6List[[1]]$effects$include,'effectName']) 
(eff.names <- eff.names)

#combine parameters and std.errors with effect names
rownames(parameters) <- eff.names
rownames(standers) <- eff.names

# print rounded to 3 decimals
round(parameters,3)
round(standers,3)

ans8 <- siena08(m6List, bound=100)

efnames <- names(ans8[1:44])
efnames <- substring(efnames, 8)

Fishers <- t(sapply(1:44,
                    function(i){c(ans8[[i]]$cjplus, ans8[[i]]$cjminus,
                                  ans8[[i]]$cjplusp, ans8[[i]]$cjminusp, 2*ans8[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers)
rownames(Fishers) <- efnames
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
#round(Fishers,3)

# we make (and save) a dataframe combining club estimates (std. errors) with fisher test statistics
par_standers <- paste(round(parameters, 3), " (", round(standers, 3), ")", sep="")
df <- as.data.frame(matrix(par_standers, nrow=44, ncol=5))
rownames(df) <- efnames
colnames(df) <- (c("Club 1 (N=27)", "Club 2 (N=58)", "Club 4 (N=9)", "Club 5 (N=76)", "Club 3 (N=159)"))
df2 <- cbind(df, round(Fishers, 3))
df2 <- df2[, c(1,2,5,3,4,7,6,9,8,10)] # reorder
save(df2, file = "test/sienaFit/meta-analysis/meta_freq_m6.RData")

```


# {-}

<br>

# Results {.tabset .tabset-fade} 

And we present the results:

## Model 1 (indegree)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m1.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```

## Model 2 (avAlt)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m2.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```

## Model 3 (avAttHigher)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m3.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```

## Model 4 (avAttLower)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m4.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```


## Model 5 (avAttHigher+Lower)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m5.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```
<br>

`r colorize("and the Fisher results excluding club 5:", "red")`


```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m5_withoutclub5.RData")

options(knitr.kable.NA = '-')

knitr::kable(df3, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics, excluding club 5") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p–values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative."))

```


## Model 6 (avSim)

```{r echo=F}
load("test/sienaFit/meta-analysis/meta_freq_m6.RData")

options(knitr.kable.NA = '-')

knitr::kable(df2, digits=3, "html", caption="Meta-analysis of parameter estimates for the objective function of kudos tie formation and running frequency dynamics across 5 Strava clubs") %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  footnote(general = "Estimates are given with standard errors in parentheses. All convergence t ratios <.10; overall maximum convergence ratio <.15.",
           alphabet  = c("Some structural network effects were rather important for some clubs, but not for others; for the latter, we fixed these effects to 0 (SE=NA).", "We fixed gender effects on kudos tie formation and running frequency dynamics to 0 in club 4. Variation between the genders was not sufficient for model convergence. Score–type tests indicated that gender effects were not significant.", "The Fisher Test combines independent p-values to test whether there is evidence in at least one of the clubs of a parameter being positive or negative"))

```

----


### References 


Meta-analysis

Last compiled on oktober, 2022

Meta-analysis

getting started

clean up

general custom functions

necessary packages

Models

Model 1 (indegree)

Model 2 (avAlt)

Model 3 (avAttHigher)

Model 4 (avAttLower)

Model 5 (avAttHigher+Lower)

additional check: are results driven by club 5?

Model 6 (avSim)

Results

Model 1 (indegree)

Model 2 (avAlt)

Model 3 (avAttHigher)

Model 4 (avAttLower)

Model 5 (avAttHigher+Lower)

Model 6 (avSim)

References